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Journal of
Structural
Journal of Structural Biology 147 (2004) 247–258
Biology
www.elsevier.com/locate/yjsbi
Use of negative stain and single-particle image processingto explore dynamic properties of flexible macromolecules
Stan A. Burgess,* Matt L. Walker, Kavitha Thirumurugan, John Trinick,and Peter J. Knight
School of Biomedical Sciences and Astbury Centre for Structural Molecular Biology, University of Leeds, Leeds LS2 9JT, UK
Received 13 January 2004, and in revised form 7 April 2004
Available online 6 May 2004
Abstract
Flexible macromolecules pose special difficulties for structure determination by crystallography or NMR. Progress can be made
by electron microscopy, but electron cryo-microscopy of unstained, hydrated specimens is limited to larger macromolecules because
of the inherently low signal-to-noise ratio. For three-dimensional structure determination, the single particles must be invariant in
structure. Here, we describe how we have used negative staining and single-particle image processing techniques to explore the
structure and flexibility of single molecules of two motor proteins: myosin and dynein. Critical for the success of negative staining is
a hydrophilic, thin carbon film, because it produces a low noise background around each molecule, and stabilises the molecule
against damage by the stain. The strategy adopted for single-particle image processing exploits the flexibility available within the
SPIDER software suite. We illustrate the benefits of successive rounds of image alignment and classification, and the use of whole
molecule averages and movies to analyse and display both structure and flexibility within the dynein motor.
� 2004 Elsevier Inc. All rights reserved.
Keywords: Negative stain; Single-particle image processing; SPIDER; Dynein; Myosin; Macromolecule; Fleximer; Flexibility
1. Introduction
The high contrast and �1.5 nm resolution achievable
by negative staining mean that this simple method still
has great utility in macromolecular microscopy. Al-
though cryo-electron microscopy followed by single-
particle image processing allows 3D structure to be
determined, many objects pose problems for this ap-
proach. For instance where the mass of the object isbelow �500 kDa, which includes most individual pro-
tein molecules or small complexes, beam damage may
make it impossible to gain enough information from
each weakly contrasted object to enable the molecular
orientation to be determined (Henderson, 1995).
Prominent substructural features reduce this limit (Orl-
ova et al., 2000), but do not abolish it. The higher
contrast of negative stain images can therefore allow
* Corresponding author. Fax: +44-113-343-4228.
E-mail address: [email protected] (S.A. Burgess).
1047-8477/$ - see front matter � 2004 Elsevier Inc. All rights reserved.
doi:10.1016/j.jsb.2004.04.004
progress to be made in discovering the structure of thesesmaller objects.
Another challenge arises where the object exists in
solution in several discrete structural states. If only a
small number of different conformations exist, and
these can be identified during preliminary processing,
it is possible to derive the 3D structure of each
conformer (Schoehn et al., 2000). However, this ap-
proach fails where the molecule is flexible, since thereis then essentially a continuum of conformers. We
refer to these flexible conformers as f leximers, to
distinguish them from conformational variants in-
duced by, for example, substrate binding by an en-
zyme. Fleximers produce ambiguity in the assignment
of orientation versus conformation for any given
image. Progress in describing the structure in two-
dimensional projections is still possible, however, us-ing single-particle processing methods. As long as one
part of the object allows alignment of the images
flexibility in other parts can then be described. Such
248 S.A. Burgess et al. / Journal of Structural Biology 147 (2004) 247–258
alignment requires a good signal-to-noise ratio in theraw images, and this is where negative staining can
bring benefits.
We have used negative staining and single-particle
processing to investigate the structure and action of the
motor proteins myosin and dynein. Except for the head
domain of dynein, the component parts of both these
molecules are too small for unstained cryo-EM to have
much chance of success, and there is evidence frommany techniques for considerable flexibility within mo-
tor proteins (Howard, 2001). Metal shadowing was used
successfully to provide initial descriptions of the struc-
ture and flexibility of the molecules (Elliott and Offer,
1978; Goodenough et al., 1987; Slayter and Lowey,
1967), but negative staining is capable of revealing
greater detail. Myosin is normally poorly preserved in
negative stain, but we found good preservation by pre-treatment of a thin carbon support film with ultraviolet
(UV) light prior to specimen application (Knight and
Trinick, 1984; Walker et al., 1985). Myosin was found to
be flexible, and we therefore adapted procedures within
the single-particle processing methodology to describe
this (Burgess et al., 1997). More recently we have ex-
tended the same general methods to describe and
quantitate acto-myosin interactions (Burgess et al.,2002; Walker et al., 2000) and dynein flexibility (Burgess
et al., 2003, 2004).
