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Anna Felicity Hobbiss
Research work coordinated by:
Oeiras, 12th July, 2016
Structural scaling and threshold
modulation of dendritic spines driven
by homeostatic plasticity
Dissertation presented to obtain the Ph.D degree in Biology | Neuroscience
Instituto de Tecnologia Química e Biológica António Xavier | Universidade Nova de Lisboa
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The work presented in this dissertation was carried out through the International
Neuroscience Doctoral Programme (INDP) at the Champalimaud Neuroscience
Programme, Champalimaud Centre for the Unknown, under the supervision of Dr
Inbal Israely, and the thesis committee supervision of Dr Megan Carey and Dr
Marta Moita. Financial support was given by a doctoral fellowship from Fundação
para a Ciênica e Tecnologia (SFRH / BD / 51265 / 2010) and Champalimaud
Foundation.
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Acknowledgements
As with true love, the course of a PhD never did run smooth. The journey along
my particular labyrinthine path was made possible only by being joined on it by
many people who at one time or another stepped onto the trail to walk with me.
Some were there just for a few steps; some trod the whole way next to me. Some
appeared with a map (or maybe new GPS co-ordinates) when every vestige of a
route seemed to have been lost. Others dropped by to keep me company when I
paused to rest. To all, your support, laughter, ideas, inspiration and kindness was
what kept me moving when my legs and mind were weary. Or, as a much better
writer than I put it, to “Force your heart and nerve and sinew, To serve your turn
long after they are gone, And so hold on when there is nothing in you, Except the
Will which says to them: ‘Hold on!’”.
Thank you to the Neuronal Structure and Function lab: Inbal Israely, Yazmín
Cortez, Ali Özgür Argunşah, Inês Vaz, Maria Royo, Ana Vaz, Catia Feliciano and
Daniela Pereira. I feel very lucky to have worked in a group of not just colleagues
but close friends. Through travails with the two-photon microscope to heated
biological and statistical discussions to over-eating at every lab dinner and a
hundred small moments shared in the open lab, it’s been an immense pleasure to
be part of this group. Although we had more than our share of technical problems
and experimental frustrations, the willingness of everyone to give up their time,
expertise, samples, experimental days and chocolate biscuits to help each other
as a team was truly inspiring. Inbal, thank you for your guidance, your concern
for us, your knowledge and advice on the intimidatingly immense world of
synaptic plasticity, and always making sure we had the best birthday cakes of
anyone in the CCU. Yazmínita Cortex, for taking us meninos horrorosos under
your wing and helping us to become better scientists and better people.
Özgürzinho, for being the one always travelling on the path with me, imparting
your Matlab expertise and keeping my spirit up with Turkish humour or beer.
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Inês, for keeping me company in the microscope room countless times, giving me
Portuguese help whenever I needed it and sharing our mutual enthusiasms for
cellular biology, home-made sangria, piñatas and mole. Maria, for being a voice of
experience and wise advice about science and the PhD, and helping to keep me
calm in the face of adversity. Ana, Catia and Daniela, for all the help and support,
and making our group such a nice place to work. To my thesis committee, Megan
Carey and Marta Moita, for not only being caring and wise advisors, but also
transcending that role to become friends.
One of the pleasures of these last 7 years has been spending them with such a
warm, diverse and crazy community. The CNP is a unique and special group,
which it was my good fortune to be part of from early in its ontogeny to its
current mature form. Thanks in particular to the Ar team for helping to create
something bigger and more successful than we ever imagined at the beginning
and which I take immense pride in; to INDP 09 (definitely the best year group);
and to all those who’ve shared conversations (scientific or otherwise), beers,
costumes, thanksgiving dinners, jam sessions and much more. There isn’t enough
space to name-drop everyone that’s been significant to me but I’ll make to thank
you in person. One special shout-out: Libbi, everything was at least twice as fun
because of you as it would have been otherwise.
Lastly, to my family – my parents who amongst everything else diligently proof-
read this thesis, my siblings, my grandparents, and other family and friends in
England and around the world, for the unwavering love, support and confidence;
for reminding me there was a world outside my PhD; and for putting up with any
long monologues I may have delivered to you on synaptic plasticity and cognition
whenever you made the mistake of mentioning memory or the brain.
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Table of contents
1 General introduction .......................................................................... 1 1.1 Learning and plasticity .................................................................................................. 2
1.2 Synaptic plasticity phenomena .................................................................................. 3
1.3 Homeostatic plasticity theory ..................................................................................... 6
1.4 Homeostasis at the synapse: from theory to experiments .......................... 12
1.5 Mechanisms of Homeostatic Synaptic Plasticity.............................................. 16
1.6 Structural changes accompanying plasticity ..................................................... 26
1.7 Interplay between Hebbian and Homeostatic plasticity .............................. 29
1.8 Outline of work in the thesis .................................................................................... 31
1.9 Bibliography .................................................................................................................... 33
2 Structural correlates of Homeostatic Synaptic Plasticity .... 47 2.1 Abstract ............................................................................................................................. 49
2.2 Introduction .................................................................................................................... 50
2.3 Materials and Methods ............................................................................................... 53
2.4 Results ................................................................................................................................ 59
2.5 Discussion ........................................................................................................................ 79
2.6 Bibliography .................................................................................................................... 84
3 Threshold modulation of LTP induction by Homeostatic
Synaptic Plasticity ..................................................................................... 89 3.1 Abstract ............................................................................................................................. 91
3.2 Introduction .................................................................................................................... 92
3.3 Materials and Methods ............................................................................................... 95
3.4 Results ................................................................................................................................ 97
3.5 Discussion ...................................................................................................................... 118
3.6 Supplementary figures.............................................................................................. 124
3.7 Bibliography .................................................................................................................. 126
4 General Discussion ......................................................................... 129 4.1 Abstract ................................................................................................................... 130
4.2 Overview of work in this thesis............................................................................. 131
4.3 Beyond synaptic scaling – silent synapses and network function ......... 132
4.4 Heterosynaptic plasticity and synaptic clustering ........................................ 134
4.5 Homeostatic Synaptic Plasticity in health and disease ............................... 137
4.6 The role of sleep in homeostasis .......................................................................... 140
4.7 Conclusion ...................................................................................................................... 141
4.8 Bibliography .................................................................................................................. 143
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Table of figures
Figure 1.1 Homeostatic feedback regulation in the nervous system ............................. 5
Figure 1.2 Synaptic scaling following chronic activity perturbations ........................... 8
Figure 2.1 Hippocampal organotypic slice cultures as a study system for
Homeostatic Synaptic Plasticity ........................................................................................ 54
Figure 2.2 Acute activity blockade does not cause structural changes ...................... 60
Figure 2.3 Activity block induces functional Homeostatic Synaptic Plasticity ....... 62
Figure 2.4 Structural correlates of Homeostatic Synaptic Plasticity ........................... 64
Figure 2.5 Distributions of spine morphologies are not affected by HSP at 48
hours ............................................................................................................................................. 66
Figure 2.6 Dendrite thickness and spine density are not affected by activity
blockade ...................................................................................................................................... 68
Figure 2.7 Firing rates return to control levels after removal of activity block ..... 70
Figure 2.8 Removal of activity block reverses HSP-dependent spine volume
increase ........................................................................................................................................ 73
Figure 2.9 Loss of Tsc1 does not impair structural HSP ................................................... 76
Figure 2.10 Chronic GABAA blockade does not induce downscaling through HSP
......................................................................................................................................................... 78
Figure 3.1 Protocol for uncaging experiments...................................................................... 98
Figure 3.2 LTP magnitude and longevity following HSP ................................................ 100
Figure 3.3 Spine growth dynamics during stimulation ................................................... 102
Figure 3.4 LTP induction had increased efficacy following HSP ................................. 105
Figure 3.5 Small spines show greater LTP after HSP ....................................................... 106
Figure 3.6 Behaviour of different sized spines during LTP ........................................... 109
Figure 3.7 Neighbouring spines respond to stimulation after HSP ........................... 112
Figure 3.8 Reduced threshold for LTP after HSP ............................................................... 115
Figure 3.9 Capacity for LTP is maintained after 72 hours of activity block ........... 117
Figure 3.10 Post-stimulation growth for real stimulations and sham stimulations
....................................................................................................................................................... 124
Figure 3.11 LTP magnitude grouped by spine volume ................................................... 124
Figure 3.12 Neighbouring spine parameters ....................................................................... 125
Figure 4.1 Structural clustering after cooperation and competition ........................ 135
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Glossary of abbreviations and chemicals
Name/ Acronym
Complete name Function
APV 2R-amino-5-phosphonovaleric acid NMDA receptor antagonist
ASD Autism Spectrum Disorder
Bicuculline GABAA antagonist
CNQX AMPA receptor antagonist
FMRP Fragile X Mental Retardation Protein
GluA1 Glutamate AMPA receptor 1 Receptor subunit of the AMPA receptor
GluA2 Glutamate AMPA receptor 2 Receptor subunit of the AMPA receptor
HSP Homeostatic Synaptic Plasticity
LTD Long Term Depression Synaptic weakening following activity
LTP Long Term Potentiation Synaptic strengthening following activity
mEPSC mini Excitatory Post-Synaptic Current Excitatory current caused by spontaneous pre-synaptic glutamate release
mTOR Mammalian Target of Rapamycin Signalling molecule fundamental in the mTOR signalling pathway
NO Nitric oxide Retrograde signal important for plasticity
PTP Post Tetanic Potential The large increase in potentiation immediately following a stimulation
PTX Picrotoxin GABAA antagonist
RA all-trans Retinoic Acid
TEA Tetraethylammonium chloride K+ channel blocker
TNFα Tumour Necrosis Factor α
TSC1 Tuberous Sclerosis Complex 1 A repressor of the mTOR pathway
TTX Tetrodotoxin Sodium channel blocker
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“Now, here, you see, it takes all the running you can do, to keep in the same
place. If you want to get somewhere else, you must run at least twice as fast as
that!”
The Red Queen
‘Through the Looking-Glass and What Alice Found There’ by Lewis Carroll
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Resumo
Os circuitos neuronais empregam mecanismos de plasticidade sináptica
homeostática (em inglês homeostatic synaptic plasticity, HSP) para manter a
atividade neuronal dentro de um intervalo óptimo, contrariando a tendência de
desvios na plasticidade do tipo Hebbiano, tanto LTP (long-term potentiation)
como LTD (long-term depression). Este mecanismo manifesta-se por uma
adaptação dos botões sinápticos, tanto aumentando como diminuindo a atividade
neuronal geral. Nesta tese de doutoramento investigamos as estruturas que se
correlacionam com a indução de HSP e determinamos como é que esta forma de
modulação sináptica tem um impacto na capacidade de troca de informação
durante a plasticidade Hebbiana. Neste estudo demonstramos que o bloqueio
prolongado da atividade de culturas organotípicas de hipocampo causa o
crescimento das espinhas dendríticas, complementando o incremento fisiológico
da capacidade sináptica. Este crescimento é reversível, voltando aos níveis
normais uma vez removido o bloqueio. Utilizando microscopia de dois fotões
e uncaging de glutamato, investigamos a fundo como é que sinapses que
sofreram adaptação provocada por HSP respondem à plasticidade Hebbiana.
