PROTEOMIC SINGLE-SYNAPSE ANALYSIS WITH ARRAY
TOMOGRAPHY
A DISSERTATION
SUBMITTED TO THE PROGRAM IN BIOPHYSICS
AND THE COMMITTEE ON GRADUATE STUDIES
OF STANFORD UNIVERSITY
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS
FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
Brad Busse
August 2011
http://creativecommons.org/licenses/by-nc/3.0/us/
This dissertation is online at: http://purl.stanford.edu/tq652pp7498
© 2011 by Brad Lee Busse. All Rights Reserved.
Re-distributed by Stanford University under license with the author.
This work is licensed under a Creative Commons Attribution-Noncommercial 3.0 United States License.
ii
I certify that I have read this dissertation and that, in my opinion, it is fully adequatein scope and quality as a dissertation for the degree of Doctor of Philosophy.
Stephen Smith, Primary Adviser
I certify that I have read this dissertation and that, in my opinion, it is fully adequatein scope and quality as a dissertation for the degree of Doctor of Philosophy.
Shaul Hestrin
I certify that I have read this dissertation and that, in my opinion, it is fully adequatein scope and quality as a dissertation for the degree of Doctor of Philosophy.
Richard Lewis
I certify that I have read this dissertation and that, in my opinion, it is fully adequatein scope and quality as a dissertation for the degree of Doctor of Philosophy.
Liqun Luo
Approved for the Stanford University Committee on Graduate Studies.
Patricia J. Gumport, Vice Provost Graduate Education
This signature page was generated electronically upon submission of this dissertation in electronic format. An original signed hard copy of the signature page is on file inUniversity Archives.
iii
Acknowledgements
This thesis would not have been possible without the contributions of pretty much
everyone I have ever known.
First off I want to thank my parents, Dale and Diane Busse, for raising me right.
My grandparents: Buck, Luella, John, and Aloise, for being such august role
models to emulate.
My undergraduate advisors Bruce McCormick and Yoonsuck Choe, for kindling
my interest in academia and neuroscience.
My graduate advisor, Stephen Smith, for his guidance, encouragement, numerous
introductions, and occasional whip-cracking.
My reading committee: Shaul Hestrin, Liqun Luo and Rich Lewis, for their valu-
able aid and prompt feedback in preparing my dissertation.
Gordon Wang, for being a good postdoc and a better friend.
Kristina Micheva and Nancy O’Rourke, for their patient proofreading and unpar-
alleled abilities at the lab bench and the microscope.
JoAnn Buchanan and Nafisa Ghouri, for their excellent technical support and
dependable repurposing of party leftovers as lab snacks.
Also Nick, Todd, Rachel and Forrest, for their friendship and myriad roles in
making the lab a lively place to work in.
Finally my fiancee, Stephanie Hold, for making my graduate career such an overall
enjoyable one in so many ways.
iv
Contents
Acknowledgements iv
1 Introduction 1
1.1 Production Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.1.1 Section discovery . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.1.2 Multistackreg . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.2 Analysis Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.2.1 Colocalization Analysis . . . . . . . . . . . . . . . . . . . . . . 5
1.2.2 Synaptogram . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.3 Synapse Classification . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2 Array Tomography: High-Resolution Three-Dimensional Immunoflu-
orescence 10
2.0.1 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.1.1 Array Tomography Procedures . . . . . . . . . . . . . . . . . . 12
2.2 Protocol A: Rodent Brain Tissue Fixation and Embedding . . . . . . 16
2.2.1 Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.2.2 Experimental Method . . . . . . . . . . . . . . . . . . . . . . 18
2.2.3 Troublesbooting . . . . . . . . . . . . . . . . . . . . . . . . . . 20
v
2.3 Protocol B: Production of Arrays . . . . . . . . . . . . . . . . . . . . 21
2.3.1 Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.3.2 Experimental Method . . . . . . . . . . . . . . . . . . . . . . 22
2.3.3 Troubleshooting . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.4 Protocol C: Immunostaining and Antibody Elution . . . . . . . . . . 25
2.4.1 Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.4.2 Experimental Method . . . . . . . . . . . . . . . . . . . . . . 27
2.4.3 Troubleshooting . . . . . . . . . . . . . . . . . . . . . . . . . . 29
2.5 Protocol D: Imaging Stained Arrays . . . . . . . . . . . . . . . . . . 30
2.5.1 Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
2.5.2 Experimental Method . . . . . . . . . . . . . . . . . . . . . . 31
2.5.3 Troubleshooting . . . . . . . . . . . . . . . . . . . . . . . . . . 33
2.6 Protocol E: Semiautomated Image Alignment . . . . . . . . . . . . . 37
2.6.1 Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
2.6.2 Experimental Method . . . . . . . . . . . . . . . . . . . . . . 38
2.6.3 Troubleshooting . . . . . . . . . . . . . . . . . . . . . . . . . . 39
2.7 Conclusion and Future Directions . . . . . . . . . . . . . . . . . . . . 39
2.7.1 Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . 40
2.8 Recipes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
3 Classical MHCI molecules regulate retinogeniculate refinement and
limit ocular dominance plasticity 53
3.0.1 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
3.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
3.2.1 Enhanced ocular dominance plasticity in KbDb−/− mice . . . . 55
vi
3.2.2 Expanded thalamocortical projections to layer 4 of KbDb−/−
mice following ME . . . . . . . . . . . . . . . . . . . . . . . . 58
3.2.3 Abnormal retinogeniculate patterning in KbDb−/− mice . . . . 58
3.2.4 Abnormal segregation of eye-specific inputs in dLGN of KbDb−/− 59
3.2.5 MHCI Immunostaining is associated with LGN synapses and C1q 60
3.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
3.3.1 MHCI function during developmental refinement of the retino-
geniculate projection . . . . . . . . . . . . . . . . . . . . . . . 62
3.3.2 H2-Kb and H2-Db may function with PirB to limit OD Plasticity
in Visual Cortex . . . . . . . . . . . . . . . . . . . . . . . . . 63
3.4 Experimental Procedures . . . . . . . . . . . . . . . . . . . . . . . . . 65
3.4.1 Animals and Genotyping of mouse lines . . . . . . . . . . . . . 65
3.4.2 Mouse surgery and OD plasticity experiments . . . . . . . . . 65
3.4.3 Arc induction . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
3.4.4 Densitometric scans of Arc induction in specific cortical layers 66
3.4.5 Transneuronal labeling . . . . . . . . . . . . . . . . . . . . . . 67
3.4.6 Anterograde labeling of retinal ganglion axons and multiple
threshold analysis . . . . . . . . . . . . . . . . . . . . . . . . . 67
3.4.7 Array Tomography . . . . . . . . . . . . . . . . . . . . . . . . 68
3.4.8 Array Tomography Cross-Correlation Analysis of synaptic mark-
ers, MHCI, and C1q . . . . . . . . . . . . . . . . . . . . . . . 69
3.4.9 Statistical analyses . . . . . . . . . . . . . . . . . . . . . . . . 70
3.4.10 Supplemental Data . . . . . . . . . . . . . . . . . . . . . . . . 70
3.4.11 Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . 70
4 Single-Synapse Analysis of a Diverse Synapse Population: Proteomic
Imaging Methods and Markers 79
vii
4.0.12 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
4.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
4.2.1 AT Resolves Individual Puncta of Multiple Synaptic Proteins
in Mouse Cortex . . . . . . . . . . . . . . . . . . . . . . . . . 83
4.2.2 Synaptic Protein Distributions Imaged by AT Correlate as Ex-
pected from Synapse Structure . . . . . . . . . . . . . . . . . 85
4.2.3 AT Immunofluorescence of Synapsin Is Highly Reliable as Synapse
Marker . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
4.2.4 Synapsin Is Detectable at Virtually All Dendritic Spines . . . 88
4.2.5 EM Analysis Supports the Identification of Synapses with Synapsin
Immunoreactivity . . . . . . . . . . . . . . . . . . . . . . . . . 88
4.2.6 Multiple Synaptic Proteins Can Be Visualized Volumetrically
as a Synaptogram Mosaic . . . . . . . . . . . . . . . . . . . . 90
4.2.7 AT Imaging Discriminates Multiple Glutamatergic and GABAer-
gic Synapse Subtypes . . . . . . . . . . . . . . . . . . . . . . . 91
4.2.8 AMPA and NMDA Receptors Distributions Vary at Different
Glutamatergic Synapses . . . . . . . . . . . . . . . . . . . . . 93
4.2.9 Synapsin Is Present at All Glutamatergic and GABAergic Synapses,
but in Varying Amounts . . . . . . . . . . . . . . . . . . . . . 93
4.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
4.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
4.5 Experimental Procedures . . . . . . . . . . . . . . . . . . . . . . . . . 100
4.5.1 Tissue Preparation . . . . . . . . . . . . . . . . . . . . . . . . 100
4.5.2 LRWhite Sections . . . . . . . . . . . . . . . . . . . . . . . . . 100
4.5.3 ImmunoEM Staining . . . . . . . . . . . . . . . . . . . . . . . 101
4.5.4 Colocalization Analysis . . . . . . . . . . . . . . . . . . . . . . 103
viii
4.5.5 Transmission and Scanning Electron Microscopy . . . . . . . . 103
4.6 Supplemental Information . . . . . . . . . . . . . . . . . . . . . . . . 103
4.7 Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
5 Single-synapse analysis of a diverse synapse population: synapse
discovery and classification 122
5.0.1 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122
5.0.2 Author Summary . . . . . . . . . . . . . . . . . . . . . . . . . 123
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124
5.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126
5.2.1 Identifying Putative Synaptic Loci . . . . . . . . . . . . . . . 126
5.2.2 Manual Classification . . . . . . . . . . . . . . . . . . . . . . . 128
5.2.3 Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . 129
5.2.4 Unsupervised Clustering . . . . . . . . . . . . . . . . . . . . . 131
5.2.5 Supervised Classification . . . . . . . . . . . . . . . . . . . . . 132
5.2.6 Post-Classification Analysis . . . . . . . . . . . . . . . . . . . 137
5.2.7 Classification Application . . . . . . . . . . . . . . . . . . . . 139
5.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141
5.3.1 Limitations and Future Work . . . . . . . . . . . . . . . . . . 142
5.4 Materials and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . 143
5.4.1 Acquisition of array tomographic volume . . . . . . . . . . . . 143
5.4.2 Normalization and background subtraction of volumetric data 145
5.4.3 Extraction of synaptic loci . . . . . . . . . . . . . . . . . . . . 146
5.4.4 PCA image treatment . . . . . . . . . . . . . . . . . . . . . . 146
5.4.5 Normalization of pairwise channel data . . . . . . . . . . . . . 146
5.4.6 Perpendicularization of cortical data . . . . . . . . . . . . . . 147
5.4.7 Software packages used . . . . . . . . . . . . . . . . . . . . . . 148
ix
6 Future Directions and Conclusions 162
Bibliography 165
x
List of Tables
2.1 Recipe: Alternative Antibody Dilution Solution with NGS (1 mL) . . . 41
2.2 Recipe: Alternative Blocking Solution with NGS (1 mL) . . . . . . . . 41
2.3 Recipe: Blocking Solution with BSA (1 mL) . . . . . . . . . . . . . . 41
2.4 Recipe: Elution Solution (10 mL) . . . . . . . . . . . . . . . . . . . . 42
2.5 Recipe: Fixative (4 mL) . . . . . . . . . . . . . . . . . . . . . . . . . 42
2.6 Recipe: Subbing Solution (300 mL) . . . . . . . . . . . . . . . . . . . 42
2.7 Recipe: Wash Buffer (50 mL) . . . . . . . . . . . . . . . . . . . . . . 43
2.8 Primary antibodies used with array tomography . . . . . . . . . . . . 44
4.1 Synaptic Antibodies Used in This Study . . . . . . . . . . . . . . . . 104
4.2 Proportion of Synapses from Different Synaptic Subtypes . . . . . . . 105
5.1 Machine Learning Algorithm Comparison . . . . . . . . . . . . . . . . 149
5.2 Estimated error rates . . . . . . . . . . . . . . . . . . . . . . . . . . . 149
xi
List of Figures
2.1 The sequence of steps for a basic immunofluorescence array tomogra-
phy process. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
2.2 Array tomographic images of layer 5 neuropil, barrel cortex of YFP-H
Thy-1 transgenic mouse . . . . . . . . . . . . . . . . . . . . . . . . . 47
2.3 Multiplexed staining for seven synaptic proteins in mouse cerebral cortex 49
2.4 A single iteration of the position-finding algorithm. . . . . . . . . . . 51
3.1 Enhanced ocular dominance plasticity in visual cortex of KbDb−/− mu-
tant mice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
3.2 Enhanced thalamocortical plasticity in KbDb−/− mutant mice . . . . 73
3.3 Incomplete segregation of RGC inputs to dLGN in KbDb−/− mutant
mice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
3.4 MHCI localization in relation to synaptic proteins during period of
retinogeniculate refinement . . . . . . . . . . . . . . . . . . . . . . . . 77
4.1 Array tomographic synapsin I immunofluorescence in the cerebral cor-
tex of an adult YFP-H mouse is punctate and consistent with synapse
identity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
4.2 Proteomic immunofluorescence AT of mouse somatosensory cortex yields
staining patterns consistent with synaptic protein distributions . . . . 108
xii
4.3 Multiple synaptic proteins are colocalized in a fashion consistent with
synaptic identity and glutamatergic and GABAergic synapse subtype 110
4.4 Dendritic spines in mouse cerebral cortex are contacted by synapsin
puncta and colocalize with other pre- and postsynaptic markers . . . 112
4.5 Ultrastructurally identified synapses are labeled with the synapsin an-
tibody . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
4.6 Synaptograms are useful for viewing proteomic information from seri-
ally sectioned single synapses . . . . . . . . . . . . . . . . . . . . . . 116
4.7 Proteomic imaging with AT reveals the diversity of cortical synapses . 118
4.8 Double innervated spines receive both a glutamatergic VGluT1 and
GABAergic synapse . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
5.1 The synaptogram as a tool for high-dimensional proteomic visualization150
5.2 Comparison of human rating to machine learning . . . . . . . . . . . 152
5.3 Unsupervised clustering of synapsin I imaged with array tomography 154
5.4 Feature importance for different molecular labels . . . . . . . . . . . . 156
5.5 Density and size of synapse classes as a function of depth through the
cortex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158
5.6 Positive and negative pairwise channel copresence . . . . . . . . . . . 160
xiii
Chapter 1
Introduction
Array tomography (AT) is a high-resolution proteomic imaging method that exploits
a combination of light and EM techniques to resolve fine details at the synapse level
across large fields of view spanning entire circuits [1, 2]. This allows us to address a
long-standing, basic problem of large-scale synapse quantification. At the genomic
level, neurons display a staggering amount of diversity in the number of their cell
types and the variety of their spatial distributions [3]. The synapses of those neurons,
each comprising hundreds of distinct protein species [4–6], have enough proteomic
complexity to potentially display even more systemic variation. For instance, it is now
clear that within each neurotransmitter category (e.g., glutamatergic, GABAergic,
cholinergic) there is substantial diversity in the expression of many intrinsic synaptic
proteins, including neurotransmitter transporters and receptors [7–16]. Yet, to date
our ability to study diverse populations of synapses in situ has been somewhat lacking.
The resolution of AT, along with its proteomic multiplexing capabilities, is well suited
to rectify that.
Much of my graduate work has centered on helping to shape AT into an imag-
ing method fit for practical use, by developing computational algorithms which take
1
advantage of AT’s particular qualities to automate its operation. The material com-
ponents of an array tomographic pipeline are an amalgamation of those already used
in fluorescence and electron microscopy [17]; indeed the physical technique has ex-
isted for some time [18]. What has enabled its rebirth as AT is the proliferation of
fluorescence immunohistochemistry and the availability of software tools needed to
image, align and analyze AT volumes. Those tools required extensive development
to graduate AT from proof of concept to working technology.
1.1 Production Methods
1.1.1 Section discovery
The automated tools created to assist array tomographic imaging needed to address
a number of interesting computational problems, beginning with data acquisition
itself. Owing to the ribbon-like structure formed by consecutive sections adjoining
each other, tissue samples prepared for AT have a rather interesting layout that defies
most imaging applications. AT sections are laid consecutively on a slide in a fashion
which is fairly predictable for low-resolution (10x) imaging, but ribbon geometry
usually includes too much variation for pure extrapolation to track them adequately.
Small lateral shifts (within a section) correspond to lateral shifts in image space, while
larger shifts (to different sections) include a z-shift component and different lateral
shifting, almost as if the field of view “wrapped around” the volume and appeared
on the other side again. Further, depending on block treatment there may be little
to no distinguishing features which might tell us when sections change.
On the other hand, our ribbons universally come with a fluorescent nuclear stain
which makes a very good fiducial marker: DAPI, which intercalates into DNA and
thus labels cell nuclei. In the mammalian cortex, cell bodies are spaced tightly enough
2
that any given field of view will image a dozen or so nuclei. With roughly 10 µm
nuclear sizes and section thicknesses in the nanometer range, the view of the corre-
sponding point on the next consecutive section will remain almost entirely unchanged.
Therefore, while determining the break between sections may be a difficult problem,
finding the same position in the next section, barring tissue damage, and assuming
you have a reasonable idea where it is to start searching, is not.
In an effort to map AT ribbons by exploiting this observation, I developed a
method which uses short extrapolations refined by cross-correlation searches. The
exact mechanics, implemented as an Axiovision plugin, are presented later in this
chapter, but the basic procedure is: a human presents, as input, two points corre-
sponding to the same lateral position on two adjacent sections. The plugin uses the
vector between them to predict the stage coordinates of the next consecutive position.
Owing to AT’s linear ribbons of regularly-spaced sections, this estimate is accurate
enough that the true point is usually somewhere in the area. To find it, we use a
cross-correlation search comparing the areas around the second and third point to lo-
cate the best match. The addition of a Kalman filter [19] (such that the tissue patch
we attempt to locate is actually the weighted running average of recent patches) aids
recovery in the face of tissue damage.
The resulting plugin worked well enough to have become a staple tool for use in
AT applications for several years. Future improvements will likely focus on improving
the user interface of the plugin, such that it becomes easier to pause, change and
correct position lists to suit the particular ribbon under analysis. Support for the
use of fiducial markers would also be highly useful, and would probably be a vital
prerequisite for any automated imaging solution.
3
1.1.2 Multistackreg
Once an array tomographic data set has been imaged, it is not immediately ready for
analysis. Ribbons curve and warp during sectioning and plating, and removal/reinsertion
invariably introduces some rotation and offset. This necessitates an alignment step
to ensure that the tissue on each section is in the same positional reference frame as
the the tissue on the next.
While some alignment utilities existed at the time of AT’s creation, they were
inadequate for our purposes. We were using the best of the ImageJ plugins, Stack-
reg [20], but it had a number of areas which needed addressing. Stackreg could at
most align standard color (RGB) images, while at the time we were already producing
volumes with up to 5 different labels in a single imaging session. Although all of these
labels began in the same reference frame, it would be incorrect to consider multiple
independent alignments to still inhabit in the same frame, and more importantly,
almost impossible to correct if there was an error. Additionally, a few of the labels
(DAPI) made for excellent alignment between sections, and the mostly punctate rest
did not. We wanted to align with the signal from DAPI, without the noise from the
other channels. My solution was to modify Stackreg to include a save/load func-
tionality. This allowed us to treat all channels in an imaging session as independent
grayscale images, align with the best, and apply to the rest.
Multiple imaging sessions also proved to be a problem, as it is difficult for a human
to insert a standard glass slide into a slide holder with nanometer precision. I further
modified Stackreg (now renamed MultiStackreg and distributed independently, at
the request of Stackreg’s author) to be capable of aligning one stack to another, each
section of one stack aligned to its corresponding index in the other stack. This allowed
us to register multiple imaging sessions into the same reference frame, at which point
the generated transformation file could be used to align all of the labels at once.
The resulting plugin, with a revamped GUI and an API to make it accessible to
4
automation, proved sufficient for our alignment needs for some time. It remains the
first step in our alignment process, though we currently use another plugin which
boasts nonlinear dewarping (bUnwarpJ) for final alignment. Future development of
alignment methods will likely include nonlinear alignment algorithms which work
robustly enough to remove the need to use MultiStackReg for rough alignment.
1.2 Analysis Methods
Once an AT volume has been imaged and aligned, it is then amenable to analysis. Due
to the size of typical array tomographic volumes, on the order of thousands to millions
of cubic microns of high resolution proteomic data, these analysis methods must
necessarily be either automated or stereological. Both have particular advantages:
automated methods survey the entire data set and are not prone to confirmation bias,
while manual methods pass data more directly to human experts for interpretation.
1.2.1 Colocalization Analysis
Most of our early concerns with AT stemmed from uncertainty in the staining process.
A given label might look specific, and upon manual inspection it might correctly asso-
ciate with other labels we’d expect to find, but large scale surveys of each individual
label just to remove confirmation bias would be very inefficient. Cross-correlation
algorithms like Pearson’s correlation coefficient could give absolute measurements of
channel overlap, but those measurements would be more dependent on label density
than label proximity - a million weakly-correlated puncta would score higher than a
few completely overlapping examples. What we needed was a way to measure relative
correlation between the channels as imaged and deliberately uncorrelated channels
with the same image properties. Comparing the two correlation measures would give
an idea of the actual relationship between the channels of interest.
5
To put a relative correlation metric to use, I decided to implement and modify
Van Steensel’s algorithm for shifting colocalization [21]. By measuring Pearson’s
correlation with one channel undergoing a variety of lateral offsets, you can study
the spatial relationship between the channels. If your data is entirely punctate, as
synaptic labels in AT are, after some amount of shifting all local relationships will
be broken and you will be measuring baseline correlation, which can be compared
to the no offset case for the relative measure. In between the offset extremes, the
falloff of the correlation allows you to infer something of the relationship between the
channels. Sharper falloff shoulders imply either smaller puncta or puncta which do
not fully overlap.
To utilize several aspects specific to array tomographic volumes, I modified Van
Steensel’s algorithm in three ways. For ease of analysis and since AT features isotropic
x- and y- resolution, I modified the algorithm to be two-dimensional, returning a more
readily-analyzed heat map of correlations with x and y shifts. AT volumes are stacks,
so my implementation used the z-axis to repeat the x-y analysis on multiple z-levels,
resulting in measurements with lower variance than the original, often to the point
where the measurements were significant even using small patches of tissue. Finally,
I added a means by which the user could easily select a smaller window of tissue
for analysis. This had the effect of improving analysis speed without (due to the
preceding modification) losing statistical significance, and ensuring that only soma-
and blood vessel-less neuropil might be analyzed if desired (though the shifts were
small enough that they did not greatly impact the measurement regardless).
The resulting tool became a useful, if niche, part of the AT analysis kit. Its best
use is in the examination of antibody labeled images as a whole, without needing to
restrict the analysis volume to areas where the label is expected, e.g. the synapse.
It remains one of the best ways to examine the specificity and reliability of novel
labels, particularly in the case where multiple antibodies to the same protein are
6
being tested, as they can then be compared with each other was well as with a third
marker. For later analysis steps, especially those focused on synapse quantification,
additional tools are required.
1.2.2 Synaptogram
The manual side of our early analysis centered around visual inspection of AT data.
The user had to first “hunt through” the data to find suitable synapse candidates,
then iterate through all of the imaged channels, building a mental map of the lo-
cal geometry of that synapse. Due to the large amount of data and high proetomic
multiplexing, this process was very slow. However, it is still the best and most reli-
able method of synapse identification available to us. It relies on the perception and
expertise of the human viewer to apply the visual segmentation which defines the
presence of necessary synaptic components and verifies that they are in the correct
orientation relative to each other. This task incorporates a great deal of a priori
knowledge concerning the stearic and functional relationships between the different
molecular labels, the variance in labeling of each particular antibody, and the partic-
ular conditions under which that tissue had been fixed, embedded, labeled, imaged,
relabeled, etc.
In order to facilitate the manual effort required to find and identify synapses, we
devised the synaptogram as a way of displaying all of the requisite proteomic data at
once. It relies on the fact that synapses are small [112]: for many tissues, all of the
information needed to identify the synapse can be found in a volume no larger than
half a micron from the middle of the synapse. In AT resolution, this comprises a cube
11 pixels to a side. By splaying out the high dimensional data into a much larger
two dimensional space, we could present the information to the users more directly.
Synaptograms, and the human classifications performed with such, have crept into
virtually all of the tools I have developed since.
7
1.3 Synapse Classification
To utilize AT in synaptic quantification requires the development of new, automated
synapse detection and classification capabilities. Manual analysis using synaptograms
is acceptable for analyzing fragmentary subsets of a few hundred synapses, but it does
not scale beyond that. The use of synaptograms eases the difficulty of per-synapse
manual classification such that the effort of classifying a set of few hundred synapses
is no longer excessive, but no matter how convenient they are to analyze individually,
the sheer number of synapses makes manual analysis of the entire data set effectively
impractical.
The development of an automated synapse quantification algorithm, which essen-
tially automates the process of creating and analyzing synaptograms, can be broken
down into a few important pieces. To identify the sites of putative synapse locations I
decided to use Synapsin I puncta, for its synaptic ubiquity and robust labeling [23,24].
I used a local maximum filter to identify the peaks of Synapsin I staining, each of
which I considered a putative synapse location, or synaptic locus.
Determining which synaptic loci were actual synapses involved implementing a
supervised learning application. In this process, humans classify a small training
set of synapse examples on the basis of the presence of absence of relevant synaptic
labels. To facilitate this, I developed a browser-based active learning scheme which
aids training by pre-classifying the training examples, such that the humans only
need to correct the errors it makes. Once the training is complete, I extract the
salient features of the example loci’s proteomic distribution and extrapolate them,
using a random forest ensemble classifier, to the rest of the unknown synapses in
order to predict how the humans would classify those as well. To establish synaptic
identity, the different channel classifications are recombined into known or suspected
combinations corresponding to synapse classes. This allows us, in addition to accurate
8
quantification of expected synapse populations, to test for novel synapse types without
having to classify them before the fact, and successfully use it to identify a few
unexpected synaptic subpopulations.
Since AT data sets are likely to increase in size as our technique becomes more
consistent and our questions weightier, automated classification will only become a
more necessary piece of the analysis tool chain. A vital addition to this classification
process, when or if AT methods are usefully adapted to superresolution techniques
like STORM or STED, will be a move away from rotation-invariant features into a
feature set which incorporates the synapse geometry resolvable in those contexts.
9
Chapter 2
Array Tomography: High-Resolution
Three-Dimensional Immunofluorescence
Micheva KD, ORourke N, Busse B, Smith SJ. 2010a. Array tomography: High-
resolution three-dimensional immunofluorescence. Cold Spring Harbor Protocols doi:
10.1101/pdb.top89.
2.0.1 Abstract
Array tomography is a volumetric microscopy method based on physical serial sec-
tioning. Ultrathin sections of a plastic-embedded tissue specimen are cut using an
ultramicrotome, bonded in ordered array to a glass coverslip, stained as desired, and
then imaged. The resulting two-dimensional image tiles can then be computationally
reconstructed into three-dimensional volume images for visualization and quantita-
tive analysis. The minimal thickness of individual sections provides for high-quality,
rapid staining and imaging, whereas the array format provides for reliable and con-
venient section handling, staining, and automated imaging. In addition, the arrays
physical stability permits the acquisition and registration of images from repeated cy-
cles of staining, imaging, and stain elution and from imaging by multiple modalities
10
(e.g., fluorescence and electron microscopy). Array tomography offers high resolution,
depth invariance, and molecular discrimination, which justify the relatively difficult
tomography array fabrication procedures. With array tomography it is possible to
visualize and quantify previously inaccessible features of tissue structure and molecu-
lar architecture. This chapter will describe one simple implementation of fluorescence
array tomography and provide protocols for array tomography specimen preparation,
image acquisition, and image reconstruction.
2.1 Introduction
Our understanding of tissue function is constrained by incomplete knowledge of tis-
sue structure and molecular architecture. Genetics, physiology, and cell biology make
it overwhelmingly clear that all cell and tissue function depends critically on the
composition and precise three-dimensional configuration of subcellular organelles and
supramolecular complexes, and that such structures may consist of very large numbers
of distinct molecular species. Unfortunately, the intricacies of tissue molecular archi-
tecture badly outstrip the analytical capability of all presently known tissue imaging
methods.
Array tomography is a new high-resolution, three-dimensional microscopy method
based on constructing and imaging two-dimensional arrays of ultrathin (70–200 nm
thickness) specimen sections on solid substrates. (The word tomography derives from
the Greek words tomos, to cut or section, and graphein, to write: The moniker array
tomography thus simply connotes the writing of a volume image from an array of
slices.) Array tomography allows immunofluorescence imaging of tissue samples with
resolution, quantitative reliability, and antibody multiplexing capacity that is greatly
superior to previous tissue immunofluorescence methods [1]. Array tomography was
developed with neuroscience applications in mind (e.g. [61,69,70]), and the following
11
description will be illustrated with examples from neuroscience and particularly from
studies of synapses and circuits in rodent brain.