Negative staining following adsorption onto carbon
has attendant concerns that the structure seen is dis-
torted or disrupted by the preparative procedure. For
example, interactions between protein and the charged
surface of the substrate may distort protein conforma-
tion. Further distortion may occur as the concentrationof salts and stain increase during drying with the addi-
tional possibility of drying induced collapse of structure.
Nevertheless, even large complexes that might be ex-
pected to collapse can show a surprising degree of fi-
delity between negative stained structure and that
determined by other techniques (e.g., Frank et al., 1991).
Our observation in raw images that the myosin head had
a large distal domain and two smaller ones in tandemcloser to the tail (Walker and Trinick, 1988), was con-
firmed when the atomic structure was solved by crys-
tallography (Rayment et al., 1993). Image processing of
such images has since shown the smaller subdomains
within the motor domain, such as the SH3 and converter
subdomains, as well as the N- and C-terminal lobes of
the light chains, each of which is about 9 kDa as re-
vealed by crystallography (Fig. 1). Moreover, confor-mational changes induced by nucleotide binding and
hydrolysis are preserved during specimen preparation,
allowing the study of myosin conformations in the
presence of ATP directly (Burgess et al., 2002), an ad-
vantage over crystallography which has been limited to
the use of ATP analogues. For dynein, images from
negative stain are compatible with preliminary data
obtained from frozen hydrated specimens (Burgesset al., 2004), indicating little gross distortion of the
molecule.
What can we learn about flexibility from negatively
stained molecules? First, drying forces may be useful in
revealing the location of weak points that deform under
stresses in vivo, which may not be detected in frozen-
hydrated specimens where such forces are absent. For
instance, negative staining of myosin molecules allowedus to conclude that the light chain domain was flexibly
attached to the motor domain (Burgess et al., 1997)
before a pliant region at their junction was identified by
crystallography (Dominguez et al., 1998). Second, ad-
sorption to the substrate may perturb a flexible molecule
in ways which provide important structural insights. For
instance, the helical order of myosin thick filaments
becomes disrupted (Knight and Trinick, 1984), and theextent of flexibility of dynein molecules depends on their
orientation (Burgess et al., 2004). These effects reveal the
reversal of polarity of myosin molecules in thick fila-
ments and an otherwise unseen component of dynein.
Third, changes in flexibility can be detected by com-
paring molecules prepared under different conditions. In
dynein molecules the stalk domain is stiffer in the ab-
sence of nucleotide than in the presence of ADP andvanadate (Burgess et al., 2003). And finally, in some
cases, where existing data are available for molecules in
solution, the flexibility deduced from electron micro-
graphs has been found to be compatible. Thus, the
persistence length of the giant muscle protein titin
(Tskhovrebova and Trinick, 2001) adsorbed to mica
agrees with that determined from light scattering solu-
tion studies, indicating that in this case where the mol-ecule is like a string of beads, its flexibility is faithfully
represented. However, with other molecular architec-
tures such good quantitative correspondence may not be
found.
Flexibility within motor proteins has long been re-
cognised as an important functional characteristic,
providing for example the in-series elastic component of
the force-generating interactions of myosin heads withactin in muscle (Huxley and Simmons, 1971). Flexibility
also allows myosin heads attached to thick filaments to
locate and form stereospecific interactions with actin
subunits in thin filaments despite the incommensurate
structures of the two filaments (Huxley, 1969). However,
an empirical description of flexibility has been long in
coming. Flexibility is generally anathema to crystallog-
raphers, and whilst structure determination by NMR isexcellent at describing inter-atomic spatial relations, it
has difficulty where domains move against one another.
Analysis of EM images by single-particle techniques
offers a way forward, and in what follows we describe
the methods we have employed both to get highly de-
tailed images of molecules in negative stain and to an-
alyse their structure and flexibility.
Fig. 1. Heads of myosin molecules. (A) Atomic structures of scallop
myosin 2 molecules with ADP and vanadate bound in the active site
(upper panel, PDB Accession No. 1DFL) or with no nucleotide bound
(lower panel, Accession No. 1DFK). Light chains are coloured yellow
and orange. The heavy chain is coloured blue with additional col-
ouring to show various motor subdomains, including the SH3 sub-
domain (red), upper (cyan) and lower (green) 50k subdomains, and the
converter subdomain (black). (B) Example class averages of negatively
stained recombinant myosin 5 heads after single-particle image pro-
cessing showing a close correspondence of motor structure to appro-
priately orientated atomic structures (A). Differences in the orientation
of the light chain-binding domain are seen, which may be due to dif-
ferences between myosins 2 and 5. Myosin 5 heads were prepared in
the presence of ATP. Scale bar: 10 nm.