Neste estudo descobrimos que 48 horas após o bloqueio da atividade neuronal o
LTP é mantido, demonstrando que a capacidade de transmissão de informação
no circuito é mantida mesmo após a adaptação. Além disso, verificamos que há
um aumento da longevidade da potenciação de espinhas dendríticas individuais,
quando comparado com neurónios nos quais a atividade nunca foi restringida, e
que o threshold para a indução de plasticidade é menor. Curiosamente,
descobrimos que a expressão de LTP é diferencialmente modulada dependendo
do tamanho inicial das espinhas dendríticas. Em espinhas pequenas a indução de
plasticidade é mais eficaz após HSP, enquanto as espinhas de grandes dimensões
são menos influenciadas por este tipo de plasticidade. Estes achados revelam que
o mecanismo de modulação induzido durante HSP resulta numa alteração da
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distribuição do tamanho das espinhas dendríticas, ocorrendo esta modificação
preferencialmente em espinhas de menores dimensões. Estes resultados
demonstram que a capacidade do circuito de codificar informação é mantida
após a HSP. No entanto, após manipulação da atividade neuronal global, espinhas
individuais podem ser moduladas, permitindo que a rede neuronal regule a troca
de informação dentro de uma amplitude ótima.
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Abstract
Neural networks employ homeostatic synaptic plasticity (HSP) to maintain
activity within an optimal range, countering the tendencies of unchecked
Hebbian LTP or LTD to saturate the network. It is manifested by synaptic scaling
of all the inputs on a neuron, either upwards to increase global activity or
downwards to decrease it. In this thesis we investigate the structural correlates
that accompany the induction of homeostatic plasticity and then determine how
this form of synaptic modulation impacts the ability of inputs to undergo
Hebbian plasticity. We show that prolonged activity blockade in organotypic
hippocampal slices causes structural growth of individual dendritic spines,
complementing the physiological increase in synaptic strength. This volume
increase is reversible, returning to control levels after the activity block is lifted.
Using glutamate uncaging and 2-photon imaging, we further investigate how
synapses which have undergone synaptic scaling respond to Hebbian plasticity.
We find that after 48 hrs of activity blockade, LTP is maintained, showing that the
ability for the network to encode information is retained. Furthermore the
longevity of potentiation of single spines is increased when compared to neurons
in which activity has not been restricted, and the threshold for plasticity
induction is decreased. Interestingly, we find that the expression of LTP is
differentially modulated depending on the initial size of the spines. In small
spines, the induction of plasticity is more efficacious after HSP, whereas larger
spines are less impacted by this form of plasticity. After HSP neighbouring spines
express short term structural plasticity. Our findings demonstrate that the gain
modulation that is induced during HSP results in a shift in the size distribution of
dendritic spines, but that such tuning is expressed preferentially at smaller
inputs. These results illustrate that after HSP the information-encoding capacity
of a network is conserved, but that synapses are individually tuned, allowing the
network to regulate the strength of its inputs to function within an optimal range.
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1
Chapter 1
1 General introduction
General Introduction: Homeostatic Synaptic
Plasticity and Nervous system
functioning
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1.1 Learning and plasticity
Living organisms are defined by two fundamental traits: their genetic make-up,
modulated by their environmental experience. One of the essential features of
organisms is their ability to sense their environment and respond accordingly,
whether it be by locomoting to a chemical stimulus in the case of bacteria,
changing patterns of cellular growth to orient towards a light source in flowering
plants, or altering gene expression patterns to metabolise a new substrate in
yeast. In the animal clade the evolution of the nervous system has made its
members uniquely accomplished at responding to stimuli presented by the
external world. Animal neurophysiology and the resultant behaviour are effected
by a combination of genetic and experience-dependent components, which allow
an animal to adapt its behaviour rapidly to better fit the environment. These
behavioural changes, known as learning, occur when some aspects of the
nervous system are modified, leading it to produce a different output – the
learned output – to the same input pattern of activation. The strength of learning
is correlated to the valence of the experience. Strongly affective stimuli, either
positive such as food consumption, or negative such as pain, will lead to bigger
consequent changes in the nervous system. These in turn will make the
behaviour which causes the experience more likely or less likely respectively.
Nervous systems are extremely well suited to quick and profound changes
because their complex multi-layered organisation allows easy modification of
their topography and functioning. This permits animals the fast environmental
adaption that has made them such a supremely diverse and successful phylum.
The ability of nervous systems to change their firing pattern and topography as a
response to activity is called neural plasticity. This broad term encompasses a
very large range of different mechanisms which can alter the activity of a neural
network. The mechanisms vary in timescales (from milliseconds to the lifetime
of an animal), in the process of induction and in the mode of implementation in
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the biological system. This thesis will focus on one of these types of plasticity: the
change of strength of synapses (the connections between neurons) known as
synaptic plasticity. This mechanism has received a great deal of theoretical and
experimental attention since it is considered one of the main biological
substrates for memory storage, due to its ability to change circuit functioning
and endure throughout an animal’s lifetime. This intense field of study has
yielded a huge literature on types of synaptic plasticity and modes of induction,
expression and maintenance. In the remainder of the thesis I will discuss two
forms of synaptic plasticity which perform fundamentally different roles within
the nervous system: Hebbian plasticity and Homeostatic plasticity.
1.2 Synaptic plasticity phenomena
1.2.1 Hebbian plasticity
In 1949, Donald Hebb proposed that if synapses could respond to a particular
input pattern by becoming stronger, this could instantiate learning within the
network (Hebb, 1949). Alterations of behaviour paralleling synaptic changes
were demonstrated in the mid 1960s in Aplysia (Kandel and Tauc, 1965). In 1973
the first experimental evidence was discovered of synaptic strengthening caused
by strong activity, and was termed Long Term Potentiation or LTP (Bliss and
Lømo, 1973). A few years later, the discovery of Long Term Depression (LTD) as
a counterpart to LTP showed that induced changes can be bi-directional (Lynch
et al., 1977). LTP and LTD came to be known collectively as Hebbian plasticity, in
accordance with Hebb’s postulate. By changing the connection strengths in a
network, Hebbian plasticity allows it to encode information, and thus it is
regarded as a major mechanism for learning and memory.
For Hebbian plasticity to be an efficient and robust mechanism for memory
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storage, it requires two important features. Firstly, it must be input specific –
that is, the changes to synaptic strength must occur at the same place that the
input signal was received, to ensure faithful encoding of the stimulus. Secondly, it
must be long-lasting, to be a realistic substrate for memories which can last for a
lifetime. These two aims are achieved through precisely calibrated signalling
mechanisms, ultimately leading to stable molecular and structural changes at the
level of the synapse, dendritic branch and whole cell.
1.2.2 Homeostatic plasticity
Although it is an extremely effective memory storage system, the
implementation of Hebbian plasticity also entails a problem, in that it is
intrinsically unstable. An increase in synaptic strengths, such as in LTP, would
increase the excitatory drive on to a post-synaptic cell, making it more likely to
fire. This in turn would increase the likelihood of more LTP; thus positive
feedback would quickly saturate the system, resulting in a hyperactive state with
saturated synaptic inputs. Conversely, excessive LTD would also proliferate,
resulting in a silent state with inputs fully depressed. Modelling studies of
Hebbian learning revealed this problem, leading to the need to introduce a
stabilising mechanism to keep the activity of the network within a dynamic
range (Malsburg, 1973; Miller and MacKay, 1994). Mechanisms which enact this
type of compensatory changes would come to be called Homeostatic Plasticity, in
accordance with other well established homeostatic mechanisms to control
physiological parameters such as body temperature and blood glucose (Cannon,
1932). All of these mechanisms use a negative feedback system, such that if the
parameter exceeds the limits of the optimal range, compensatory pathways are
activated to push it back to within the correct boundaries. In the specific case of
nervous system activity, two distinct but related parameters need to be kept
within functioning ranges (Figure 1.1). One is network firing rates, which if left
unchecked could reach pathologically high (epileptic) or low (silent) levels. The
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other is synaptic strengths, because it is a variable range of synaptic weights
which gives a network such an immense capacity for information storage. If all
synapses become saturated or conversely fully depressed, the information
stored in the cell and network will be lost. Conveniently, the regulation of
synaptic strengths will also have the benefit of maintaining the network activity
levels in check, so both of these parameters can be adjusted by controlling the
range of synaptic weights of a neuron.
Figure 1.1 Homeostatic feedback regulation in the nervous system
Activity in the nervous system has an optimal level of activity, whether measured
through action potential firing or synaptic strength. If, through changes in input or
Hebbian plasticity processes, the level of activity is pushed out of the ideal bounds, the
processing of the network and its storage capacity will be compromised. Homeostatic
processes use negative feedback mechanisms to restore the system parameters to their
ideal intermediate state.
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1.3 Homeostatic plasticity theory
The first studies to show that loss of function could induce compensatory
changes in the nervous system were at the neuromuscular junction. Since the
middle of the 20th century, muscles deprived of innervation from motor neurons
were known to show increased sensitivity to neurotransmitters such as
acetylcholine and to applied current, a phenomenon known as ‘denervation
sensitivity’ (Axelsson and Thesleff, 1959). This was described in multiple
systems, from the frog skeletal muscle to cat gastrocnemius and iris muscles
(Brown, 1937; Nicholls, 1956; Axelsson and Thesleff, 1959). In central
mammalian neurons, bi-directional shifts in firing rates of cultured cortical
neurons were demonstrated in the early 1990s (Ramakers et al., 1990; Corner
and Ramakers, 1991). A long-term blockade of activity caused hyper-excitability
once the blockade was released, whilst chronically increased activity lead to
decreased firing rates. No mechanism that could account for these changes was
proposed however, and the findings were not linked to homeostasis of the
network. Instead they were attributed to an altered time course of development.
Meanwhile, studies in invertebrate central pattern generators revealed that if
rhythmic activity was disturbed by an experimental perturbation, over time
compensatory changes could restore the rhythmicity of the circuit (Turrigiano et
al., 1994; Thoby-Brisson and Simmers, 1998). In 1994, Miller and Mackay
theorised that network stabilisation could be achieved at a synaptic level, a
mechanism which I will henceforth refer to as Homeostatic Synaptic Plasticity
(HSP) (Miller and MacKay, 1994). The detailed mechanism they proposed was a
“non-specific decay of all synaptic strengths, provided the rate of this decay is set
for the cell as a whole to cancel the total increase due to specific, Hebbian
plasticity”. In essence, rather than any one particular synapse or set of synapses
changing weight to compensate for activity changes, the whole synaptic
population is affected equally, such that whilst the overall sum synaptic
strengths and therefore activity of a neuron is changed, the relative weight
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differences between synapses is preserved. The advantage of such a system, as
opposed to other proposed methods such as a ‘competition rule’ between inputs
(Malsburg, 1973), is that differences in weights between synapses are still
preserved, so the computational and storage capacity of the network is not
compromised and HSP will not erase the information set by Hebbian plasticity.