2.1.1 Array Tomography Procedures
A sequence of eight steps for a very basic array tomography protocol is illustrated
in Figure 2.1. Array tomography begins with (Step 1) the chemical fixation of the
specimen, followed by (Step 2) dissection and embedding in resin (LR White). Resin-
embedded specimen blocks are then (Step 3) mounted in an ultramicrotome chuck,
trimmed, and prepared for ultrathin sectioning. Block preparation includes careful
trimming of the block edges and application of a tacky adhesive to the top and bottom
block edges. As shown in the magnified detail of Step 3, this adhesive causes the
spontaneous formation of a stable splice between successive serial sections as they are
cut by the ultramicrotomes diamond knife blade. The automated cycling of a standard
ultramicrotome produces automatically a ribbon up to 45 mm in length, which may
consist of more than 100 serial sections held on a water surface. Ribbons are then
manually transferred to the surface of a specially coated glass coverslip (Step 4). The
resulting array can be stained using antibodies or any other desired reagents (Step 5).
After immunostaining, arrays can be imaged using fluorescence microscopy (Step 6).
The minimal thickness of array sections promotes very rapid and excellent staining
and imaging, whereas the array format promotes convenient and reliable handling
of large numbers of serial sections. The individual two-dimensional section images
are then computationally stitched and aligned into volumetric image stacks (Step 7)
to provide for three-dimensional image visualization and analysis (Figure 2.2). The
volumetric image stacks are stored electronically for analysis and archiving (Step
8). Although array tomography procedures are at present relatively complex and
demanding in comparison to many other imaging methods, each of the steps lends
itself potentially to automated and highly parallel implementations, and for many
12
applications the advantages outlined below can easily justify this extra effort.
Resolution
The volumetric resolution of fluorescence array tomography compares very favorably
with the best optical sectioning microscopy methods. The axial resolution limit for
array tomography is simply the physical section thickness (typically 70 nm). For a
confocal microscope, the z-axis resolution is limited by diffraction to e700 nm. The
confocals limiting z-axis resolution is usually worsened, however, by spherical aber-
ration when a high-numerical-aperture (high-NA) objective is focused more deeply
than a few micrometers into any tissue specimen. Array tomography physical sec-
tioning thus improves on ideal confocal optical sectioning by at least an order of
magnitude. Spherical aberrations also adversely impact the lateral resolution of con-
focal microscopes as they are focused into a tissue depth. Array tomography avoids
this problem, because the high-NA objective is always used at its design condition
(immediate contact between specimen and coverslip), with no chance of focus depth
aberration. The degradation of lateral resolution that occurs at focus depths of just a
few micrometers can easily exceed a factor of 2 (see http://www.microscopy.fsu.edu/),
so a very conservative approximation would imply that array tomography using or-
dinary high-NA, diffraction-limited optics would improve volumetric resolution (the
product of improvements in x-, y-, and z-axes) by a factor of 40 (= 2 x 2 x 10). The
improved volumetric resolution realized by array tomography can be very significant.
For instance, individual synapses in situ within mammalian cortex generally cannot
be resolved optically from their nearest neighbors by confocal microscopy but can be
resolved quite reliably by array tomography [1].
13
Depth Invariance
The major limitation to quantitative interpretation of whole-mount tissue immunoflu-
orescence images arises from reductions in both immunostaining and imaging efficien-
cies as focal plane depth increases. Diffusion and binding regimes typically limit the
penetration of labeling antibodies to the first few micrometers below the surface
of a tissue, even after multiday incubations. Imaging efficiency likewise decreases
with depth, as increasing spherical aberration and light scattering reduce signals
profoundly with focal plane depths of just a few micrometers. These staining and
imaging efficiency gradients make any quantitative comparison of specimen features
at different depths with whole-mount (e.g., confocal) volume microscopy difficult and
unreliable. Array tomography completely circumvents depth dependence issues, be-
cause each specimen volume element is stained identically owing to minimal section
thickness, and imaged identically because every section is bonded directly to the
coverslip surface.
Multiplexity
Traditional multicolor immunofluorescence techniques have provided compelling evi-
dence for the localization of multiple molecular species at individual subcellular com-
plexes. For example, because there is a very large number and a great diversity of
distinct molecules at individual synapses, there is a pressing need for imaging tech-
niques that can simultaneously discriminate many more than the three or four species
that can be distinguished by standard multicolor immunofluorescence. Attempts have
been made in the past to improve the multiplexity of immunofluorescence microscopy
by repeated cycles of staining, imaging, and stain elution, but the results have been
disappointing owing to the tendency of antibody elution treatments to destroy sam-
ples. In array tomography, specimens are stabilized by the embedding resin matrix
14
and by tight attachment to the coverslip substrate. An example of multiplexed stain-
ing with array tomography is shown in Figure 2.3. We have shown as many as nine
cycles of staining, imaging, and elution thus far [1]. With four fluorescence colors per
cycle, this would mean that 36 or more antigens could be probed in one specimen.
We now routinely acquire four colors in each of three cycles for a total of 12 marker
channels. Although 12–36 markers may still fall short of the degree of multiplex-
ing needed to fully probe the many and diverse molecules composing a synapse, it
is a substantial advance in comparison to traditional multicolor immunofluorescence
methods.
Volume Field of View
In principle, array tomography offers unique potential for the acquisition of high-
resolution volume images that extend seamlessly over very large tissue volumes. The
depth invariance of array tomography noted above eliminates any fundamental limit
to imaging in depth, whereas the availability of excellent automated image mosaic ac-
quisition, alignment, and stitching algorithms allows tiling over arbitrarily large array
areas. Ultimate limits to the continuous arrayable volume will be imposed by diffi-
culties in tissue fixation, processing, and embedding (owing to diffusion limitations)
as thicker volumes are encountered, and by mechanical issues of ultramicrotome and
diamond knife engineering as block face dimensions increase. Successful array tomog-
raphy has already been shown for volumes with millimeter minimum dimensions, and
it seems likely that volumes with minimum dimensions of several millimeters (e.g.,
an entire mouse brain) may be manageable eventually.
In practice, the size of seamless array tomography volumes is limited by the re-
quirement that numerous steps in the fabrication, staining, and imaging of arrays be
performed through many iterations without failure. At present, the most error-prone
steps are those involved in array fabrication, whereas the most time-consuming are
15
those involved in image acquisition. Ongoing engineering of array fabrication mate-
rials and processes will advance present limits to the error-free production of large
arrays, whereas image acquisition times will be readily reducible by dividing large ar-
rays across multiple substrates and imaging those subarrays on multiple microscopes.
The following protocols describe one simple implementation of immunofluores-
cence array tomography suitable for any laboratory with standard equipment and
some expertise in basic fluorescence microscopy and ultrathin sectioning. In addi-
tion, algorithms designed to fully automate the acquisition of array images are de-
scribed for the benefit of any laboratory having or planning to acquire the appropriate
automated fluorescence microscopy hardware and software.
2.2 Protocol A: Rodent Brain Tissue Fixation and
Embedding
Careful preparation of the tissue is essential for successful array tomography. These
steps take time to complete and require some practice to perfect.
2.2.1 Materials
CAUTION: See full Cold Spring Harbor citation, Appendix 6 for proper handling
of materials marked with <!>. See the end of the chapter for recipes for reagents
marked with <R>.
Reagents
• Ethanol <!>, 4◦C
• Fixative <R>
16
• Isoflurane <!> (VWR International)
• LR White resin <!> (medium grade, SPI Supplies 2646 or Electron Microscopy
Sciences 14381)
• Mice
• Wash buffer <R>, 4◦C
Equipment
• Capsule mold (Electron Microscopy Sciences 70160)
• Dissection instruments: handling forceps, small scissors, bone rongeur, forceps
#5, small spatula, scalpel
• Gelatin capsules, size 00 (Electron Microscopy Sciences 70100)
• Guillotine
• Microscope, dissection
• Microwave tissue processor system (PELCO with a ColdSpot set at 12◦C; Ted
Pella, Inc.) (optional)
• Oven (set at 51◦–53◦C)
• Paintbrush, fine
• Petri dishes, 35-mm
• Scintillation vials, glass, 20-mL
17
2.2.2 Experimental Method
Dissecting and Fixing Tissue
1. Anesthetize the rodent with isoflurane.
2. Remove head using the guillotine.
3. In a hood, using the dissection tools quickly remove the brain and plunge it into
a 35-mm Petri dish filled with fixative (room temperature). Remove the tissue
region of interest.
4. Transfer tissue to a scintillation vial with fixative solution. Use e1 mL of fixative
per vial, or just enough to cover the tissue; excessive liquid volume will cause
overheating in the microwave.
5. Microwave the tissue in the fixative using a cycle of 1 min on/1 min off/1 min
on at 100–150 W. After this and each subsequent cycle feel the glass vial to
check for overheating. If solutions are getting too warm (>37◦C), decrease the
amount of liquid added.
6. Microwave using a cycle of 20 sec on/20 sec off/20 sec on at 350–400 W. Repeat
three times.
7. Leave the tissue at room temperature for e1 h.
If a microwave is unavailable, fix the samples at room temperature for up to 3
h or overnight at 4◦C. Tissue can also be fixed by perfusion.
8. Prepare ethanol dilutions: 50%, 70%, 95%, and 100% in ultrapure H2O. Keep
at 4◦C.
9. Wash the tissue in wash buffer (4◦C) twice for 5 min each.
18
10. Transfer the tissue to a 100-mm Petri dish, cover with wash buffer, and under a
dissecting microscope dissect the tissue into smaller pieces (<1 mm in at least
one dimension).
11. Return the samples to scintillation vials and rinse them twice with wash buffer
for 15 min each at 4◦C.
12. Change to 50% ethanol (4◦C) and microwave the samples for 30 sec at 350 W.
Use just enough liquid to cover the tissue; excessive liquid volume will cause
overheating.
If a microwave processor is unavailable, Steps 12–20 can be performed for 5 min
per step on the bench.
13. Change to 70% ethanol (4◦C) and microwave the samples for 30 sec at 350 W.
Processing Samples that Contain Fluorescent Proteins
If processing samples with fluorescent proteins, then complete Steps 14–16. If
samples do not contain fluorescent proteins, then skip Steps 14–16, and instead
continue with Step 17.
14. Change one more time to 70% ethanol and microwave for 30 sec at 350 W.
15. Change to a mixture of 70% ethanol and LR White (1:3; if it turns cloudy add
1–2 extra drops of LR White) and microwave for 30 sec at 350 W.
16. Go to Step 20.
Processing Samples that Do Not Contain Fluorescent Proteins
17. Change to 95% ethanol (4◦C) and microwave for 30 sec at 350 W.
19
18. Change to 100% ethanol (4◦C) and microwave for 30 sec at 350 W. Repeat once.
19. Change to 100% ethanol and LR White resin (1:1 mixture, 4◦C) and microwave
for 30 sec at 350 W.
Embedding Brain Tissue
20. Change to 100% LR White (4◦C) for 30 sec at 350 W. Repeat two more times.
21. Change to fresh LR White (4◦C) and leave either overnight at 4◦C or 3 h at
room temperature.
22. Using a fine paintbrush, place the tissue pieces at the bottom of gelatin capsules
(paper labels can also be added inside the capsule) and fill to the rim with LR
White.
See Troubleshooting.
23. Close the capsules well and put in the capsule mold.
Gelatin capsules are used because they exclude air that inhibits LR White
polymerization. The little bubble of air that will remain at the top of the
capsule will not interfere with the polymerization.
24. Put the mold with capsules in the oven set at 51◦–53◦C. Leave overnight (e18–24
hours).
2.2.3 Troublesbooting
Problem (Step 22): It is difficult to orient the tissue.
Solution: If tissue orientation is important, it should be dissected in a shape that
will make it naturally sink in the resin the desired wayfor example, for mouse cerebral
20
cortex, a 300-µm coronal slice can be cut and trimmed to a rectangle, e1 x 2 mm,
that includes all of the cortical layers. Alternately, if the tissue is elongated and has
to be cut perpendicular to the long axis, the capsules can be positioned on the side,
instead of standing up in the mold.
2.3 Protocol B: Production of Arrays
Once the tissue has been embedded, the arrays are prepared. This protocol requires
familiarity with ultramicrotome sectioning for electron microscopy.
2.3.1 Materials
CAUTION: See full Cold Spring Harbor citation, Appendix 6 for proper handling
of materials marked with <!>. See the end of the chapter for recipes for reagents
marked with <R>.
Reagents
• Borax
• Contact cement (DAP Weldwood)
• Subbing solution <R>
• Tissue, fixed and embedded as in Protocol A
• Toluidine blue
• Xylene <!>
21
Equipment
• Coverslips (for routine staining: VWR International Micro Cover Glasses, 24 x
60-mm, No.1.5, 48393-252; for quantitative comparison between different arrays:
Bioscience Tools High Precision Glass Coverslips CSHP-No1.5-24 x 60)
• Diamond knife (Cryotrim 45; Diatome) (optional)
• Diamond knife (Histo Jumbo; Diatome)
• Eyelash tool
• Marker
• Razor blades
• Paintbrush, fine
• Slide warmer set at 60◦C
• Staining rack (Pacific Southwest Lab Equipment, Inc. 37-4470 and 4456)
• Syringe
• Transfer pipettes, extra fine-tip polyethylene (Fisher Scientific 13-711-31)
• Ultramicrotome (e.g., Leica EM UC6)
2.3.2 Experimental Method
1. Prepare subbed coverslips. They can be prepared in advance and stored in
coverslip boxes until needed.
i Put clean coverslips into the staining rack.
22
ii Immerse the rack in the subbing solution and remove bubbles formed at the
surface of the coverslips using a transfer pipette.
iii After 30–60 sec, lift out and drain off excess liquid. Leave the coverslips in
a dust-free place until they are dry.
2. Using a razor blade, trim the block around the tissue. A blockface e2 mm wide
and 0.5–1 mm high works best.
3. Using a glass knife or an old diamond knife cut semithin sections until you
reach the tissue. Mount a couple of the semithin sections on a glass slide and
stain with 1% toluidine blue in 0.5% borax. View the stained sections under a
microscope to determine whether they contain the region of interest and decide
how to trim the block.
4. Trim the block again, to ensure that the blockface is not too big and the leading
and trailing edges of the blockface are parallel. The Cryotrim 45 diamond knife
works well for this purpose.
5. Using a paintbrush, apply contact cement diluted with xylene (1:2) to the lead-
ing and trailing sides of the block pyramid. Blot the extra glue using a tissue.
6. Insert a subbed coverslip into the knife boat of the Histo Jumbo diamond knife.
You may need to push it down and wet it using the eyelash tool. Make sure
that the knife angle is set at 0◦.
7. Carefully align the block face with the edge of the diamond knife. If the block
starts cutting at an angle, the leading and trailing edge of the block face will
no longer be parallel.
8. Start cutting ribbons of serial sections (70–200 nm) with the diamond knife. In
general, thinner sections stick better to the glass.
23
See Troubleshooting.
9. When the desired length of the ribbon is achieved, carefully detach it from the
edge of the knife by running an eyelash along the outer edge of the knife. Then
use the eyelash to gently push the ribbon toward the coverslip, so that the edge
of the ribbon touches the coverslip at the interface of the glass and the water.
The edge of the ribbon will stick to the glass.
10. Using a syringe, slowly lower the water level in the knife boat until the entire
ribbon sticks to the glass.
11. Remove the coverslip from the water and label it on one edge. Also, mark
the position of the ribbon by circling it with a marker on the backside of the
coverslip.
This allows you to keep track of the samples and provides a way to tell which
side of the coverslip the ribbon is mounted on (without a label, after the ribbon
dries, it is not possible to tell which side it is on).
12. Let the ribbon dry at room temperature and place the coverslip on the slide
warmer (e60◦C) for 30 min. The slides can be stored at room temperature for
at least 6 mo.
2.3.3 Troubleshooting
Problem (Step 8): The ribbons curve.
Solution: Sometimes, even when the leading and trailing edges of the blockface are
parallel, the ribbons are curved. This can happen when there is more resin around the
tissue on one side of the block than the other. As the section comes in contact with
water it expands, however, the resin and tissue expand to different degrees, causing
24
curving of the ribbon. Thus, make sure that the extra resin is trimmed on either side
of the block.
Problem (Step 8): The ribbons break.
Solution: Trim the block using a very sharp razor blade or, even better, the
Cryotrim diamond knife. Make sure that the blockface is at least twice as wide as it
is high. Apply glue again and take care to align the block so the edge of the blockface
is parallel to the knife edge.
2.4 Protocol C: Immunostaining and Antibody
Elution
The tissue arrays are prepared for imaging by binding primary antibodies against
specific cellular targets followed by secondary fluorescent antibodies. Alternatively,
fluorescent proteins can be used that have been introduced into the tissue before
dissection.
2.4.1 Materials
CAUTION: See Appendix 6 for proper handling of materials marked with <!>. See
the end of the chapter for recipes for reagents marked with <R>.
Reagents
• Alternative antibody dilution solution with normal goat serum (NGS) <R>
• Alternative blocking solution with NGS <R>
• Blocking solution with bovine serum albumin (BSA) <R>
• Elution solution <R>
25
• Glycine
• Mounting medium: SlowFade Gold antifade reagent with DAPI <!> (Invitrogen
S36939) or without DAPI (Invitrogen S36937)
• Primary antibodies, see Table 2.8
A detailed list of antibodies that have been tested for array tomography is
available from www.smithlab.stanford.edu.
• Secondary antibodies: for example, the appropriate species of Alexa Fluor 488,
594, and 647, IgG (H+L), highly cross-adsorbed (Invitrogen)
• Tissue sectioned as in Protocol B
• Tris buffered saline tablets (Sigma-Aldrich T5030)
Equipment
• Microcentrifuge
• Microscope slides (precleaned Gold Seal Rite-On micro slides; Fisher Scientific
12-518-103)
• PAP pen (ImmEdge Pen, Vector Laboratories H-4000)
• Petri dishes, 100-mm diameter
• Slide warmer set at 60◦C
• Transfer pipettes, extra fine-tip polyethylene (Fisher Scientific 13-711-31)
26
2.4.2 Experimental Method
1. Encircle the ribbon of sectioned tissue with a PAP pen.
2. Place the coverslip into a humidified 100-mm Petri dish and treat the sections
with 50 mM glycine in Tris buffer for 5 min.
3. Apply blocking solution with BSA for 5 min.
If there is a problem with high background staining, see the alternate blocking
and staining protocol beginning with Step 21.
4. Dilute the primary antibodies in blocking solution with BSA. Approximately
150 µL of solution will suffice to cover a 30-mm-long ribbon.
5. Centrifuge the antibody solution at 13,000 revolutions per minute (rpm) for 2
min before applying it to the sections.
6. Incubate the sections in primary antibodies either overnight at 4◦C or for 2 h
at room temperature.
Primary antibodies are diluted to 10 µg/mL, although the best concentration
will need to be determined for each antibody solution.
7. Rinse the sections three to four times with Tris buffer for a total of e20 min.
Wash the sections using a manual perfusion method, simultaneously adding Tris
buffer on one end and removing if from another with plastic transfer pipettes.
8. Dilute the appropriate secondary antibodies in blocking solution with BSA
(1:150 for Alexa secondaries).
9. Centrifuge secondary antibody solution at 13,000 rpm for 2 min.
10. Incubate the sections in secondary antibodies for 30 min at room temperature
in the dark.
27
11. Rinse the sections three to four times with Tris buffer for e5 min each.
12. Wash the coverslip thoroughly with filtered ultrapure H2O to remove any dust
or debris, leaving some H2O on the sections so that they do not dry out.
13. Mount the sections on a clean, dust-free microscope slide with SlowFade Gold
Antifade containing DAPI.
14. Image the sections as soon as possible after immunostaining, or at least the same
day. If you are planning to restain the sections with additional antibodies, elute
the antibodies (Steps 15–19) as soon as possible after imaging.
Elute Antibodies Before Restaining
15. Add filtered ultrapure H2O around the edge of the coverslip to help slide it off
the microscope slide.
Wash the coverslip gently with filtered ultrapure H2O to rinse off the mounting
medium.
16. Apply elution solution for 20 min.
17. Gently rinse the coverslips twice with Tris, allowing them to sit for 10 min with
each rinse.
18. Rinse the coverslips with filtered ultrapure H2O and let them air dry completely.
19. Bake the coverslip on a slide warmer set to 60◦C for 30 min.
Staining the Sections Multiple Times
20. Restain using the Steps 2–13 above or store array at room temperature until
needed.
28
See Troubleshooting.
Alternative Staining Method to Reduce Background
21. Proceed through Steps 1 and 2 of the staining protocol above.
22. Incubate the sections for 30 min with alternative blocking solution with NGS.
If secondary antibodies are made in donkey, use normal donkey serum; if sec-
ondary antibodies are made in horse, use normal horse serum, etc. This protocol
can only be used if all of the secondary antibodies are made in the same animal.
23. Dilute the primary and secondary antibodies in alternative antibody dilution
solution with NGS.
24. Follow the rest of the staining protocol above, using the solutions with NGS.
2.4.3 Troubleshooting
Problem (Step 20): There is incomplete elution of antibodies.
Solution: To check for incomplete elution, which could interfere with subsequent
antibody staining, perform the following control experiment. Stain with the antibody
of interest and image a region that you can relocate later. Elute and apply the
secondary antibody again. Image the same region as before, using the same exposure
time; this will give an estimate of how much primary antibody was left after the
elution. Increase the exposure time to determine if longer exposure times reveal the
initial pattern of antibody staining. If the first antibody was not eluted sufficiently, try
longer elution times. Some antibodies elute poorly (e.g., rabbit synapsin or tubulin)
and, if followed by a weaker antibody, may still be detectable after the elution. In
such cases, begin the experiment with the weaker antibodies.
29
2.5 Protocol D: Imaging Stained Arrays
Tissue arrays are imaged using a conventional wide-field fluorescence microscopy.
Images can be captured manually or, with the appropriate software and hardware,
the process can be automated.
2.5.1 Materials
Reagents
• Immunostained brain sections prepared as in Protocol C
Equipment
• Digital camera (Axiocam HR, Carl Zeiss)
• Fluorescence filters sets (all from Semrock) YFP, 2427A; GFP, 3035B; CFP,
2432A; Texas Red, 4040B; DAPI, 1160A; FITC, 3540B; and Cy5, 4040A
• Illuminator series 120 (X-Cite)
• Objective (Zeiss Immersol 514 F Fluorescence Immersion Oil)
• Piezo Automated Stage (Zeiss)
• 10x Plan-Apochromat 0.45 NA
• 63x Plan-Apochromat 1.4 NA oil objective
• Software (e.g., Zeiss Axiovision with Interactive Measurement Module, Au-
tomeasure Plus Module and Array Tomography Toolbar; the toolbar can be
downloaded from http://www.stanford.edu/ebbusse/work/downloads.html)
• Upright microscope (Zeiss Axio Imager.Z1)
30
2.5.2 Experimental Method
Manual Image Acquisition
1. Focus on your sample using the 10x objective. Find the ribbon by focusing on
the DAPI label or another bright label that is not prone to bleaching. Once you
have found the right general area of the sample, switch to the 63x objective.
See Troubleshooting.
2. Find the exact area of the sample that you want to image. Choose a landmark
that you can use to find the same spot in the next section. A useful landmark
should not change dramatically from one section to the next (e.g., a DAPI-
stained nucleus or blood vessel). Because the sections are 70–200 nm thick we
can often follow the same nucleus through the entire length of a long array. Line
up your landmark with a crosshair in the middle of the field.
3. Set the correct exposure for each of your fluorescence channels.
4. Beginning with the first section, collect an image of your area of interest.
5. Manually, move to the same area of the next section. The glue on the edge
of each section is autofluorescent, so you can tell when you have moved to the
next section. Align your landmark carefully in each section to assure that your
image alignment will run smoothly.
See Troubleshooting.
6. Continue to the end of the ribbon, collecting an image from each section. Align
your stack of images using Protocol E.
31
Automated Image Acquisition
Although we have developed our automated tools to work with Zeiss Axiovision
software, any microscopy software suite (such as Micro-Manager) controlling
an automated stage should be adaptable to this approach. Some steps may be
altered or eliminated, depending on your framework and implementation.
7. With the 10x objective, find the ribbon by focusing on the DAPI label or another
bright label that is not prone to bleaching.
See Troubleshooting.
8. Acquire a mosaic image of the entire ribbon with the MosaicX Axiovision mod-
ule, using a bright label that does not vary much between sections, such as
DAPI.
9. Find the top left and bottom right corners of the ribbon and use them to define
the limits of the mosaic in the Mosaic Setup dialog.
10. Set three to four focus positions along the length of the ribbon and enable focus
correction.
11. Collect the mosaic image. Convert the mosaic to a single image with the Convert
Tile Images dialog, setting the Zoom factor to 1 so that the resulting image is
the same size.
See Troubleshooting.
12. Choose a point of interest to be imaged in the ribbon. Place a marker on
that point via Measure → Marker. Place another marker at the same spot in
the next consecutive section. Create a table of the x and y coordinates of the
markers, DataTable, via Measure → Create Table, with the list option. This
allows Axiovisions Visual Basic scripts to read the marker locations.
32
See Troubleshooting.
13. With the large, stitched image selected, call PrepImage and MarkLoop from
the Array Tomography toolbar.
14. The preceding step will create a file (csv) with a list of the coordinates for the
same position in each section, which will be automatically saved in the same
folder as the mosaic and with the same name as the stitched image. To load
the position list, go to Microscope → Mark and Find, click the New icon, and
then the Import Position list button. In the Mark and Find dialog, switch to
the Positions tab which will let you review or edit the calculated positions by
double-clicking on any position.
15. Collect one field of view at each point via Multidimensional Acquisition with the
position list checkbox set. We recommend using a bright label that is present
throughout the field as the first channel, setting it to autofocus at each position.
Review your images at the end to make sure they are all in focus.
See Troubleshooting.
2.5.3 Troubleshooting
Problem (Steps 1 and 7): Sections cannot be found under the microscope.
Solution: Use DAPI in the mounting mediumit will stain the nuclei brightly and
make it easy to find the sections with the 10x objective. Make sure the coverslip has
been mounted with the sections on the same side as the mounting medium and that
there are no bubbles in the immersion oil.
Problem (Steps 5 and 15): Sections are wrinkled.
Solution: Section wrinkling can occur at several steps in the procedure. First, it
can occur during array preparation if the coverslip is put on the slide warmer while
33
the ribbon is still wet. Make sure that the sections are dry before putting them on
the slide warmer. It can also occur if the blockface is too big (>1 x 2 mm) or sections
are too thick (>200 nm). Second, wrinkles can be caused by improper subbing of
the coverslips. The gelatin must be 300 Bloom (measure of stickiness, higher number
indicates stickier) and should not be heated above 60◦C during solution preparation.
Third, sections can wrinkle if the ribbon is stored with the mounting solution for >2
days. Finally, wrinkling can occur after antibody elution, especially with sections 200
nm thick. Make sure that the solutions are applied gently during the elution and the
array is completely dry before putting it on the slide warmer.
Problem (Steps 5 and 15): There is no staining or fluorescent signal.
Solution: Use a high-power, high-NA objectiveideally a 63x oil objective. Only
immunofluorescence with antibodies against abundant antigens (e.g., tubulin, neu-
rofilament) will be visible with a low-power objective. Also, check if there are two
coverslips stuck to each other; this will make it impossible to focus at higher magni-
fication.
Problem (Steps 5 and 15): Punctate staining is seen with a seemingly random
distribution.
Solution: Immunostaining with thin array sections (≤200 nm) looks different from
staining on thicker cryosections or vibratome sections. Because a very thin layer of
tissue is probed, many stains that appear continuous on thicker sections will appear
punctate with array tomography. A 3D reconstruction of a short ribbon (10–20 sec-
tions) can be helpful for comparison. You may also need to test antibody performance.
First, compare the antibody staining pattern to that of different antibodies against
the same antigen or a different antigen with a similar distribution. For example, a
presynaptic marker should be adjacent to a postsynaptic marker. Other common
controls for immunostaining can be used, such as omitting primary antibodies, stain-
ing a tissue that does not contain the antigen, etc. Second, specific controls for array
34
tomography include comparison of the antibody staining patterns from adjacent sec-
tions or from consecutive stains (i.e., stain → image → elute → stain with the same
antibody → image the same region → compare). Not all antibodies that work well
for other applications will work for array tomography.
Problem (Steps 5 and 15): There is high background fluorescence.
Solution: Background fluorescence can have many causes. Often, there is high
autofluorescence when using the low-power (but not high-power) objectives. If the
autofluorescence levels are high with the 63x objective, try the following. First,
check whether the immersion oil is designed to be used with fluorescence. Second,
labeling marks on the back of the coverslip can dissolve in the immersion oil causing
autofluorescencewipe labels off with ethanol before imaging. Third, use high-quality
fluorescence filter sets. Fourth, try a longer fluorescence quenching step (glycine
treatment in Protocol C, Step 2), the alternative staining method (Protocol C, Step
21), or introduce an additional quenching step with 1% sodium borohydride in Tris
buffer for 5 min.