S.A. Burgess et al. / Journal of Structural Biology 147 (2004) 247–258 249
1.1. Negative staining
We use a single, continuous carbon film method for
negative staining. In general this produces less even
staining than the double-carbon �sandwich� techniqueused by others (Frank, 1996), but an advantage is that it
is relatively quick and easy. In our hands it produces a
higher yield of useable grids. For large proteins it typi-
cally produces single-sided staining of the molecule
(Frank, 1996). This is a severe shortcoming for three-dimensional reconstruction but in other situations, sin-
gle-sided staining can provide useful information about
the �face� of the molecule in contact with the carbon
(Verschoor et al., 1989). Commonly, the orientation of
molecules on carbon is heavily biased in favour of par-
ticular views.
1.1.1. Preparation of continuous carbon support films
The preparation of grids containing thin and intact
carbon films is a very important part of the procedure.
Carbon is evaporated from 1.0mm carbon thread (Agar
Scientific) at a height of �17 cm onto freshly cleaved
mica in a coating unit with an operating vacuum of
�10�6 torr. The current passed through the carbon
thread is increased rapidly to maximum, which ruptures
the thread. This speed helps create carbon films withconsistent thickness. A thickness of �8 nm provides a
good compromise between strength and low electron
scattering. Care is taken to avoid oil contamination of
the vacuum by minimising the duration of direct
pumping by the rotary pump and by cooling the diffu-
sion pump baffles with liquid nitrogen (a cold trap in the
vacuum chamber may also be used). Freshly made car-
bon films are collected onto 300 or 400 mesh coppergrids by floating the carbon onto the surface of a bath of
distilled water and then raising prearranged grids from
under the water surface to collect the carbon. The
�rough� side of the carbon (i.e., the surface not in contact
with mica) is therefore uppermost on the grid and is the
side to which the specimen is applied. Grids are then left
to dry overnight at room temperature. They may also be
dried more quickly in a warm oven although this tendsto cause more breakage.
1.1.2. Treatment with ultraviolet light
Grids are rendered hydrophilic by treatment with UV
light from a lamp emitting over a broad range of wave-
lengths. The lamp we use (low pressure mercury vapour
type R51, UV Products, Pasadena, CA) has had the low-
pass (black light) filter removed and generates ozone inthe air around it which may be involved in the modifi-
cation of the carbon surface. Grids are placed about 5 cm
from the lamp in an enclosed environment (e.g., card-
board box) which retains the ozone. Grids are typically
irradiated for about 40min, although this depends on the
specimen to be examined; filamentous proteins (e.g., F-
actin) require less time than single molecules. Also,
freshly made carbon may need less time as it is alreadyslightly hydrophilic. After UV irradiation, grids remain
hydrophilic for around 4 h. Details of the surface
chemistry of this process are poorly understood. How-
ever, it leads to a thin, even layer of stain upon drying,
and may promote interactions between the specimen
protein molecules and the carbon substrate which sta-
bilises them against any adverse effects of the stain.
1.1.3. Buffers and protein concentration
For good quality negative staining, attention must be
given to the constituents of the specimen buffer. Milli-
molar concentrations of reducing agents such as dithi-
othreitol and mercaptoethanol adversely affect the
quality of staining and increase the occurrence of stain
artefacts. If reducing agents cannot be avoided, micro-
250 S.A. Burgess et al. / Journal of Structural Biology 147 (2004) 247–258
molar amounts can be tolerated. The choice of bufferanion is not critical, although we have obtained partic-
ularly good results with MOPS, which is also a conve-
nient buffer for specimen storage by drop-freezing in
liquid nitrogen. In general, high concentrations of salt
(0.5M) and nucleotide (millimolar) also impair the
quality of staining, as do detergents, glycerol, and su-
crose. If these buffer constituents cannot be avoided or
removed by dilution before application to the grid, awashing step can be introduced before staining. The
wash buffer should be similar to the specimen buffer, but
lacking the undesirable constituents. A typical wash
buffer we use is 25mM KCl, 10mM MOPS, 1mM
EGTA, and 1mM MgCl2, pH 7.0. However, a washing
step does raise the possibility of some rearrangement of
the protein on the carbon substrate (Walker et al., 2000).
Suspensions of single molecules are applied to thegrid at a concentration of between 20 and 100 nM,
which usually gives an appropriate distribution of par-
ticles for subsequent windowing and single-particle
analysis. Particle density also seems to affect stain depth.
If the particle density is too low, the depth of stain is
often too shallow for good staining.
1.1.4. Staining
After application of a drop of the sample (and a
possible washing step with 2–3 drops of wash buffer), the
grid is stained with 2–3 drops of 1% aqueous uranyl
acetate previously passed through a 0.2 lm filter (Ac-
rodisc, Gelman Laboratory Sciences). Using a pasteur
pipette each drop of solution (sample, wash, and stain)
Fig. 2. Variations in stain depth affect the appearance of molecules. (A) Grada
heads. Enlarged regions (boxes) from deep (B) and shallow (C) stain show
contrast balancing show accumulation of stain around particles in shallow sta
is touched onto the grid surface and then �flicked� offrapidly to avoid migration onto the underside of the
grid. Excess stain is then removed by touching the torn
edge of a piece of filter paper (Grade 1) to the edge of
the grid.