1.3.1 Synaptic scaling
The process outlined by Miller and Mackay can be mathematically expressed as a
multiplicative scaling of synaptic strengths (Figure 1.2). The first experimental
evidence for such synaptic scaling came from experiments in primary cultures of
rat cortical neurons (Turrigiano et al., 1998). Activity was chronically perturbed
by application of pharmacological agents for 48 hours, using either Bicuculline (a
GABAA receptor antagonist) to increase firing rates by preventing inhibitory
transmission, or Tetrodotoxin (TTX) (a voltage-gated Na+ channel antagonist) to
block all cell firing. Synaptic strengths were assayed by measuring miniature
excitatory post-synaptic currents (mEPSCs) of treated cells. These occur through
spontaneous (i.e. action potential-independent) pre-synaptic neurotransmitter
release. The size of the post-synaptic current (as measured by whole-cell patch
clamp at the soma) is proportional to the strength of the synapse; thus a
recording of mEPSCs can be used to sample the synaptic strengths of the
recorded neuron. Bicuculline application for 48 hrs led to initially increased
activity, and compensatorily decreased mEPSC amplitudes. Conversely, TTX
application causing activity blockade led to increased mEPSC amplitudes. In
keeping with the mechanism proposed by Miller and MacKay, perturbations of
activity caused a linear multiplicative scaling of mEPSC amplitudes, implying that
all the synapses had been altered proportionally by the same scaling factor. This
and subsequent work supporting work established HSP as a novel form of neural
plasticity by which the output of a system can be modulated whilst the
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information content is preserved (O’Brien et al., 1998; Turrigiano et al., 1998;
Desai et al., 1999). Since it requires the integration of network activity,
homeostatic plasticity acts at long timescales (on the order of hours to days), and
in a global, cell-to-network-wide fashion, as opposed to the fast, single synapse
resolution of Hebbian plasticity.
1.3.2 Other types of Homeostatic Plasticity
Since the initial discovery, it has become clear that homeostatic plasticity is a
broad class of phenomena acting at different levels and through different
mechanisms, a combination of which may be co-opted for use in any one
situation (Davis, 2006, 2013; Karmarkar and Buonomano, 2006; Maffei and
Turrigiano, 2008; O’Leary and Wyllie, 2011; Turrigiano, 2011). As well as the
aforementioned post-synaptic scaling, network stabilisation can be achieved in
Figure 1.2 Synaptic scaling following chronic activity perturbations
Different sizes synapses have a different AMPA receptor content which is set by Hebbian
plasticity processes. Chronic changes in activity cause the population of synapses to scale the
number of AMPA receptors in the post-synaptic density either down or up, to counteract the
shift. The relative numbers of AMPA receptors, and thus the strengths of the synapses, is
preserved after the scaling has taken place.
9
numerous other ways (mirroring the abundant mechanisms for instantiating
Hebbian plasticity (Nelson and Turrigiano, 2008)). The most prominent of these
alternative means of establishing homeostasis are briefly described below.
1.3.2.1 Pre-synaptic homeostatic plasticity
Pre-synaptic changes in LTP and LTD are well established, and have more
recently been confirmed as a mechanism of homeostatic plasticity, in systems as
diverse as the neuromuscular junction in Drosophila and Xenopus to central
nervous system synapses (Davis and Müller, 2014). One interesting question is
whether the pre-synaptic changes are triggered by aberrant levels of activity in a
cell autonomous way, or whether trans-synaptic signalling from the post-
synaptic partner is the requisite signal of activity levels. In fact it is likely that
both mechanisms are at play (Jakawich et al., 2010), although there is increasing
evidence to suggest that a retrograde signal from the post to the pre-synaptic
side is the major determinant controlling aspects of pre-synaptic strength
(Petersen et al., 1997; Davis et al., 1998; Nick and Ribera, 2000; Frank et al.,
2006; Branco et al., 2008; Lindskog et al., 2010). The identity of the signal is yet
to be discovered, although in Drosophila TOR signalling is crucial (Penney et al.,
2012) and endostatin has been identified as a possible candidate (Wang et al.,
2014). Various different modifications of pre-synaptic function have been
observed, including quantal size (i.e. how much neurotransmitter each pre-
synaptic vesicle holds) (Davis et al., 1998), speed of vesicle uptake (Thiagarajan
et al., 2005), number of docked vesicles (Murthy et al., 2001) and release
probability (Sandrock et al., 1997; Davis et al., 1998; Frank et al., 2006; Lindskog
et al., 2010; Zhao et al., 2011; Mitra et al., 2012; Wang et al., 2016). The latter
mechanism may be caused by changes in calcium influx into the pre-synaptic
bouton which promotes vesicle fusion (Frank et al., 2009; Zhao et al., 2011),
mediated by plasticity in calcium channel expression levels in pre-synaptic
terminals (Jensen et al., 2009).
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1.3.2.2 Intrinsic threshold of excitability
An alternative to changing the strength of synaptic signalling is to shift the
intrinsic excitability of the cell, so that for the same amount of synaptic input the
output activity is either increased or decreased (Zhang and Linden, 2003;
Turrigiano, 2011). This can be achieved by altering the expression of ion
channels in the cell membrane, thus changing the threshold at which a cell will
fire. This type of plasticity was observed in early studies on the lobster
somatogastric ganglion circuit, where neurons isolated from their normal inputs
regain their in vivo bursting patterns after 3 – 4 days in a calcium-dependent
manner (Turrigiano et al., 1994). Changes in intrinsic excitability have been
observed both in vitro following activity blockade and in vivo after sensory
deprivation (Desai et al., 1999; Breton and Stuart, 2009). Various channels have
been implicated in changing the membrane conductance. Very clearly important
are potassium channels, which are down-regulated in response to chronic
activity decrease (Desai et al., 1999; Cudmore et al., 2010; Lee and Chung, 2014;
Lee et al., 2015). In addition, the HCN channel, which reduces excitability by
increasing the leak current of the membrane, is down-regulated in dendrites in
response to sensory deprivation (Breton and Stuart, 2009). Meanwhile, sodium
channel conductance is up-regulated after activity blockade (Desai et al., 1999).
These changes to intrinsic excitability appear to require calcium signalling
(especially for down-shifts after excessive activity), with both L-type calcium
channels and NMDA channels being implicated (O’Leary et al., 2010; Lee and
Chung, 2014). Various generalised models have shown that if multiple different
channels can adjust their levels based on activity, the circuit activity is robust to
perturbations (Liu et al., 1998; O’Leary et al., 2014). One interesting thing to note
about plasticity of intrinsic excitability is that, although it may take hours to days
to develop (in keeping with other types of homeostatic plasticity), the actual
switch between different patterns of firing can take less than 1 hr (Turrigiano et
al., 1994). This occurs because small changes to conductances can flip neurons
11
between two bistable activity states, one with low levels of firing and one with
bursting behaviour. A system can therefore potentially change between firing
behaviours very quickly by employing this type of plasticity, allowing for fast
adaption to different levels of activity.
1.3.2.3 Excitation-inhibition balance
Network activity can be altered by changing the balance between excitation and
inhibition (E/I ratio) in the neural circuit. The E/I ratio has been especially
studied in the cortex, where it affects many functions such as gain and feature
selectivity (Barth et al., 2004). Its role in homeostatic plasticity was confirmed in
vivo after rats were dark reared for 2 days, resulting in an increased excitatory
and decreased inhibitory drive (Maffei et al., 2004). Successive studies have also
demonstrated how changes to the E/I ratio can balance network activity whilst
still maintaining cortical function (Atallah and Scanziani, 2009; Pouille et al.,
2009; Xue et al., 2014). For example, a neuron receiving inputs with an increased
strength has an increased threshold to fire due to correspondingly enhanced
feed-forward inhibition (Pouille et al., 2009). This has the effect of stabilising the
system, whilst simultaneously making the cortex sensitive to a wide range of
input strengths. In the hippocampus, balanced levels of excitation and inhibition
produced gamma oscillations which are critical for its function (Atallah and
Scanziani, 2009). Indeed, individual cells themselves receive balanced amounts
of excitation and inhibition, stabilising output in a cell-autonomous manner (Xue
et al., 2014). One candidate for modulating the relative strengths of excitation
and inhibition is nitric oxide (NO), which is known to play fundamental roles in
Hebbian pre-synaptic plasticity, and also in homeostasis for both excitatory and
inhibitory cells (reviewed in (Hardingham et al., 2013)); as well as other
signalling molecules such as Npas4 (Spiegel et al., 2014).
In addition to changes to the E/I balance, homeostatic changes to inhibitory
activity alone have become the focus of increasing interest (Wenner, 2011).
12
Classic studies on visual deprivation documented that the density of GABAergic
cells was reduced following the loss of visual input or activity deprivation
(Hendry and Jones, 1986; Benevento et al., 1995; Rutherford et al., 1997, 1998).
Detailed studies into mechanisms have revealed that inhibitory synapses show
many of the same homeostatic mechanisms when exposed to activity
perturbations as excitatory synapses, including bidirectional shifts in activity
(Karmarkar and Buonomano, 2006), synaptic scaling (Kilman et al., 2002;
Holopainen and Lauren, 2003; Chang et al., 2010) and pre-synaptic changes
(Hartman et al., 2006). This being said the relationship between activity shifts
and inhibition is not a simple one, since visual deprivation during the critical
period can also cause potentiation of inhibition rather than the expected
depression (Maffei et al., 2006; Nahmani and Turrigiano, 2014). These results
suggest that the timing of the activity shift is crucial for determining how the
system will respond.
1.4 Homeostasis at the synapse: from theory to experiments
1.4.1 Computational modelling of Homeostatic Synaptic Plasticity
The homeostatic synaptic plasticity field has a rich history of combining
theoretical predictions and experimental findings. Indeed modelling studies
were quick to reveal the intrinsic instability of Hebbian processes, leading to the
first proposals of homeostatic mechanisms. Initial papers exploring the
computational dynamics of LTP and LTD had to compensate for the positive
feedback inherent in Hebbian theory, for instance by providing constraints to the
expressed plasticity (Oja, 1982), by setting an overall constant sum synaptic
strength thus forcing competition for plasticity between inputs (Malsburg,
1973), or by using a sliding threshold of plasticity to balance LTP and LTD as
seen in the seminal BCM rule of Hebbian plasticity (Bienenstock et al., 1982).
13
Initially these stabilising mechanisms were built into the Hebbian models as a
fundamental aspect of the plasticity. Opposing this, a train of thought proposing
that in fact Hebbian and homeostatic plasticity were separable processes began
to arise. As was referred to earlier in this chapter, the first explored mechanism
cell-wide synaptic strength changes proposed that a multiplicative decay (as
opposed to a linear decay) would preserve information content in the network
whilst constraining the output activity (Miller and MacKay, 1994).
Since these beginnings, the computational theory of homeostatic plasticity has
expanded hugely, with the development of models which reflect realistic
physiological parameters for synaptic strength changes. HSP has been shown
influence Hebbian plasticity (Litwin-Kumar and Doiron, 2014), STDP (Clopath et
al., 2010), connectivity (Tetzlaff et al., 2011) and working memory (Renart et al.,
2003). Interestingly, some discrepancies between theory and experimental data
have arisen. In particular, the timescale over which homeostasis occurs is still
under debate. A recent study formulated a model where homeostasis needs to
have a fast rate detector (on the order of seconds to minutes) to stabilise
Hebbian plasticity (Zenke et al., 2013). Experimentally however, HSP is induced
over hours to days (see parts 1.5.1.1 and 1.5.1.2 for more details). Very recently a
fast form of pre-synaptic homeostasis was reported which could fulfil the
conditions required (Wang et al., 2016). Future work theoretical and
experimental is needed to resolve how the different timescales interact in
dynamic systems.