Problem (Steps 5 and 15): Green fluorescent protein (GFP)/YFP fluorescence is
lost.
Solution: First, confirm that the tissue was dehydrated only to 70% ethanol (Pro-
tocol A, Step 14). Second, make sure you are using a high-power, high-NA objective.
To check for GFP fluorescence use a short array with ultrathin sections (<200 nm).
Let it sit for 5–10 min or more with Tris-glycine (50 mM glycine in Tris), mount over
a glass slide and look with the 63x objective. GFP can bleach very fast, so work
quickly to find the region with GFP fluorescence. For acquiring images, select the
region of interest with another stain (e.g., Alexa 594) and focus. Do not use the DAPI
stain for this purpose, because it can cause DAPI to bleed into the GFP channel. In
cases of weak GFP fluorescence, GFP antibodies may help identify GFP-positive cell
bodies and large processes, but are generally not useful for thinner processes. GFP
35
antibodies for array tomography include Roche 11814460001 (mouse), MBL 70 (rab-
bit), Invitrogen A11122 (rabbit), NeuroMabs 75-131 (mouse), GeneTex GTX13970
(chicken). All of these antibodies should be used at 1:100 dilution.
Problem (Step 11): The Convert Tile Images step keeps downsampling the stitched
image.
Solution: In the Tools→ Options→ Acquisition menu, change the Mx. MosaicX
image size to the maximum allowed: 1000000000 pixel.
Problem (Step 12): The microscopy software is not designed for array tomography.
Solution: We have developed an algorithm that automates position finding in the
arrays by using simple extrapolation to estimate the neighborhood of an unknown
point and then refining the estimate with an autocorrelation search. Given two known
points Pn and Pn1, we find the next point Pn+1 such that Pn+1 = Pn + (Pn [Pn1])
(Figure 2.4). This does not take into account ribbon curvature or changes in section
width, but gives a rough approximation of the unknown points locale. Pn+1 becomes
the center of an autocorrelation search to find the points true position. The size
of the search varies with the width of the sections; larger sections will have larger
warping and curvature effects, and any miscalculation in the estimate of Pn+1 will
be magnified.
To conduct the search, the algorithm compares the area centered at Pn+1 with
a Kalman-filtered image of recently processed points. Although our fiducial labels
(DAPI and tubulin immunostaining) have minor variations from section to section, it
does not disrupt the accuracy of the correlation search. To make the Kalman-filtered
image at each iteration, use the area around the current Pn, newSample, to update
the image using the following pseudocode: image = 0.3 x image + 0.7 x newSample.
The purpose of using the Kalman filter, when newSample alone would do, is to add
a measure of robustness to the algorithm. If the ribbon is damaged or has aberrant
staining on a single section, using newSample alone may result in the algorithm going
36
off course. With a running average of previous iterations to compare with, a defect
in a single section has a good chance of being ignored. This process continues until
one end of the ribbon is reached, then starts in the other direction.
We developed an implementation of this algorithm in Visual Basic script for Zeiss
Axiovision, available from http://www.stanford.edu/ebbusse/work/downloads.html,
and would welcome any ports to other microscopy software.
Problem (Step 15): Autofocus does not work using Axiovision.
Solution: The autofocus does not work every time. Typically, e5% of the images
collected with autofocus may be out of focus. In that case, you can move to the
positions on the ribbon with bad focus, focus by hand, and collect individual images.
Replace the out-of-focus images with the newly focused ones in the stack before to
alignment. If 10% or more of the images are out of focus, you can try using the
autofocus with a different channel. Pick a channel with antibody staining that is
bright, and present throughout the field of view. Using a channel with dim or sparse
immunostaining will not work well.
Problem (Step 15): Autofocus is grayed out.
Solution: In the Tools → Options → Acquisition menu, check the box marked
either Use calibration-free Autofocus or Enable new Autofocus.
2.6 Protocol E: Semiautomated Image Alignment
Successful array tomography requires that the captured images be properly stacked
and aligned. Software to achieve these ends is freely available.
37
2.6.1 Materials
Software
• Fiji can be obtained at http://pacific.mpi-cbg.de/wiki/index.php/Main Page
• MultiStackReg is available at
http://www.stanford.edu/ebbusse/work/downloads.html
2.6.2 Experimental Method
1. Load your images into Fiji. If using Axiovision, Fijis Bio-Formats Importer
plugin can read .zvi files directly.
2. Pick a channel that is relatively invariant from one section to the next (e.g.,
DAPI or tubulin), and select a slice near the middle of the ribbon.
3. Align the sections of that channel using affine in MultiStackReg (Fiji), but do
not save over the misaligned stack. Save the resulting transformation matrix.
This is the intrasession matrix.
See Troubleshooting.
4. Using MultiStackReg, apply that matrix to the other channels of the same
imaging session.
5. For each subsequent imaging session, choose the same channel. Align the new
(misaligned) channel to the old (misaligned) channel, saving the matrix. This
is the intersession matrix.
6. For each channel in that imaging session, first apply the intersession matrix
from Step 5 and then the intrasession matrix from Step 3.
7. Repeat until all imaging sessions have been registered.
38
2.6.3 Troubleshooting
Problem (Step 3): The alignment steps are not working properly.
Solution: Detailed instructions with graphical illustration, compiled by Andrew
Olson, are available at http://nisms.stanford.edu/UsingOurServices/Training.html.
If an affine transformation does not align the images well, try either the rigid body
then affine or try rigid body alone. For each registration step, save the transformation
matrix and apply it to the other channels in sequence.
MultiStackReg is an extension of the StackReg ImageJ plugin, which is depen-
dent on TurboReg [20]. TurboReg aligns a single pair of images using a pyramid
registration scheme. StackReg aligns an entire stack by calling TurboReg on each
pair of consecutive slices in the stack, propagating the alignment to later slices. The
two principle changes added by MultiStackReg are the ability (1) to load and save
transformation matrices and (2) to align one stack to another by registering each pair
of corresponding sections independently. MultiStackReg can process TurboReg align-
ment files in the same manner as the files it generates for itself, so if your alignment
is failing owing to a single section, it is possible to manually align that section in
TurboReg, apply that transform to a copy of the stack, and splice the two together.
2.7 Conclusion and Future Directions
One important application of array tomography in the field of neuroscience is the
analysis of synapse populations. With this method it is possible to resolve individ-
ual synapses in situ within brain tissue specimens. Because 10 or more antibodies
can be used on an individual sample, the molecular signature of each synapse can
be defined with unprecedented detail. The throughput of the technique is inherently
high, approaching the imaging of one million synapses per hour. Compared with
3D reconstruction at the electron microscopic level, array tomography can image
39
much larger volumes and provide information about the presence of a much larger
number of molecules, but cannot presently provide the fine ultrastructure of electron
microscopy. On the other hand, the amount of effort involved in array tomography
may not be warranted for all studies. If it is not considered critical to resolve indi-
vidual synapses, immunostaining of vibratome sections or cryosections and confocal
microscopy imaging may be sufficient.
Currently, we are focused on developing array tomography in three directions.
First, we are refining current staining and imaging approaches to image larger and
larger tissue volumes with more antibodies. Second, we are combining light and
electron microscopic imaging to visualize both immunofluorescence and ultrastructure
on the same tissue sections. Finally, we are applying advanced computational methods
for data analysis, in particular with the goal to both count and classify millions of
synapses on a routine basis.
2.7.1 Acknowledgements
We thank JoAnn Buchanan and Nafisa Ghori for their help in refining the methods.
This work was supported by grants from McKnight Endowment Fund for the Neu-
rosciences, the National Institutes of Health (NS 063210), The Gatsby Charitable
Foundation, and the Howard Hughes Medical Institute.
2.8 Recipes
CAUTION: See Appendix 6 for proper handling of materials marked with <!>.
Recipes for reagents marked with <R> are included in this list.
40
Table 2.1: Recipe: Alternative Antibody Dilution Solution with NGS (1 mL)
Reagent Quantity Final concentrationTween (1%) (make the stock solution usingTween-20 [Electron Microscopy Sciences25564])
100 µL 0.1%
NGS (Invitrogen PCN5000) 30 µL 3%Tris buffer 870 µL
Prepare on the same day it is used. NGS can be kept frozen in aliquots for severalmonths.
Table 2.2: Recipe: Alternative Blocking Solution with NGS (1 mL)
Reagent Quantity Final concentrationTween (1%) (make the stock solution usingTween-20 [Electron Microscopy Sciences25564])
100 µL 0.1%
NGS (Invitrogen PCN5000) 100 µL 10%Tris buffer 800 µL
Prepare on the same day it is used. NGS can be kept frozen in aliquots for severalmonths.
Table 2.3: Recipe: Blocking Solution with BSA (1 mL)
Reagent Quantity Final concentrationTween (1%) (make the stock solution usingTween-20 [Electron Microscopy Sciences25564])
50 µL 0.05%
BSA (10%) (AURION BSA C [acety-lated BSA], Electron Microscopy Sciences25557)
10 µL 0.1%
Tris buffer 940 µL
Prepare the same day. The 1% Tween stock (10 µL Tween in 1 mL of H2O) and the10% BSA stock can be kept at 4◦C for several months.
41
Table 2.4: Recipe: Elution Solution (10 mL)
Reagent Quantity Final concentrationNaOH <!>, 10 N 200 µL 0.2 N%SDS <!> (20%) 10 µL 0.02%Distilled H2O 10 mL
Can be prepared in advance and stored at room temperature for several months.
Table 2.5: Recipe: Fixative (4 mL)
Reagent Quantity Final concentrationParaformaldehyde <!> (8%, EM grade;Electron Microscopy Sciences 157-8)
2 mL 4%
PBS, 0.02 M (use PBS powder, pH 7.4[Sigma-Aldrich P3813])
2 mL 0.01 M
Sucrose 0.1 gm 2.5%
Prepare the same day as it will be used.
Table 2.6: Recipe: Subbing Solution (300 mL)
Reagent Quantity Final concentrationGelatin from porcine skin, 300 Bloom(Sigma-Aldrich G1890)
1.5 g 0.5%
Chromium potassium sulfate (Sigma-Aldrich 243361)
0.15 g 0.05%
Distilled H2O 300 mL
Prepare the same day. Dissolve the gelatin in 290 mL of distilled H2O by heating to<60◦C. Dissolve 0.15 gm of chromium potassium sulfate in 10 mL of H2O. Whenthe gelatin solution cools down to e37◦C, combine the two solutions, filter, and pourinto the staining tank. Use fresh.
42
Table 2.7: Recipe: Wash Buffer (50 mL)
Reagent Quantity Final concentrationGlycine 187.5 mg 50 mMSucrose 1.75 g 3.5%PBS, 0.02 M 25 mL 0.01 MDistilled H2O 25 mL
Can be prepared in advance and stored at 4◦C for up to 1 month; discard if itappears cloudy.
43
Table 2.8: Primary antibodies used with array tomography
Antibody Source Supplier DilutionSynapsin I Rabbit Millipore AB1543P 1:100PSD95 Mouse NeuroMabs 75-028 1:100VGluT1 Guinea pig Millipore AB5905 1:1000GAD Rabbit Millipore AB1511 1:300Gephyrin Mouse BD Biosciences 612632 1:100Tubulin Rabbit Abcam ab18251 1:200Tubulin Mouse Sigma-Aldrich T6793 1:200Neurofilament 200 Rabbit Sigma-Aldrich N4142 1:100
44
Figure 2.1: The sequence of steps for a basic immunofluorescence arraytomography process.
45
Figure 2.1
46
Figure 2.2: Array tomographic images of layer 5 neuropil, barrel cortex ofYFP-H Thy-1 transgenic mouse [71] Yellow fluorescent protein (YFP) expressionin a subset of pyramidal cells (green), Synapsin I immunostaining (white), PSD95(red), DAPI staining of nuclear DNA (blue). (A) Four-color fluorescence image ofa single, ultrathin section (200 nm). (B) Volume rendering of a stack of 30 sectionsafter computational alignment as described in this chapter.
47
Figure 2.2
48
Figure 2.3: Multiplexed staining for seven synaptic proteins in mouse cere-bral cortex (layer 2/3, barrel cortex) using five cycles of staining and elution. Thisvolume of 18 x 16 x 1.3 µm was reconstructed from 19 serial sections (70 nm each).Individual synapsin puncta 1, 2, and 3 colocalize with synaptophysin and VGlut1 andare closely apposed to PSD95 and thus appear to be excitatory synapses. Synapsinpuncta 4–7 colocalize with synaptophysin, but do not have adjacent PSD95 puncta.Puncta 6 and 7 also colocalize with GAD and VGAT and thus have the characteristicsof inhibitory synapses.
49
Figure 2.3
50
Figure 2.4: (Top) A fragment of an array tomography ribbon stained with DAPI.(Bottom) A closer view of two sections in the ribbon showing a single iteration ofthe position-finding algorithm. An established field (red x) is used to maintain areference patch (red square) for a correlation-based search (green square) to find thenext point (green circle).
51
Figure 2.4
52
Chapter 3
Classical MHCI molecules regulate
retinogeniculate refinement and
limit ocular dominance plasticity
Datwani A, McConnell MJ, Kanold PO, Micheva KD, Busse B, Shamloo M, Smith
SJ, Shatz CJ. Classical MHCI molecules regulate retinogeniculate refinement and
limit ocular dominance plasticity. Neuron. 2009 Nov 25;64(4):463-70.
3.0.1 Abstract
Major histocompatibility complex Class I (MHCI) genes were discovered unexpect-
edly in healthy CNS neurons in a screen for genes regulated by neural activity. In
mice lacking just 2 of the 50+ MHCI genes H2-Kb and H2-Db, ocular dominance (OD)
plasticity is enhanced. Mice lacking PirB, an MHCI receptor, have a similar pheno-
type. H2-Kb and H2-Db are expressed not only in visual cortex, but also in lateral
geniculate nucleus (LGN) where protein localization correlates strongly with synaptic
markers and complement protein C1q. In KbDb−/− mice developmental refinement of
53
retinogeniculate projections is impaired, similar to C1q−/− mice. These phenotypes
in KbDb−/− mice are strikingly similar to those in β2m−/−TAP1−/− mice, which lack
cell surface expression of all MHCIs, implying that H2-Kb and H2-Db can account
for observed changes in synapse plasticity. H2-Kb and H2-Db ligands, signaling via
neuronal MHCI receptors, may enable activity-dependent remodeling of brain circuits
during developmental critical periods.
3.1 Introduction
Brain circuits are refined by early spontaneous activity and later on by sensory ex-
perience [25, 26]. In the developing mammalian visual system, spontaneous activ-
ity generated in retinal ganglion cells (RGCs) and relayed to the lateral geniculate
nucleus (LGN) drive initially intermixed retinogeniculate connections to refine into
non-overlapping eye-specific layers; blockade or perturbation of waves prevents refine-
ment [27, 28]. How neural activity ultimately leads to refinement is not well under-
stood, but it is known that synapse elimination, as well as synapse strengthening and
stabilization, are involved [29–32].
To discover genes downstream of spontaneous retinal activity, we conducted an
unbiased screen in which activity was blocked by tetrodotoxin (TTX) during the
period of eye-specific segregation [33]. TTX not only prevents retinogeniculate re-
finement [34,35], but unexpectedly also downregulated the expression of MHC Class
I mRNA in neurons. MHCI (Major Histocompatibility Class I), a large and highly
polymorphic family of 50+ genes, some with well known roles in the immune system
and T-cell function [36], were not previously thought to be expressed by neurons in
the healthy brain, let alone regulated by neural activity.
Here, by examining mice lacking both H2-Kb and H2-Db (Histocompatibility Locus
2 [37], we show that these 2 genes are required for retinogeniculate refinement. In the
54
immune system H2-Kb and H2-Db bind and signal not only via the T-cell receptor, but
also via PirB (Paired immunoglobulin-like receptor B), an innate immune receptor [38]
also expressed in visual cortical neurons [39]. We report that KbDb−/− mice have
enhanced ocular dominance (OD) plasticity, similar to PirB mutant mice [39]. These
findings imply a novel role for these 2 specific MHCI molecules and cognate immune
receptors in activity-dependent plasticity during CNS development. Given the recent
association between human Histocompatibility Locus (HLA) and Schizophrenia [40–
42], our findings also reveal a novel way to explore potential links between immune
and nervous systems: via the disruption of normal MHCI regulation of plasticity at
neuronal synapses.
3.2 Results
3.2.1 Enhanced ocular dominance plasticity in KbDb−/− mice
In the immune system, H2-Kb and H2-Db bind and signal via a variety of recep-
tors, including PirB [38, 43]. Cortical neurons not only express PirB, but also mice
lacking PirB have enhanced OD plasticity following monocular deprivation or eye re-
moval [39]. Because cortical neurons express both H2-Kb and H2-Db [44], Figure S1,
we examined if OD plasticity is also perturbed in KbDb−/− mutant mice. To create
a large imbalance in visually driven inputs to neurons normally receiving binocular
inputs, one eye was deprived (MD) or enucleated (ME) at P22, just at the onset of
the critical period in visual cortex but after eye segregation in the LGN is adult-
like [45–47].
The spatial extent of input from the eyes to visual cortex was assessed functionally
at P31 by in situ hybridization for the immediate early gene Arc. After a 30-minute
monocular light exposure, Arc mRNA is upregulated (induced) rapidly [39, 47] in
55
visually stimulated cortical neurons (Figure 3.1A). Following ME or MD, this method
reports OD plasticity reliably, as measured by the pronounced expansion in width of
Arc mRNA signal in visual cortex ipsilateral to the stimulated (open) eye (Syken et
al., 2006; Tagawa et al., 2005). This expansion in width of Arc induction correlates
with the well-known strengthening of the open eye following monocular deprivation
assessed in physiological studies measuring single units [48], visually-evoked potentials
(VEPs) [45,49] or optical imaging [50,51] and faithfully detects OD plasticity with the
advantage here of laminar and cellular resolution. As expected [47], ME in wildtype
mice (WT: KbDb+/+) led to a 51% increase in width of Arc induction in layer 4 of
visual cortex ipsilateral to the remaining eye, as compared to normally reared WT
mice (Figure 3.1C).
By contrast, Arc induction in KbDb−/− mice following ME is even wider; indicat-
ing that OD plasticity following ME is greater in KbDb−/− than WT mice. However,
in KbDb−/− mice reared with normal vision, the width of Arc induction ipsilateral to
the stimulated eye is already about 22% greater than WT (cf. Figure 3.1B,C). This
initially expanded ipsilateral representation might account entirely for the greater
OD plasticity observed in KbDb−/− mice. Thus, to compare the extent of OD plas-
ticity between the different genotypes, we computed a plasticity index (=width of
Arc induction following ME/width following normal visual rearing; Figure 3.1D) that
factors in the different starting points for each genotype. The plasticity index for
KbDb−/− (2.16 ± 0.11) is 65% larger than for WT (1.51 ± 0.08, Figure 3.1D), in-
dicating that even after correcting for the expanded representation of the ipsilateral
eye in normally reared KbDb−/− mice, OD plasticity is significantly enhanced.
To exclude the possibility that enhanced OD plasticity after eye removal inKbDb−/−
mice is due to an injury response, rather than visual deprivation, experiments were re-
peated using MD at similar ages (Figure 3.1C). A more modest expansion in width of
Arc induction was detected than after ME [47]. Nevertheless following MD, the width
56
of Arc signal in KbDb−/− mice, as well as the plasticity index, is 17% greater than
that in WT mice (plasticity index: 1.48 ± 0.103 KbDb−/−; 1.31 ± 0.105 KbDb+/+; P
= 0.042; Figure 3.1C, D). These differences in OD plasticity in KbDb−/− vs. WT mice
are also not likely due to differences in visual ability. We examined this possibility
behaviorally in normally reared mice using a visual acuity water maze test [52, 53].
Acuity is similar in WT and KbDb−/− mice (Figure S2: A-F), suggesting that the
expanded representation of the ipsilateral eye within the BZ of visual cortex following
MD or ME is not from degraded vision in the mutant mice. Indeed, following ME
in mutant mice, visual acuity is, if anything, slightly better than in WT mice (Fig-
ure S2: G-I), possibly reflecting a behavioral consequence of enhanced OD plasticity.
Thus, like PirB [39], H2-Kb and/ or H2-Db appear to limit the extent of OD plasticity
following MD or ME during the critical period.
The vast majority of MHCI cell surface proteins can be abrogated by genetically
deleting β2m, a subunit needed for MHCIs, and/or the peptide loading-associated
molecule TAP1 [36,54]. In the brains of β2m−/−, or β2m−/−TAP1−/− mice, there are
changes in hippocampal synaptic plasticity, homeostatic scaling [44,55], motor learn-
ing [56], pheromone driven behavior [57], and motoneuron axon regeneration [58]. In
addition, following ME here, the width of Arc induction in visual cortex of β2m−/−
and β2m−/−TAP1−/− mice is similar to that in KbDb−/− mice (Figure 3.1C, 3.1D,
and data not shown: β2m−/−TAP−/−+ME: 2258.8 ± 120.2 µm, n = 38 mice, P
= 0.0032; KbDb−/−+ME: 2404.6 ± 105.1 µm, n = 25 mice, P = 0.0017) The plas-
ticity index is also 44% greater than for WT (β2m−/−TAP−/− = 2.08 ± 0.140;
β2m+/+TAP1+/+ = 1.64 ± 0.080; P = 0.0078), similar to that for KbDb−/− mice
(2.16 ± 0.11). These observations suggest that loss of just H2-Kb and H2-Db can
account for enhanced OD plasticity seen in visual cortex of mice lacking surface ex-
pression of the majority of MHCI proteins.
57
3.2.2 Expanded thalamocortical projections to layer 4 of KbDb−/−
mice following ME
The expansion of Arc signal in layer 4 following ME or MD could arise from at
least two non-mutually exclusive possibilities: (1) thalamocortical axons could have
a wider distribution and therefore contact more neurons in layer 4; and/or (2) the
expansion in Arc signal could result from changes in intracortical connections. To
explore these possibilities, transneuronal tracing, using 3H-proline [39, 48, 59] was
performed after ME in WT (n = 5) or KbDb−/− (n = 7) mice. Similar to Arc
induction, transneuronal tracing labels a wider patch in KbDb−/− than in WT mice
(Figure 3.2B, C). Even so, the width measured from transneuronal transport is about
70% smaller than that from Arc induction (cf. Figure 3.1C, 3.2C)), suggesting that
expansion of thalamocortical axons alone cannot account entirely for the increased
OD plasticity observed in KbDb−/−mice; changes in intracortical connections likely
also contribute.
3.2.3 Abnormal retinogeniculate patterning in KbDb−/− mice
Blockade of neural activity prevents developmental refinement of retinogeniculate pro-
jections [27] and down-regulates MHCI mRNA and protein [33,55]. KbDb−/− mutant
mice resemble the situation in which 2 MHCI molecules have been completely down-
regulated by activity blockade. To examine if H2-Kb and H2-Db could be involved
in refinement of the retinogeniculate projection, we first assessed expression in the
dLGN by immunostaining (Figure S1C). Protein can be detected in LGN neurons at
P9, during the period of activity-dependent refinement of the retinogeniculate projec-
tion. Expression has decreased by P34, after refinement is complete, consistent with
transient expression of MHCI mRNA in the LGN [44].
58
To assess the distribution of retinal ganglion cell (RGC) terminals, we made in-
traocular injections of different fluorophores into the each eye (Experimental Proce-
dures, [60]. The LGN territory occupied by RGC projections was assessed at P34, 3
weeks after projections normally have completely segregated (Figure 3.3A, Figure S3).
The percent LGN area occupied by projections from the ipsilateral eye in KbDb−/−
mice (24.99 ± 2.97%) is almost twice that of WT (12.75 ± 2.42%, Figure 3.3B), while
total LGN area is similar between genotypes.
3.2.4 Abnormal segregation of eye-specific inputs in dLGN
of KbDb−/−
An increase in area of the ipsilateral eye projection to the LGN could come at the
cost of contralateral eye territory, or if RGC inputs failed to segregate [60]. Using
fluorescent double labeling to examine projections from both eyes simultaneously at
P34, significantly increased overlap of RGC projections from the two eyes was ob-
served in KbDb−/− vs. WT mice (Figure 3.3; for both hemispheres see Figure S3B).
These changes in the retinogeniculate projection of KbDb−/− mice also almost exactly
phenocopy β2m−/−TAP1−/− mice (Figure S3B; [44]). Using multiple threshold anal-
ysis (Experimental procedures [60]), we find that overlap of RGC inputs, measured
by pixels common to both red and green channels, is significantly greater in all MHCI
mutant mouse lines over WT controls (Figure S3C, D). For example, at 60% maxi-
mal threshold, the percent of dLGN area with overlapping pixels is 19.10 ± 2.80% in
KbDb−/−; 15.50 ± 3.01% in β2m−/−TAP1−/−; but only 4.23 ± 1.77% in WT (Figure
S3D). On average across examined intensity thresholds there is approximately a 3–5
fold increase in overlap in mutant mice over WT (Figure S3D). These findings suggest
that normal retinogeniculate refinement may require just H2-Kb and H2-Db.
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Given the enhanced OD plasticity noted in visual cortex of mutant mice, we won-
dered if RGC projections in mutant mice remain stable even after retinogeniculate
segregation is normally completed. If not, ME might cause further expansion of the
RGC projection within the LGN. Intraocular injections of CTB-AF488 were per-
formed to label retinal afferents subsequent to ME between P22-31 in WT (n = 6)
and KbDb−/− (n = 6) mice and the percent LGN area occupied by the ipsilateral
RGC projection was measured. There was no change observed in either genotype,
the developmentally-expanded ipsilateral eye inputs remained stable. This obser-
vation correlates with our finding that the expression of both H2-Kb and H2-Db is
developmentally downregulated by P34, and demonstrates that the enhanced OD
plasticity seen in visual cortex of mutant mice during the critical period is not due to
a reorganization of RGC projections following ME.
3.2.5 MHCI Immunostaining is associated with LGN synapses
and C1q
A direct role for H2-Kb and/or H2-Db in refinement of RGC projections would be sup-
ported by synaptic localization of these proteins. To determine if MHCI localizes near
synapses in vivo, Array Tomography (AT) [1] was used to examine immunostained
LGN sections at P7, at time when eye-specific layer formation is in progress and
LGN expression levels of C1q [61] and MHCI (Figure S1; [44]) are high. This method
permits immunostaining of the same ultrathin 70nm section repeatedly for known
synaptic markers, as well as for MHCI and other molecules of interest (Figure 3.4A).
Serial sections are then tomographically reassembled, rendering a 3-dimensional im-
age containing patterns of protein localization. In AT images, MHCI is localized
in a punctate pattern, often closely associated both with PSD-95 and/ or synapsin
puncta (Figure 3.4A), consistent with previous observations of hippocampal neurons
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in vitro [55].
Higher resolution examination of 4 serial sections (Figure 3.4B) shows MHCI
puncta associated with excitatory and inhibitory synapses, as well as with C1q, a com-
plement protein recently also found to be required for RGC refinement and synapse
elimination [61]. To assess these relationships quantitatively, a cross-correlation pixel
analysis was performed (Figure 3.4C, D and Figure S4 online; Experimental proce-
dures). As expected, pre- (synapsin I) and post- (postsynaptic density-95) synaptic
markers are highly correlated with each other, while inhibitory (GAD) and excitatory
(vGluT2) markers are not correlated with each other since they are not associated
with similar synaptic types. Close association of MHCI molecules and C1q at synapses
is consistent with the observation here that the specific MHCI molecules H2-Kb and
H2-Db, like C1q, are needed for retinogeniculate synapse refinement.
3.3 Discussion
In mice lacking expression H2-Kb and H2-Db, retinal projections to the LGN fail to
refine completely, and within visual cortex, OD plasticity is enhanced. These changes
phenocopy those present in β2m−/−TAP1−/− mice, which lack stable cell-surface
expression of most of the 50+ MHCIs [54]. Because H2-Kb and H2-Db mRNA and
protein are present in neurons within LGN and visual cortex, we propose that these
specific classical MHCI family members are not only required for activity-dependent
refinement and plasticity in the visual system, but can account for the majority of
the abnormalities observed in the visual system of β2m−/−TAP1−/− mice.
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3.3.1 MHCI function during developmental refinement of the
retinogeniculate projection
Many experiments have shown that when neural activity is blocked or altered, seg-
regation of RGC afferents from the two eyes within the dLGN is incomplete [28, 62].
Here we show that H2-Db and H2-Kb are expressed in the dLGN primarily during
the period of eye-specific segregation and that, in their absence, RGC afferents fail
to segregate fully. The Array Tomography data provides strong support for the pro-
posal that these MHCI proteins are located near or at synapses, but the resolution
of the method is still not sufficient to conclude that they are situated at the pre-
or the postsynaptic membrane. Here, we found a higher correlation between MHCI
and PSD-95 than between MHCI and synapsin, consistent with previous observa-
tions of immunostaining associated with neuronal dendrites in vivo [33, 56], as well
as with synapses and PDS-95 in vitro [55]. Together, these observations place MHCI
at the postsynaptic membrane, but this suggestion must await electron microscopy,
fix-insensitive MHCI antibodies and or biochemical fractionation for confirmation.