1.2. Microscopy
Low-dose images (�300 e�/nm2) undoubtedly offerhigher resolution (Unwin, 1974). However, upon irra-
diation stain migration can improve the definition
against the background of fine structures like the coiled-
coil tail of myosin (Walker et al., 1991). Although this
improvement in signal-to-noise ratio occurs at the ex-
pense of resolution, molecules imaged under high dose
reveal structures as small as 9 kDa (Fig. 1B) and single
a-helices can be seen in raw micrographs (Walker andTrinick, 1986). For our recent work on dynein mole-
cules, we wanted to image its flexible coiled-coil stalk
with optimal clarity for subsequent image processing
(see below). In a previous study (Samso et al., 1998)
using low-dose conditions the stalk was not seen either
in raw images or after single-particle image processing.
Therefore, as for myosin, we use high dose conditions.
We do not routinely monitor the dose of each micro-graph, but as a guide, viewing for approximately 10 s at
a magnification of 40 000� followed by a 1 s exposure
corresponds to a dose of between about 5000 and
100 000 e�/nm2.
The quality of staining between different grids is
always variable, even when they are prepared under the
tion of stain depth across a single micrograph of recombinant myosin 5
ing individual molecules. Same regions (D and E, respectively) after
in (E), but not in deeper stain (D). Scale bars: (A) 200 nm; (B–E) 50 nm.
S.A. Burgess et al. / Journal of Structural Biology 147 (2004) 247–258 251
same conditions and on the same day. Grids aresearched at low magnification to locate areas which are
negatively stained (appear darker). Usually, these areas
show a gradation in stain depth (Fig. 2). Molecules in
deeper stain appear surrounded by a fairly uniform
darker background (Figs. 2B and D), whereas those in
shallower stain appear outlined by an accumulation of
stain (Figs. 2C and E). Molecules in extremely shallow
stain (not shown) are visibly damaged by the beam andare avoided. To ensure that particles selected for image
processing are as homogeneous as possible, we choose
Fig. 3. Negatively stained dynein molecules. (A) Field showing particles in
dynein. The stem is �25 nm long, the stalk is �15 nm long, and the head dom
visible in well-stained areas. (B) Gallery of selected particles. Each particle is
outer rings are indicated by circles in the lower right panel. Scale bars: (A)
them from regions of similar stain depth. In our ex-perience, those from shallow stain (Figs. 2C and E)
show the greatest detail after image processing
(Fig. 1B).
1.3. Image processing
All image processing described in this section is per-
formed using procedures written in the SPIDER suite ofprograms (Frank et al., 1996). SPIDER procedures are
batch files which implement SPIDER commands within
shallow stain. Inset: Cartoon showing the arrangement of domains in
ain is �13 nm in diameter. Stems and stalks as well as heads are directly
shown in a window of the size used for the initial alignment. Inner and
100nm; (B) 50 nm.
252 S.A. Burgess et al. / Journal of Structural Biology 147 (2004) 247–258
loops and with conditional statements. As such, they areextremely flexible allowing the image processing strategy
to be customised to suit molecules with unusual shapes
and behaviours. One such example is the microtubule
motor protein dynein.
Dynein has two extended and flexible structures,
called the stalk and stem, which emerge from a ring-like
head (Fig. 3). Of the two structures, the stalk is the
smaller, and predicted to consist of an a-helical coiledcoil. Understanding this structure is vital to under-
standing the mechanism of dynein since this coiled coil
carries at its distal end the all-important ATP-sensitive
microtubule-binding domain. Currently, the three-di-
mensional structure of the entire dynein molecule is
unknown, and the mechanism of this motor is poorly
understood. What follows is a description of the image
processing strategies we have used to align, classify, andanalyse images of negatively stained dynein molecules.
The resulting two-dimensional images have provided
new insights into dynein�s structure and mechanism
(Burgess et al., 2003, 2004).
1.3.1. Micrograph digitisation
Micrographs are digitised using a Leafscan 45 (Leaf
Systems, Southborough, MA) or an Imacon Flextight848 (Imacon A/S, Copenhagen, Denmark) film scanner
at 16-bit optical resolution, and with a pixel size of
20 lm, corresponding to �0.5 nm at the specimen.
1.3.2. Particle picking and windowing
Particle picking involves positioning a cursor over
each particle and recording its image coordinates.