1.4.2 The spatial expression and scale of HSP
Homeostatic synaptic plasticity has been shown to occur widely across the
nervous system. The majority of studies have been conducted in cultured cells,
from the cortex (Turrigiano et al., 1998), hippocampus (Lissin et al., 1998),
peripheral neurons such as spinal cord (O’Brien et al., 1998), and somatogastric
ganglion cells (Turrigiano et al., 1994). In vivo homeostatic synaptic plasticity in
14
various systems has also been demonstrated, although fewer studies exist due to
the increased difficulty of the experimental protocol. The majority of studies are
from sensory cortices, where activity reduction can be achieved through sensory
deprivation protocols such as ocular deprivation (Desai et al., 2002; Maffei et al.,
2004; Goel and Lee, 2007; Maffei and Turrigiano, 2008; Keck et al., 2013) or
whisker trimming (Greenhill et al., 2015). Other sub-cortical areas such as the
hippocampus (Pawlak et al., 2005; Echegoyen et al., 2007) and superior
colliculus (Chandrasekaran et al., 2007), and the spinal cord (Knogler et al.,
2010; Garcia-Bereguiain et al., 2013) have also been used to demonstrate
homeostatic synaptic responses. The details of in vivo homeostasis and its
implications for nervous system functioning are discussed later in this chapter in
Section 1.5.2.
A complicating factor for the study of homeostasis is that although the basic
processes employed seem to be universal, different synapses show different
responses to shifts in activity. Even within single structures such as the
hippocampus, different synaptic sites (dentate to CA3 and CA3 to CA1) show
differing or even opposing responses (Kim and Tsien, 2008; Lee et al., 2013).
Compounding the somewhat bewildering array of modes of synaptic
homeostasis is the question of the spatial scale on which the homeostatic signal
acts. Processes such as regulation of the excitation/inhibition balance are by
definition network-level phenomena, but synaptic processes can be the result of
much smaller-scale integrations of activity. Synaptic homeostasis is normally
described and modelled as a cell-wide process, whereby neuron activity is
integrated over long time-periods and all spines are then scaled in concert.
Theoretically, this type of cell-wide scaling differentiates HSP from Hebbian
plasticity and so the information encoded in synaptic weights is not lost
(Toyoizumi et al., 2014). Although there is a weight of experimental evidence to
support wide-scale scaling, most of the induction protocols involve a global up or
down-regulation of activity, so it is not possible to differentiate between global
15
or local plasticity processes. In fact, studies looking at smaller neuronal
segments, from individual dendrites (Sutton et al., 2006; Branco et al., 2008)
down to single spines (Hou et al., 2008, 2011; Béïque et al., 2011), have indeed
seen evidence for homeostasis occurring at these levels, in both the upward and
downward directions. This spine-specific scaling was induced by manipulating
the activity levels of upstream cells, and then examining the post-synaptic sites
they connected to. Its expression involved changes in AMPAR content in the
spines, similarly to global induction protocols (Hou et al., 2008, 2011).
Neighbouring spines receiving normal levels of input remained unchanged,
suggesting that homeostatic regulation was local to the individual synapse. In the
visual cortex in vivo however, selective synapse silencing produced a
potentiation of all synapses including those not affected by the deprivation
protocol, suggesting that a more global homeostatic effect occurs (Greenhill et al.,
2015). The potential for synapse-specific homeostasis presents an interesting
conundrum for how HSP and Hebbian plasticity can co-exist, since the induction
of one type would seem to be antagonistic to the other. In Hebbian plasticity,
reduced signalling (i.e. low levels of calcium in the spine) leads to depression of
the spine, but in homeostasis reduced activity would lead to potentiation. It will
be intriguing in the future to see whether more evidence for synapse-specific
HSP emerges.
Interestingly, there is also disagreement in the literature about whether post-
synaptic homeostasis is induced by changes in pre-synaptic activity (i.e. the
synapse’s own glutamatergic drive), or post-synaptic activity (implying some
integration of the activity of the post-synaptic cell). Two studies saw a
decoupling of post-synaptic spiking activity with synaptic scaling, leading to the
conclusion that neurotransmission is the crucial induction signal for scaling (Hou
et al., 2008; Fong et al., 2015). Other studies (Burrone et al., 2002; Ibata et al.,
2008; Goold and Nicoll, 2010) have demonstrated that indeed activity changes in
only the post-synaptic cell do change the input synaptic strength. It is likely
16
therefore that both pre- and post-synaptic changes have the capacity to induce
homeostasis.
1.5 Mechanisms of Homeostatic Synaptic Plasticity
1.5.1 Molecular pathways of HSP
Following the discovery of HSP an intense area of research has been to uncover
the molecular pathways involved. Unsurprisingly given the diversity of
homeostatic phenomena, many different molecules have been seen to play a role
in HSP. Various overlapping pathways have been identified, which depend on the
protocol used to induce HSP. Below I will discuss some of the different molecular
signatures of these alternate HSP forms.
1.5.1.1 Up-scaling induced by reduction in excitatory drive
The original synaptic scaling discovery used TTX, a sodium channel blocker, to
prevent spiking activity in cultured neurons (Turrigiano et al., 1998). This has
the effect of both blocking evoked neurotransmission (i.e. reducing excitatory
drive) and also post-synaptic spiking. Notably however, miniature transmission
(i.e. spontaneous vesicle fusion) still occurs under these conditions, so in
contrast to some of the protocols described further on in this chapter, there is
not a total absence of AMPA and NMDA activation. This difference is important
mechanistically to determine the time-course and molecular identity of the
scaling (Sutton et al., 2006). Sensory deprivation in vivo, which again has the
result of reducing excitatory drive without pharmacologically blocking any of the
receptors, should also induce this same type of HSP.
The synaptic strength changes of all different types of HSP appear to be mediated
by changes in the AMPA receptor content at the synapse. Activity reduction
protocols result in an increased AMPAR content causing functional up-scaling,
17
whilst increased activity leads to down-scaling due to less synaptic AMPAR. The
AMPAR subunit required for the expression of the scaling differs depending on
the induction protocol. There are 4 AMPAR subunit types; GluA1, GluA2 and
GluA3, and GluA4, which have different functional properties. The full AMPA
receptor is tetrameric, normally consisting of dimers of dimers which commonly
combine GluA2 with either GluA1, 3 or 4 (Isaac et al., 2007). GluA2-lacking
AMPARs have an important functional role to play in synaptic plasticity since
they are calcium permeable, so will enhance calcium-dependent signalling
pathways in synapses. GluA2-lacking AMPARs are thus known to be involved in
the early stages of LTP, whereas GluA2-containing receptors are inserted at later
stages during what seems to be a consolidation process.
When excitatory drive is reduced using either TTX or sensory deprivation, both
the GluA1 and GluA2 subunits are up-regulated at synapses in a correlated
fashion (Wierenga et al., 2005). It is GluA2 however that appears be to the crucial
component, since knock down of GluA2 but not GluA1 abolishes scaling (Gainey
et al., 2009). GluA2 is also inserted on a surprisingly fast timescale, with changes
being visible after only 4 hours of activity block (despite most protocols of
scaling showing changes only at the 24 hour timepoint) (Ibata et al., 2008).
GluA1 may also have a role to play in the functional expression of scaling
however, since phosphorylation of this subunit through PKA can cause up-
scaling (Diering et al., 2014). Similarly single-spine induction protocols also
appear to up-regulate, and require, GluA2-lacking AMPA receptors (identifiable
through pharmacological or electrophysiological methods) (Hou et al., 2008;
Béïque et al., 2011), as do some developmental homeostasis processes in vivo
(Garcia-Bereguiain et al., 2013). Further on in this chapter I will explore the
important role that GluA2-lacking receptors play in a different type of synaptic
homeostasis, a fast retinoic acid-dependent form (see section 1.5.1.2).
Although AMPA receptors appear to be the clear candidate for the functional
18
expression of scaling, a lot of work remains to understand the molecular
mechanisms involved. An increasingly complex set of pathways is emerging to
span the gap between the start point of sensing of activity changes, to the
insertion or removal of the AMPARs at the other end. As with other types of
plasticity, calcium is looking increasingly promising as a candidate for initiating
the plastic changes (Liu et al., 1998). In particular, a drop in Ca2+ levels at the
soma can induce synaptic scaling (Ibata et al., 2008); a mechanism which makes
intuitive sense for the detection of cell-wide activity (as opposed to local
dendritic calcium changes which initiate Hebbian plasticity). The change in Ca2+
concentration appears to be sensed by CaMKIV, one of the calcium/calmodulin-
dependent protein kinase (CaMK) family (Ibata et al., 2008). CaMKs play many
important signalling roles in neurons, especially in plasticity through their role in
calcium sensing. Another member of the family, CaMKII, is an extremely
important and abundant signalling molecule in Hebbian synaptic plasticity. Two
of the subunits which compose the complete CaMKII molecule, CaMKIIα and
CaMKIIβ, change in proportion in response to activity blockade with TTX
(Thiagarajan et al., 2002), with β levels rising whilst α is down-regulated (this
shift is reversed after chronic increase of activity with bicuculline). β subunits
have a higher sensitivity to calcium so are activated by lower calcium levels. This
shift will therefore act to sensitise the quietened system to any activity. CaMKII
in turn regulates the recruitment of GKAP, a post-synaptic scaffolding protein
which is necessary for synaptic scaling (Shin et al., 2012).
Biology is a frugal system, and in HSP, as in multiple other physiological
processes, molecules have been co-opted from other purposes to form part of
these signalling pathways. For example, Tumour Necrosis Factor α (TNFα), a
pro-inflammatory cytokine, is crucial for the synaptic scaling process, both in
vitro (Stellwagen and Malenka, 2006) and in vivo (Kaneko et al., 2008; Steinmetz
and Turrigiano, 2010), where it acts to maintain the synapses in an up-scaled
state (Steinmetz and Turrigiano, 2010). Importantly, it is not required for LTP or
19
LTD (Stellwagen and Malenka, 2006; Kaneko et al., 2008; Knogler et al., 2010) – a
crucial clue that at the molecular level these different types of plasticity can be
differentiated. Adding another component to the HSP signalling circuit, TNFα is
derived from glia surrounding the neurons undergoing scaling (Stellwagen and
Malenka, 2006), through their sensitivity to glutamate signalling. It is possible
that HSP can co-opt glial processes more easily that Hebbian plasticity since its
induction usually involves a wide-scale activity change (rather than activation at
single inputs). Indeed, glial and cell adhesion molecules are emerging as
important players in the HSP process (Thalhammer and Cingolani, 2014).
Towards the end of the scaling pathway, stargazin, an AMPA auxiliary subunit,
has been shown to play a crucial role in AMPAR trafficking in the synapse upon
induction of HSP (Louros et al., 2014). Stargazin is known to regulate the
delivery of AMPARs to the synapse. It is up-regulated upon activity blockade,
whereupon it is phosphorylated to promote scaling.
Alongside these major signalling players, numerous other molecules have been
shown to influence scaling, including BDNF (Rutherford et al., 1998), MSK1
(Corrêa et al., 2012), Beta-3 integrins (Cingolani et al., 2008), STEP61 (Jang et al.,
2015) and MHC1 (Goddard et al., 2007). It is clear that, as for Hebbian plasticity,
HSP is an extremely complex process which requires many layers of control for
its successful expression.