The failure of RGC axons from the two eyes to segregate completely from each
other even in the presence of intact retinal wave activity [44] implies H2-Kb and/or
H2-Db may function as molecular read-outs for activity-dependent synapse weaken-
ing and elimination. Other immune system molecules have also been implicated in
retinogeniculate synapse elimination. Like MHCI, neuronal pentraxins (e.g., NP1
and NP2), proteins homologous to immune system pentraxins, are upregulated by
neuronal activity [63]. In addition, the complement proteins C1q and C3 are present
in the dLGN during retinogeniculate refinement [61]. NP1−/−NP2−/− as well as
C1q−/− and C3−/− mutant mice have dLGN phenotypes strikingly similar to those
reported here. The localization of MHCI and C1q is highly correlated, implying that
these two molecules could interact and function together in developmental remodeling
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at excitatory as well as inhibitory synapses. Note that PirB cannot be detected in
the LGN during the period of retinogeniculate remodeling [39], but CD3ζ, a signaling
component for other immune receptors, is present. CD3ζ mutant mice also have de-
fects in retinogeniculate refinement [44], implying that H2-Db and H2-Kb in the LGN
may collaborate with a CD3ζcontaining receptor.
The phenotypes observed in complement-deficient and in MHCI mutant mice are
stable and persist into adulthood. In contrast, the defect in NP1−/−NP2−/− is
transient [63]. Thus it may be that a series of tightly developmentally regulated events
operate sequentially to establish connectivity, then to stabilize and cluster glutamate
receptors (a process involving neuronal pentraxins [63] and finally to remodel and
eliminate synaptic inputs in an MHCI-C1q activity-dependent manner. The precise
mechanisms of how synapses are tagged for elimination based on their activity is far
from understood, but MHCI appears well-suited to act in this process, given that
action potential blockade downregulates both mRNA and protein [33,55].
3.3.2 H2-Kb and H2-Db may function with PirB to limit OD
Plasticity in Visual Cortex
The presence of enhanced OD plasticity in the visual cortex of mutant mice studied
here is notable. First, it argues for a role for H2-Kb and H2-Db in limiting the extent of
strengthening of the open (remaining) eye following an imbalance in activity created
by eye removal or closure. Just how the open eye is able to gain so much functional
territory in the visual cortex of KbDb−/ mice following visual deprivation remains to
be fully explored. Following ME in the mutant mice, we have shown that there is
a large mismatch in extent of expansion of the ipsilateral thalamocortical projection
as assessed using transneuronal tracing compared with that assessed using induction
of Arc mRNA. The expansion, as measured from anatomical tracing, is far less than
63
that measured functionally from the response of cortical neurons to visual stimulation,
suggesting that the enhanced OD plasticity in KbDb−/ mice may reflect not only the
presence of a larger pool of ipsilateral geniculocortical axons, but also changes within
the cortex. Future experiments will be required to elucidate how loss of these 2
MHCI family members alters the details of connectivity, as well as rules of synaptic
plasticity, at the cellular level.
H2-Kb and H2-Db join a very limited number of other molecules whose loss of func-
tion also results in enhanced, rather than diminished, OD plasticity. This small group
includes Nogo signaling (NgR−/−, NogoA/B−/− mice; [64] and PirB [39], as well as
infusion of tPA [65] which alters the extracellular matrix in cortex. PirB is expressed
on certain cells of the innate immune system and is a known receptor for H2-Kb and
H2-Db [43]. PirB is expressed in subsets of neurons including many pyramidal neurons
of the cerebral cortex (but notably is not detected in LGN neurons) and PirB has
recently been shown to bind Nogo [66]. Mice lacking PirB have enhanced OD plastic-
ity [39] almost identical to that seen here in KbDb−/ mice. Together these observations
suggest that PirB functions as a neuronal receptor for H2-Kb and H2-Db, possibly even
in collaboration with NgR/Nogo. In cultured cortical neurons, PirB immunostaining
is associated with axonal growth cones and is located near synapses [39], while MHCI
immunostaining is present in neuronal dendrites and colocalizes with PSD-95 [55],
implying a model in which postsynaptic MHCI interacts across the synapse with
presynaptic PirB [67]. Thus, MHCI-PirB signaling may operate either in parallel or
in conjunction with these other molecules to regulate negatively the strength and
stability of synaptic connections in an activity-dependent manner.
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3.4 Experimental Procedures
3.4.1 Animals and Genotyping of mouse lines
The Institutional Animal Care and Use Committees at Harvard Medical School and
Stanford University approved all protocols. Animals were raised in a pathogen-free
facility, all mutant mice were outwardly normal. β2m−/−TAP1−/− mice were ob-
tained from D. Raulet [68]. Wildtype (β2m+/+TAP1+/+) and singly mutant mice
were derived by backcrossing β2m−/−TAP1−/− to the same background strain of
C57BL/6 wildtype mice purchased from Charles River (Wilmington, MA) for more
than five generations to obtain mice carrying the wildtype alleles for both β2m and
TAP1 genes. To avoid genetic drift, homozygous parents in each of these four colonies
are obtained from a common breeding colony containing mixed heterozygote geno-
types. Genotyping of β2m and TAP1 alleles was performed by PCR as described
previously [44]. KbDb−/− mice on a C57BL/6 genetic background were obtained
from H. Ploegh [37] and maintained as a homozygous breeding colony. Age-matched
C57BL/6 controls were purchased from Charles River (Wilmington, MA).
3.4.2 Mouse surgery and OD plasticity experiments
For monocular enucleation (ME) experiments to assess OD plasticity mice were anes-
thetized at P22 with isofluorane, one eye was removed (if needed sterile gelfoam was
inserted in the orbit to minimize bleeding). Eyelids were trimmed and sutured with
6-0 sterile surgical silk. A drop of Vetbond (3M, St. Paul, MN) was put on sutured
eyelids to prevent reopening. For monocular deprivation (MD) experiments, mice
were anesthetized at P25 and the procedure was identical as described above except
the eye was not removed until the day prior to Arc induction.
65
3.4.3 Arc induction
At P31 (for ME) or at P34 (for MD) one eye was removed under anesthesia (unless ME
had already been performed at an earlier age, as in OD plasticity experiments); mice
were revived and put in total darkness for 8 - 12 hours to minimize basal levels of Arc
mRNA in visual cortex. Mice were returned to a lighted environment for 30 minutes
to induce Arc mRNA in the cortex driven by vision through the remaining eye.
After light exposure, mice were euthanized with Halothane (Halocarbon, River Edge
NJ), brains were removed, flash-frozen in M-1 embedding media (Thermo Scientific,
Waltham, MA) and 14µm thick coronal sections were processed for situ hybridization
with Arc antisense riboprobe [39, 47]. Arc plasmid was provided by Dr. P. Worley,
Johns Hopkins University, Baltimore, MD.
3.4.4 Densitometric scans of Arc induction in specific corti-
cal layers
Quantitative analysis of Arc induction by stimulation of the ipsilateral eye was per-
formed in MATLAB (Mathworks, Inc, Natick, MA) by line scans across layer 4 of
primary and secondary visual cortex as described previously [47]: for each animal the
width of Arc mRNA signal two standard deviations above background was measured
in 3 - 4 sections scanned in randomized order, blind to genotype and manipulation.
Slides from different animals and manipulations were interleaved and only reassembled
once all width measurements were computed. Between 7-28 animals of each genotype
were studied; average widths of Arc induction were computed for each animal and
displayed in cumulative histograms.
66
3.4.5 Transneuronal labeling
To visualize the pattern of geniculocortical projections to layer 4 of mouse visual
cortex 1-2 µl of L-[2,3,4,5-3H]-proline (GE Healthcare, Cat#TRK750; approx. 100
Ci/mmol) was injected intraocularly at a concentration of 150-200µCi/µl dissolved
in 0.1M PBS pH 7.4; a week later mice were euthanized with euthasol 50mg/kg,
brains were frozen in M-1 mounting medium (ThermoShandon) and 14 µm coronal
cryosections were prepared on glass superfrost Plus slides (Fisher scientific). Sections
were fixed in 4% paraformaldehyde in PBS, pH 7.4, washed twice in 0.1M PBS and
dehydrated through a graded ethanol series. Sections were coated with NTB-2 emul-
sion (Kodak, Inc.), dried in dark room, and then stored at 4◦C. After 2-3 months,
slides were developed and imaged using dark field microscopy. Width measurements
of transported radioactive signal were measured in MATLAB (Mathworks, Inc) by
making line scans across layer 4 of primary and secondary visual cortex, as described
for Arc in situ measurements above and as used previously [47].
3.4.6 Anterograde labeling of retinal ganglion axons and mul-
tiple threshold analysis
P31-34 mice were anesthetized with isofluorane (in the case of LGN plasticity ex-
periments ME was performed from P22-31) then 1 - 2µl of cholera toxin B (CTB)
subunit conjugated to AF488 was injected in the right eye and AF594 in the left
(1mg/mL dissolved in 0.2% DMSO in nuclease-free water; Invitrogen, Inc. Carlsbad,
CA). After 24 hours, animals were overdosed with euthasol (50mg/kg), brains fixed
by transcardial perfusion of 0.1M PBS then ice-cold 4% PFA in 0.1M PBS. Brains
were removed, postfixed overnight in 4% PFA in 0.1M PBS at 4◦C, then sectioned
coronally using a vibratome at 100µm. Sections were mounted with prolong antifade
67
gold media (Invitrogen, Inc. Carlsbad, CA) on glass sides, and coverslipped for imag-
ing on a Zeiss LSM 510 META confocal microscope (Carl Zeiss MicroImaging, Inc.
Thornwood, NY)
All analysis was performed blind to genotype. To minimize variability analysis
was performed on dLGN sections where the ipsilateral projection area was the largest,
typically at the middle of the rostral-caudal extent of the dLGN (Figure S3B online).
All images were acquired such that the peak intensity values were just below saturat-
ing and multiple threshold analysis was carried out for the series of signal thresholds,
described previously [60]. In brief, dLGN sections were imaged in the red and then
green channel, and by varying each ipsi and contra channel at each intensity threshold
of 20%, 40%, 60%, 80% and 100% of maximum. To obtain overlap measurements,
the amount of overlapping red and green pixels in LGNs in both hemispheres was
measured in Image J (NIH, Bethesda, MD) using the Colocalization plugin tool and
displayed as yellow pixels (Figure 3.3A lower panel, overlap). The total area of over-
lapping pixels was represented as a percentage of total dLGN area (Figure 3.3C).
3.4.7 Array Tomography
Two postnatal day (P)7 mice, were perfused intracardially with 0.1M PBS, followed
by 4% paraformaldehyde in 0.1M PBS and the tissue was processed for array to-
mography [1]. The LGN was dissected out, further fixed in the same fixative using
microwave irradiation (PELCO 3451 laboratory microwave system; Ted Pella), then
dehydrated in ethanol and embedded in LRWhite resin (medium grade, SPI). Serial
ultrathin sections (70 nm) were cut on an ultramicrotome (Leica), mounted on subbed
coverslips and immunostained using either of two MHCI antibodies that yielded sim-
ilar AT staining patterns (Ox18, 1:50 AbDSerotec Cat#MCA51G or ErHr52, 1:10
BMA Cat#T2105). For secondary antibodies, Alexa 488, Alexa 594 and Alexa 647
(Invitrogen, 1:150) from the appropriate species were used. Up to 3 antibodies from
68
different hosts were applied together and imaged, followed by an antibody elution.
Sections were then restained with a different set of antibodies and re-imaged. Other
antibodies included those against synaptic proteins: synapsin I (rabbit, Millipore
AB1543P, 1:100), PSD-95 (mouse, NeuroMabs 75-028, 1:100), GAD65/67 (rabbit,
Millipore AB1511, 1:300), vGluT2 (guinea pig, Millipore AB2251, 1:1000), as well as
a C1q antibody (goat, Quidel A301, 1:300; ref. 31). Sections were mounted using
SlowFade Gold antifade reagent with DAPI (Invitrogen). Imaging was done on a Zeiss
AxioImager.Z1 fluorescence microscope with AxioCam HRm CCD camera, using a
Zeiss 63x/1.4 NA Plan Apochromat objective. Images were aligned using ImageJ and
the Multistack Reg plugin.
3.4.8 Array Tomography Cross-Correlation Analysis of synap-
tic markers, MHCI, and C1q
To examine the spatial relationship between known synaptic markers and MHCI
and C1q, we used a cross-correlation analysis method similar to that described pre-
viously [21]. For each pair of channels analyzed, a cross-correlation score Si was
computed over a range of lateral offset distances for images in the two channels.
Differences in the mean brightness in different channels were corrected by repeating
the analysis with one channel transposed (and therefore uncorrelated) to obtain a
baseline score St. To ensure that only labeled molecules in the synaptic neuropil
were included, the DAPI channel was used as a mask to remove nuclear and somatic
staining from the analysis. Si/St = Cr, describing the correlation (Cr) between two
channels as a multiple of their baseline correlation. A Cr of 1 indicates no correla-
tion, Cr >> 1 indicates high correlation, and Cr < 1 indicates negative correlation.
This method of analysis enables one to directly compare the correlation of different
immunolabels in a channel-independent manner (Figure 3.4D).
69
3.4.9 Statistical analyses
Statistical comparisons were performed using Excel (Microsoft Corp., Redmond, WA).
Tests on independent groups were Mann Whitney U test for comparisons of cumula-
tive distributions (widths of Arc mRNA signal or radioactively labeled thalamocorti-
cal terminals in layer 4) with population samples of unequal size, two-tailed Students
t test for group comparisons of LGN areas, two-way ANOVA for LGN pixel overlap
across multiple thresholds, and one-way ANOVA for Plasticity Index comparisons.
3.4.10 Supplemental Data
Supplemental Data include four figures and Supplemental Experimental Procedures
and can be found with this article online at http://www.cell.com/neuron/S0896-
6273(09)00844-7.
3.4.11 Acknowledgements
We thank members of the Shatz lab for helpful suggestions and comments. For tech-
nical assistance we thank B. Printseva, M. Marcotrigiano, J. Neville-Golden, and P.
Kemper. β2m−/−TAP1−/− mice were a gift from D. Raulet (UC Berkeley, Berkeley
CA) and KbDb−/− mice from H. Ploegh (MIT, Cambridge MA). We thank Dr. Beth
Stevens (Harvard Medical School, Boston MA) for providing the C1q antibody and
helpful discussions on using Array Tomography. Thanks also to Matthew Priestley
and Nay Lui Saw of the Stanford Institute for Neuroinnovation and Translational
Neuroscience NeuroBehavior Core Faci lity. This work was supported by NIH R01
EY02858, NIH R01 MH071666, the G. Harold and Leila Y. Mathers Charitable Foun-
dation, the Dana Foundation (CJS & AD), and NIH T32CA09361 (MJM).
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Figure 3.1: Enhanced ocular dominance plasticity in visual cortex of KbDb−/−
mutant mice (A) Schematic of mouse visual system. Binocular zone (BZ), locatedbetween primary (V1) and secondary (V2) visual cortex, receives input from botheyes. Arc mRNA induction was used to map cortical neurons driven by stimulationof one eye (Experimental procedures, [47]). Below: darkfield autoradiograph of Arc insitu hybridization in a coronal section from a P34 WT (KbDb+/+) mouse; 30 minutesof visual stimulation upregulates Arc mRNA in neurons of layers 2-4 and 6 of visualcortex (layer 5 neurons only express very low levels of Arc mRNA). Box indicatesregion of Arc mRNA upregulation driven by ipsilateral eye stimulation within the BZ;broad induction of Arc mRNA is present throughout V1 and V2 contralateral to thestimulated eye, Scale = 900µm. (B) Ipsilateral eye representation in cortex expandsmore in KbDb−/− than in WT (KbDb+/+) mice following monocular enucleation (ME)during the critical period (P22-31). Top: In situ hybridization for Arc mRNA inKbDb+/+ (upper) and KbDb−/− (lower) visual cortex ipsilateral to the remaining eye;arrows indicate borders of signal in layer 4. Below: cumulative histograms of meanwidth of Arc induction in layer 4 ± sem for KbDb+/+ (upper) and KbDb−/− (lower)mice reared with normal vision (open symbols) or mice that received ME (filledsymbols). Note increased width of Arc induction following ME in both genotypes.(KbDb+/++ME: 1426.3 ± 89.7 µm, n = 18 mice vs. KbDb+/+ normal vision: 945± 58.3 µm, n = 2 8 mice; P < 0.05; KbDb−/−+ME: 2404.6 ± 105.1 µm, n = 25mice; P = 0.0017). Note also that width of Arc induction in KbDb−/− mutant micereared with normal visual experience (open squares) is slightly larger than that ofnormally-reared KbDb+/+ mice (open circles): KbDb−/−: 1111 ± 50.8 µm, n = 25mice; KbDb+/+: 945 ± 58.3 µm n = 2 8 mice. Each symbol represents the average ofseveral scanned sections from a single animal ± sem. Scale = 400µm. (C) Averagewidth of Arc induction in layer 4 for normally reared KbDb+/+ or KbDb−/− mice (openbar) vs. mice receiving monocular visual deprivation from P25-34 (MD: gray bar, n= 7) or from P22-31 (ME: black bar, n = 25). (D) Plasticity Index (see text) revealsgreater OD plasticity in KbDb−/− than in KbDb+/+ visual cortex by MD and ME, (*)statistical significance determined by one-way ANOVA. Error bars = standard errorof mean (sem) in B and C, and root mean square error (RMSE) in D.
71
Figure 3.1
72
Figure 3.2: Enhanced thalamocortical plasticity in KbDb−/− mutant mice (A)Schematic of connections in mouse visual system: thalamocortical axon terminalsinnervate layer 4 of cortex. Below, transneuronal transport of H3proline followingintraocular injection reveals (age P34; Experimental procedures) a broad contralateralsignal (left), and smaller ipsilateral patch (right) in layer 4. Scale=1500µm. (B)Higher magnification of representative sections from KbDb+/+ (top) and KbDb−/−
(bottom) mice. Arrows indicate measurement borders used. Scale = 450µm. (C)Population averages for width ± sem of layer 4 label for each genotype after ME.P<0.05; (Mann-Whitney U test).
73
Figure 3.2
74
Figure 3.3: Incomplete segregation of RGC inputs to dLGN in KbDb−/− mu-tant mice (A) RGC projections labeled by intraocular injections of CTB AF594(red) into the contralateral (contra) eye and CTB AF488 (green) into the ipsilat-eral (ipsi) eye. Top: Merged fluorescent micrographs of dLGN from KbDb+/+ andKbDb−/− mice at P34. Middle (green pixels): ipsilateral eye projection pattern (in-tensity threshold = 60% of maximum). Bottom (yellow pixels): overlapping pixelsfrom ipsilateral and contralateral eye projections each at an intensity threshold 60%of maximum (Figure S3 and Experimental procedures). Note ectopic patches of ipsi-lateral eye projections not eliminated during development in KbDb−/− mice. Scale =150µm. (B, C) Mean % of dLGN area ± sem of ipsilateral eye projection and meanoverlapping pixels in both channels. More ipsilateral territory as well as overlappingpixels are present in mutant dLGN: KbDb+/+ = 5 mice, KbDb−/− = 5 mice, (*) P <0.05 (two-tailed t-test).
75
Figure 3.3
76
Figure 3.4: MHCI localization in relation to synaptic proteins during pe-riod of retinogeniculate refinement (A) AT micrographs reconstructed from 25serial ultrathin sections (each 70nm thick) of P7 LGN showing MHCI immunos-taining (green) in relation to known synaptic markers (Syn=synapsin (orange),PSD=postsynaptic density-95 (white), GAD=glutamic acid decarboxylase 65/67(cyan)), as well as to C1q=complement protein C1q (magenta). DAPI stain of nuclei,blue. Scale = 5µm. (B) Four serial sections of 2 different synapsin positive puncta:Left example is characteristic of an excitatory synapse (close apposition of presy-naptic marker synapsin with postsynaptic excitatory synapse marker PSD-95); Rightexample is characteristic of an inhibitory synapse (overlap of synapsin with GADand absence of PSD-95). MHCI and C1q are closely associated with both types ofsynapses. (C) Single ultrathin section, showing colocalization between MHCI punctaand PSD, Synapsin, GAD and C1q. Bottom right, zoomed in view of puncta num-bered on left, showing immunofluoresence signal in separate channels, Scale = 2µm.Note colocalization of signal for MHCI, PSD and C1q at puncta #1 and 2. (D) Crosscorrelations showing pairwise comparisons of degree of spatial overlap between punctaimmunostained for the various markers (Experimental procedures and Figure S4 on-line). Synapsin vs. PSD95 shows strongest correlation, and GAD vs. vGluT2 theweakest. MHCI is more correlated with C1q than with other markers. Si/St = Cr,the correlation ratio between two channels as a multiple of their baseline correlation.Cr = 1 indicates no correlation, Cr >> 1 indicates high correlation, Cr < 1 indicatesnegative correlation.
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Figure 3.4
78
Chapter 4
Single-Synapse Analysis of a
Diverse Synapse Population:
Proteomic Imaging Methods and
Markers
Micheva KD, Busse B, Weiler NC, O’Rourke N, Smith SJ. Single-synapse analysis
of a diverse synapse population: proteomic imaging methods and markers. Neuron.
2010 Nov 18;68(4):639-53.
4.0.12 Abstract
A lack of methods for measuring the protein compositions of individual synapses in
situ has so far hindered the exploration and exploitation of synapse molecular diver-
sity. Here, we describe the use of array tomography, a new high-resolution proteomic
imaging method, to determine the composition of glutamate and GABA synapses in
somatosensory cortex of Line-H-YFP Thy-1 transgenic mice. We find that virtually
79
all synapses are recognized by antibodies to the presynaptic phosphoprotein synapsin
I, while antibodies to 16 other synaptic proteins discriminate among 4 subtypes of
glutamatergic synapses and GABAergic synapses. Cell-specific YFP expression in the
YFP-H mouse line allows synapses to be assigned to specific presynaptic and postsy-
naptic partners and reveals that a subpopulation of spines on layer 5 pyramidal cells
receives both VGluT1-subtype glutamatergic and GABAergic synaptic inputs. These
results establish a means for the high-throughput acquisition of proteomic data from
individual cortical synapses in situ.
4.1 Introduction
Rapidly accumulating physiological and genetic evidence establishes that the molec-
ular diversity of synapses extends far beyond that envisioned by traditional classi-
fication schemes based solely on neurotransmitter identity. For instance, it is now
clear that within each neurotransmitter category (e.g., glutamatergic, GABAergic,
cholinergic) there is substantial diversity in the expression of many intrinsic synaptic
proteins, including neurotransmitter transporters and receptors [7–16]. Until synapse
molecular diversity is properly fathomed, it is likely to be a troublesome source of
variability in physiological and neurodevelopmental experimentation. Conversely, a
systematic understanding of synapse diversity (i.e., the synaptome) is likely to provide
valuable new perspectives on the organization of synaptic circuitry (i.e., the connec-
tome), its development, plasticity and disorders. It is easy to envision, for instance,
that a potential catalog of molecular synapse types [4] would help explorations of
the synaptic basis of specific memory or disease processes to focus more fruitfully on
specific synapse subpopulations.
To place a possible molecular catalog of synapse types on a firm footing, two
broad experimental challenges remain. First, it is essential that synapse populations
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be explored at the single-synapse level. Until recently, the only way to reliably resolve
and characterize individual synapses was by way of electron microscopy (EM). While
traditionally a time-consuming and very volume-limited method, recent advances in
EM [72–76] have greatly improved its throughput, even offering the possibility of
detailed neuronal circuit reconstruction. Nonetheless, EM still provides only very
limited proteomic discrimination (although Anderson et al. [75] describe a very pow-
erful new approach to integrating small-molecule discrimination with EM). Second,
synapse diversity must be explored in situ, in ways that retain full fidelity to the
intact tissue setting and allow for the acquisition of as much information as possible
about circuit context and cellular morphology.
Array tomography (AT) is a high-resolution proteomic imaging method [1, 17]
that exploits a combination of light and EM approaches to resolve fine details at the
level of synapses across large fields of view spanning entire circuits. Of prime sig-
nificance to the present application, AT allows the immunofluorescence resolution of
single synapses within cortical neuropil, where such resolution is highly problematic
for other optical methods. Additionally, AT can acquire many more dimensions of
immunofluorescence information about single synapses than previous methods (up
to 17 in the present work, as compared to the standard immunofluorescence limit of
three or four). AT also benefits from greatly improved quantitative reliability, since
both staining and imaging are completely independent of depth within a tissue sam-
ple. Finally, AT delivers very high experimental throughput: our present automated
methods acquire image data at a rate of approximately one million synaptic protein
puncta per hour. Such throughput will help advance the analysis of synaptic diversity
from the anecdote to the realm of solid bioinformatics. AT thus seems uniquely suited
to meet the challenges of exploring the molecular diversity of cortical synapses.
Here, we describe array tomographic immunofluorescence methods for the single-
synapse analysis of mouse cortex, focusing on the discrimination and analysis of
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glutamatergic and GABAergic synapses. Toward a goal of identifying every single
cortical synapse as unambiguously as possible, we evaluated antibody markers to
presynaptic proteins likely to be common to all synapses, such as synaptophysin,
bassoon, and synapsin. We find that antibodies to the presynaptic phosphoprotein
synapsin I [23, 24] are particularly robust and useful, labeling the vast majority of
cortical synapses with a minimum of labeling at nonsynaptic loci. For increased
confidence in synapse identification, we also develop here a basis for conjoint use of
multiple synaptic markers. We argue that antibodies to the glutamatergic synaptic
proteins VGluT1, VGluT2, PSD-95, GluR2, NMDAR1, and the GABAergic synaptic
proteins GAD, VGAT, and gephyrin can be used both to distinguish reliably between
glutamatergic and GABAergic synapses and begin the work of searching for finer
synapse molecular subtypes within these broad categories.
4.2 Results
A Note about Color Use
We have adopted a colorblind-friendly scheme in as many figures as possible. In
figures with only two immunofluorescence channels (Figures 4.3, 4.6, 4.7, 4.8C–D),
we use magenta and green as additive colors, such that regions of overlap display as
white. When three or more channels need to be displayed (Figures 4.1, 4.2, 4.4, 4.8A–
B), we represent each channel with a nontransparent color plane. In this case, colors
are nonadditive, for example, white in such figures is not the result of magenta and
green overlap but rather a distinct color representing a distinct immunofluorescence
channel.
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4.2.1 AT Resolves Individual Puncta of Multiple Synaptic
Proteins in Mouse Cortex
Figure 4.1 offers a panoramic view of a volume of somatosensory cortex from a YFP-
H Thy-1 transgenic mouse [71] representative of the specimens used in the present
work. The volume image rendered here was acquired by automated AT imaging
of a mosaic of 52 fields per section over 60 serial sections (200 nm each) in three
fluorescence spectral channels, comprising a total of 9360 individual image tiles. The
tiles were stitched and aligned in three dimensions and rendered as described in
Experimental Procedures. This volume is 12 µm thick, 0.5 mm wide and extends a
distance of 1.4 mm from the pial surface of the cortex through all cortical layers past
the subcortical white matter and into a portion of the underlying striatum. The three
fluorescence channels represented are YFP fluorescence (green), anti-tubulin (blue),
and anti-synapsin I (magenta) immunofluorescence. The vast information content
of the volume presented in Figure 4.1 is better appreciated from dynamic volume
renderings as in Movie S1 in the Supplemental Information available online.
YFP fluorescence in the cortex of line H mouse represents a soluble YFP marker
transgenically expressed in a large subset of layer 5 pyramidal neurons. This mouse
line was used because the YFP-expressing neurons provide a useful anatomical frame-
work, with their apical dendrites extending all the way to layer 1 and their axons
forming conspicuous bundles in the white matter. However, YFP fluorescence is not
necessary for the subsequent single synapse analysis, which can be performed also
in a wild-type mouse. In addition to the YFP labeled neurons, the apical dendrites
of pyramidal cells not expressing YFP are evident from tubulin immunostaining of
their core microtubule bundles (Figure 4.1D). Finally, the presence of aggregates
of synapsin I protein in the neuropil is apparent from the magenta puncta, which
can be individually discerned (Figure 4.1C–H). The spatial distribution of synapsin
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immunoreactivity is consistent with that expected of neocortical synapses. For ex-
ample, cell bodies appear as circular spaces largely devoid of synapsin puncta. The
few puncta observed within such voids are actually situated in front or behind the
cell bodies, as the depth of this volume is larger than the diameters of most cortical
cell bodies. There is no staining in blood vessels and very few puncta in the white
matter (Figure 4.1H). All of the data that follow were collected from arrays similar
to that represented in Figure 4.1, but image acquisition was, for expediency’s sake,
carried out in single fields of view in layers 4 and 5, corresponding to the areas in
Figure 4.1E and 4.1F, respectively. Thinner, 70 nm sections were used to increase z
resolution and the sampling of each synapse.