Flexibility within dynein molecules and differences inorientation on the grid cause the particles to appear
rather variable in raw micrographs (Fig. 3A). For
elongated, multi-domain particles like these, it is neces-
sary to pick the same feature within each molecule since
this determines its position within the window which
affects the subsequent alignment process. For dynein we
pick the centre of the head domain, placing this feature
at the centre of each resulting windowed image(Fig. 3B).
The window size is chosen to include the stem and
stalk plus an additional border of background. During
subsequent alignment, image transformations (i.e., ro-
tations and translations) introduce artefacts into each
image near its edges. Therefore, the window must be
sufficiently large to ensure that these artefacts occur only
in the image background and not within the particleitself. For particles that are variable in length, like dy-
nein, particular care must be given to this when choos-
ing the window size. Larger windows are therefore
better but they reduce the speed of alignment, which
may need to be performed several times with different
parameters before it is successful. Larger windows also
increase the likelihood of containing neighbouring par-
ticles, although these can be obscured by applying acircular mask to each window (see below). To minimise
both the window size and the subsequent image trans-
lations during alignment, the centre of each head is
picked with greater precision than would otherwise be
required.
All windowed images undergo pre-processing steps
before alignment to minimise differences between them
arising from residual differences in stain depth and even-ness. A ramp of pixel densities is calculated and removed
from each image and then the pixel density distribution is
�floated� using an arithmetic operation to adjust the mean
pixel density to zero and the standard deviation to one. A
circular mask, if necessary, is applied at this stage. In this
case, particles are sufficiently well separated that the
majority of windows contain no intruding particles
(Fig. 3B), avoiding the need for masking.The number of particles required for successful
alignment and classification is difficult to predict be-
cause it depends on the homogeneity of the preparation
(orientation and conformation). However, we generally
aim to start with a minimum of 2000–4000 particles
which allows us subsequently to remove misaligned or
poorly stained molecules after initial classification yet
leaves enough particles for further analysis.
1.3.3. Initial alignment
Image alignment is critical since all subsequent pro-
cessing depends upon it. In our experience, some parti-
cles always fail to align, particularly if they are
heterogeneous in conformation. Therefore it is impor-
tant both to minimise these by exploring a range of
alignment strategies and to discard the failures that re-main. The high contrast of negative stain facilitates this
step because the morphology is easier to see in individ-
ual images. Reference-free strategies are used in general,
unless otherwise stated.
Successful alignment of dynein images requires the
use of a modified iterative procedure in which a rota-
tional alignment is performed first in each cycle, fol-
lowed by a translational alignment. This is the oppositeorder to that normally employed with roughly globular
particles which benefit from translational alignment
first. Another crucial parameter for alignment is the
choice of inner and outer radii for rotational alignment.
For rotational alignment of the stems it is necessary to
choose an outer radius large enough to encircle all the
stems. It is also absolutely necessary to exclude the head
from rotational alignment by using a larger than usualinner ring radius (Fig. 3B, circles). The stem is a small
feature within a large region of background staining.
Apparently, including the head makes the head compete
with the much weaker signal arising from the stem,
leading to many rotational misalignments of the stem.
After each iteration of rotational and translational
alignment we re-centre the aligned images using a
S.A. Burgess et al. / Journal of Structural Biology 147 (2004) 247–258 253
model image generated from a previous alignmentattempt. This is necessary to prevent images from
becoming translated substantially within the image
window. When this does occur, molecules escape the
inner and outer radii defined for rotational alignment
(which remain fixed relative to the image window)
thereby decreasing the likelihood of successful rota-
tional alignment. Re-centring also allows for error
checking. Any image translated by more than a user-defined value can be considered erroneous and is
reset to its original position. This can prevent the
accumulation of translational errors in the early it-
erative cycles. Therefore, an image with a final
translation of precisely (0, 0) indicates a misaligned
particle and this value can be used as a criterion to
eliminate the image at the end of alignment. The
value of the maximum permissible translation vector
Fig. 4. Initial alignment and classification of dynein particles. (A) Global aver
global variance here and in all subsequent figures, the contrast has been inv
variances are shown in darker shades, lower variances in paler shades. (C) M
averages showing flexibility within the most common orientations: (D) left vi
class is shown in the lower right corner of each panel. Each panel is 72 nm w
is chosen empirically and depends not only on thesize of the window, but also on the accuracy with
which the centre of each particle is identified during
particle picking.
The success of alignment can be assessed in a number
of ways. Re-centring (described above) permits, in
principle, a quantitative assessment of convergence of
alignment. The common alignment between iterations
means that the magnitude of translations and rotationsfor each image should tend towards zero as alignment
proceeds. However, for heterogeneous particles we
prefer to assess the success of alignment more directly by
examining class averages produced by a subsequent
classification step and also by examining all individual
aligned images on the screen. The stem and stalk of
dynein provide useful landmarks on the molecule for
this purpose.
age and (B) global variance of 3057 images after seven iterations. In the
erted from the usual appearance produced in SPIDER. Thus, higher
ask used for initial classification into 150 classes. (D–F) Selected class
ews, (E) side views, and (F) right views. The number of images in each
ide.