1.5.1.2 Retinoic acid dependent up-scaling
In 1998, just subsequent to the first description of HSP (Turrigiano et al., 1998),
changes in the accumulation of AMPA receptors following chronic increases or
decreases of synaptic signalling were described (O’Brien et al., 1998). When APV,
an NMDA receptor antagonist, was applied in conjunction with an activity
blocker for 72 hrs to eliminate synaptic signals, AMPA receptor levels increased
in the synapses. Subsequent work using this protocol of NMDA receptor
20
blockade elucidated that this form of HSP is mechanistically different to the one
described in the previous section (Ju et al., 2004; Sutton et al., 2004, 2006; Aoto
et al., 2008; Sarti et al., 2012). The reason for this is that not only is activity-
dependent glutamate release inhibited, but also the activation of post-synaptic
NMDA receptors by spontaneous glutamate release (miniature synaptic
transmission) is prevented. The loss of miniature signalling causes an up-
regulation of translation in the dendrite, accompanied by up-scaling of synaptic
strengths, which can be observed only an hour after application of the combined
drugs (Sutton et al., 2004, 2006). In contrast, synaptic scaling caused by only
action potential blockade can take 24 hours or more (Sutton et al., 2006). This
fast form of HSP has been found to rely on a newly discovered function for all-
trans retinoic acid (RA), a signalling molecule previously known for its role as a
morphogen during development (Aoto et al., 2008; Chen et al., 2014). Two hours
of RA application induces synaptic scaling, and scaling caused by TTX + APV is
blocked by inhibitors of RA signalling (Aoto et al., 2008; Sarti et al., 2012). These
two pathways – RA-mediated and non-RA mediated – remain mechanistically
distinct even at the timescales where both are evident, i.e. at more than 24 hours
of treatment, at which time TTX-mediated synaptic scaling is not affected by RA
inhibition, but TTX + APV scaling is abolished (Soden and Chen, 2010; Wang et
al., 2011).
Blockade of NMDA receptors inhibits Ca2+ entry thus lowering Ca2+ levels in the
dendrite, which has been found to be a crucial step for the up-regulation of RA
synthesis (Pawlak et al., 2005; Wang et al., 2011). This difference in Ca2+
signalling may explain why TTX + APV co-ops RA-mediated HSP, whilst TTX
alone does not. As mentioned in the previous section, Ca2+ signalling in the soma,
rather than the dendrites, was important for TTX-induced HSP (Ibata et al.,
2008). Calcium concentration is sensed by calciuneurin, a Ca2+-dependent
phosphotase, which then promotes RA synthesis (Arendt et al., 2015). In addition
to calcium sensing, RA-synthesis and GluA1 synthesis also happen locally at
21
dendrites (Ju et al., 2004; Maghsoodi et al., 2008). This could give a vital insight
into the different spatial scales at which HSP is seen in the literature. Non-RA
mediated HSP, which is sensed at a cell-wide level, could co-opt scaling at all
synapses simultaneously, whereas RA-mediated, which senses activity and
initiates signalling mechanisms at a local level, could act on much more
restricted spatial scales.
I have referred to this type of HSP as the ‘RA-dependent form’, and differentiated
it from the previously described non-RA dependent form. It is probable that it is
not only the RA component, but the whole signalling cascade which differs
between these two types. Much less is known however about the other
components. One better explored aspect involves protein synthesis. The scaling
response requires both eEF2 (Sutton et al., 2007), and FMRP (Soden and Chen,
2010) which are both involved in the control of translation in dendrites. The
requirement for protein translation and transcription will be explored more fully
in section 1.5.2 but the de novo synthesis of proteins is an important part of
scaling expression.
As with the previously described forms of HSP, RA-dependent scaling is effected
by AMPAR insertion into the synapse. Whereas GluA2 seems to be the major
subunit player in non-RA dependent HSP, the RA-dependent form is instantiated
by GluA2-lacking receptors, likely GluA1 homomers (Ju et al., 2004; Thiagarajan
et al., 2005; Shepherd et al., 2006; Sutton et al., 2006). These receptors are
dendritically synthesised which may account for the fast dynamics of this type of
scaling, since the receptors do not have to be newly transcribed and transported
from the soma (Ju et al., 2004). Some recent work has shed light on how the
synthesis of AMPAR subunits is controlled through interactions with micro-RNAs
(miRNAs). These short lengths of RNA have received great attention in recent
years for their regulatory roles in mRNA translation. Two different miRNAs have
been identified which modulate AMPAR translation in opposing directions
22
during synaptic scaling – miR-92a which suppresses GluA1 synthesis and is
down-regulated upon activity blockade (Letellier et al., 2014), and miR124 which
suppresses GluA2 synthesis during activity blockade, leading to the formation of
GluA2-lacking AMPARs (Hou et al., 2015).
1.5.1.3 Down-scaling
HSP would be of limited use if it could only implement scaling in one direction.
Indeed, scaling downwards in response to over-activity is also known in the
literature (O’Brien et al., 1998; Turrigiano et al., 1998; Goold and Nicoll, 2010;
Hou et al., 2011). It is worth noting however that there are many fewer studies
than for up-scaling, and the vast majority of these are in primary cultured
neurons, instead of more the physiologically relevant systems of slices or in vivo.
This may be partly due to the increased experimental challenges of chronically
increasing activity (though see (Corrêa et al., 2012) for the use of environmental
enrichment as an activity increase paradigm), but it could also represent that the
system is not symmetric with regards to the methods used to implement up-
scaling versus down-scaling, and so down-scaling is harder to observe.
That down-scaling is a qualitatively different process to up-scaling is shown by
the different pathways involved. For instance, CaMKII, fundamental for the
expression of up-scaling, is not required for down-scaling (Goold and Nicoll,
2010; Hou et al., 2011). A change in calcium level is still the most likely means by
which activity levels are sensed, as shown by the necessity of various neuronal
components which control intra-cellular calcium, including NMDA receptors and
voltage-gated calcium channels (Goold and Nicoll, 2010; Hou et al., 2011). Other
calcium-binding molecules must therefore take the calcium-sensing role that
CaMKII played in up-scaling. Indeed, different members of the CaMK signalling
cascade, most prominently CaMKK (which activates CaMKIV and CaMKI), are
necessary for the expression of down-scaling (Goold and Nicoll, 2010).
23
Once over-activity has been sensed, a process to decrease synaptic strength must
be enacted. Here the immediate early gene Arc plays an important role (Bateup
et al., 2013a; Korb et al., 2013). As well as acting as a marker for recent activity,
Arc is known to play a role in LTD by stimulating the endocytosis of glutamate
receptors (Bramham et al., 2008). It is unsurprising that it also proves essential
for the weakening of synapses in response to chronic over-activity, since AMPA
removal is responsible for the functional post-synaptic change after downwards
HSP (O’Brien et al., 1998). Arc is up-regulated after increased activity (Bateup et
al., 2013b), and also acts to down-regulate transcription of GluA1 (Korb et al.,
2013). Arc’s well-documented fast induction after synaptic activity (for which it
has become known as an immediate-early gene) may have another consequence;
that down-scaling is induced at shorter timescales than up-scaling. Using
optogenetic methods to increase activity of single cells, the removal of GluA1
receptors in spines was seen to take place as early as 30 minutes into the light
treatment (Hou et al., 2011). Other studies report strong effects after only a few
hours of treatment (Bateup et al., 2013a, 2013b). Surprisingly however, Arc KO
neurons are still capable of synaptic down-scaling, possibly implying that a
number of parallel redundant pathways can achieve this result (Shepherd et al.,
2006).
Finally, a host of other molecular players have also been shown to be necessary
for synaptic down-scaling, including PP1 I-2 (Siddoway et al., 2013), MeCP2 (Qiu
et al., 2012; Zhong et al., 2012) and components of the Plk pathway and CDK
(Seeburg and Sheng, 2008; Seeburg et al., 2008) (for a full review of molecular
pathways see (Siddoway et al., 2014)).
1.5.2 The involvement of proteins in HSP
Proteins are fundamental building blocks of cellular processes, and plasticity is
no exception. As with Hebbian plasticity, de novo protein synthesis seems to be
necessary to maintain HSP for extended periods of time. Synthesis of GluA1
24
occurs locally in dendrites in response to RA-mediated HSP (Ju et al., 2004;
Sutton et al., 2004, 2006). FMRP, an important player in controlling levels of
protein synthesis, is required for this process (Soden and Chen, 2010). Non-RA
mediated HSP however requires not just translation but also transcription –
reinforcing the theory already expounded in this introduction that the non-RA
form is a global cell wide process, whereas the RA-form is more local (Ibata et al.,
2008). Similarly, global downscaling required not just translation but also
transcription (Goold and Nicoll, 2010). These findings reinforce the fundamental
role new proteins play in the expression and maintenance of HSP.
1.5.3 HSP in vivo
The majority of work on HSP has been conducted in culture systems, either in
primary neuron cultures (Turrigiano et al., 1998) or in organotypic slices
(Karmarkar and Buonomano, 2006). This is due to the increased tractability of
the in vitro system, where chemical manipulations, and their molecular and
physiological effects, are much more easily controlled and assessed than in the
live animal. In vitro systems have allowed huge strides to be made in
understanding how HSP works using these systems. An increasing number of
studies however have now addressed the question of how HSP is expressed in
intact nervous systems.
Many of the in vitro studies on the mechanisms and expression of HSP have been
borne out through in vivo studies. Multiplicative synaptic scaling has been seen
in various sensory cortices after deprivation, including visual (Desai et al., 2002;
Goel and Lee, 2007; Keck et al., 2013) and somatosensory (Greenhill et al., 2015),
as well as in other regions such as the spinal cord (Knogler et al., 2010; Garcia-
Bereguiain et al., 2013). As a counter example, synaptic scaling was absent after
in vivo TTX infusion in the hippocampus (Echegoyen et al., 2007).
25
One potentially confounding factor for drawing a universal conclusion about HSP
in vivo is that the type and extent expressed is highly influenced by
developmental age (Desai et al., 2002; Knogler et al., 2010). In sensory cortices,
as well as other parts of the brain, the neurons progress though periods with
differing capacities of plasticity expression. The peak, normally during infancy or
childhood, is known as the critical period, and is the time when much of the
framework for the sensory processing is established. Without activity during this
period, the neurons can never recover their optimal functioning into adulthood
(Wiesel and Hubel, 1963). This inability to recover from deprivation suggests
that homeostasis is suppressed in some areas during the critical period –
possibly because compensatory mechanisms could hinder the development of
acuity. Indeed at the retinocollicular synapse, mice without normal retinal waves
throughout development (due to deletion of a critical protein) have larger
receptive fields, compensating for the lower peak responses (Chandrasekaran et
al., 2007). In the visual cortex, studies from the Turrigiano lab found that
homeostasis in layer 4 visual cortical neurons was prominent in early life (Desai
et al., 2002; Maffei et al., 2004) but was switched off by P21 (Maffei et al., 2006),
indicating that by the beginning of the visual critical period homeostasis is
suppressed. In upper layers of the cortex however, plasticity remains prominent
through the critical period (Maffei and Turrigiano, 2008) and into adulthood
(Goel and Lee, 2007). An interesting study which examined the length of time
needed to induce homeostatic responses at different developmental ages in the
hippocampus found that in early life only 15 hours of activity shift was needed,
but later in development more than 40 hours was required for HSP induction
(Huupponen et al., 2007). These differences may reflect that fact that throughout
development, the subunit composition of receptors is known to change, altering
the functional properties of the circuit. One example is the NMDA receptor, a
cation channel which acts as a coincidence detector for pre- and post- synaptic
activity and thus is vital for circuit refinement and Hebbian plasticity. This
26
receptor has a developmental switch, from being enriched in the subunit
GluN2B, which is highly calcium permeable and has a prolonged channel
opening, to GluN2A, which with its decreased calcium permeability reduces the
capacity for plasticity (Paoletti et al., 2013). These differences in synaptic
receptors may make different types of homeostasis more or less likely,
depending on the developmental stage they manifest. What is clear is that HSP,
and indeed other complementary mechanisms of homeostasis, exist in a complex
interplay throughout different periods of the lifetime of an animal.