While the distribution of synapsin puncta resembles that of cortical synapses, it
is not clear whether all synapsin puncta represent synapses and whether all synapses
are immunoreactive for synapsin. Synapsin I is highly concentrated in presynap-
tic boutons [23] and has been used extensively as a general synaptic marker, but
a one-to-one relationship between synapsin puncta and synapses has not yet been
demonstrated. Therefore, it cannot be assumed that synapsin immunofluorescence
data alone are sufficient for synapse identification. Also, there are other proteins, for
example synaptophysin and bassoon, which are highly concentrated at presynaptic
boutons and could be useful as general markers for synapses. To evaluate candidate
cortical synapse markers, we developed a panel of antibodies that label a variety
of pre- and postsynaptic proteins (Table 4.1; see Supplemental Experimental Proce-
dures for antibody characterization). Because of the proteomic capability of AT to
immunostain sections multiple times with different sets of antibodies, we were able to
measure numerous pre- and postsynaptic markers at every putative synaptic locus.
An example of multiple antibody labeling is shown in Figure 4.2, which represents
a volume rendering from layer 4 of the somatosensory cortex of a YFP-H mouse
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immunostained with 10 different antibodies against synaptic proteins (synapsin, bas-
soon, VGluT1, VGluT2, PSD-95, GluR2, NMDAR1, GAD, VGAT, gephyrin) and
one against tubulin. The sequence of antibody application is presented in Table S1
(data set KDM-SYN-090416). In addition, two other fluorescent labels (YFP and
DAPI) were imaged, making a total of 13 fluorescent channels collected from each
section.
4.2.2 Synaptic Protein Distributions Imaged by AT Corre-
late as Expected from Synapse Structure
Some of the antibody markers in Table 4.1 are expected to be present at all synapses
in cortical neuropil, while others are specific for particular synapse subtypes. For ex-
ample, as universal presynaptic proteins, synapsin and synaptophysin puncta overlap
(Figure 4.3A). The majority of synapsin puncta also overlap with VGluT1, known to
be present in most cortical glutamatergic synapses. In addition, synapsin puncta are
closely apposed with PSD-95 puncta as would be anticipated from imaging pre- and
postsynaptic proteins at single synapses. GAD puncta, expected to label inhibitory
GABAergic synapses, overlap with a small subset of synapsin puncta, and synapsin
levels are generally lower in these synapses.
To quantify globally the extent of spatial correlation among various synaptic
marker candidates, we designed a correlation matrix test based on the van Steensel
method [21]; see Experimental Procedures for full description. The basic idea is to
test for the effect of very small relative displacements between pairs of marker images
on a measurement of image overlap. Because of the abundance of many synaptic
markers, overlapping spatial distributions might occur by chance. If the association
between two channels is real, however, then any shift of one channel relative to the
other will decrease the observed degree of colocalization. On the other hand, if two
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channels tend to be mutually exclusive, then a shift will increase the degree of colo-
calization. Finally, if the association between two channels is occurring by chance,
then a shift will not substantially affect the degree of colocalization. Using a 20 x
20 x 6.3 µm3 volume of neuropil from data set KDM-SYN-091207 (Table S1), we
computed a cross-correlation score for pairs of channels over a range of lateral offset
distances. From the 17 antibodies used in this dataset, we focused on the general
presynaptic markers synapsin, synaptophysin and bassoon, as well as several specific
markers for glutamatergic (VGluT1, VGluT2, PSD-95, and GluR2) and GABAergic
synapses (GAD and VGAT).
The cross-correlation score is represented in Figure 4.3B as a grid of false col-
ored squares with centers corresponding to the score at 0 offset and each pixel shift
equal to 0.1 µm offset. To visualize the data, different channel pairs are also shown
as immunofluorescent images from a small area of a single section of the same data
set. As can be seen in the correlation matrix, both synapsin and synaptophysin,
and to a lesser extent bassoon, colocalize with all other synaptic markers, includ-
ing those of smaller subsets of synapses that contain VGluT2 or GAD. All synaptic
markers are anticorrelated with tubulin, which labels microtubules within dendrites
and cell bodies. VGluT1 and VGluT2, found in cortical glutamatergic synapses, do
not colocalize with the GABAergic markers. PSD-95 and GluR2, both present at
the postsynaptic side of glutamatergic synapses, correlate strongly with each other
and more weakly with the presynaptic glutamatergic markers. GAD and VGAT,
presynaptic markers for GABAergic synapses, show strong correlation. An interest-
ing distinction can be made between the presynaptic markers with respect to their
colocalization with postsynaptic markers. Presynaptic markers that are associated
with synaptic vesicles (e.g., synapsin, synaptophysin, VGluTs) show high colocaliza-
tion among themselves, while their colocalization with postsynaptic markers such as
PSD-95 and GluR2 is weaker. On the other hand, the presynaptic marker bassoon,
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which labels the presynaptic active zone, shows similar colocalization with both pre-
and postsynaptic markers. This is due to the fact that the synaptic vesicle cluster is
situated far enough from the postsynaptic density to be resolved by AT. On the other
hand, the presynaptic active zone is only one synaptic cleft (around 20 nm) away
from the postsynaptic density which is below the resolution capabilities of AT. For
example, in single section images in Figure 4.3B, synapsin puncta are seen next to
PSD-95 and GluR2 puncta, while bassoon overlaps with these postsynaptic markers.
4.2.3 AT Immunofluorescence of Synapsin Is Highly Reliable
as Synapse Marker
A single marker protein detectable at all synapses and only at synapses would be
very useful for many purposes, but thus far there has been no conclusive demonstra-
tion of any such marker. While numerous markers, e.g., intrinsic proteins of synaptic
vesicles, might be localized at every chemical synapse, the usefulness of any such
antibody marker would be diminished if it were found at nonsynaptic loci as well.
From the colocalization matrix of Figure 4.3B, it is evident that both synapsin and
synaptophysin colocalize strongly with all other synaptic markers and thus might be
useful as general markers for synapses. Further examination of the immunofluores-
cence images revealed, however, that synaptophysin immunoreactivity is also fairly
often detectable at obviously extrasynaptic sites, e.g., in cell body and dendritic cy-
toplasm and nuclei (Figure 4.3A). Synaptophysin puncta moreover tend to be smaller
and less continuous than synapsin puncta. For these reasons, the synapsin I antibody
appeared to be the stronger candidate as a reliable synaptic marker and was subjected
to further evaluation.
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4.2.4 Synapsin Is Detectable at Virtually All Dendritic Spines
Almost all dendritic spines in adult cortex receive synapses and therefore a general
synaptic marker should be present at these sites. To determine the distribution of
synapsin puncta at spines, we reconstructed the apical dendrites of YFP-positive
layer 5 pyramidal cells extending through layer 4 in tissue that was immunostained
for both pre- and postsynaptic proteins (Figure 4.4). Immunofluorescence reveals
PSD-95 puncta within spine heads that are closely associated with both synapsin
and bassoon puncta. Two dendritic segments from data set KDM-SYN-091207 were
used to quantify the number of spines contacted by synapsin puncta. Only synaptic
marker immunofluorescence within 0.5 µm of the YFP dendrites was considered for
this analysis. One of the dendritic segments was 45 µm in length, 2 µm in width
and had 131 spines (2.9 spines/µm). From the 116 spines included in their entirety
within the imaged volume, 114 had at least one synapsin punctum associated with
them. The other dendritic segment was 93 µm long, 1.7 µm wide, and had 117 spines
(1.3 spines/µm), from which 110 were completely included in the volume. All of
these spines were associated with a synapsin punctum. Thus, more than 99% of the
dendritic spines on layer 5 pyramidal neurons were in the immediate vicinity of a
synapsin punctum. Moreover, other pre- and postsynaptic proteins colocalized with
the synapsin puncta at these dendritic spines (100% of synapsin puncta with pre- and
98% with postsynaptic markers).
4.2.5 EM Analysis Supports the Identification of Synapses
with Synapsin Immunoreactivity
The use of the synapsin antibody as a general synaptic marker was also assessed at
the EM level. Postembedding immunoEM using synapsin and a secondary antibody
conjugated to colloidal gold (15 nm) labeled presynaptic boutons, as identified by
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the presence of synaptic vesicles and adjacent postsynaptic densities (Figure 4.5C).
The relatively low density of immunogold labeling is likely due to the addition of
0.1% glutaraldehyde and 0.1% OsO4 during tissue preparation, which is necessary
for ultrastructural preservation but significantly impairs immunogenicity. Indeed,
synapsin immunofluorescence on sections from tissue treated this way is much weaker
than on our conventional sections used for the rest of this study (Figure S2).
We then compared synapsin immunofluorescence with the corresponding ultra-
structure to assess what proportion of synapses identified at the EM level are fluores-
cently labeled. Because the tissue preparation for EM significantly reduces synapsin
immunolabeling this analysis will result in an underestimate of the presence of synapsin
at synapses. Serial sections from tissue prepared for EM observation and mounted
on coverslips were first immunofluorescently labeled with the synapsin antibody and
imaged with the fluorescent microscope. The sections were then post-stained with
uranyl acetate and lead citrate and viewed in the SEM using the backscattered elec-
tron detector. The fluorescent and SEM images were aligned using the DAPI signal
and the nuclei as viewed in the SEM. The bright DAPI-stained puncta in the nuclei
correspond to the electron dense heterochromatin masses (Figure 4.5A). A compar-
ison of the ultrastructurally identified synapses and synapsin immunofluorescence
revealed that 91% of synapses (279 out of 305) were synapsin positive on at least
one section. The intensity of synapsin immunofluorescence did not correlate with the
size of the synapse as seen in the SEM. For example, some big presynaptic boutons
(asterisk on Figure 4.5B) were very weakly labeled. Thus, despite the reduced im-
munoreactivity with conjugate immunofluorescence-SEM imaging, 91% of synapses
were synapsin positive, which is consistent with synapsin being a reliable marker for
immunofluorescent imaging of cortical synapses.
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4.2.6 Multiple Synaptic Proteins Can Be Visualized Volu-
metrically as a Synaptogram Mosaic
The large number of immunofluorescence stains used with AT presents a challenge for
visualization. The traditional color coding cannot be used for so many channels, and
volume reconstruction along any single axis can obscure weak labels or show false colo-
calization of markers. Therefore, we devised a representation for multichannel volu-
metric image data, called a synaptogram (Figure 4.6), that is useful for single-synapse
analysis. All possible synaptic loci are identified with synapsin immunostaining and
represented by single channel serial sections in a matrix where each section occupies
a column and each channel a row. Sections represent a 1 x 1 µm2 area centered on
the centroid of the synapsin punctum. With synaptograms many antibody labels
can be visualized simultaneously and spatial relationships among labeled structures
can be examined with precision and relative ease. For example, the synaptograms in
Figure 4.6 use 18 different fluorescent signals: the general synaptic markers synapsin,
synaptophysin, and bassoon (two different antibodies), eight glutamatergic markers,
four GABAergic markers, and two structural markers (data set KDM-SYN-091207;
Table S1). The glutamatergic synapse on the left has a distinct synaptogram appear-
ance compared to the GABAergic synapse on the right. Both synapses contain the
general synaptic markers synapsin, synaptophysin, and bassoon. The glutamatergic
synapse contains presynaptic VGluT1 as well as a number of postsynaptic scaffold
and receptor markers (PSD-95, MAGUK, GluR2, NMDA receptor subunits). The
GABAergic synapse is distinguished by the presence of presynaptic GAD and VGAT
as well as the postsynaptic scaffold protein gephyrin and GABAA receptor subunit.
Both of the synapses are adjacent to a YFP-positive process and it appears that the
glutamatergic synapse is making a contact with this process (postsynaptic markers
overlap with the YFP signal), while the GABAergic synapse is not (postsynaptic
90
markers away from the YFP process).
The synaptogram makes it easy to check for the continuity of a given marker
punctum from one serial section to the next, and the 3D colocalization of multiple
markers that would be expected at a true synapse. The synaptogram can also be
useful in identifying fluorescence signals that are clearly not synaptic structures, such
as staining artifacts, and excluding them from the analysis. For example, the presence
of a synaptic vesicle immunofluorescence signal in just one isolated section is unlikely
to originate from an actual synapse, because synaptic vesicle clusters almost always
have a minimum extent greater than the 70 nm thickness.
4.2.7 AT Imaging Discriminates Multiple Glutamatergic and
GABAergic Synapse Subtypes
To begin characterizing the diversity of cortical synapses we focused on a panel of 10
antibodies. Synapsin and bassoon were used as general synaptic markers. VGluT1,
VGluT2, PSD-95, GluR2, and NMDAR1 were used as markers for glutamatergic
synapses. The vesicular glutamate transporters VGluT1 and VGluT2 were included
because their expression reportedly varies depending on the intracortical or subcorti-
cal origins of the synapses. In particular it is believed that VGluT2 is predominantly
expressed in thalamocortical synapses [77–79]. GAD, VGAT, and gephyrin were used
as markers for GABAergic synapses. This combination of antibodies allowed the iden-
tification of two general types of synapses: glutamatergic and GABAergic, with the
glutamatergic synapses further subdivided into subtypes containing VGluT1, both
VGluT1 and 2, VGluT2, and other (lacking VGluT1 and 2) (Figure 4.7A).
Synapses formed by axons of layer 5 pyramidal neurons belonged to the glu-
tamatergic VGluT1 subtype (Figure 4.7D), as evidenced by examination of YFP-
positive presynaptic boutons. From 96 YFP synapses in layer 4 and 110 YFP synapses
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in layer 5, all had associated VGluT1, not VGluT2, immunofluorescence.
To further evaluate the reliability of our approach to single-synapse analysis, we
calculated the proportion of synapses falling into each synaptic subtype from two
experiments performed on tissue sections from the same region of the same animal
but with a different order of antibody application (Table S1; data sets KDM-SYN-
090416 and KDM-SYN-091207). A cluster of glutamatergic markers was considered
to be a synapse only if both pre- and postsynaptic markers were present. The re-
quirement for the presence of pre- and postsynaptic markers was not applied to the
GABAergic synapses, because it is not yet known whether the postsynaptic scaffold
gephyrin is present at all synapses of this type [80]. Instead, we adopted the less strin-
gent criterion of colocalization of several presynaptic markers belonging to different
compartments (e.g., cytoplasmic, presynaptic active zone, and vesicular). Thus, in
addition to the presence of a ubiquitous synaptic marker (e.g., synapsin or bassoon)
and the cytoplasmic GAD, we required the presence of the vesicular marker VGAT,
which has been shown to specifically localize to presynaptic boutons [81]. The results
obtained from these two experiments were very similar (Table 4.2). Approximately
84% synapses were glutamatergic and 16% GABAergic. There were almost twice as
many VGluT2 containing synapses (VGluT1 + 2 subtype and VGluT2 only subtype)
in layer 4 compared to layer 5 (21.4% versus 12.9%). On average, only 4% of synapsin
puncta were not associated with other synaptic proteins and therefore most likely do
not represent synapses. The proportion of glutamatergic and GABAergic synapses,
as well as the preference of VGlut2 synapses for layer 4 are consistent with previous
studies [77, 79, 82]. These results, reproducible between two separate experiments,
confirm the reliability of single-synapse analysis with AT. It should be noted that
only one animal was analyzed quantitatively, and the results are aimed at evaluating
the technique, not providing reference proportions of synapse types in the mouse so-
matosensory cortex. This will, undoubtedly, require a larger number of animals and
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is beyond the scope of the present study.
4.2.8 AMPA and NMDA Receptors Distributions Vary at
Different Glutamatergic Synapses
There is great variability in the expression levels, subunit composition, and local-
ization of AMPA and NMDA receptors at synapses, which significantly affects their
functional properties [11]. To further characterize cortical synapses based on the type
of postsynaptic receptors present (Figure 4.7C), 110 synapses were randomly selected
in each of layers 4 and 5 using synapsin immunostaining. Inhibitory synapses were
excluded from the analysis. The distribution of glutamatergic receptors was very
similar in both cortical layers. The great majority of excitatory synapses contained
both AMPA and NMDA receptors, as identified with antibodies against GluR2 and
NMDAR1 respectively (85.4% in layer 4 and 84.5% in layer 5). In 11.8% of synapses
in both layers only AMPA receptors could be detected and in 2.7% of synapses in
layer 4 and 3.6% in layer 5only NMDA receptors. GluR2 and, to a lesser extent,
NMDA labels often extended through more sections than PSD-95, or were observed
in sections adjacent to PSD labeling.
4.2.9 Synapsin Is Present at All Glutamatergic and GABAer-
gic Synapses, but in Varying Amounts
In addition to enabling the study of synaptic diversity, the establishment of markers
for synapse subtypes also allowed us to revisit the question of whether synapsin is
expressed in all cortical synapses. From the conjugate immunofluorescence-SEM anal-
ysis, it was observed that 91% of ultrastructurally identified synapses were labeled for
synapsin, but, as mentioned before, those were suboptimal conditions for immunos-
taining. To better understand what proportion of synapses are labeled with synapsin
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in our tissue prepared for immunofluorescence, synapses were identified using different
combinations of pre- and postsynaptic markers, excluding synapsin. Glutamatergic
VGluT1 synapses were identified by the combination of VGluT1-PSD-95 antibodies,
VGluT2 synapses with VGluT2-PSD-95 antibodies and GABAergic synapses with
VGAT and gephyrin antibodies. One hundred synapses from each group were chosen
randomly and the synapsin immunofluorescence associated with them was measured
on all sections through the synapse. Synapsin immunofluorescence was above back-
ground levels in all analyzed synapses (Figure 4.7B). VGluT1 synapses contained the
highest average synapsin levels (151 ± 7 arbitrary units) compared to VGluT2 (111
± 7 a.u.) and VGAT synapses (81 ± 4 a.u.). A significant proportion of VGluT2
(17%) and VGAT synapses (12%) contained low levels of synapsin immunofluores-
cence (below 40 a.u.) compared to only 2% of the VGluT1 synapses.
These data further confirm that synapsin I can be used as a general synaptic
marker because it appears to be present in all mouse glutamatergic and GABAergic
cortical synapses. However, synapsin content as detected with immunofluorescence
varies depending on synapse type with some VGluT2 and VGAT synapses exhibiting
low levels of synapsin. Thus, applying a simple intensity threshold of synapsin im-
munofluorescence should be avoided because this can lead to underestimation of the
synapse subtypes exhibiting low synapsin levels. Double Innervated Spines on Layer
5 Pyramidal Neurons Are Contacted by a VGluT1 and a GABAergic Synapse
Double innervated dendritic spines are an intriguing synaptic arrangement in-
volved in cortical plasticity and are found in a variety of species and cortical areas.
Until now they have been observed only by EM [82–85]. Very little is known about
the identity of either the input or the target in this arrangement [86, 87] especially
in the mouse somatosensory cortex. AT can resolve neighboring synapses, as can
be seen on Figure 4.5B, where adjacent synapses imaged in SEM are represented
by separate immunofluorescent synapsin puncta. We therefore used AT to further
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characterize dually innervated spines. Examples of dendritic spines receiving two
synaptic inputs, one glutamatergic and one GABAergic, are presented in Figure 4.8.
These spines emerge from the apical dendrites of YFP-positive layer five pyramidal
neurons. From two data sets (KDM-SYN-090416 and KDM-SYN-091207), 22 un-
equivocal double-innervated spines were identified, i.e., both pre- and postsynaptic
markers were present for the two inputs. Of those spines, 82% received a glutamater-
gic VGluT1 input and a GABAergic input. The remaining 18% received a gluta-
matergic VGluT2-containing (VGluT2 only, 9%, and VGluT1 and 2, 9%) as well as a
GABAergic input. The great majority of the glutamatergic synapses contained both
AMPA and NMDA receptors (91%), one spine was seen with NMDA receptors only,
and one spine had neither AMPA nor NMDA receptor markers present. Thus, layer
5 pyramidal neurons have apical dendrites that contain spines innervated by both
excitatory and inhibitory synapses, and the glutamatergic input to these spines is
predominantly VGluT1 positive.
4.3 Discussion
In the present study, we identify a synapsin I antibody as a reliable marker for cor-
tical synapses. Synapsin I associates exclusively with small synaptic vesicles and is
concentrated in presynaptic boutons [23]. The great majority of synapses are thought
to contain synapsin and this protein has been used extensively as a general synap-
tic marker. However, the list of possible exceptions to this rule has grown recently
and now includes ribbon synapses in the retina [88], reticulogeniculate synapses [89],
and some GABAergic- and VGluT2-containing synapses in the cerebral cortex [90].
AT, because of its increased sensitivity of immunofluorescence detection, ability to
analyze multiple antibody stains and use immunofluorescence in conjunction with
EM, allowed us to examine in detail the relationship between synapsin puncta and
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synapses in the mouse cerebral cortex.
For the synapsin antibody to be relied upon as a universal synapse marker, the
following conditions had to be confirmed: (1) synapsin labels the great majority
of synapses; (2) synapsin background labeling is minimal and can be differentiated
from the labeling of synapses; and (3) synapsin imaging has the resolution for dis-
cerning adjacent synapses. This study presents multiple lines of evidence that a
rabbit polyclonal synapsin I antibody labels the vast majority of synapses in the
mouse somatosensory cortex. For example, conjugate SEM of sections stained and
imaged for synapsin immunofluorescence revealed that 91% of ultrastructurally iden-
tified synapses are immunofluorescently labeled on at least one section. Because the
preparation of the tissue for EM sharply decreases antigenicity, this is a conserva-
tive estimate of the number of synapsin positive synapses. In tissue prepared for
immunofluorescence, more than 99% of dendritic spines have an adjacent synapsin
punctum. Also, all glutamatergic and GABAergic synapses identified by different
combinations of pre- and postsynaptic markers contain synapsin label, albeit some
at low levels. The background synapsin immunofluorescence is very low and only
occasional puncta can be seen in cell body or large dendrite cytoplasm, or nuclei.
Very few synapsin puncta (around 4%) exist alone, away from other synaptic mark-
ers. Synapsin immunofluorescence colocalizes with other presynaptic markers such as
bassoon, VGluT1, VGluT2, VGAT, and GAD and is also found immediately adjacent
to postsynaptic markers such as PSD-95, GluR2, NMDAR1, and gephyrin. These re-
lationships were confirmed both at the synapse population level using a normalized
cross-correlation analysis and for individual synapses using synaptograms. Finally,
synapsin labeling with AT allows for the resolution of juxtaposed synapses as can be
seen from conjugate EM. At the light level, when multiple immunofluorescent labels
are used, adjacent synaptic puncta can be observed that colocalize with different sets
of pre- and postsynaptic antibodies and thus clearly belong to two different synapses.
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The reliability of synapsin as a universal synaptic marker can be strengthened with
the concomitant use of multiple strategically chosen synaptic markers. This not only
helps the unequivocal identification of synapses but also allows the positive identifi-
cation of glutamatergic and GABAergic synapses within cortical neuropil and reveals
the existence of several synaptic subtypes within those broad categories. Based on
the presence of vesicular glutamate transporters, the glutamatergic synapses could be
divided into those containing only VGluT1, only VGluT2, both VGluT1 and 2, and
neither VGlut1 or 2. Synapsin immunofluorescence was detected in all synapses but
varied in intensity depending on the synapse type. It was highest in VGluT1 con-
taining synapses, followed by VGluT2 synapses and GABAergic synapses. Previous
studies [90] have noted this heterogeneity of synapsin content in cortical synapses of
rats. Using confocal microscopy in Vibratome sections, synapsin was observed in e90%
of VGluT1 puncta and only 30%–50% of VGluT2 and VGAT puncta. This discrep-
ancy with our results is probably due to the inability to detect small synapsin puncta
using confocal microscopy. Previously, we observed fewer small synapsin puncta with
confocal microscopy of Vibratome sections compared to AT on LR White sections pre-
pared from the same animal [1]. The varying synapsin content in different synapse
types is probably related to the different functional properties of the synapses. For
example, release probability is low at VGluT1 synapses and high at VGluT2 [77] and
many VGAT synapses (e.g., synapses made by parvalbumin-containing fast-spiking
interneurons). Interestingly, there is evidence that synaptophysin is also expressed at
a lower level in GABAergic synapses [91].
The proportions of different synaptic types was found to be very similar in layers
4 and 5, with the exception of VGluT2 containing synapses which, as observed in
previous studies [77], were more prominently represented in layer 4. In addition, the
existence of a sizable population of glutamatergic synapses that contain both VGluT1
and 2 was detected. There were several-fold more synapses containing both VGluT1
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and 2 (15% in layer 4 and 10% in layer 5) than purely VGluT2 synapses (6% in layer
4 and 2.5% in layer 5). It was previously thought that the expression of VGluT1 and
2 in synapses in the adult animal is mostly complementary [77], but later studies have
revealed the existence of both VGluTs in the same cortical synapses [92], particularly,
the thalamocortical terminals in layer 4 [79]. VGluT1, VGluT1 and 2, and VGluT2
containing synapses appear to have distinct intracortical and subcortical origins, but
the exact details of their identities are still being explored [77–79]. Interestingly, the
expression of the two vesicular glutamate transporters can be regulated by activity
in opposite directions [78]. Thus, determining the VGluT1 and 2 content of synapses
may provide information about their synaptic activity as well. Including more molec-
ular markers in the single-synapse analysis is expected to reveal additional synaptic
categories and contribute further to our understanding of synaptic diversity.
AT also allowed us to observe double innervated spines at the light level and to be-
gin characterizing their input. The YFP fluorescence expressed in pyramidal neurons
in the YFP-H mouse line conveniently outlines dendritic spines, but similar analysis
can also be performed in wild-type mice on neurons labeled by intracellular microin-
jections of fluorescent tracers or by in utero electroporation. Between 5% and 30%
of cortical dendritic spines in a variety of species are thought to receive two synaptic
inputs, one excitatory and one inhibitory [82, 83, 93]. This intriguing synaptic ar-
rangement is involved in cortical plasticity and is an example of a specific synapse
subtype, namely inhibitory synapses on spines in layer 4, that changes in response to
modifications in sensory experience and/or learning [82, 84, 85]. Sensory deprivation
induced by whisker removal results in a selective reduction of inhibitory synapses
on spines, while increased sensory stimulation or classical conditioning involving the
whiskers results in a selective increase in inhibitory synapses on spines. No direct
evidence existed about the identity of either the dendritic spines or their inputs in
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mouse somatosensory cortex. A recent study in the frontal cortex of young rats sug-
gests that double-innervated spines are preferentially targeted by VGluT2-containing
thalamocortical afferents, while the inhibitory input is from all subtypes of cortical
interneurons [87]. Using AT, we were able to observe the double innervated spines of
layer 5 pyramidal neurons in mouse somatosensory cortex at the light level and con-
firm that they receive one glutamatergic and one GABAergic synapse. We also show
for the first time that the glutamatergic input is predominantly VGluT1-containing
and that both AMPA and NMDA receptors are present at the postsynaptic site. The
addition of synaptic markers such as neuropeptide markers could provide information
about the identity of the inhibitory input.
4.4 Conclusions
Here, we demonstrate the usefulness of AT for the proteomic examination of indi-
vidual synapses in natural brain tissue with full preservation of neuroanatomical and
circuit context information. As efficient automated analysis strategies are developed
to complement the inherently high throughput of array tomographic image acquisi-
tion, this tool should open new doors to the large-scale bioinformatic exploration of
the molecular diversity and architecture of synapses. One likely consequence of such
exploration could be the development of new schemes for the differentiation and cata-
loging of molecular synapse types. By isolating specific subsets of synapses, a synapse
catalog could help enormously in pinpointing the specific synapse changes involved in
particular neurological disorders [94] or forms of neural plasticity [82,84,95–97]. AT’s
unique abilities to extract simultaneously rich proteomic and fine-scale structural in-
formation also suggests that the method may substantially advance ongoing efforts
to integrate the structural and molecular views of neuronal microcircuit function.
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4.5 Experimental Procedures
4.5.1 Tissue Preparation
All procedures related to the care and treatment of animals were approved by the
Administrative Panel on Laboratory Animal Care at Stanford University. Four adult
mice: three C57BL/6J and one YFP-H [71], were used for this study. The ani-
mals were anesthetized by halothane inhalation and their brains quickly removed and
placed in 4% formaldehyde and 2.5% sucrose in phosphate-buffered saline (PBS) at
room temperature. Each cerebral hemisphere was sliced coronally into three pieces
and fixed and embedded using rapid microwave irradiation (PELCO 3451 laboratory
microwave system with ColdSpot; Ted Pella, Redding CA) as described in [17]. To
preserve YFP fluorescence in the YFP-H mouse, the tissue was dehydrated only up
to 70% ethanol.
For EM, the tissue was processed as above except that the fixative also contained
0.1% glutaraldehyde, and a postfixation step was added with osmium tetroxide (0.1%)
and potassium ferricyanide (1.5%) with rapid microwave irradiation, 3 cycles of 1 min
on-1 min off-1 min on at 100W, followed by 30 min at room temperature.