254 S.A. Burgess et al. / Journal of Structural Biology 147 (2004) 247–258
Applied to images of dynein, the alignment strategydescribed above produces a global average image in
which the head and stem are visible (Fig. 4A). The stalk
is not seen but a faint indication of its presence is indi-
cated in the upper left region of the global variance
image (Fig. 4B).
1.3.4. Initial classification and segregation
For image classification our preferred method is K-means clustering. Previously, we found this method to
perform better than hierarchical ascendant classification
when molecules display continuous flexibility (Burgess
et al., 1997). Here, classification was done directly on all
image pixels within the mask (see below), that is, with-
out prior correspondence analysis and the selection of
factors. For dynein images a mask was chosen that en-
compasses the head and stem only (Fig. 4C). We basethe shape of the mask on the shape of the global vari-
ance image, rather than the global average, because the
former provides a better indication of the diversity of
fleximers than the latter. Attempts to classify the images
using masks also encompassing the stalks were unsuc-
cessful (see below). At this stage of processing we rou-
tinely create a series of classifications using different
masks and for each mask, different numbers of classes.This allows the investigator to assess the alignment
qualitatively and to build up a picture of the reliable
features in the images. The shape of the final mask
(Fig. 4C) is thus arrived at by trial and error to ensure
that the heads and stems of all molecules are included
within it, whilst including a minimum of background.
Having chosen a suitable mask for classification, it is
then necessary to choose an appropriate number ofclasses. There is no simple guide to do this, since every
data set is different in terms of stain variability, distri-
bution of particle orientations, particle heterogeneity,
and flexibility. One is trying to satisfy the competing
requirements of merging images to achieve noise re-
duction while preserving diversity. Starting with many
small classes allows the investigator to better detect the
diversity that exists within the data set. K-means clus-tering produces classes with broadly similar numbers of
particles (unlike some of the merging criteria in hierar-
chical ascendant classification), which can be helpful at
this stage. In general we find that 20–40 images per class
provides reasonable noise suppression of well-stained
molecules. Most classes thus produced are reliable,
however some can be misleading because they contain
significant numbers of misaligned, poorly stained orheterogeneous particles. Therefore, it is important to
check the individual images within each class. Careful
examination of individual images within heterogeneous
classes can sometimes reveal useful information. For
example, with dynein we found a perturbed structure
that was critical to our interpretation of the more usual
appearance (Burgess et al., 2003).
Dynein adopts three characteristic orientations of thehead on the carbon substrate, but multiple conforma-
tions exist within each of these (Figs. 4D–F). This ne-
cessitates the use of many classes (number of
classes¼ 150, average number of images per class �20).
Conformational variability is caused by flexibility within
the stem, near its connection with the head. The stalk is
also flexible as evidenced by its absence in these small
classes. Stalks were subsequently found to flex inde-pendently of the stem (see below) producing too many
different fleximer structures to form homogeneous clas-
ses. This problem is compounded by this alignment
which, for each orientation of the head fails to achieve a
precise alignment of the heads and hence the base of the
stalks. This happens because flexibility between the head
and stem cause both these domains (and hence the stalk)
to become slightly misaligned in favour of a global(head+ stem) alignment. To overcome this problem we
employed the following strategy. We identified classes
showing a particular head orientation and combined
them to form a new data set. To do this we used the
�markers� command in WEB to select each good class of
a particular head orientation from a gallery of all class
averages. Then, using a specially written procedure,
these coordinates were converted into class numbers andindividual particles from each class were extracted from
the document files produced by K-means clustering and
combined into a single, new document. Classes showing
poor detail were excluded at this stage. Thus, we created
three segregated data sets (Figs. 5A and B) which were
then processed separately in a second round of align-
ment and classification.
1.3.5. Second alignment and classification
Within each of the three data sets segregated on the
basis of orientation we obtain a common frame of refer-
ence by aligning them according to their head domains.
This is an entirely independent alignment from the first,
using re-windowed images, this time with a much smaller
window to exclude the stem and stalk. We also apply a
soft-edged circular mask to each image to further obscurethese structures. Reference-free alignment was performed
as before except with inner and outer radii for rotational
alignment modified to include only the head. Each ori-
entation-specific data set was aligned separately, but us-
ing the same parameters. Their global �head-aligned�image averages show considerably improved detail
(Fig. 5C) indicating the success of alignment. The align-
ment parameters (hrot, xsh, and ysh) thus determined werethen applied to windowed images large enough to contain
the whole molecule. This provides an alignment of one
part of themolecule (the head) allowing us to examine the
flexibility of the other parts (stem and stalk) relative to it.