1.6 Structural changes accompanying plasticity
As is detailed above, much has been elucidated regarding the molecular
pathways and functional expression of HSP. An area which is still to be explored
is the structural consequences of HSP for the dendritic spines and the neuron.
The structure of a neuron – its physical size and shape – is strongly influential
with regards to its function. The high degree of specialisation seen in different
neuron subtypes (consider the extreme differences between pyramidal cells,
Purkinje cells and motor neurons), shows how precisely adapted different
neuron types are for their roles. Although there is undoubtedly a large genetic
component, structural parameters such as dendrite branching, spine number or
axon diameter are not fixed but can undergo activity and time-dependent
changes throughout development and into adulthood.
In this thesis I will focus on structural changes to the dendritic arbour, since
there is a wider scope for plasticity in the dendrites than the axon, and the
dendrite and spines provide a visual read-out to predict downstream responses
to activity. There is some evidence for structural changes to the axon upon
homeostatic plasticity induction but this is outside the scope of this work
(Yamahachi et al., 2009). Dendritic structural changes strongly influence the
27
function of the cell by changing the electrical and biochemical properties of its
inputs, and thus its firing pattern. A simplistic example is that if a cell develops
more dendritic spines it has increased input and thus will be more likely to fire
(although this can of course be counterbalanced by other type of modifications
such as threshold changes). A more subtle example occurs at the level of a single
spine, where by increasing the spine volume there is more room for both
receptors (such as AMPARs) and also organelles such as endoplasmic reticulum,
which could enhance signalling at the synapse and so strengthen it. Hebbian
plasticity is known to cause structural changes, for both LTP (Matsuzaki et al.,
2004) and LTD (Ramiro-Cortés and Israely, 2013). Much less is known about
structural correlates with regards to homeostatic plasticity. In particular, it
remains to be discovered whether indeed structural changes are a hallmark of
HSP, and if so whether they mirror the changes seen in Hebbian plasticity or
whether they employ parallel mechanisms.
Much of the evidence for structural changes following HSP comes not from a
mammalian system but from Drosophila, a classic model for homeostasis studies.
Activity blockade throughout development causes large structural re-
organisations, including increases in dendritic arbour size and complexity
(Tripodi et al., 2008) and a larger synaptic glomerulus (Kremer et al., 2010).
Drosophila synapses are not located on dendritic spines however, so the
adaptations seen in may be different to those in a chordate system. In
mammalian neurons, there is ample evidence that the number of synaptic
connections changes after HSP. Here the developmental stage of activity
deprivation determines the structural adaptations, with decreased synapses if
deprivation occurs before synaptogenesis, but increased synapses if it occurs
after (Stroemer et al., 1995; Burrone et al., 2002; Kirov et al., 2004; Zuo et al.,
2005a; Arendt et al., 2013). Complementing this, synaptic elimination follows
chronic increases in activity (Goold and Nicoll, 2010). However, see (Wallace and
Bear, 2004; Thiagarajan et al., 2005) for experiments which result in unchanged
28
or decreased spine density following reduced activity. Interestingly, spine
turnover can still occur even throughout chronic blockade of activity (Yasumatsu
et al., 2008).
Not only spine number but also the structure of spines themselves can change
with shifts in activity, although there is conflicting evidence over the dynamics of
this process. Sensory deprivation in vivo and chemical blockade of activity have
been reported to cause spine volume increases, accompanied by corresponding
increases in the size of the pre-synaptic terminals (Murthy et al., 2001; Wallace
and Bear, 2004; Keck et al., 2013). This affect was seen to be reversible after re-
exposure to activity (Wallace and Bear, 2004). On the other hand, various studies
have reported no change to spine volume following chemical manipulations or
single synapses activity loss (Yasumatsu et al., 2008; Béïque et al., 2011).
Presumably, these differences relate to the induction protocols used in the
specific experiments.
The individual morphology of the spines also influences function. Spines are
traditionally divided into 4 different classes; mushroom, stubby, thin and
filopodia, with mushroom spines being the most mature and filopodia the most
immature (Harris et al., 1992). In a normal system, a mixture of these different
types is present. Throughout development the distribution of spine types evolves
to the mature state. Certain neurodevelopmental disorders are known to skew
this distribution. For example, loss of the Fragile X Mental Retardation protein 1
(FMRP1), which in humans causes the mental retardation disorder Fragile X
Syndrome, leads to an over-proliferation of thin spines and filopodia (i.e.
immature spines) in cortical and hippocampal regions (Irwin et al., 2000).
Conversely, mutations in MeCP2, a transcriptional regulator, which in humans
causes Rett syndrome, leads to increased spine volume (Xu et al., 2014). This
inability to maintain the correct balance of spine types may well be causal for the
cognitive impairments which accompany these disorders. It is currently
29
unknown whether the expression of HSP changes the distribution of spine types
in a neural system (although it is interesting to note that both FMRP and MeCP2
have been implicated in synaptic scaling (Soden and Chen, 2010; Qiu et al.,
2012)). It is clear that for this important topic, more work is needed to fully
establish the role that structural modifications play in homeostatic synaptic
plasticity.
1.7 Interplay between Hebbian and Homeostatic plasticity
Since the discovery of plasticity, the majority of research has been conducted in
reduced systems such as primary cultures or brain slices. These allow for precise
control of biological parameters such as activity, connectivity and molecular
signalling, allowing isolation of the crucial elements of plasticity without external
confounding factors. Through this approach we have gleaned a vast and detailed
knowledge of molecular, cellular and network level pathways of plasticity
processes. Studying single types of plasticity in isolated systems does, however,
ignore the fact that in vivo these processes will take place on a background of
many integrated activity events. The way in which different processes – for
example homeostatic plasticity and Hebbian plasticity – affect each other is of
great interest to understand how intact nervous systems integrate many
different and sometimes conflicting sources of information.
Hebbian plasticity shares many of the traits already described for HSP.
Mechanistically, Hebbian plasticity is dependent on signalling mechanisms
triggered by synaptic activity. These normally involve calcium influx into the
spine through NMDA receptors. Changes in synaptic strength are effected by
changing the number of AMPA receptors in the post-synaptic density. AMPA
receptors may be ‘held’ in preparation either in vesicles in the spine or dendrite,
or in the extra-synaptic membrane. Upon receiving the LTP stimulus, these
30
receptors can move into the post-synaptic density, increasing the electrical
conductance of the spine. An increase in synaptic strength is also correlated with
an increase in spine volume, such that under normal conditions there is a linear
relationship between the strength of the spine and its volume (Matsuzaki et al.,
2004).
It is not known whether the increase of conductance seen after homeostatic
AMPA insertion is functionally equivalent to classical Hebbian LTP/LTD. It may
instead represent a separate mechanism, such as a ‘priming’ of the synapses to
allow them to respond more strongly to stimuli in the future. Since there is a
large overlap between the endpoints of both Hebbian plasticity and HSP, it is still
to be elucidated whether the two interfere with each other or if the signalling
mechanisms are distinct. Theoretical studies have predicted that the two
processes are separable (Toyoizumi et al., 2014), and indeed that HSP may
influence the stability of Hebbian plasticity (Rabinowitch and Segev, 2006). Only
a limited number of studies have sought to address this question experimentally,
using a variety of HSP induction mechanisms, including global activity blockade
(Arendt et al., 2013), NMDA receptor blockade (Félix-Oliveira et al., 2014) and
single-spine inactivation (Lee et al., 2010). These different protocols will each
induce a mechanistically different type of HSP as detailed previously in this
introduction. However, in all cases LTP is reported to be stronger after HSP,
whether it is induced through electrical stimulation of axon bundles (Arendt et
al., 2013; Félix-Oliveira et al., 2014) or by single-spine stimulation through local
glutamate release (Lee et al., 2010). The mechanistic reasons for this difference
are related to the NMDA receptor content of the synapses after HSP. There is
reported to be an increase in silent (i.e. AMPAR-lacking) synapses after
prolonged activity blockade, but no change in the subunit composition of the
receptor (Arendt et al., 2013). However, a different activity reduction protocol
demonstrated an increase in the concentration of the NMDAR subunits GluN1
and GluN2B (Lee et al., 2010). GluN2B is known to bind CamKII with high affinity
31
and have higher calcium permeability than its counterpart GluN2A (Barria and
Malinow, 2005; Paoletti et al., 2013). This switch therefore seems to ‘prime’ the
system for more activation following the LTP stimulus, increasing the input to
the downstream cell.
There are many unknowns still to be investigated in the domain of Hebbian
plasticity and HSP interactions. These include the structural consequences for
spines, the long-term plasticity dynamics, and which spines in a population will
be more prone to express plasticity. We will address these topics experimentally
in the remainder of this thesis.
1.8 Outline of work in the thesis
In this thesis, we examine how the structural correlates of HSP relate to the
functional changes that have been described. In chapter 2, we report the
structural changes which occur at dendritic spines that are induced by chronic
blockade of activity, and the corresponding functional changes that accompany
them. We assess the ways in which the structure changes, and elaborate on what
the results imply for homeostatic plasticity theory. We test how the system can
recover after activity blockade is removed, both structurally and functionally, to
see whether flexibility is retained after HSP. We finally expose the slices to
chronic increases of activity to assess structural and functional changes.
In chapter 3, we investigate how HSP and Hebbian plasticity co-exist together.
For this we induce LTP after the expression of HSP, and use a structural read-out
of plasticity to draw conclusions about the capability of a system to retain
information after HSP. We investigate what structural parameters determine
how the capacity of the spines to express LTP, and how this is altered by the
expression of HSP. We also use different strengths of induction to determine
32
what the threshold for plasticity is under different conditions. Finally we
examine the effect of HSP on neighbouring spine plasticity.
Together, the experiments in this thesis help to shed light on how HSP can affect
the structural parameters of neurons, and the long-term consequences this will
have for the functioning of the neural circuit.
33
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Chapter 2
2 Structural correlates of Homeostatic Synaptic Plasticity
Structural correlates of
Homeostatic Synaptic
Plasticity
48
Contributions:
Anna F. Hobbiss and Inbal Israely designed experiments.
Anna F. Hobbiss performed experiments and analysed data.
Anna F. Hobbiss wrote the chapter.
Inbal Israely, Yazmín Ramiro Cortés, Ali Özgür Argunşah, Maria Royo and Inês Vaz
contributed with discussion about the work.
49
2.1 Abstract
Homeostatic synaptic plasticity is known to change the strength of synapses in
response to chronic shifts in activity levels, through a process of multiplicative
synaptic scaling. It is not known whether structural parameters such as the
volume of a spine also scale simultaneously. Here we show that chronic activity
blockade using the Na2+ channel blocker TTX causes spines to increase in
volume, paralleling observed functional changes. At 48 hours, scaling is supra-
linear with an over-representation of large spines. By 72 hours scaling has re-
linearised. When activity blockade was removed, firing rate changes returned to
control levels by 96 hours, whereas structural changes required until 120 hours
to reverse the growth. Our data suggest that homeostatic synaptic plasticity
causes structural changes to spines in addition to functional changes. The circuit
is still functional after the expression of HSP and retains the ability to undergo
further homeostatic shifts if activity is again altered. In contrast, chronically
increased activity using GABAA blockers did not lead to synaptic down-scaling or
spine volume decreases in our system.