4.5.2 LRWhite Sections
Ribbons of serial ultrathin (70 nm) sections were cut with an ultramicrotome (EM
UC6, Leica Microsystems, Wetzlar, Germany) as described in [17]. The ribbons were
mounted on subbed coverslips (coated with 0.5% gelatin and 0.05% chromium potas-
sium sulfate) and placed on a hot plate ( e60◦C) for 30 min. For SEM imaging, the
subbed coverslips were also carbon coated using a Denton Bench Top Turbo Carbon
Evaporator (Denton Vacuum, Moorestown, NJ). Subbed and carbon coated cover-
slips were also prepared for mounting ribbons of sections to be used for multiple
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immunostaining rounds (>6). For transmission electron microscope (TEM) the sec-
tions were collected on formvar-coated nickel grids. Immunofluorescence Staining and
Antibodies
Staining was performed as described in [17]. The coverslips with sections were
mounted using SlowFade Gold antifade with DAPI (Invitrogen, Carlsbad CA). To
elute the applied antibodies, the mounting medium was washed away with dH2O and
a solution of 0.2 M NaOH and 0.02% SDS in distilled water was applied for 20 min.
After an extensive wash with Tris buffer and distilled water, the coverslips were dried
and placed on a hot plate (60◦C) for 30 min.
The primary antibodies and their dilutions are listed in Table 4.1. Only well
characterized commercial antibodies were used and they were evaluated specifically for
AT as described in Supplemental Experimental Procedures. For immunofluorescence,
Alexa Fluor 488, 594, and 647 secondary antibodies of the appropriate species, highly
preadsorbed (Invitrogen, Carlsbad CA) were used at a dilution 1:150. The sequence
of antibody application in the multiround staining is presented in Table S1.
4.5.3 ImmunoEM Staining
The staining protocol was similar to the immunofluorescence staining with the addi-
tion of two steps in the beginning: treatment for 1 min with saturated sodium meta-
periodate solution in dH2O to remove osmium and 5 min with 1% sodium borohydride
in Tris buffer to reduce free aldehydes resulting from the presence of glutaraldehyde
in the fixative. A 15 nm gold labeled goat anti-rabbit IgG secondary antibody (SPI
Supplies, West Chester, PA) was used at 1:25 for 1 hr. After washing off the sec-
ondary antibody, the sections were treated with 1% glutaraldehyde for 1 min to fix
the antibodies in place and the sections were post-stained with 5% uranyl acetate for
30 min and lead citrate for 1 min. Fluorescence Microscopy and Image Processing
Sections were imaged on a Zeiss Axio Imager.Z1 Upright Fluorescence Microscope
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with motorized stage and Axiocam HR Digital Camera as described in [17]. Briefly,
a tiled image of the entire ribbon of sections on a coverslip was obtained using a
10x objective and the MosaiX feature of the software. The region of interest was
then identified on each section with custom-made software and imaged at a higher
magnification with a Zeiss 63x/1.4 NA Plan Apochromat objective, using the image-
based automatic focus capability of the software. The resulting stack of images was
exported to ImageJ, aligned using the MultiStackReg plugin and imported back into
the Axiovision software to generate a volume rendering. When a ribbon was stained
and imaged multiple times, the MultiStackReg plugin was used to align the stacks
generated from each successive imaging session with the first session stacks based on
the DAPI channel, then a second within-stack alignment was applied to all the stacks.
To reconstruct large volumes of tissue (Figure 4.1), we first used Zeiss Axiovision
software to stitch together the individual high-magnification image tiles and produce
a single mosaic image of each antibody stain for each serial section in the ribbon. We
created a z stack of mosaic images for each fluorescence channel, and then grossly
aligned the stacks using the MultiStackReg plugin. Finally, to remove non-linear
physical warping introduced into the ribbons by the sectioning process, we used a sec-
ond ImageJ plugin, autobUnwarpJ (available at http://www.stanford.edu/enweiler),
which adapts an algorithm for elastic image registration using vector-spline regular-
ization [98].
For the figures, images representing single sections were upscaled using bicu-
bic interpolation. No other image processing was used except for adjustment of
brightness/contrast in some of the channels (NMDA receptor subunits and gephyrin).
Synapsin immunofluorescence was not adjusted. For volume renderings, meant only
for visual appreciation, more extensive image processing was used to adequately illus-
trate the spatial distribution and relationship between different markers. No quan-
tifications or substantive comparisons were based on these images.
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4.5.4 Colocalization Analysis
To examine the spatial relationships between synaptic markers, we developed a colo-
calization detection function similar to the van Steensel method [21]. Using a 20 x
20 x 6.3 µm3 volume of neuropil, for each pair of channels we computed the three-
dimensional normalized cross-correlogram [99] for a range of lateral offsets approx-
imating the size of a synapse, using Eaton’s extension of the MATLAB function
normxcorr2 (http://www.cs.ubc.ca/edeaton/tut/normxcorr3.html). Pairs of labeled
channels with nonrandom associations (either positive or negative) should demon-
strate a nonzero correlation effect which asymptotically approaches 0 at offset ranges
exceeding their scale of interaction.
4.5.5 Transmission and Scanning Electron Microscopy
The immunostained TEM grids were imaged using a JEOL TEM1230 equipped with
a Gatan 967 slow-scan, cooled CCD camera. For SEM following fluorescence imaging,
the immunostained arrays were washed with dH2O to remove the mounting medium
and post-stained with 5% uranyl acetate in H2O for 30 min and lead citrate for 1 min.
The arrays were imaged on a Zeiss Sigma scanning electron microscope equipped with
field emission gun using the backscattered electron detector at 10 kV. SEM images
were aligned with the corresponding immunofluorescent images in ImageJ using the
TurboReg plugin [20]. The nuclei as viewed with DAPI fluorescence, and the SEM
defined the identical regions in the two imaging modes.
4.6 Supplemental Information
Supplemental Information includes three figures, one table, and one movie and can
be found with this article online at doi:10.1016/j.neuron.2010.09.024.
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4.7 Acknowledgments
This work was supported by grants from the National Institutes of Health (NS063210),
Gatsby Charitable Trust, Howard Hughes Medical Institute (Collaborative Innovation
Award #43667), by funds from the Stanford’s BioX Program, Stanford’s Departments
of Neurosurgery and Neurology and Neurological Science, and by a gift from Dr.
Lubert Stryer. We thank Nafisa Ghori for her expert technical help and JoAnn
Buchanan and Gordon Wang for help and advice. We thank Profs. Liqun Luo,
Miriam Goodman, and Thomas Clandinin (Stanford University) and Bradley Hyman
(Harvard Medical School) for their very helpful comments on the manuscript.
Table 4.1: Synaptic Antibodies Used in This StudyAntibody Localization Species Source Cat. No. Dilution
All synapses Synapsin I presynaptic Rabbit Millipore AB1543P 1:100Bassoon presynaptic Mouse Abcam ab13249 1:100Bassoon presynaptic Rabbit Synaptic Systems 141003 1:100Synaptophysin presynaptic Mouse Abcam ab8049 1:10Synaptophysin presynaptic Rabbit Abcam ab68851 1:100
Glutamatergic VGluT1 presynaptic Mouse NeuroMab 75-066 1:100VGluT1 presynaptic Guinea Pig Millipore AB5905 1:1000VGluT2 presynaptic Guinea Pig Millipore AB2251 1:1000PSD-95 postsynaptic Mouse NeuroMab 75-028 1:100panMAGUK postsynaptic Mouse NeuroMab 75-029 1:100GluR2 postsynaptic Mouse Millipore MAB397 1:50GluR2/3 postsynaptic Rabbit Millipore AB1506 1:100NMDAR1 postsynaptic Mouse Millipore MAB363 1:200NMDAR2A postsynaptic Mouse Millipore MAB5216 1:25NMDAR2B postsynaptic Mouse NeuroMab 75-101 1:500
GABAergic GAD presynaptic Rabbit Millipore AB1511 1:300VGAT presynaptic Mouse Synaptic Systems 131 011 1:100Gephyrin postsynaptic Mouse BD Biosciences 612632 1:100GABAAR1 postsynaptic Mouse NeuroMab 75-136 1:100
See also Figure S3.
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Table 4.2: Proportion of Synapses from Different Synaptic SubtypesSynaptic Subtype Layer 4 Layer 5
Exp. 1 Exp. 2 Exp. 1 Exp. 2VGluT1 60.1% (149) 57.5% (100) 66.7% (156) 67.3% (113)VGluT1+2 15.3% (38) 15.5% (27) 9.0% (21) 11.9% (20)VGluT2 5.6% (14) 6.3% (11) 2.6% (6) 2.4% (4)Other glutamatergic 3.6% (9) 4.6% (8) 5.6% (13) 2.4% (4)GABAergic 15.3% (38) 16.1% (28) 16.2% (38) 16.1% (27)All synapses 100% (248) 100% (174) 100% (234) 100% (168)All synapsin puncta 262 183 242 172Not synapse 5.3% (14) 4.9% (9) 3.3% (8) 2.3% (4)
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Figure 4.1: Array tomographic synapsin I immunofluorescence in the cere-bral cortex of an adult YFP-H mouse is punctate and consistent withsynapse identity.(A) A volume rendering of 60 serial sections (200 nm each) through the entire corti-cal depth, including portions of the striatum. While all subsequent experiments andanalysis were performed on thinner, 70 nm sections, the thicker sections in this casehave allowed us to collect a larger volume and to better visualize the extensive den-drites of pyramidal neurons. Synapsin (magenta), tubulin (blue), and YFP (green).Scale bar, 50 µm.(B) A close up of layer 5 pyramidal neurons labeled with YFP.(C–H) Zoomed-in view of layers 1 (C), 2/3 (D), 4 (E), 5a (F), 6a (G), and whitematter and striatum (H).Scale bar for (B)–(H), 10 µm. See also Movie S1 for a more revealing rendering of thesame image volume and Figure S1 for comparison of different synapsin antibodies.
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Figure 4.1
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Figure 4.2: Proteomic immunofluorescence AT of mouse somatosensory cor-tex yields staining patterns consistent with synaptic protein distributions.Volume rendering from 20 sections, 70 nm each, from an array stained with 11 anti-bodies (Table S1, data set KDM-SYN-090416).(A) Tubulin (blue), synapsin (magenta), YFP (green), and DAPI (gray) fluorescence.(B–D) The boxed area in (A). DAPI (gray) and YFP (green). (B) Distribution of allpresynaptic boutons as labeled with synapsin (magenta). (C) Distribution of VGluT1(red), VGluT2 (yellow), and VGAT (cyan) presynaptic boutons. (D) Postsynapticlabels: GluR2 (blue), NMDAR1 (white), and gephyrin (orange) next to synapsin(magenta).Scale bar 10 µm. See also Table S1 for sequence of antibody application.
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Figure 4.2
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Figure 4.3: Multiple synaptic proteins are colocalized in a fashion consistentwith synaptic identity and glutamatergic and GABAergic synapse subtype.(A) Volume rendering of 20 sections (70 nm) from the mouse somatosensory cor-tex immunostained for synapsin (magenta) and synaptophysin, VGluT1, PSD-95, orGAD (green). Colocalization of the magenta and green channels is displayed as white.DAPI, blue. These volume renderings are from an array stained with 17 antibodies(Table S1, data set KDM-SYN-091207). Scale bar, 5 µm.(B) Colocalization matrix of nine synaptic markers and tubulin (left) and correspond-ing pairwise representation of the channels on a small area (4 x 4 µm) of a single sec-tion (right). For each pair of channels we computed a cross-correlation score over arange of lateral offset distances for images in the two channels. The cross-correlationscore is represented as a grid of false colored squares with their center representingthe score at 0 offset and each pixel equal to 0.1 µm offset.(C) For a subset of channel comparisons, the cross-correlation score is plotted as afunction of the lateral offset. Each trace is obtained by averaging 16 equally spacedradii. Left, with no lateral shift the normalized cross-correlation is equal to thePearson correlation coefficient and at shifts beyond the rough size of a synapse thecorrelation drops to e0 for all channels. Right, the same is normalized such thateach curve is 1.0 in the no-shift case. Pre-presynaptic and post-postsynaptic channelcomparisons drop off sharply, while pre-postsynaptic do not.
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Figure 4.3
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Figure 4.4: Dendritic spines in mouse cerebral cortex are contacted bysynapsin puncta and colocalize with other pre- and postsynaptic mark-ers.Volume rendering of 45 sections from dataset NAOR-081118 (Table S1). To bettervisualize the synaptic markers associated with dendritic spines, only immunofluores-cence within 0.5 µm of the YFP dendrite was displayed.(A) In the left panel, a 20 µm long segment from a spiny dendrite of a layer 5 pyramidalcell (green) is shown as it traverses layer 4. In each subsequent panel the labeling of asynaptic protein is added. PSD-95 (blue), bassoon (yellow), and synapsin (magenta).The postsynaptic protein PSD-95 is found within spine heads and closely apposed tothe presynaptic proteins bassoon and synapsin (arrow). Scale bar, 2 µm.(B) The opposite side of the spine marked with an arrowhead in (A) at higher mag-nification. Scale bar, 0.5 µm.See also Figure S2.
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Figure 4.4
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Figure 4.5: Ultrastructurally identified synapses are labeled with thesynapsin antibody.(A and B) Conjugate synapsin immunofluorescence and SEM of the adult mousecerebral cortex. Synapsin (magenta) and DAPI (blue) signal as obtained with thefluorescence microscope are overlaid on the SEM image from the same section. (B)Four serial sections through the boxed region in (A). Boxed region is section #2 inthe series. The majority of presynaptic boutons are consistently labeled from sectionto section (arrows), but some are labeled only on few sections with a weak signal(asterisk). Scale bar, 0.5 µm.(C) A TEM image of postembedding gold immuno-EM for synapsin. The 15 nm goldparticles label presynaptic terminals as identified by the presence of synaptic vesiclesand postsynaptic density. Scale bar, 0.5 µm.See also Figure S2 for effect of tissue processing on synapsin immunostaining.
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Figure 4.5
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Figure 4.6: Synaptograms are useful for viewing proteomic information fromserially sectioned single synapses.A glutamatergic (left) and a GABAergic synapse (right) are shown. Each squarerepresents an area of 1 x 1 µm from a single 70 nm section. Each section through thesynapsin punctum occupies a column and each antibody label a row. See also TableS1 for sequence of antibody application.
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Figure 4.6
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Figure 4.7: Proteomic imaging with AT reveals the diversity of corticalsynapses.(A) Examples of synaptograms representing the main synapse subtypes observed inmouse somatosensory cortex with the current antibody panel.(B) Synapsin content of different synaptic subtypes. For each subtype, 100 synapseswere randomly selected using the VGluT1-PSD-95, VGluT2-PSD-95, and VGAT-gephyrin channels and synapsin immunofluorescence was measured on each sectionthrough the synapse. Top panel, histograms of synapsin immunofluorescence in thethree synapse subtypes. Lower panel, scatterplot of synapsin intensity versus therespective vesicular transporter immunofluorescence for each synapse.(C) Examples of glutamatergic synapses with different postsynaptic receptor combi-nations.(D) Example of a synapse made by the axon of a YFP-positive layer 5 pyramidalneuron.
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Figure 4.7
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Figure 4.8: Double innervated spines receive both a glutamatergic VGluT1and GABAergic synapse.(A and B) Volume rendering of dendritic spines from YFP-positive pyramidal celldendrites (green), each receiving 2 synaptic inputs on the head. The glutamater-gic synapses are represented by postsynaptic PSD-95 label (blue) and presynap-tic synapsin (magenta). The GABAergic synapses are represented by postsynapticgephyrin (orange) and presynaptic GAD (cyan). The labels are added consecutivelyfrom left to right. Additional synapses not contacting the spines are also observedwithin the reconstructed volume.(C and D), Single sections through double innervated spines labeled with multipleantibodies. For each spine, the two adjacent sections where most of the markers werepresent was chosen. Each panel shows the spine (green) and one synaptic marker(magenta). Direct overlap of the two labels is seen as white. The punctuated lineseparates adjacent sections. (C’) and (D’) show a volume rendering of the spines in(C) and (D) with the plane of the single sections represented in gray. Scale bar, 1µm.
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Figure 4.8
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Chapter 5
Single-synapse analysis of a diverse
synapse population: synapse
discovery and classification
5.0.1 Abstract
Synapses of the mammalian central nervous system are recognized today as being
highly diverse in function and in molecular composition. Indeed, synapse diversity
per se is likely to be critical to brain function, since the abilities of synaptic circuits to
store and retrieve memories, and to adapt homeostatically to developmental and en-
vironmental change are thought to be rooted primarily in activity-dependent plastic
changes in specific subsets of individual synapses. Unfortunately, the measurement of
synapse diversity has been restricted by limitations of methods capable of measuring
synapse properties at the level of individual synapses. Array tomography is a new
high-resolution, high-throughput proteomic imaging method that has the potential
to very substantially advance the measurement of unit-level synapse diversity across
large and diverse synapse populations. Here we introduce and compare automated
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feature extraction and classification algorithms designed to discriminate, classify, and
quantify synapses from high-dimensional array tomographic data much too volumi-
nous for manual analysis. We identify a random forest classifier, trained from exam-
ples classified by human experts, as being particularly suitable to high-throughput
synapse classification. We demonstrate the use of this method to quantify laminar
distributions synapses in mouse somatosensory cortex and validate the classification
process by detecting the presence of known but uncommon proteomic profiles. We
suggest that such classification and quantification is likely to be highly useful in iden-
tifying specific subsets of synapses exhibiting plasticity in response to perturbations
of the environment or sensory periphery.
5.0.2 Author Summary
Synaptic connections are fundamental to every aspect of brain function. There is
growing recognition of the individual synapse as the key sites of the functional plastic-
ity that allows brain circuits to store and retrieve memories and to adapt to changing
demands and environments. There is also growing recognition that many neurologi-
cal, psychiatric, neurodevelopmental and neurodegenerative disorders must be under-
stood at he level of individual synapses and proteomically-defined synapse subsets.
Here, we introduce and validate computational analysis tools designed to complement
array tomography, a new high-resolution proteomic imaging method, to enable the
analysis of diverse synapse populations of unprecedentedly large size at the single-
synapse level. We expect these new single-synapse classification and analysis tools
to substantially advance the search for the specific physical traces, or engrams, of
specific memories in the brains synaptic circuits. We also expect these same tools
to be useful for identifying the specific subsets of synapses that are impacted by the
various synaptically-rooted afflictions of the brain.
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5.1 Introduction
Synapses are fundamental to every aspect of brain function. They are recognized
today as being highly complex structures and highly diverse in both function and
molecular composition. At the structural level, individual synapses of the mam-
malian central nervous system are thought to comprise hundreds of distinct protein
species [4–6], and genomic and gene expression data available implies very strongly
that there are multiple isoforms of many of these proteins and that their expression
is differentially patterned across the brains many different neuron types [3]. It thus
seems inescapable that synapses of the brain, even within traditional transmitter-
defined synapse categories (e.g., glutamatergic, GABAergic, cholinergic, etc.), must
be highly diverse in protein composition. This conclusion is consistent with the avail-
able functional data, where physiological studies report wide differences in synaptic
transmission as different brain regions and pathways are examined (again, even when
results are compared only within traditional neurotransmitter categories). Moreover,
the well-known functional plasticity of both synapse structure and synapse function
in response to electrical activity implies directly that even an otherwise homogeneous
synapse population must become heterogeneous or diverse after individual synapses
experience differential activity. In this light, it seems likely that synapse diversity
per se may be critical to the proper function of neural circuitry. For instance, there
is now widely believed that the plasticity (and therefore resulting diversity) of indi-
vidual synapses is fundamental to memory storage and retrieval and to many other
aspects of neural circuit adaptation to environmental change [100,101].
Unfortunately, the measurement of synapse diversity has been restricted by limi-
tations of methods capable of measuring synapse properties at the level of individual
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synapses. Array tomography (AT) is a new high-resolution, high-throughput pro-
teomic imaging method that has the potential to very substantially advance the mea-
surement of unit-level synapse diversity across large and diverse synapse populations.
AT uses multiple cycles of immunohistochemical labeling on thin sections of resin-
embedded tissue to image the proteomic composition of synapse-sized structures in
a depth-invariant manner. We have applied AT to freshly-fixed mouse cerebral cor-
tex, where our volumes have typical sizes of thousands to millions of µm3 of tissue,
contain millions of individually-resolved synapses, and label over a dozen multiplexed
proteomic markers.
With proper analysis, the informational density of array tomographic volumes has
numerous potential applications. Synapse-level resolution of large volumes of tissue
is an ideal tool for addressing interesting hypotheses concerning principles like synap-
tic scaling [100], structural arrangement [102], and novel synapse types [103, 104].
Combined with connectomic data [105, 106], genetic models [107, 108] or dye filling
techniques [109, 110], array tomography can also address questions regarding pro-
teomic distributions in specific subsets of cells. We are interested in investigations
of this nature and others in the mouse cerebral cortex, where the anatomical distri-
bution of synapses, aside from cortical layer cytoarchitectonics, is currently largely
unexplored.
Developing a Method of Synapse Quantification
To utilize array tomography to its fullest extent requires the development of new
synapse detection and classification capabilities. Simple analysis, using repeated hu-
man observation of a fraction of the channels available in the full volume, may be
acceptable for analyzing fragmentary subsets of a few hundred synapses but cannot
scale beyond that. We have developed tools and methods to assist in handling the
high proteomic dimensionality of array tomographic volumes (Figure 5.1), principally
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the synaptogram [2], which splays out a small 3d volume surrounding a single synapse
into a larger 2d image. This eases the difficulty of per-synapse manual classification
such that the effort of classifying a set of few hundred synapses is no longer excessive,
but no matter how convenient they are to analyze individually, the sheer number of
synapses makes manual analysis of the entire data set effectively impractical.
Given that just a few hundred analyzed examples can be obtained with a reason-
able expenditure of effort, there are two approaches to consider. The first is to use
those examples as a representative sample, in a manner similar to stereology. That
may work well for some questions, but not others. Rare or novel synapse types and
cortical laminar distributions would be especially difficult to study. An alternative,
which this paper will present, is to take that sample of accurately classified synapses
and extrapolate its decision-making information to the much larger population of
unclassified individuals.
5.2 Results
5.2.1 Identifying Putative Synaptic Loci
The first necessary step in our classification process is to locate the sites which
may contain synapses. Despite their appreciable proteomic diversity [111], cortical
synapses are small: from the ostensible midpoint of the synapse, all relevant synaptic
protein labeling can fit within a 500 nanometer radius for mouse cortex [112]. Given a
reliable method of locating synapses, all information needed to verify and type those
synapses can be had from the local volume surrounding them, greatly reducing the
spatial analysis needed per synapse. To avoid confusion with actual synapses, we
refer to these sorts of putative synapse locations as ”synaptic loci.” They are specific
places which might be synaptic.
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In order to find putative synapses to help limit the necessary search space, we are
using an antibody targeting Synapsin I. Synapsin is a scaffolding protein reportedly
found in all cortical synapses [113], and labeled antibodies targeting synapsin have
previously been used on their own to estimate synapse counts [114]. A Millipore
Rabbit anti-Synapsin I antibody (Millipore AB1543P) demonstrates robust and re-
liable labeling, and is likely to be colocalized with all relevant synaptic markers [2].
For these reasons the core of our analysis uses Synapsin I labeling to derive a list of
locations likely to contain synapses from which to begin small volumetric searches for
confirmation. Our approach is to use the brightest point of each Synapsin I punc-
tum as the site of a possible synapse to designate a local volume for further analysis,
without attempting to explicitly determine the punctum boundaries.
We prefer our local maxima-based approach over thresholding-based segmentation
because the latter has a number of issues arising from AT’s largely anisotropic reso-
lution (e200nm x e200nm x 70nm). This anisotropy, combined with (often unknown)
epitope density and labeling variance means that any segmented punctum boundary
is at best an estimate. An approach using local maxima, paired with a voxel-based
rotation-invariant feature set, is not affected by the exact boundaries of the puncta
of interest, but by the puncta themselves.
While our approach to synapse discovery sidesteps segmentation, it does so at
the cost of introducing potential false positives: background local maxima which
segmentation would have discarded, but whose peak brightness rises over our low
threshold for consideration. However, it is possible to filter those out in later clas-
sification. Conversely, this method is ideal for teasing apart “clumps” of synaptic
labeling, where multiple synapses exist in close proximity but can be resolved by the
Rayleigh criterion and thus having separate local maxima.
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5.2.2 Manual Classification
Using Human Experts
Humans can visually identify the synaptic category of a given locus via the use of
synaptograms (Figure 5.1), using the spatial juxtaposition of a number of relevant
synaptic molecules for classification [2]. Glutamatergic synapses, for example, will
by definition have at least one vesicular glutamate transport protein and at least one
post synaptic density scaffolding protein present. Similarly, GABAergic synapses can
be identified by the presence of glutamic acid decarboxylase (GAD) and a vesicular
GABA transport protein.
This process of human synapse identification is the best and most reliable method
of synapse identification available to us. It relies on the perception and expertise of
the human viewer to apply the visual segmentation which defines the “presence” of
necessary labels. This task incorporates a great deal of a priori knowledge concerning
the stearic and functional relationships between the different molecular labels, the
variance in labeling of each particular antibody, and the particular conditions under
which that tissue had been fixed, embedded, labeled, imaged, relabeled, etc.
Although manual classification of fluorescence data is orders of magnitude faster
than EM stereology, it is still orders of magnitude slower than needed to keep up
with the synaptic output rate of AT volumes. For that, we decided to use human-
generated classifications as training data, then liberally applied a number of clustering
and supervised learning methods to quantitatively mimic the human decision making
process.
Human Rater Agreement
In order to gauge the reliability of any single human expert’s rating, we performed a
qualitative test of the consistency of human classification. We presented a set of one
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hundred randomly-selected synaptograms to a group of six human raters who were
familiar with the task of interpreting synaptograms, and instructed them to classify
the set based on whether or not the synaptogram was centered on a glutamatergic
synapse. Once collated, we considered the true classification of a given synaptogram
to be that of a simple majority vote. When we compared each rater’s performance
relative to the average, we found an average accuracy rate of 77.7%, with a standard
deviation of 10.1% (Figure 5.2). The largest source of variance arose from the self-
reported stringency of the raters, in how much ambiguity they found acceptable when
classifying a locus as positive.
5.2.3 Machine Learning
Machine learning methods come in two broad categories. Supervised learning al-
gorithms, trained using a sufficient number of human rated synapses, are capable
of producing numerical descriptions of human judgment as it is applied to synapse
classification, as well as extrapolating that judgment to the hundreds of thousands
of synapses which comprise an average data set. Unsupervised clustering, on the
other hand, when applied to raw synaptic loci or already classified synapses is a great
approach to the discovery of marginal classes or subtle subtypes.
Feature Extraction
The first step in constructing a computational framework for either form of synapse
classification is to find a set of explicit measurements which span the feature space
that human raters implicitly search. We are using a small set of ad hoc, channel-
independent, rotationally invariant features to measure the spatial distribution of
each channel’s fluorescence about the synaptic locus. These features are calculated
per voxel, without relying on segmentation, combinatorial information or a priori
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geometrical information, in keeping with the rationale behind finding the loci in a
similarly parameter-independent manner. The equations used to calculate the four
features are given below.
For every voxel i in the local 11x11x11 voxel window with brightness b and pixel-
wise distance from the synaptic locus d:
IntegratedBrightness = B =∑i∈V
bi (5.1)
LocalBrightness =∑i∈V
bi
di2 (5.2)
CenterofMass =
∑i∈V bidi
B(5.3)
MomentofInertia =
∑i∈V bidi
2
B(5.4)
Of these features, the Integrated Brightness is the simplest to describe, as it is
the sum of all the pixel values within 5 pixels. Local Brightness is also the sum of all
values within 5 pixels, but the contribution of each pixel is reduced by the square of
its distance from the locus. It can be used as a metric for estimating the volume of the
punctum without segmentation because nearby pixels (more likely to be part of the
punctum) contribute much more heavily than distant ones (more likely to be noise
or neighbors). The remaining features, Center of Mass and Moment of Inertia, treat
the puncta brightness as if it is a mass distribution in a synaptogram-sized object,
and respectively compute the distance to the center of that object and its angular
inertia for a rotation about the locus. Collectively, these four features do a good job
of describing the fluorescence distribution in a synaptogram.
The result of this feature extraction, when performed on a multidimensional image
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of c channels, is a 4c-long numerical vector of proteomic measurements describing the
putative synapse. This analysis is repeated for each of p synaptic loci in the data
set, giving us a p x 4c matrix of measurements to be further analyzed. To enhance
consistency between data sets, which may well have different imaging conditions, we
normalize each of the extracted features by dividing by the population’s mean score.