The weakness of the stem and stalk in the resulting global
averages (Fig. 5E) confirms their flexibility, while global
variances indicate their ranges of positions (Fig. 5F).
Fig. 5. Segregation of dynein molecules according to orientation, and second alignment. (A) Averages and (B) variances of combined classes in each
of the three typical views after initial alignment and classification. Note the poor detail in the heads and clear visibility of the stems. Number of
particles are: left, n ¼ 1658; side, n ¼ 219; right, n ¼ 346. (C) Averages and (D) variances after a second alignment of each segregated data set using
features within the head domain only (head alignment). (E) Averages and (F) variances after head-alignment is applied to the original, larger images.
S.A. Burgess et al. / Journal of Structural Biology 147 (2004) 247–258 255
For each orientation of dynein, classification of the
stem and stalk regions is performed separately and in-
dependently. Again we use K-means clustering. Masks
need to be sufficiently large to enclose all positions of thestalk (Figs. 6A and B) and stem (Figs. 6C and D). Since
this also encloses a considerable area of background,
which is also �seen� by the classification procedure, it is
vital for optimal classification that these domains be
maximally contrasted by stain (using high dose condi-
tions).
Classification is exquisitely sensitive to mask shape.
We therefore try several different, though related masks.For each mask we also classify into many different
numbers of classes before choosing the most useful one
(Figs. 6E and F). This choice is subjective. Our defini-
tion of the best classification in this context is that which
satisfies the following criteria simultaneously: it pro-
duces the most classes with clearly defined structures of
interest while displaying the widest variety of their
conformations and retaining the most individual images.Performing multiple classifications in this way helps to
avoid producing spurious results.
1.4. Quantitative measurements from class averages
Image averages of stalks and stems contain a wealth
of information about the structure and flexibility of
dynein molecules. First, however, it is necessary toidentify those �good� classes with meaningful informa-
tion to ensure that measurements taken from them are
representative of their constituent individual images.
Some stalk class averages show no clear stalk (Fig. 6E).
Others appear good but inspection of their constituent
images reveals a number of inappropriately assigned
images. This situation probably arises because of thelarge size of the mask compared to the size of the stalk
and its occasional poor staining. Therefore, we exam-
ine carefully individual images within each class to
assess the validity of that class average before taking
measurements from it. This is a subjective process.
Concerns about bias (e.g., weighting in favour of un-
representative fleximers) can be alleviated by starting
with a large data set. For example with dynein we haveanalysed two data sets of left views. One of these
(Fig. 6) was prepared in the absence of nucleotide
(apo-dynein) and consists of 1658 images. The other
(Burgess et al., 2003) was prepared in the presence of
ATP and vanadate which produces an ADP�Vi–dyneincomplex and consists of 1733 images.
In each good class we mark the position of the end
of the stalk interactively. We also mark the point atwhich it attaches to the head, which we do only once
because it is visible in the global average. The co-or-
dinates of the two ends of the stalk are then used to
calculate its chord length and angle (relative to an ar-
bitrary axis, see cartoon, Fig. 6G). The same process is
applied to the stems. Within the common frame of
reference of the aligned heads, this data can be plotted
as a histogram to reveal the distribution of stem andstalk angles (Fig. 6G) after first weighting each value
according to the number of images in the class from
which it was derived. Note that the number of
Fig. 6. Classification of stem and stalk positions in left views of dynein molecules. Masks used for classification, encompassing the stalks (A) and
stems (C) are indicated alongside corresponding variance images (B and D) showing in outline the position of the mask. Gallery of all 70 class
averages from (E) stalk and (F) stem classifications. Some stalk class averages show poor detail or no stalk, whereas the larger stem is seen in all
classes. (G) Histogram showing the distribution of stalk and stem chord angles measured according to the arbitrary vertical axis shown in the
cartoons. The distributions are continuous.
256 S.A. Burgess et al. / Journal of Structural Biology 147 (2004) 247–258
measurements thus determined is less for stalks than
stems, because many stalk classes are poor.
From this data set quantitative measurements were
obtained for both stem and stalk from a sizeable pro-portion of individual molecules (�45%). Combining
the information allowed us to describe several impor-
tant aspects of the whole molecule (Burgess et al.,
2003). For example, by plotting stem angle versus stalk
angle for individual molecules, we were able to show
that their movements are not coupled. By calculating
the end-to-end length of individual molecules we alsoshowed that the molecule shortens when prepared in
the absence of ATP and vanadate. And by plotting
histograms of the angle between stem and stalk in
S.A. Burgess et al. / Journal of Structural Biology 147 (2004) 247–258 257
individual images we showed a systematic change inangle between molecules in the two nucleotide condi-
tions.