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2.2 Introduction
Homeostatic synaptic plasticity (HSP) causes global increases or decreases of
synaptic strength by changing the AMPA receptor content at synapses (O’Brien
et al., 1998; Turrigiano et al., 1998). This serves as a negative feedback
mechanism to counteract activity levels that are in a sub-optimal state, and bring
them back to within an ideal range. Synaptic strength changes have been shown
to increase in a linear multiplicative fashion, known as synaptic scaling, whereby
the relative synaptic strengths on a neuron are preserved. Although much has
been elucidated regarding the molecular pathways of HSP, very little is known
about how this affects the structure of the dendritic spine, the site of an
excitatory contact. Spines compartmentalise molecular processes and the
electrical coupling of the synapse to the dendrite, so changes to their volume and
morphology strongly modulate synaptic strength. Indeed, there is a very tight
structure-function relationship coupling the volume of the spine to its
conductance, with conductance increasing linearly with the volume of the spine
(Matsuzaki et al., 2001; Smith et al., 2003). Hebbian plasticity processes which
change the conductance of the spine also cause corresponding increases or
decreases in spine volume (Matsuzaki et al., 2004; Ramiro-Cortés and Israely,
2013). Whether similar structural processes exist in relation to HSP is an open
question under active investigation.
The current literature regarding spine size changes after chronic activity shifts is
equivocal. The first study into structural components of the synapse (Murthy et
al., 2001) showed that molecular components of both the pre-synaptic side, such
as the active area zone, and the post-synaptic side, i.e. the size of the post-
synaptic density, showed correlated increases after activity blockade. In
accordance with these results, two studies in vivo (Wallace and Bear, 2004; Keck
et al., 2013) also examined spine sizes in the visual cortex after sensory
deprivation, and found that the manipulation caused the average spine size to
51
increase. This increase was reversible, with spines reducing to control levels
when animals were once more exposed to light. In contrast to this, various
studied in vitro have reported different structural dynamics after activity
changes. In organotypic slice cultures, spine fluctuations were measured over
several days of chemical activity manipulations (Yasumatsu et al., 2008). Here it
was observed that although activity blockade changes the dynamics of turnover
(with decreased turnover following blockade), it did not cause spine size changes
over days. Similarly, after a single-spine activity reduction protocol (Béïque et al.,
2011), no spine size difference was seen between silenced and control spines.
These contradictory results are surprising; it is not clear whether they are
attributable to different induction protocols or another experimental factor, or
whether the age or brain region studied can play a role. There is still clearly
considerable work to be done to reach a consensus on how HSP affects spine
sizes.
Another structural parameter which is subject to plasticity is spine density, and
this too is a source of contention in the HSP literature. Some studies have
reported a change in spine density following activity shifts; either an increase is
the number of spines following activity blockade (Stroemer et al., 1995; Burrone
et al., 2002; Kirov et al., 2004; Zuo et al., 2005b; Arendt et al., 2013) or a decrease
in spine density following activity decreases (Goold and Nicoll, 2010). Other
studies both in vivo and in culture have reported however that HSP induction
doesn’t cause a change in spine density (Wallace and Bear, 2004; Thiagarajan et
al., 2005). As with the spine size data described above, some of these results may
be explained by differences in HSP induction protocol, but exactly which
induction mechanisms lead to which spine density and size changes is still
subject to much debate.
Here we subject organotypic slice cultures to long-term activity manipulations of
up to 72 hours. We assess the induction of HSP using electrophysiological
52
methods, and then examine the structural consequences of this induction. We
find that indeed HSP does have structural correlates, with 48 hours of activity
blockade using the Na2+ channel blocker TTX causing up-scaling of spine sizes in
whilst leaving the spine morphology distribution and spine density unchanged.
This increase in spine size is reversible, with removal of activity blockade leading
to decreases in spine volume over many days. Unlike the case for up-scaling,
chronic application of the GABAA blockers bicuculline and picrotoxin do not
cause downscaling either of functional synaptic strengths or of spine sizes.
53
2.3 Materials and Methods
Throughout this thesis we use mouse organotypic hippocampal slice cultures to
study the structural effects of HSP at the level of individual synapses (Figure
2.1). Organotypic cultures allow for long-term chemical manipulations of
activity, which in vivo or acute slice methods could not support. The
developmental progress of neurons in slice cultures in terms of structure and
physiology remains very similar to that of neurons in vivo (De Simoni et al.,
2003), which makes this a good model to study the physiological processes of the
system. In contrast to primary neuronal cell cultures (in which most of the
previous HSP studies have been performed), the neuron structure and the
cellular composition of the system is preserved. This is especially important
when considering how a network-level effect, in this case altered activity levels,
will change the functioning of the system. Additionally, due to the laminar
organisation of the hippocampus, most of the connections between cells are
maintained in a slice, reducing the effects of rewiring (Gähwiler, 1997).
2.3.1 Slice culture preparation
Mouse organotypic slice cultures were prepared using p7-10 C57BL/6J mice.
Hippocampi were dissected and 350 µm slices were cut with a chopper in ice-
cold artificial cerebral spinal fluid (aCSF) containing 2.5 mM KCl, 26 mM NaHCO3,
1.15 mM NaH2PO4, 11 mM D-glucose, 24 mM sucrose, 1 mM CaCl2 and 5 mM
MgCl2. The slices were cultured on membranes (Millipore), and maintained at an
interface with the following media: 1x MEM (Invitrogen), 20% horse serum
(Invitrogen), 1 mM GlutaMAX (Invitrogen), 27 mM D-glucose, 30 mM HEPES, 6
mM NaHCO3, 2mM CaCl2, 2mM MgSO4, 1.2% ascorbic acid, 1 µg/ml insulin. The
pH was adjusted to 7.3, and osmolarity adjusted to 300–310 mOsm. All chemicals
were from Sigma unless otherwise indicated. Media was changed every 2-3 days.
54
2.3.2 Biolistic Transfection
Pyramidal neurons from hippocampal organotypic slice cultures were
transfected using a Helios gene gun (Bio-Rad) after 4–7 days in vitro (DIV). Gold
beads (10 mg, 1.6 µm diameter, Bio-Rad) were coated with 100 mg AFP-plasmid
DNA (a GFP variant) according to the manufacturer’s protocol and delivered
biolistically to the slices, using a pressure of 160-180 psi.
Figure 2.1 Hippocampal organotypic slice cultures as a study system for Homeostatic
Synaptic Plasticity
a) Study system. Hippocampi from postnatal (p) 7-9 mice were dissected, sliced and cultured
for 7-9 days before the start of the experiment. All cells studied in this thesis were CA1
pyramidal cells. Imaging experiments were performed on secondary or tertiary apical
dendrites. b) Timeline for homeostatic synaptic plasticity induction. Slices were cultured at
p7-9 (days in vitro (DIV) 0). Sparse GFP labelling of pyramidal cells was achieved by biolistic
transfection at DIV 4-7. Chemical manipulations of activity were started at DIV 7-9
(designated 0 hours in experiments), and continued for up to 72 hours.
55
2.3.3 Homeostatic plasticity induction by activity block
Tetrodotoxin (TTX) (1 µM, Tocris), Picrotoxin (100 µM, Abcam) or Bicuculline
(50 µM, Tocris) were added to the culture media at 7-9 DIV. The day of
application was then designated day 0 for experiments. Control experiments
were maintained in normal culture media, and were age-matched to treated
slices for experiments. For bicuculline and picrotoxin experiments an equivalent
volume of vehicle (DMSO) without the active compound was added to the control
culture medium. TTX was dissolved in H2O so no vehicle was necessary.
2.3.4 Patch clamp electrophysiology
Hippocampal slice cultures were perfused continuously with aCSF (as above,
with the addition of 0.5 µM TTX for all mEPSC recordings, and picrotoxin or
bicuculline respectively for experiments with these chemicals) for a pre-
incubation period of 15 to 30 min. Whole cell voltage-clamp recordings were
performed in CA1 pyramidal neurons, using 7–8 MΩ electrodes. For mEPSC
recordings, the internal solution contained 135 mM Cs-methanesulfonate, 10 mM
CsCl, 10 mM HEPES, 5 mM EGTA, 2 mM MgCl2, 4 mM Na-ATP and 0.1 mM Na-
GTP, with the pH adjusted to 7.2 with KOH, at 290-295 mOsm. Cells were voltage
clamped at -65 mV. Cellular recordings in which series resistance was higher
than 25 MΩ were discarded. Stability was assessed throughout the experiment,
with cells whose series resistance changed more than 30% being discarded.
mEPSCs recordings were started 3 minutes after break-in and continued for 10
minutes. Signals were acquired using a Multiclamp 700B amplifier (Molecular
Devices), and data was digitized with a Digidata 1440 at 3 kHz. mEPSC events
were detected off-line using Mini-Analysis Program (Synaptosoft). Events
smaller than 15 pA were classified as indistinguishable from noise and were
discarded. For spontaneous activity recordings, slices were perfused
continuously with aCSF without the addition of TTX for a pre-incubation period
of 5 to 10 min. The internal solution for the electrodes contained 136.5 mM K-
56
Glucagonate, 9 mM NaCl, 17.5 mM KCl, 10 mM HEPES, 0.2 mM EGTA, and 0.025
mM Alexa 594, with the pH adjusted to 7.2 with KOH, at 280-290 mOsm. In
current clamp with no external current applied, an IV curve was first recorded to
check for pyramidal-type firing patterns. Spiking events were then recorded for a
period of 6-9 minutes. The addition of Alexa-594 allowed cells to be imaged post-
recording to ensure they were pyramidal neurons. At the 48 hr timepoint,
activity was found to be highly correlated within slices (data not shown) so all
recordings from this timepoint are from cells in different slices. At 96 hours no
significant correlation was seen, so 1-3 cells were recorded per slice.
2.3.5 Two-photon imaging
Two-photon imaging was performed on a BX61WI Olympus microscope, using a
galvanometer-based scanning system (Prairie Technologies /Bruker) with a
Ti:sapphire laser (910 nm for imaging AFP; Coherent), controlled by PrairieView
software (Prairie Technologies). Slices were perfused with oxygenated aCSF
containing 127 mM NaCl, 2.5 mM KCl, 25 mM NaHCO3, 1.25 mM NaH2PO4, 25 mM
D-glucose, 2 mM CaCl2 and 1 mM MgCl2 (equilibrated with O2 95%/CO2 5%) at
room temperature, at a rate of 1.5 ml/min. Activity blockade experiments
contained an additional 0.5 µM TTX in the aCSF for both experimental condition
and control; increased activity experiments contained either 50 µM bicuculline
or 100 µM picrotoxin in the aCSF. Secondary or tertiary apical dendrites of CA1
neurons (where the apical trunk is counted as the primary branch) were imaged
using a water immersion objective (60 x, 1.0 NA, Olympus LUMPlan FLN) with a
digital zoom of 8x. 2-3 dendrites were imaged per neuron. Z-stacks (0.5 µm per
section) were collected at a resolution of 1024 x 1024 pixels, resulting in a field
of view of 25.35 x 25.35 µm. 3 images were taken per dendrite at 5 minute
intervals, with the reported spine volume being the average of the 3 images.
Images were taken at the highest possible fluorescence value without leading to
57
saturation of the image, to achieve maximum accuracy of volume quantification
(see below).
2.3.6 Spine volume determination
To determine the volume of spines we used SpineS, a custom built Matlab plug-in
which employs semi-automatic detection, automatic alignment and
segmentation of spine heads (Argunsah et al., 2016). Spine volume was
calculated using the summed fluorescence intensity of the spine, normalised to
the median fluorescence intensity of the dendrite, to correct for changes in
overall fluorescence levels in a cell. Fluorescence intensity was converted to real
volumes by taking Full Width Half Max (FWHM) measurements of spines
(Matsuzaki et al., 2004) and calculating a conversion factor between the two
measures. For the homeostatic plasticity analysis, all spines with a discernible
head within the field of view were included in the analysis, with the exception of
spines that were obstructed by other structures. This resulted in between 6 – 40
spines scored per dendrite.