5.2.4 Unsupervised Clustering
Although visual analysis is the traditional and preferred method of examining biolog-
ical data, long strings of numbers such as our feature vectors are difficult for humans
to visualize. In response, high-dimensional numerical measurements have often been
approached using some form of dimensionality reduction as a first step in numerical
analysis. Simply put, reducing a long string of numbers to a short string of numbers
makes them easier graphically display and understand. Principal Component Anal-
ysis (PCA) is a venerable method of dimensionality reduction which has seen use in
similar applications [115,116], and has proven useful in ours as well.
Our PCA result, illustrated in Figure 5.3, identifies some synaptic populations
but does not separate them sufficiently for classification. The loci tend to aggregate
in clusters which correspond to a few of the broader synaptic categorizations, namely
GABAergic and two common subtypes of glutamatergic synapses. We identified the
clusters using multivariate regression, that is, taking a few of the more distant exam-
ples and inferring the contribution of channels which brought them from the mean.
We had hoped when applying PCA to find discrete, easily-separable clusters corre-
sponding to each class, but in the reduced dimensionality of PCA, simple thresholds
are insufficient for proper class discrimination.
We had hoped the dimensionality reduction accomplished by the above methods
would have proven amenable to simple thresholding. If that where the case, multi-
variate regression might have led to identification and, combined with a measure of
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the statistical significance of cluster separation, classification of unknown synapses
based solely on where they fell in the unsupervised plot. Since our clusters were not
so cleanly separable, we resorted to a more subtle stratagem involving supervised
learning.
5.2.5 Supervised Classification
The “supervision” of supervised learning refers to the supervised training set, a ran-
dom or semi-random collection of human-rated examples from which the machine
learning algorithm (MLA) infers the rules for classification to extrapolate onto novel
synapses. To generate each item of the set, we presented a synaptogram to a human
trainer, who rated the synaptogram in one or more binary categories representing
the presence or absence of channels relevant to synapse classes of interest. We could
then associate those categorizations with the already-derived feature vectors of those
examples, compiling them into a library of “correct” classifications for training.
MLA Selection
Another necessary choice in supervised learning is that of the MLA used as a classi-
fier. In an early training experiment, we created a training set of 200 examples clas-
sified into glutamatergic/non-glutamatergic and GABAergic/non-GABAergic cate-
gories. We fed these results into an assortment of MLAs available in MATLAB with
minimal parameter optimization. The error rates of the various MLAs are presented
in Table 5.1. Although many of these algorithms performed well, the random forest
ensemble [117] slightly edged out the competition and earned its place as our classifier
of choice.
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Global Feature Importance
An additional point in favor of the random forest ensemble was the useful poste-
rior metrics made readily available in MATLAB’s random forest implementation (the
TreeBagger class). Posterior metrics are methods of analyzing the process of clas-
sification after classification. Their primary purpose is to relate information about
why a given locus was classified one way or another, and meta-information such as
the relationship between classes and the features which proved more important than
others during classification.
Each decision tree in a random forest is a series of optimal feature threshold
branches with decisions for leaves. By keeping track of which feature was used for
each branch point, along with the confidence that branch point engenders, we could
gauge the importance of the various features relative to each other. Overall, our
local brightness feature proved most useful, with the rest decreasing in performance.
Normalized to the local brightness importance, feature values were 0.76, 1.00, 0.59
and 0.55 for the integrated brightness, local brightness, center of mass and moment of
inertia features, respectively. Though the local brightness feature may have outshone
the others, all proved useful in classification.
Channel-based Classification
In order to facilitate the discovery of novel synapse populations, we decided to classify
loci based on channel presence, rather than synapse identity. For the MLA choice
analysis and the human agreement test our raters classified synaptograms based on
full synapse classes: whether or not the synaptogram included all the requisite markers
for a synapse of the class in question. While this strategy was effective at classifi-
cation of known synapse types, for the full analysis we wished to be able to extend
the analysis to detect synapse configurations which the raters did not have a priori
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knowledge of. Such novel synapse types might be discarded as being the product of
noise or aberrancies in the data, if they were accessible at all. For example, suppose
there was a small population of glutamatergic synapses which also expressed VGAT.
If all you have is glutamatergic/non-glutamatergic, there’d be no way of discovering
it even though the data is there.
We chose a classification system which addresses these concerns by using multiple
MLAs per synapse type, each trained to detect a single proteomic marker. A given
locus can be said to be a synapse of a certain class if all requisite markers of that class
are present at that locus. To avoid confusion, we elected to highlight the difference
by identifying synapses which contain a given marker as “marker synapses,” and loci
which contain a given marker (but may not be synaptic), “marker loci” or “marker
positive loci.” For example, rather than using a glutamatergic synapse classifier to
detect glutamatergic synapses, we use individual classifiers for the relevant channels
(VGluT1, VGluT2, PSD95), and then use their outputs in the same logical way (
(VGluT1 ∪ VGluT2) ∩ PSD95) to identify glutamatergic synapses. Using the previ-
ous example of VGAT-positive glutamatergic synapses, it would be straightforward to
add a ∩ VGAT to the equation, and see if the resulting population occurs significantly
above chance.
Active Learning and Rare Classes
In most supervised learning models, training set examples are sampled entirely at
random in order for the training set to have the same statistical properties of the full
data set. This can be inefficient for us in the of case of uncommon channels. The less
common a given channel is, the more negative results a human has to sort through
before reaching a usable number of positive results. For example, VGluT3 positive
loci can be identified in much the same manner as VGluT1 or VGluT2 loci, but due
to their paucity in the cortex (we see roughly 1.2 VGluT3+ loci per one thousand
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negative loci), human raters would have to classify excessive numbers of negative loci
for each positive locus in the training set.
In order to address this possibility, our classification process is a two-phased non-
random selection of training examples. The first phase is to “prime” the training set
data for rare classes by choosing one of each class’s requisite presynaptic channels and
randomly sampling a subset from the loci for which the channel’s local brightness is
more than two standard deviations above the mean. A number of class subsets gen-
erated in this manner are collated, each class contributing to the negative examples
of the rest. The second phase is an “active” training process in which a human rater
and the MLA being trained work in tandem to speed training, a technique known
as active learning [118]. At each step, the half-trained classifier selects a few exam-
ples, half of which it thinks are positive and half negative, to present to the rater for
verification and feedback.
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In pseudocode, the training proceeds according to the following algorithm:
while Human wishes to train do
Load training synaptogram population, P
Human selects a synaptic category
Train RFE using partially classified training set T , display predicted error rate
while Human wishes to add training examples do
Randomly choose c, where c ∈ [True, False]
Randomly choose a synaptogram s from subpopulation Pc, the elements of P
classified as c
Display s and c to human for verification
Add/Update s in T to reflect human input
end while
end while
The application of active learning in this instance was inspired by a similar effort
by Dr. Badrinath Roysam (personal correspondence) for use in the classification of
cell types in confocal volumes.
The net effect of the training modification is to focus the human role more on
verification and correction than strict classification. Aside from accomplishing the
goal of efficiently training classifiers for rare classes, we find that the active version
seems to be much less of a strain on human patience than de novo training, even
that aided by synaptograms. It also reduces the necessary training set size to roughly
twice the number of requisite positive synapses in the training set, despite the rarity
of the class in question.
Once the human raters are satisfied with their training sets, we pass the entire
data volume through the classifiers for identification, and collate the results into a
combinatorial set of vectors.
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5.2.6 Post-Classification Analysis
After classification, the predicted presence of each channel for a given locus can be
derived from the percentage of decision trees in the random forest ensemble which
attest to its presence. This effectively serves as a confidence metric for the entire
ensemble, and is generally referred to as the “posterior probability.” An instance
with a posterior probability of 1.0 is unequivocally positive for the class in question,
one of 0.0 is undeniably negative. In this manner, we reduce the 4c-long numeric
feature vector to a c1 -long numeric posterior vector, representing the presence or
absence of all c1 relevant channels. We can then use these vectors in a combinatorial
fashion to recreate synaptic classes. Glutamatergic VGluT1-expressing synapses, for
example, should at a minimum be positive (posterior probability ≥ 0.5) for VGluT1
and PSD95.
Per-Channel Feature Importance
Since our labeled channels occupy a number of spatial niches in the canonical synapse,
we were interested in determining which features contributed most to which channel
classifier, in case that reflected the differential distribution. The results are shown
in Figure 5.4. The channels which differ from the norm (Figure 5.4-A) in selecting
the center of mass or moment of inertia features as their most important included
VGluT2, VGluT3 and VAChT. These channels are all presynaptic, which eliminates
spatial differentiation as a cause, but interestingly they are all uncommon to rare.
TH, also rare, did not display this behavior, and also differs from the rest in that
”neighboring” puncta were deemed acceptable for positive classification. This may
suggest that for rare classes where neighbor discrimination is important, determining
whether a discovered punctum is part of the synapse in question or a close neighbor
plays a bigger role in the accuracy rate than discovering the punctum in the first
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place.
OOB Error
The training process of the random forest classification itself provides a reliable ap-
proximation of its error rate. During training, each tree in a random forest excludes
a random fraction of examples from its construction, which can later be used in the
manner of cross-validation testing to gauge the accuracy of that tree. More precisely,
each training example can function as withheld data for a sub-random forest ensemble
composed of the fraction of decision trees to have excluded it during training, and,
taken in aggregate, are an estimate of the performance of the full forest. This is called
the “out-of-bag error” [117]. OOB performance for the classes we are interested in
can be found in Table 5.2. The OOB error can be interpreted as a self-estimation of
the classifier’s true error rate.
Comparison to Human Rating
To quantitatively examine this system’s performance when applied to real synapse
classification, we ran our human accuracy test set through the VGluT1 and PSD95
classifiers, then compared the combined output (VGluT1 ∩ PSD95) loci with that
given by humans. Although these two channels had the worst OOB performance, the
intersection of the two was about as accurate as the best human raters. We performed
a receiver operating characteristics analysis to describe the classifier performance in a
more detailed fashion; it is shown in Figure 5.2B. The fact that the worst OOB error
is still equal to the agreement of human raters implies the output of the classifiers
should be usable with the same degree of confidence as that of human raters.
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5.2.7 Classification Application
Synapse Class Definition
The use of a channel-based classification process allows us somewhat greater flexibility
in the definition of synaptic classes. Our lab has years of experience in recognizing
VGluT1-glutamatergic, VGluT2-glutamatergic and GABAergic synapses [2], which
compose the majority of synapses in the cortex, and all are defined by the presence
of at least two specific markers, in addition to Synapsin I. For this paper we have
also included a number of labels targeting synaptic populations for which we haven’t
found a robust label for a ubiquitous second protein. This includes VGluT3-positive
synapses, cholinergic (vesicular acetylcholine transporter [VAChT]) and dopaminer-
gic/noradrenergic (tyrosine hydroxylase [TH] positive) synapses. It is our intention
to find such corroborating labels before these channels are used in a full experiment.
Additionally, dopaminergic synapses have been reported not to express much of the
Synapsin I/II isoforms, if they express them at all [119]. Since we are using a Synapsin
I marker to discover putative synapse loci, those which are positive for TH may ac-
tually be identifying simple synaptic complexes - dopaminergic synapses adjoining
those of another class.
Cortical Depth Analysis
One straightforward application of synapse-classified array tomography can be had
via cytoarchitectonics, as seen in Figure 5.5. We first segregated the data into a
number of synaptic classes, then subdivided those into 10 µm bins stretching from
the pial surface of the cortex to the striatum. We calculated the density of each bin’s
population, and averaged the Synapsin I local brightness feature to estimate the mean
synapse size. Overall, the synaptic densities were nearly twice as high as expected
in the literature [120], but tissue shrinkage during LR White embedding [121] can
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potentially account for some or all of that.
Although a sample size of one precludes statistical certainty and excessive hypoth-
esizing about function, there are three interesting effects observed by this analysis.
First, there is an increase in VGluT2-positive synapse density in layer IV, which we
expected given the laminar characterization of VGluT2-expressing synapses [2]. Sec-
ond, we notice a decrease in the density of parvalbumin-positive GABAergic synapses
in layers I and VI, similar to [122]. Finally, we find that VGluT1-positive synapses in
layer 5a, though not more dense than elsewhere in the cortex, are somewhat larger.
Pairwise Proteomic Analysis
Another promising possibility is the use of data sets classified in our per-channel
fashion to search for unexpected proteomic combinations which may correspond to
novel synaptic subsets, particularly of rare classes. In any volume, some background
noise is to be expected: given the spatial distribution of synapses, it is inevitable that
some synapses will have asynaptic puncta, or those belonging to nearby synapses,
expressed in the region of analysis. Assuming that two classified markers have inde-
pendent distributions, the expected number of loci in a volume which will be classified
positive for both is the product of their probabilities, Eij = Pi ∗Pj. We can compare
this with Fij, the number of colocalized loci actually found in the data set, and use
a two-tailed binomial test to check for significance and reject stochastic noise as an
explanation.
For example, VGluT3 has previously been intimated to be present in a very small
subset of cortical GABAergic synapses [123]. Since we have labeled both GABAergic
synapses and VGluT3 puncta in the course of classifying their respective categories,
we can simply retain those GABAergic synapses which classed VGluT3-positive. A
two-tailed binomial test can tell if the overlap we observe (82 synapses) is significantly
different from that we would expect by multiplying the two class probabilities together
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(43 synapses). Those are small numbers in a data set of nearly a million classified
synapses, but the difference between them is significant (p < 0.001).
Using the nine classified channels in our present analysis, we ran binomial tests to
calculate the normalized pairwise relationship between each of them. Our results are
presented in Figure 5.6. The significant results match our expected relationships for
the most part - GAD, VGAT, parvalbumin all colocalize, as do VGluT1/PSD95 and
VGluT2/PSD95, and all three categories are mutually exclusionary. There are a few
points of interest - as mentioned, VGluT3 colocalizes with all GABAergic channels
and excludes itself from PSD95, corroborating the literature’s suggestion of VGluT3
as a supporting neurotransmitter and not a primary glutamatergic synapse class on
its own [124]. Additionally, TH generally avoids both VGluT1 and VAChT, but
shows positive copresence with VGluT2 (though this relationship disappears in the
striatum).
5.3 Discussion
Synapse Class Discovery
When we began the class discovery process as shown in Figure 5.6, we expected re-
lationships based on our preconceived notions of a few synapse classes: that GAD,
VGAT and parvalbumin should all be copresent to some extent [122], and that VG-
luT1 and VGluT2 should each colocalize with PSD95 (but not with each other) [125].
The other channels, VGluT3, VAChT and TH, had fewer performance expectations.
Of them, VGluT3 had the most interestingly unexpected behavior, avoiding the com-
mon glutamatergic markers and colocalizing instead with GABAergic synapses. That
the literature corroborates these as possible roles of VGluT3 in the cortex [124] lends
a degree of confidence that our analysis returns usable results. Another interesting re-
sult is the cortical-only localization of VGluT2 and TH. Dopaminergic (TH) neurons
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have been reported to express VGluT2 in rat cultures [126], midbrain and hypotha-
lamus [127]. With current volume sizes we only find a dozen of these appositions,
however, so it would be problematic to assert certain confirmation.
Human Consensus Formation
The variance of human raters raises a few interesting questions to look into in the
future. Two of the six raters (#2 and #6) self-reported using a stricter standard
of classification than the rest: when an example was at all doubtful, they classified
it as being negative. Effectively, these raters elected to position themselves on the
left side of the ROC curve, trading an increase in false negatives for reduced false
positives. Depending on the application, stricter classification may be preferable.
Based on our experiences, we would recommend taking time to discuss questionable
examples and reasons for rating them one way or another. Such conversations are
rather illuminating and very effective at getting everyone to agree on a common
standard of classification.
5.3.1 Limitations and Future Work
There are two significant limitations to the questions which can be asked using this
method. The first and strictest: an array tomography volume is a decidedly terminal
snapshot of a piece of tissue. This precludes experiments which would watch a par-
ticular cell or dendrite change over time, or in response to learning [128], except in
animal models which are stereotyped enough for different animals to have equivalent
nervous systems, namely C. Elegans [129] and Drosophila [130]. Synapse populations
are assumed to be fairly invariant between individual mice (and presumably humans),
however, which allows us to study changes to synaptic classes as a whole in response
to plasticity or disease.
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The second limitation is more easily rectified. Our analysis partially depends on
limiting the scope of the problem to that required to identify synapses at locations al-
ready suspected to contain a synapse. For common synapse classes this is easy. They
all express Synapsin I, so wherever we find our Synapsin I marker, there may be a
synapse. As mentioned, we have already begun to abut the usefulness of Synapsin I,
which may not be expressed in dopaminergic synapses. Using a pan-Synapsin anti-
body would be a straightforward solution to catching all dopaminergic synapses, but
it is fully possible that other, more exotic synapse types would not express Synapsin
at all.
Establishing a robust system for synapse classification in array tomographic vol-
umes opens up a number of avenues for addressing biological questions. It allows us
to conduct single-synapse analyses in large regions of tissue, which lets us study rare
or spatially-segregated populations. It helps us discover new synaptic populations
and novel variations on known synapse types, and gives us an unprecedented level of
control over the proteomic complexity we can bring to bear.
5.4 Materials and Methods
5.4.1 Acquisition of array tomographic volume
All procedures related to the care and treatment of animals were approved by the
Administrative Panel on Laboratory Animal Care at Stanford University. All volumes
were acquired from mouse cortex, line C57BL/6J, using the methodology given in [2].
One adult mouse was used for this study. The animal was anesthetized by
halothane inhalation and its brain quickly removed and placed in 4% formaldehyde
143
and 2.5% sucrose in phosphate-buffered saline (PBS) at room temperature. Its cere-
bral hemisphere was sliced coronally into three pieces and fixed and embedded us-
ing rapid microwave irradiation (PELCO 3451 laboratory microwave system with
ColdSpot; Ted Pella, Redding CA) as described in [17]. The tissue was dehydrated
up to 70% ethanol.
Ribbons of serial ultrathin (70 nm) sections were cut with an ultramicrotome
(EM UC6, Leica Microsystems, Wetzlar, Germany) as described in [17]. The ribbons
were mounted on subbed coverslips (coated with 0.5% gelatin and 0.05% chromium
potassium sulfate) and placed on a hot plate (60 C) for 30 min. For SEM imaging,
the subbed coverslips were also carbon coated using a Denton Bench Top Turbo
Carbon Evaporator (Denton Vacuum, Moorestown, NJ). Subbed and carbon coated
coverslips were also prepared for mounting ribbons of sections to be used for multiple
immunostaining rounds (>6).
Staining was performed as described in [17]. The coverslips with sections were
mounted using SlowFade Gold antifade with DAPI (Invitrogen, Carlsbad CA). To
elute the applied antibodies, the mounting medium was washed away with dH2O and
a solution of 0.2 M NaOH and 0.02% SDS in distilled water was applied for 20 min.
After an extensive wash with Tris buffer and distilled water, the coverslips were dried
and placed on a hot plate (60C) for 30 min.
The primary antibodies and their dilutions are listed in [2], Table 1. Only well
characterized commercial antibodies were used and they were evaluated specifically for
AT as described in Supplemental Experimental Procedures. For immunofluorescence,
Alexa Fluor 488, 594, and 647 secondary antibodies of the appropriate species, highly
preadsorbed (Invitrogen, Carlsbad CA) were used at a dilution 1:150. The sequence
of antibody application in the multiround staining is presented in [2], Table S1.
Sections were imaged on a Zeiss Axio Imager.Z1 Upright Fluorescence Microscope
with motorized stage and Axiocam HR Digital Camera as described in [17]. Briefly,
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a tiled image of the entire ribbon of sections on a coverslip was obtained using a
10 objective and the MosaiX feature of the software. The region of interest was
then identified on each section with custom-made software and imaged at a higher
magnification with a Zeiss 63/1.4 NA Plan Apochromat objective, using the image-
based automatic focus capability of the software. The resulting stack of images was
exported to ImageJ, aligned using the MultiStackReg plugin and imported back into
the Axiovision software to generate a volume rendering. When a ribbon was stained
and imaged multiple times, the MultiStackReg plugin was used to align the stacks
generated from each successive imaging session with the first session stacks based on
the DAPI channel, then a second within-stack alignment was applied to all the stacks.
To reconstruct large volumes of tissue, we first used Zeiss Axiovision software to
stitch together the individual high-magnification image tiles and produce a single mo-
saic image of each antibody stain for each serial section in the ribbon. We created a
z stack of mosaic images for each fluorescence channel, and then grossly aligned the
stacks using the MultiStackReg plugin. Finally, to remove non-linear physical warping
introduced into the ribbons by the sectioning process, we used a second ImageJ plu-
gin, autobUnwarpJ (available at http://www.stanford.edu/enweiler), which adapts
an algorithm for elastic image registration using vector-spline regularization [98].
5.4.2 Normalization and background subtraction of volumet-
ric data
Before analyzing imaged volumes, we subtracted the background from each fluorescent
channel using a 10x10 pixel (1 µm2) rolling ball filter to remove systematic apunctal
background fluorescence, then normalized each slice of the stack without saturating
any pixels, such that the brightness histogram of each section was stretched as much
as possible without loss of information. No other image processing, including removal
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of fluorescence due to foreign material, nonspecific staining, etc, was performed before
analysis.
5.4.3 Extraction of synaptic loci
To extract a list of putative synapse locations from raw volume data, we first identi-
fied individual synapsin puncta by convolving the synapsin channel with a 3x3x3 local
maxima filter; retaining all voxels with a brightness ≥ those of its 26-voxel neighbor-
hood. Then, we passed the synapsin maxima through a connected component filter
to reduce peak voxel clumps (caused by discretization of the fluorescence data) to
centroids, and discarded those below a deliberately low threshold (10% of the total
brightness range) as being too dim to represent a real synapse. What remained was
a list of putative synapse locations, or “synaptic loci,” so named for their central role
in later classification steps.
5.4.4 PCA image treatment
The color of each point in the PCA figure was determined by taking the extreme
outliers of the three clusters, determining their feature composition via multivariate
regression, taking the dot product of the feature weight vectors with the feature vector
of each locus, and assigning that to red, green or blue for the VGluT1, VGluT2, and
GABA clusters respectively. Colors were manually normalized to be of approximately
equal intensity, and synaptic loci not strongly represented in any of the three colors
were removed to better visualize cluster relationship.
5.4.5 Normalization of pairwise channel data
To produce the pairwise channel copresence map, for each marker pair (i, j) we cal-
culated the probability of co-occurrence Eij = Fi/N ∗ Fj/N , where Fi is the number
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of loci found to be positive for i, and N is the number of total loci in the population.
Multiplying by N gives us the expected population, Eij. We compared this num-
ber with the observed population Fij using difference over sum normalization to find
the normalized pairwise relationship Rij = (Fij − Eij)/(Fij + Eij). These relation-
ships made pairwise comparisons easy to interpret, with one minor counter-intuitive
exception: markers which comprised a substantial proportion of the synaptic loci
population (VGluT1 and PSD95) had reduced values, even with themselves, owing
to their high Eij. To bring those into the same reference frame as the rest, we nor-
malized again using the reciprocal of the sum of the relationship identity reciprocals,
that is, Nij = Rij ∗ (1/Rii + 1/Rjj)/2. Finally, since the previous setup disrupted
negative relationship scaling such that the most negative pairs (VGluT1 vs GABAer-
gic markers) reached nearly -3.0, we multiplied the positive ratings by 3 to match
once more.
5.4.6 Perpendicularization of cortical data
To simplify the calculation of the cortical depth-dependent metrics used in Figure 5.5,
such that any given Y-value represented tissue at the same cortical depth, we needed
to correct a minor slant in the raw volume. We measured the degree of tissue slope
using the pial surface and the white matter/striatum boundary, and imposed an affine
transformation on the loci, linearly interpolating them to be level. The underlying
data and the features used to classify the loci were not changed as a result of this
process.
147
5.4.7 Software packages used
Image normalization, locus discovery and feature extraction were implemented and
performed using Fiji (http://pacific.mpi-cbg.de/). Training set generation was im-
plemented as a browser-based application, coded in Python, to permit our experts to
work at their leisure. We used R for interactive classification for its ease of Python
integration, but the final random forest classifiers, trained on the complete training
set alone, used MATLAB (the TreeBagger class). Imaris was used to render the data
for visualization of Figure 5.1.
All implemented code used in this analysis is available at http://stanford.edu/ebbusse/.
148
Table 5.1: Machine Learning Algorithm Comparison
LDA QDA NB NBkd RFE kNN SVMGlut 0.128 0.114 0.110 0.104 0.084 0.176 0.222
GABA 0.044 0.036 0.062 0.052 0.036 0.070 0.178
Comparison of various supervised machine learning algorithms. A smalltraining set was used to compare the error rates of multiple MLAs when classifyingglutamatergic and GABAergic synapses in an early data set. From left to right:Linear Discriminant Analysis (LDA); Quadratic Discriminant Analysis (QDA);Naive Bayesian filter, gaussian distribution assumption (NB); Naive Bayesian filter,normalized kernel distribution assumption (NBkd); Random Forest Ensemble(RFE); k-means clustering (kNN); Support Vector Machine (SVN). k-meansclustering, an unsupervised clustering method, was included for comparison’s sake.
Table 5.2: Estimated error rates
Channel OOB ErrorGAD 0.0400VGAT 0.0767PV 0.0814VGluT3 0.1146VGluT2 0.0716VGluT1 0.0690PSD95 0.1215VAChT 0.1394TH 0.0333
Out-of-bag (OOB) error rate estimates for various classified markers.Order of markers is the same as in Figure 5.6. Each classifier had a minimumtraining set size of one hundred examples.
149
Figure 5.1: The synaptogram as a tool for high-dimensional proteomic visu-alization. (A) A maximum projected volume of Synapsin I labeling. 41 slices, 70nmper slice, total thickness of 2.87 µm. (B) Randomly-colored segmentation of individ-ual synapsin puncta. (C) Rendering of a single punctum from the volume showingsynapsin (white), imaged together with VGluT1 (red), PSD95 (green), GluR2 (blue),GAD (magenta) and VGAT (magenta). From top to bottom: all proteomic mark-ers, glutamatergic presynaptic labels, glutamatergic postsynaptic labels, GABAergiclabels. This appears to be a glutamatergic synapse. (D) The synaptogram derivedfrom the same synapse. Synapsin, top row, is repeated in red for the rest to providespatial context. Not shown, sixteen other colors and two redundant labels (synapsinand VGluT1). Scale bar: 5 um, size of synaptogram/render volume, 1100 nm x 1100nm x 630 nm
150
Figure 5.1
151
Figure 5.2: Comparison of human rating to machine learning. (A) Accuracyrates. i-vi - When compared against the average decisions of their peers in a VGluT1synapse discrimination task, humans performed at different accuracy levels basedon their stringency of classification. vii - The random forest ensemble, (VGluT1∩ PSD95), trained by human rater 1, performed comparably to the humans. (B)Receiver operating characteristics (ROC) curve, for VGluT1 and PSD95 classifica-tions on human-rated data. The ROC curve describes the tradeoff between reducingfalse positives (left side of the curve) and maximizing true positives (right side of thecurve). The displayed diagonal line represents chance, with better classifiers occupy-ing large areas between the diagonal and their own curves. A perfect classifier wouldhave no rounded corner; there would be no need to compromise. In this case, ourclassifiers perform well.
152
Figure 5.2
153
Figure 5.3: Unsupervised clustering of synapsin I imaged with array tomog-raphy. When the first and third principle components of the local brightness featureeq 1 are plotted against each other, they form clusters identifiable as known synapticsubtypes.
154
Figure 5.3
155
Figure 5.4: Feature importance for different molecular labels. (A) When allclasses were averaged, our local brightness feature (ii) saw the most use, followed byintegrated brightness (i), center of mass (iii) and moment of inertia (iv). (B-J) GAD,VGAT, PV, VGluT3, VGluT2, VGluT1, PSD95, VAChT, and TH (respectively)each make slightly different use of the feature set. VGluT3, VGluT2, and VAChTare notable in that they rely most heavily on features other than local brightness.
156
Figure 5.4
157
Figure 5.5: Density and size of synapse classes as a function of depth throughthe cortex. (A) Synapse density through the cortex. ** - VGluT2 synapses peak inlayer IV. PV-positive GABAergic synapse density is slightly decreased in layer I, andsignificantly lacking in layer VI. (B) Synapse size estimated using the synapsin localbrightness measurement. * - VGluT1 size peaks in layer Va (p < 0.05).
158
Figure 5.5
159
Figure 5.6: Positive and negative pairwise channel copresence. Symbols de-note interesting comparisons with statistical significance of p < 0.001. Red squaresrepresent label pairs which are copresent more than expected, blue squares less thanexpected by chance. * - GABAergic markers are copresent with each other, but avoidglutamatergic and TH markers. ˆ - VGluT1/2 are copresent with PSD95, but notwith each other. # - VGluT3 is present with all three GABAergic markers, butavoids VGluT1 and PSD95. & - VGluT2 shows some presence with TH. e - TH tendsto avoid VAChT.