Caution should be taken when interpreting the ap-
parent flexibility seen in molecules adsorbed to a sub-
strate, since distortion during specimen preparation
(e.g., flattening) may have contributed to it or even
caused it. Therefore, it may not represent the true flex-
ibility of the molecule in solution. Indeed, we havefound that for dynein molecules adsorbed in different
head orientations, different extents of stem flexibility are
seen (Burgess et al., 2004). Nevertheless, these images
may still be useful because distortions by external forces
still show the locations within the molecule where flex-
ibility is most likely to occur.
1.5. Movie making
Quantitative information like the angle of the stalk
and stem can be used to sort a series of images into a
meaningful sequence suitable for playing as frames of
a movie. The advantage of movies is that they provide a
rapid and intuitive means of viewing large numbers of
images (class averages or individual images), so are
useful for comparing the images within a class to test forconsistency with the class average. They also enable the
identification of subtle differences between individual
frames which may be overlooked in a gallery. Image
sequences of dynein made using stem and stalk angles
produce movies illustrating differences in the way the
stalk flexes in molecules prepared in different nucleotide
conditions (Burgess et al., 2003). However, caution
should be taken to avoid spurious inferences being de-rived from the sequence in which the frames are played.
The sequence of frames is imposed by the investigator
and therefore does not, for instance, represent a se-
quence of events in time. For example, the order of
images in Fig. 7C should not be taken to indicate that
Fig. 7. Whole molecule averages of dynein. (A) Examples of individual head
this alignment. (C) Whole-molecule averages of molecules shown in (A) cre
erages, illustrated for the last panel in (D). (E) Whole-molecule averages sho
aligned images.
the stem moves progressively from one extreme positionto the other. When displayed as a movie (Burgess et al.,
2003) such a sequence looks like a pendulum swinging,
but there is no reason to suppose that the molecule ac-
tually behaves in this manner. Instead, we suppose that
it exhibits random, thermally driven fluctuations about
its lowest energy conformation.
1.6. Generating whole-molecule averages
So far classification of dynein has only been possible
for one part of the molecule at a time. For illustrative
purposes it is helpful to show fleximers in which both the
stem and stalk are visible simultaneously. To do this we
splice together image averages of stalk and stem classes
to create low-noise images of whole molecules (Figs. 7C
and D). Importantly, it is necessary to create whole-molecule images only for those fleximers that actually
exist, since not all combinations of stem and stalk occur
(Burgess et al., 2004). We do this by selecting each
molecule in which both stem and stalk were classified
successfully (Fig. 7A) and splicing together the two class
averages into which it had been assigned (Fig. 7C). For
dynein, this process is simple, requiring the splicing to-
gether of two entirely non-overlapping regions(Fig. 7D). For other molecular shapes, it may be nec-
essary to use masks to achieve this.
1.7. Alignment of class averages
To obtain a single frame of reference between apo-
and ADP�Vi–dynein data sets, which is meaningful in
terms of how the motor works, we decided to performan additional alignment of the molecules according to
the bases of their stems (cargo-binding domains). In-
stead of using images of individual dynein stems, we
used class averages showing noise-reduced stems from
head-aligned images (Burgess et al., 2004). This way we
-aligned dynein molecules and (B) corresponding global average from
ated by splicing together their corresponding stalk and stem class av-
wn in (C) after alignment of their stems. (F) Global average of stem-
258 S.A. Burgess et al. / Journal of Structural Biology 147 (2004) 247–258
avoided losing any images to misalignment, thereby al-lowing us to apply all the stem and stalk positional data
obtained previously to a newly transformed frame of
reference (i.e., stem alignment). Briefly, to each stem
class average a mask was applied to obscure the head
(the stalk is not visible). One class average was used as a
reference image and the others were aligned with respect
to it. This third alignment (Figs. 7E and F) provides a
basis from which to plot the two-dimensional distribu-tion of the stalk tip position (i.e., the microtubule-
binding domain) that results from the combined
flexibility of the stalk and stem (Burgess et al., 2004).
Since molecules in both apo- and ADP�Vi-conditionsare aligned to a common origin, the new alignment al-
lows us to see their overlapping distributions, suggesting
how dynein might operate in situ.
2. Conclusion
Negative stain images of molecules contain a wealth
of information which can be extracted by customising
the alignment and classification strategies within single-
particle image processing. Uniquely, large-scale flexi-
bility within macromolecules is amenable to study usingappropriately designed single-particle processing tech-
niques. Despite progress in cryo-electron microscopy we
envisage that negative staining will continue as a useful
additional technique for the study of macromolecular
function.
Acknowledgments
We thank J. Sellers for recombinant myosin 5 and K.
Oiwa and H. Sakakibara for dynein. This work was
supported by NIH (J.T.) and BBSRC (P.J.K.).
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