160
Figure 5.6
161
Chapter 6
Future Directions and Conclusions
For the past few years the image processing tools required by AT have been sub-
stantially refined through the efforts of our lab and others. The algorithms I have
presented here, though initially developed in completely different programming en-
vironments, compose the seed around which a unified image processing pipeline is
precipitating. This is largely thanks to a strong emphasis on open source software,
which encourages the quick adoption and modification of useful third party code.
This process is still ongoing, but I have every confidence that in the near future we
will have a single piece of AT software capable of performing every image processing
step required to go from ribbon to volume.
One thing which will require additional attention is the future state of array
tomographic analysis. In this dissertation I have presented solutions to two analyt-
ical problems: one of label validation and one of synapse quantification. These are
powerful tools which can facilitate a wide array of biological experiments, but they
barely scratch the surface of the hypotheses AT can potentially address. For ex-
ample, without even leaving the context of proteomics, we have previously used the
close apposition of synapses and labeled dendrites as evidence of connections [2]. This
tells us something of the population of synapses onto that dendrite, but not with the
162
confidence necessary to positively identify the specific synapses in an specific circuit.
That would require a segmentation-based approach to synapse discovery, where we
segregate the pre- and postsynaptic sides of the cleft, and look for whatever the cir-
cuit has been labeled with in the pre- and postsynaptic boutons. Those circuits also
can be traced with AT’s resolution, barring some axonal processes, but suffer from a
similar problem as synapse identification: there’s too much material to be segmented
for manual methods to be efficient and no current automated solutions (most all of
which are closed source) perform acceptably. Both of these problems can probably
be solved with similar expenditures of time and effort as the synapse quantification I
have presented, but they too are but two specialized analytical methods.
I would argue that AT could stand to benefit more from a more general-purpose
approach to analytics, akin to that which ImageJ offers for image processing [131].
The strength of ImageJ (and its descendant, FIJI) lies in enabling the quick adap-
tation of image processing capabilities through its open source framework. Likewise,
a general open-source analytics package should be structured to encourage users to
extend its segmentation and classification functionality to address similar but subtly
different applications without forcing them to reimplement algorithms from scratch.
This should make it simpler to then apply associative feature measurements like Sholl
analysis, freeing up developer time for more substantive science.
As for the field of Neuroscience as a whole, the next decade is likely to see a
pressing need for exactly the kind of large-scale biological imaging AT excels at.
Computational modeling environments like the Blue Brain project [132] are already
capable of simulating hundreds of thousands of NEURON models with performance
sufficient to allow for in silico experimentation [133], and owing to the parallel archi-
tecture of the brain this capability will probably increase in the near future at about
the same exponential rate as general computing power. Given how much is currently
unknown about molecular physiology at the circuit level, projects like Blue Brain will
163
need to rely on gross and almost certainly inaccurate assumptions unless and until
circuit-level connectomic or proteomic imaging can experimentally test them [134].
Array tomography is presently well-suited to address these sorts of questions, and is
on a trajectory which should presently make it a powerful platform for exploratory
science in its own right.
164
Bibliography
1. Micheva K, Smith S (2007) Array tomography: a new tool for imaging the
molecular architecture and ultrastructure of neural circuits. Neuron 55: 25–
36.
2. Micheva K, Busse B, Weiler N, O’Rourke N, Smith S (2010) Single-synapse
analysis of a diverse synapse population: proteomic imaging methods and
markers. Neuron 68: 639–53.
3. Lein E, Hawrylycz M, Ao N, Ayres M, Bensinger A, et al. (2007) Genome-wide
atlas of gene expression in the adult mouse brain. Nature 445: 168–76.
4. Grant S (2007) Toward a molecular catalogue of synapses. Brain research
reviews 55: 445–9.
5. Sheng M, Hoogenraad C (2007) The postsynaptic architecture of excitatory
synapses: a more quantitative view. Annual review of biochemistry 76: 823–
47.
6. Sassoe-Pognetto M, Frola E, Pregno G, Briatore F, Patrizi A (2011) Under-
standing the molecular diversity of gabaergic synapses. Frontiers in cellular
neuroscience 5: 4.
165
7. Gupta A, Wang Y, Markram H (2000) Organizing principles for a diversity
of gabaergic interneurons and synapses in the neocortex. Science (New York,
NY) 287: 273–8.
8. Hausser M, Spruston N, Stuart G (2000) Diversity and dynamics of dendritic
signaling. Science (New York, NY) 290: 739–44.
9. Staple J, Morgenthaler F, Catsicas S (2000) Presynaptic heterogeneity: Vive
la difference. News in physiological sciences: an international journal of phys-
iology produced jointly by the International Union of Physiological Sciences
and the American Physiological Society 15: 45–49.
10. Cherubini E, Conti F (2001) Generating diversity at gabaergic synapses.
Trends in neurosciences 24: 155–62.
11. Craig A, Boudin H (2001) Molecular heterogeneity of central synapses: affer-
ent and target regulation. Nature neuroscience 4: 569–78.
12. Cull-Candy S, Brickley S, Farrant M (2001) Nmda receptor subunits: diver-
sity, development and disease. Current opinion in neurobiology 11: 327–35.
13. Grabowski P, Black D (2001) Alternative rna splicing in the nervous system.
Progress in neurobiology 65: 289–308.
14. Huang Y, Bergles D (2004) Glutamate transporters bring competition to the
synapse. Current opinion in neurobiology 14: 346–52.
15. Mody I, Pearce R (2004) Diversity of inhibitory neurotransmission through
gaba(a) receptors. Trends in neurosciences 27: 569–75.
16. Grant S (2006) The synapse proteome and phosphoproteome: a new paradigm
for synapse biology. Biochemical Society transactions 34: 59–63.
166
17. Micheva K, O’Rourke N, Busse B, Smith S (2010) Array tomography: high-
resolution three-dimensional immunofluorescence. Cold Spring Harbor proto-
cols 2010: pdb.top89.
18. Newman G, Jasani B, Williams E (1983) A simple post-embedding system
for the rapid demonstration of tissue antigens under the electron microscope.
The Histochemical journal 15: 543–55.
19. Kalman R (1960) A new approach to linear filtering and prediction problems.
Journal of Basic Engineering 82: 35–45.
20. Thevenaz P, Ruttimann U, Unser M (1998) A pyramid approach to subpixel
registration based on intensity. IEEE transactions on image processing : a
publication of the IEEE Signal Processing Society 7: 27–41.
21. van Steensel B, van Binnendijk E, Hornsby C, van der Voort H, Krozowski
Z, et al. (1996) Partial colocalization of glucocorticoid and mineralocorticoid
receptors in discrete compartments in nuclei of rat hippocampus neurons.
Journal of cell science 109 ( Pt 4): 787–92.
22. Arellano J, Benavides-Piccione R, Defelipe J, Yuste R (2007) Ultrastructure of
dendritic spines: correlation between synaptic and spine morphologies. Fron-
tiers in neuroscience 1: 131–43.
23. De Camilli P, Cameron R, Greengard P (1983) Synapsin i (protein i), a nerve
terminal-specific phosphoprotein. i. its general distribution in synapses of the
central and peripheral nervous system demonstrated by immunofluorescence
in frozen and plastic sections. The Journal of cell biology 96: 1337–54.
167
24. Hilfiker S, Pieribone V, Czernik A, Kao H, Augustine G, et al. (1999)
Synapsins as regulators of neurotransmitter release. Philosophical transactions
of the Royal Society of London Series B, Biological sciences 354: 269–79.
25. Katz L, Shatz C (1996) Synaptic activity and the construction of cortical
circuits. Science (New York, NY) 274: 1133–8.
26. Knudsen E (2004) Sensitive periods in the development of the brain and be-
havior. Journal of cognitive neuroscience 16: 1412–25.
27. Huberman A, Feller M, Chapman B (2008) Mechanisms underlying develop-
ment of visual maps and receptive fields. Annual review of neuroscience 31:
479–509.
28. Stellwagen D, Shatz C (2002) An instructive role for retinal waves in the
development of retinogeniculate connectivity. Neuron 33: 357–67.
29. Butts D, Kanold P, Shatz C (2007) A burst-based ”hebbian” learning rule at
retinogeniculate synapses links retinal waves to activity-dependent refinement.
PLoS biology 5: e61.
30. Chen C, Regehr W (2000) Developmental remodeling of the retinogeniculate
synapse. Neuron 28: 955–66.
31. Hooks B, Chen C (2006) Distinct roles for spontaneous and visual activity in
remodeling of the retinogeniculate synapse. Neuron 52: 281–91.
32. Mooney R, Madison D, Shatz C (1993) Enhancement of transmission at the
developing retinogeniculate synapse. Neuron 10: 815–25.
33. Corriveau R, Huh G, Shatz C (1998) Regulation of class i mhc gene expression
in the developing and mature cns by neural activity. Neuron 21: 505–20.
168
34. Shatz C, Stryker M (1988) Prenatal tetrodotoxin infusion blocks segregation
of retinogeniculate afferents. Science (New York, NY) 242: 87–9.
35. Sretavan D, Shatz C, Stryker M (1988) Modification of retinal ganglion cell
axon morphology by prenatal infusion of tetrodotoxin. Nature 336: 468–71.
36. Zinkernagel R, Doherty P (1979) Mhc-restricted cytotoxic t cells: studies on
the biological role of polymorphic major transplantation antigens determin-
ing t-cell restriction-specificity, function, and responsiveness. Advances in
immunology 27: 51–177.
37. Schott E, Bonasio R, Ploegh H (2003) Elimination in vivo of developing t cells
by natural killer cells. The Journal of experimental medicine 198: 1213–24.
38. Takai T (2005) Paired immunoglobulin-like receptors and their mhc class i
recognition. Immunology 115: 433–40.
39. Syken J, Grandpre T, Kanold P, Shatz C (2006) Pirb restricts ocular-
dominance plasticity in visual cortex. Science (New York, NY) 313: 1795–800.
40. Purcell S, Wray N, Stone J, Visscher P, O’Donovan M, et al. (2009) Common
polygenic variation contributes to risk of schizophrenia and bipolar disorder.
Nature 460: 748–52.
41. Shi J, Levinson D, Duan J, Sanders A, Zheng Y, et al. (2009) Common variants
on chromosome 6p22.1 are associated with schizophrenia. Nature 460: 753–7.
42. Stefansson H, Ophoff R, Steinberg S, Andreassen O, Cichon S, et al. (2009)
Common variants conferring risk of schizophrenia. Nature 460: 744–7.
43. Nakamura A, Kobayashi E, Takai T (2004) Exacerbated graft-versus-host dis-
ease in pirb-/- mice. Nature immunology 5: 623–9.
169
44. Huh G, Boulanger L, Du H, Riquelme P, Brotz T, et al. (2000) Functional
requirement for class i mhc in cns development and plasticity. Science (New
York, NY) 290: 2155–9.
45. Frenkel M, Bear M (2004) How monocular deprivation shifts ocular dominance
in visual cortex of young mice. Neuron 44: 917–23.
46. Rossi F, Pizzorusso T, Porciatti V, Marubio L, Maffei L, et al. (2001) Require-
ment of the nicotinic acetylcholine receptor beta 2 subunit for the anatomical
and functional development of the visual system. Proceedings of the National
Academy of Sciences of the United States of America 98: 6453–8.
47. Tagawa Y, Kanold P, Majdan M, Shatz C (2005) Multiple periods of functional
ocular dominance plasticity in mouse visual cortex. Nature neuroscience 8:
380–8.
48. Antonini A, Fagiolini M, Stryker M (1999) Anatomical correlates of functional
plasticity in mouse visual cortex. The Journal of neuroscience: the official
journal of the Society for Neuroscience 19: 4388–406.
49. Iny K, Heynen A, Sklar E, Bear M (2006) Bidirectional modifications of visual
acuity induced by monocular deprivation in juvenile and adult rats. The
Journal of neuroscience: the official journal of the Society for Neuroscience
26: 7368–74.
50. Cang J, Kalatsky V, Lowel S, Stryker M (2005) Optical imaging of the intrinsic
signal as a measure of cortical plasticity in the mouse. Vis Neurosci 22: 685–
691.
51. Hubener M (2003) Mouse visual cortex. Current opinion in neurobiology 13:
413–20.
170
52. Prusky G, West P, Douglas R (2000) Behavioral assessment of visual acuity
in mice and rats. Vision research 40: 2201–9.
53. Robinson L, Bridge H, Riedel G (2001) Visual discrimination learning in the
water maze: a novel test for visual acuity. Behavioural brain research 119:
77–84.
54. Ljunggren H, Van Kaer L, Sabatine M, Auchincloss H, Tonegawa S, et al.
(1995) Mhc class i expression and cd8+ t cell development in tap1/beta 2-
microglobulin double mutant mice. International immunology 7: 975–84.
55. Goddard C, Butts D, Shatz C (2007) Regulation of cns synapses by neuronal
mhc class i. Proceedings of the National Academy of Sciences of the United
States of America 104: 6828–33.
56. McConnell M, Huang Y, Datwani A, Shatz C (2009) H2-k(b) and h2-d(b)
regulate cerebellar long-term depression and limit motor learning. Proceedings
of the National Academy of Sciences of the United States of America 106:
6784–9.
57. Loconto J, Papes F, Chang E, Stowers L, Jones E, et al. (2003) Functional
expression of murine v2r pheromone receptors involves selective association
with the m10 and m1 families of mhc class ib molecules. Cell 112: 607–18.
58. Oliveira A, Thams S, Lidman O, Piehl F, Hokfelt T, et al. (2004) A role for
mhc class i molecules in synaptic plasticity and regeneration of neurons after
axotomy. Proceedings of the National Academy of Sciences of the United
States of America 101: 17843–8.
171
59. LeVay S, Stryker M, Shatz C (1978) Ocular dominance columns and their
development in layer iv of the cat’s visual cortex: a quantitative study. The
Journal of comparative neurology 179: 223–44.
60. Torborg C, Feller M (2004) Unbiased analysis of bulk axonal segregation pat-
terns. Journal of neuroscience methods 135: 17–26.
61. Stevens B, Allen N, Vazquez L, Howell G, Christopherson K, et al. (2007)
The classical complement cascade mediates cns synapse elimination. Cell 131:
1164–78.
62. Penn A, Riquelme P, Feller M, Shatz C (1998) Competition in retinogeniculate
patterning driven by spontaneous activity. Science (New York, NY) 279:
2108–12.
63. Bjartmar L, Huberman A, Ullian E, Renterıa R, Liu X, et al. (2006) Neuronal
pentraxins mediate synaptic refinement in the developing visual system. The
Journal of neuroscience: the official journal of the Society for Neuroscience
26: 6269–81.
64. McGee A, Yang Y, Fischer Q, Daw N, Strittmatter S (2005) Experience-driven
plasticity of visual cortex limited by myelin and nogo receptor. Science (New
York, NY) 309: 2222–6.
65. Mataga N, Nagai N, Hensch T (2002) Permissive proteolytic activity for visual
cortical plasticity. Proceedings of the National Academy of Sciences of the
United States of America 99: 7717–21.
66. Atwal J, Pinkston-Gosse J, Syken J, Stawicki S, Wu Y, et al. (2008) Pirb is a
functional receptor for myelin inhibitors of axonal regeneration. Science (New
York, NY) 322: 967–70.
172
67. Shatz C (2009) Mhc class i: an unexpected role in neuronal plasticity. Neuron
64: 40–5.
68. Dorfman J, Zerrahn J, Coles M, Raulet D (1997) The basis for self-tolerance
of natural killer cells in beta2-microglobulin- and tap-1- mice. Journal of
immunology (Baltimore, Md: 1950) 159: 5219–25.
69. Smith S (2007) Circuit reconstruction tools today. Current opinion in neuro-
biology 17: 601–8.
70. Koffie R, Meyer-Luehmann M, Hashimoto T, Adams K, Mielke M, et al. (2009)
Oligomeric amyloid β associates with postsynaptic densities and correlates
with excitatory synapse loss near senile plaques. Proc Natl Acad Sci 106:
4012–4017.
71. Feng G, Mellor R, Bernstein M, Keller-Peck C, Nguyen Q, et al. (2000) Imag-
ing neuronal subsets in transgenic mice expressing multiple spectral variants
of gfp. Neuron 28: 41–51.
72. Denk W, Horstmann H (2004) Serial block-face scanning electron microscopy
to reconstruct three-dimensional tissue nanostructure. PLoS biology 2: e329.
73. Harris K, Perry E, Bourne J, Feinberg M, Ostroff L, et al. (2006) Uniform serial
sectioning for transmission electron microscopy. The Journal of neuroscience
: the official journal of the Society for Neuroscience 26: 12101–3.
74. Knott G, Marchman H, Wall D, Lich B (2008) Serial section scanning electron
microscopy of adult brain tissue using focused ion beam milling. The Journal
of neuroscience: the official journal of the Society for Neuroscience 28: 2959–
64.
173
75. Anderson J, Jones B, Yang J, Shaw M, Watt C, et al. (2009) A computational
framework for ultrastructural mapping of neural circuitry. PLoS biology 7:
e1000074.
76. Kasthuri N, Lichtman J (2010) Neurocartography. Neuropsychopharmacol-
ogy: official publication of the American College of Neuropsychopharmacology
35: 342–3.
77. Fremeau R, Troyer M, Pahner I, Nygaard G, Tran C, et al. (2001) The ex-
pression of vesicular glutamate transporters defines two classes of excitatory
synapse. Neuron 31: 247–60.
78. De Gois S, Schafer M, Defamie N, Chen C, Ricci A, et al. (2005) Homeostatic
scaling of vesicular glutamate and gaba transporter expression in rat neocor-
tical circuits. The Journal of neuroscience : the official journal of the Society
for Neuroscience 25: 7121–33.
79. Graziano A, Liu X, Murray K, Jones E (2008) Vesicular glutamate trans-
porters define two sets of glutamatergic afferents to the somatosensory tha-
lamus and two thalamocortical projections in the mouse. The Journal of
comparative neurology 507: 1258–76.
80. Fritschy J, Harvey R, Schwarz G (2008) Gephyrin: where do we stand, where
do we go? Trends in neurosciences 31: 257–64.
81. Chaudhry F, Reimer R, Bellocchio E, Danbolt N, Osen K, et al. (1998) The
vesicular gaba transporter, vgat, localizes to synaptic vesicles in sets of glycin-
ergic as well as gabaergic neurons. The Journal of neuroscience : the official
journal of the Society for Neuroscience 18: 9733–50.
174
82. Micheva K, Beaulieu C (1995) An anatomical substrate for experience-
dependent plasticity of the rat barrel field cortex. Proceedings of the National
Academy of Sciences of the United States of America 92: 11834–8.
83. Jones E, Powell T (1969) Morphological variations in the dendritic spines of
the neocortex. Journal of cell science 5: 509–29.
84. Knott G, Quairiaux C, Genoud C, Welker E (2002) Formation of dendritic
spines with gabaergic synapses induced by whisker stimulation in adult mice.
Neuron 34: 265–73.
85. Jasinska M, Siucinska E, Cybulska-Klosowicz A, Pyza E, Furness D, et al.
(2010) Rapid, learning-induced inhibitory synaptogenesis in murine barrel
field. The Journal of neuroscience: the official journal of the Society for
Neuroscience 30: 1176–84.
86. Dehay C, Douglas R, Martin K, Nelson C (1991) Excitation by geniculocortical
synapses is not ’vetoed’ at the level of dendritic spines in cat visual cortex.
The Journal of physiology 440: 723–34.
87. Kubota Y, Hatada S, Kondo S, Karube F, Kawaguchi Y (2007) Neocortical
inhibitory terminals innervate dendritic spines targeted by thalamocortical
afferents. The Journal of neuroscience: the official journal of the Society for
Neuroscience 27: 1139–50.
88. Mandell J, Townes-Anderson E, Czernik A, Cameron R, Greengard P, et al.
(1990) Synapsins in the vertebrate retina: absence from ribbon synapses and
heterogeneous distribution among conventional synapses. Neuron 5: 19–33.
175
89. Kielland A, Erisir A, Walaas S, Heggelund P (2006) Synapsin utilization differs
among functional classes of synapses on thalamocortical cells. The Journal of
neuroscience: the official journal of the Society for Neuroscience 26: 5786–93.
90. Bragina L, Candiracci C, Barbaresi P, Giovedı S, Benfenati F, et al. (2007)
Heterogeneity of glutamatergic and gabaergic release machinery in cerebral
cortex. Neuroscience 146: 1829–40.
91. Grønborg M, Pavlos N, Brunk I, Chua J, Munster-Wandowski A, et al. (2010)
Quantitative comparison of glutamatergic and gabaergic synaptic vesicles un-
veils selectivity for few proteins including mal2, a novel synaptic vesicle pro-
tein. The Journal of neuroscience : the official journal of the Society for
Neuroscience 30: 2–12.
92. Nakamura K, Watakabe A, Hioki H, Fujiyama F, Tanaka Y, et al. (2007)
Transiently increased colocalization of vesicular glutamate transporters 1 and
2 at single axon terminals during postnatal development of mouse neocortex:
a quantitative analysis with correlation coefficient. The European journal of
neuroscience 26: 3054–67.
93. LeVay S, Gilbert C (1976) Laminar patterns of geniculocortical projection in
the cat. Brain research 113: 1–19.
94. Luscher C, Isaac J (2009) The synapse: center stage for many brain diseases.
The Journal of Physiology 587: 727–9.
95. Hofer S, Mrsic-Flogel T, Bonhoeffer T, Hubener M (2009) Experience leaves
a lasting structural trace in cortical circuits. Nature 457: 313–7.
176
96. Xu T, Yu X, Perlik A, Tobin W, Zweig J, et al. (2009) Rapid formation and
selective stabilization of synapses for enduring motor memories. Nature 462:
915–9.
97. Yang G, Pan F, Gan W (2009) Stably maintained dendritic spines are associ-
ated with lifelong memories. Nature 462: 920–4.
98. Arganda-Carreras I, Sorzano C, Marabini R, Carazo J, Ortiz-de Solorzano C,
et al. (2006) Consistent and elastic registration of histological sections using
vector-spline regularization. Springer, 85–95 pp.
99. Lewis J (1995) Fast template matching. Canadian Image Processing and
Pattern Recognition Society, pp. 120–123.
100. Turrigiano G, Nelson S (2004) Homeostatic plasticity in the developing ner-
vous system. Nature reviews Neuroscience 5: 97–107.
101. Citri A, Malenka R (2008) Synaptic plasticity: multiple forms, functions, and
mechanisms. Neuropsychopharmacology : official publication of the American
College of Neuropsychopharmacology 33: 18–41.
102. Garner C, Zhai R, Gundelfinger E, Ziv N (2002) Molecular mechanisms of cns
synaptogenesis. Trends in neurosciences 25: 243–51.
103. Hioki H, Fujiyama F, Nakamura K, Wu S, Matsuda W, et al. (2004) Chemi-
cally specific circuit composed of vesicular glutamate transporter 3- and pre-
protachykinin b-producing interneurons in the rat neocortex. Cerebral cortex
(New York, NY : 1991) 14: 1266–75.
104. Gutierrez R (2005) The dual glutamatergic-gabaergic phenotype of hippocam-
pal granule cells. Trends in neurosciences 28: 297–303.
177
105. Briggman K, Helmstaedter M, Denk W (2011) Wiring specificity in the
direction-selectivity circuit of the retina. Nature 471: 183–8.
106. Anderson J, Jones B, Watt C, Shaw M, Yang J, et al. (2011) Exploring the
retinal connectome. Molecular vision 17: 355–79.
107. Li L, Tasic B, Micheva K, Ivanov V, Spletter M, et al. (2010) Visualizing the
distribution of synapses from individual neurons in the mouse brain. PloS one
5: e11503.
108. Kuhlman S, Huang Z (2008) High-resolution labeling and functional manip-
ulation of specific neuron types in mouse brain by cre-activated viral gene
expression. PloS one 3: e2005.
109. Katz Y, Menon V, Nicholson D, Geinisman Y, Kath W, et al. (2009) Synapse
distribution suggests a two-stage model of dendritic integration in ca1 pyra-
midal neurons. Neuron 63: 171–7.
110. Davie J, Clark B, Husser M (2008) The origin of the complex spike in cerebellar
purkinje cells. The Journal of neuroscience : the official journal of the Society
for Neuroscience 28: 7599–609.
111. Bayes A, Grant S (2009) Neuroproteomics: understanding the molecular or-
ganization and complexity of the brain. Nature reviews Neuroscience 10:
635–46.
112. Dani A, Huang B, Bergan J, Dulac C, Zhuang X (2010) Superresolution imag-
ing of chemical synapses in the brain. Neuron 68: 843–56.
113. De Camilli P, Cameron R, Greengard P (1983) Synapsin 1 (protein 1), a nerve
terminal-specific phosphoprotein. Journal of Cell Biology 96: 1337–1354.
178
114. Evans N, Facci L, Owen D, Soden P, Burbidge S, et al. (2008) Abeta(1-42)
reduces synapse number and inhibits neurite outgrowth in primary cortical
and hippocampal neurons: a quantitative analysis. Journal of neuroscience
methods 175: 96–103.
115. Jackson JE (1991) A User’s Guide to Principal Components. John Wiley and
Sons.
116. Belkacem-Boussaid K, Pennell M, Lozanski G, Shana’ah A, Gurcan M (2010)
Computer-aided classification of centroblast cells in follicular lymphoma. An-
alytical and quantitative cytology and histology / the International Academy
of Cytology [and] American Society of Cytology 32: 254–260.
117. Breiman L (2001) Random forests. Machine Learning 45: 5-3-2.
118. Settles B (2009) Active learning literature survey. Computer Sciences Tech-
nical Report 1648, University of Wisconsin–Madison.
119. Bogen I, Boulland J, Mariussen E, Wright M, Fonnum F, et al. (2006) Absence
of synapsin i and ii is accompanied by decreases in vesicular transport of
specific neurotransmitters. Journal of neurochemistry 96: 1458–66.
120. Schuz A, Palm G (1989) Density of neurons and synapses in the cerebral
cortex of the mouse. The Journal of comparative neurology 286: 442–55.
121. Lawton D, Oswald W, McClure J (1995) The biological reality of the interlacu-
nar network in the embryonic, cartilaginous, skeleton: a thiazine dye/absolute
ethanol/lr white resin protocol for visualizing the network with minimal tissue
shrinkage. Journal of microscopy 178: 66–85.
179
122. Gonchar Y, Wang Q, Burkhalter A (2007) Multiple distinct subtypes of
gabaergic neurons in mouse visual cortex identified by triple immunostain-
ing. Frontiers in neuroanatomy 1: 3.
123. Fremeau R, Burman J, Qureshi T, Tran C, Proctor J, et al. (2002) The identi-
fication of vesicular glutamate transporter 3 suggests novel modes of signaling
by glutamate. Proceedings of the National Academy of Sciences of the United
States of America 99: 14488–93.
124. Fremeau R, Voglmaier S, Seal R, Edwards R (2004) Vgluts define subsets of
excitatory neurons and suggest novel roles for glutamate. Trends in neuro-
sciences 27: 98–103.
125. Fremeau R, Kam K, Qureshi T, Johnson J, Copenhagen D, et al. (2004) Vesic-
ular glutamate transporters 1 and 2 target to functionally distinct synaptic
release sites. Science (New York, NY) 304: 1815–9.
126. Dal Bo G, St-Gelais F, Danik M, Williams S, Cotton M, et al. (2004)
Dopamine neurons in culture express vglut2 explaining their capacity to re-
lease glutamate at synapses in addition to dopamine. Journal of neurochem-
istry 88: 1398–405.
127. Kawano M, Kawasaki A, Sakata-Haga H, Fukui Y, Kawano H, et al. (2006)
Particular subpopulations of midbrain and hypothalamic dopamine neurons
express vesicular glutamate transporter 2 in the rat brain. The Journal of
comparative neurology 498: 581–92.
128. Fu M, Zuo Y (2011) Experience-dependent structural plasticity in the cortex.
Trends in neurosciences 34: 177–87.
180
129. White JG, Southgate E, Thomson JN, Brenner S (1986) The structure of
the nervous system of the nematode caenorhabditis elegans. Philosophical
Transactions of the Royal Society B: Biological Sciences 314: 1–340.
130. Cardona A, Saalfeld S, Arganda I, Pereanu W, Schindelin J, et al. (2010) Iden-
tifying neuronal lineages of drosophila by sequence analysis of axon tracts. The
Journal of neuroscience : the official journal of the Society for Neuroscience
30: 7538–53.
131. Abramoff M, Magelhaes P, Ram S (2004) Image processing with imagej. Bio-
photonics International 11: 36–42.
132. Markram H (2006) The blue brain project. Nature reviews Neuroscience 7:
153–60.
133. Migliore M, Cannia C, Lytton W, Markram H, Hines M (2006) Parallel net-
work simulations with neuron. Journal of computational neuroscience 21:
119–29.
134. Mishchenko Y, Hu T, Spacek J, Mendenhall J, Harris K, et al. (2010) Ultra-
structural analysis of hippocampal neuropil from the connectomics perspec-
tive. Neuron 67: 1009–20.
181