2.3.7 Spine distribution and dendrite thickness
2-photon dendrite images were traced using Neurolucida software (MBF
Bioscience), adjusting tracing manually to follow the thickness of the dendrite.
Spine positions and spine type (mushroom, stubby, thin or filopodia) were
manually assigned.
2.3.8 Release of activity block
Slices were incubated for 48 hours in TTX as above. They were then removed
from TTX-containing media and moved to plates containing normal media. Media
was changed at least once more in the subsequent 2 days, to ensure complete
removal of TTX from the environment. Slices were imaged or recorded using
patch-clamp electrophysiology as described above.
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2.3.9 TSC1 knock-out mice generation
Tsc1fl/fl mice were crossed with Tsc1fl/+ Emx1Cre+/- mates. Pups with either a
Tsc1fl/fl Emx1Cre+ genotype (referred to as KOs (knock outs)) or a Tsc1fl/fl
Emx1Cre- genotype (referred to as controls) were used for cultures. Experiments
were performed blind to genotype.
2.3.10 Statistical analysis
All statistical analysis was done in Graphpad Prism 5. Stars represent the
degrees of significance, with 1 star (*) meaning p < 0.05; ** meaning p < 0.01; ***
meaning p < 0.001.
59
2.4 Results
2.4.1 Homeostatic Synaptic Plasticity induced by TTX-dependent
activity blockade
To chronically reduce activity levels in the slices, we used the Na+ channel
blocker Tetrodotoxin (TTX) to prevent action potentials, thus rendering the
slices completely silent. Although all activity is suppressed, other physiological
processes remain intact during TTX treatment. Neurons can survive for up to a
week until significant apoptosis occurs (Schonfeld-Dado and Segal, 2009), and
spine generation can still occur (Annis et al., 1994). Importantly, TTX does not
cause glial cell death even by 12 days, so any effects we see on neurons are not
due to changes in glial composition of the slices (Schonfeld-Dado and Segal,
2009). We therefore decided to examine the effects of TTX incubation for up to
72 hours, within the timeframe where there is no compromise of the health of
the slices.
2.4.1.1 HSP induced by TTX requires chronic blockade of activity
HSP has been reported to work over a wide range of timescales. Simple activity
blockade using TTX induces the expression of HSP after 1-2 days (Sutton et al.,
2006). Other protocols, which combine the silencing of action potentials and
blockade of NMDA receptors, induce HSP on much shorter timescales of around
2 hours (Sutton et al., 2004; Aoto et al., 2008; Wang et al., 2011). This form of fast
HSP is retinoic acid-dependent (Chen et al., 2014) (see part 1.5.1.2). For this
study, we decided to use only activity blockade via TTX, rather than the
induction protocol of activity blockade coupled with NMDA receptor blockade, in
order to mimic the physiological conditions in which HSP would occur such as
sensory deprivation. Our first step was therefore to verify that our activity
blockade protocol had no short-term consequences for the spines. We imaged
dendrites over 2 hours of TTX treatment, and compared spine volumes to that
60
of dendrites imaged without any activity block. We saw no structural differences
between the neurons in TTX and those without it (Figure 2.2). Seeing as a linear
relationship between spine volume and conductance has been demonstrated
(Matsuzaki et al., 2001; Smith et al., 2003), the lack of changes over 2 hours
indicates that acute TTX treatment does not in itself induce structural or
functional plasticity.
2.4.1.2 Activity blockade over days causes functional synaptic scaling
We next sought to verify that as would be expected, activity blockade causes
functional HSP in our organotypic culture system. As a measure of functional
synaptic strength, we used whole cell patch clamp to record mini excitatory post-
Figure 2.2 Acute activity blockade does not cause structural changes
a) TTX application for 120 minutes did not lead to any changes in volume for the treated
spines. Imaging over 120 minutes also had no effect on spine volume, since the control also
showed no changes (p>0.05, ANOVA, post-hoc Bonferroni). n = 43 spines 3 cells control, 45
spines 3 cells TTX.
61
synaptic potentials (mEPSCs) from CA1 pyramidal neurons. mEPSCs are the
result of action potential-independent fusion of a vesicle to a pre-synaptic
terminal, which releases glutamate into the synaptic cleft. This binds to
glutamate receptors on the post-synaptic terminal, resulting in channel opening
and current influx to the post-synaptic terminal. To ensure that no action
potential-dependent vesicle fusion occurred, we performed all experiments in
the presence of 0.5 µM TTX to block cell firing. The ‘control’ condition has thus
undergone acute TTX application for approximately 20 minutes (which does not
elicit plasticity, Figure 2.2) compared to the 48 hours treatment experienced in
the chronic ‘TTX’ condition. We clamped neurons at -65 mV to ensure that NMDA
receptors would be blocked and the currents we recorded were purely AMPA-
receptor mediated. The size of the mEPSC recorded at the soma can be
interpreted as proportional to the post-synaptic strength of the synapse, because
it will increase with increasing numbers of post-synaptic AMPA receptors. Since
spontaneous vesicle release happens stochastically, we can use the sizes of the
mEPSCs recorded over a period of time as a representative sampling of the
population of synaptic strengths of the neuron.
We recorded mEPSC amplitudes from neurons which had undergone 48 or 72
hours of TTX treatment, and compared them to age-matched controls (Figure
2.3a). We saw a significant increase in mEPSC amplitude in the TTX treated
condition by 48 hours, which was maintained at 72 hours (Figure 2.3b). This
demonstrates that synaptic strength was increased by TTX treatment in
accordance with the theory of HSP and experimental reports (Turrigiano et al.,
1998). We next checked whether the distribution of mEPSCs could be scaled
linearly as previously demonstrated (Turrigiano et al., 1998), to indicate that the
phenomenon of synaptic scaling had occurred. We saw that the distributions of
the treated events could indeed be scaled using linear equations, to overlay with
the control distributions (Figure 2.3 c). The fitting equations were: 48 hours Y =
X * 1.45 – 6.06, and 72 hours Y=X * 1.10 – 0.01.
62
Figure 2.3 Activity block induces functional Homeostatic Synaptic Plasticity
a) Example whole cell recording traces of mEPSCs from a control cell and a cell incubated in
TTX for 48 hours. Scale bar = 32 pA, 655 ms. Inset examples of average sized mEPSCs for the
48 hour timepoint. Scale bar = 20 pA, 30 ms. b) mEPSC amplitudes increase following TTX
treatment at both the 48 hour (p<0.001, n = 552 control, 742 TTX) and 72 hour (p<0.001,
Kruskal-Wallis, post-hoc Dunn, n= 1164 control, 1302 TTX) timepoints. c) Control amplitudes
can be scaled using first order equations to fit the recorded TTX amplitudes (48 hour scaling
shown in green: TTX = Control * 1.45 – 6.06. 72 hour scaling shown in blue: TTX = Control *
1.10 – 0.01). d) mEPSC intervals are altered differentially by TTX treatment at different
timepoint. Intervals are reduced at 48 hours (p<0.001, Kruskal-Wallis, post-hoc Dunn n = 544
control, 734 TTX ), but are increased by 72 hours (p<0.001 Kruskal-Wallis, post-hoc Dunn n =
1154 control, 1290 TTX).
63
We then examined the frequency of mEPSCs; a proxy for pre-synaptic release
probability of the synaptic population. We saw different results between the two
timepoints we examined, with inter-mEPSC interval decreasing after treatment
at 48 hours, but increasing at 72 hours (Figure 2.3 d). Previous data about HSP
has been variable with regard to mEPSC frequency changes, (unlike the
consensus for mEPSC amplitude) with some studies reporting no change in
mEPSC frequency (O’Brien et al., 1998; Turrigiano et al., 1998; Kim and Tsien,
2008) whilst others find it altered (Thiagarajan et al., 2002, 2005). Although this
could be in part due to the system of study used, our data suggest that the length
of activity blockade could also explain these differences, with different cellular
mechanisms of compensation employed along a time-course of inactivity. The
studies in the literature are reported at various different timepoints, spanning
the range we examine here.
2.4.1.3 Functional synaptic scaling is accompanied by increases in spine
volume
After confirming the induction of functional HSP and synaptic scaling by
electrophysiology, we next investigated whether activity blockade had structural
consequences for spines. We used 2-photon microscopy to image apical (stratum
radiatum) dendrites of CA1 pyramidal neurons (Figure 2.4 a) which had been
pre-incubated for 0-72 hours in TTX (see Figure 2.1 for protocol). We measured
the volumes of all possible spines in the image using SpineS, a custom-written
Matlab plug-in, which uses summed fluorescence intensity to calculate volume
(Argunsah et al., 2016). Spines from TTX-incubated cells were significantly
bigger than those from control cells by 48 hours, and remained so by 72 hours
(Figure 2.4 b), showing that in accordance with the functional increases in
synaptic strength seen after TTX block, spines volumes do also increase in a
correlated manner.
We then investigated whether the distributions of volumes were linearly scaled
64
(as had been the case for the functional HSP (Figure 2.3 c)). We saw that at 48
hours, volumes of TTX spines scaled supralinearly, being best fit by the function
Y = X2 * 5.44 + X * 0.25 + 0.055; in contrast, by 72 hours spine volumes scaled
linearly, being fit by Y = X * 1.26 – 0.004 (Figure 2.4 c). The supralinear scaling
at the 48 hours timepoint was due to a preponderance of large spines when
compared to controls. This result may indicate an interesting dissociation
Figure 2.4 Structural correlates of Homeostatic Synaptic Plasticity
a) Representative example images of dendrites at either 0 hours or 48 hours after TTX
treatment or in control media. Asterisks indicate all the analysed spines for the respective
dendrite. Inset: zoomed images of individual spines at each time point. b) TTX treatment
causes an increase in spine volume by 48 hours of incubation (p<0.001, 2-way ANOVA,
post-hoc Bonferroni). This increase is maintained by 72 hours (p<0.05, 2-way ANOVA,
post-hoc Bonferroni). n of spines per timepoint = 103, 245, 285, 258 control; 172, 232,
208, 214 TTX. c) At 48 hours, TTX volumes show supralinear scaling with respect to
control (TTX = Control2 * 5.44 + Control * 0.25 + 0.55), whereas at 72 hours TTX volumes
scale linearly from controls (TTX = Control * 1.26 – 0.004)
65
between the structure of the spine and the functional synaptic strengths, because
these large spines are not reflected in the mEPSC population data. By 72 hours
however the population structural distribution had reshaped to show linear
scaling between the control and the TTX population, suggesting that the
customary linear structure-function relationship (Matsuzaki et al., 2001; Smith et
al., 2003) has reasserted itself (Figure 2.4 c). It may be that since HSP is a very
slow process, the time taken for the normal processes which couple the volume
of the spine to its functional strength are also much longer, and so whilst the
functional expression of synaptic scaling has reached a stable distribution by 48
hours, the correlating structure takes an extra day to stabilise equally.
2.4.1.4 Distributions of types of spines after HSP is not altered
Up to this point in the thesis I have referred to structural changes only in the
context of the overall volume of spines. There are other parameters which can
change the signalling capabilities of spines, including head morphology
(Matsuzaki et al., 2001) and neck length and width (Tønnesen et al., 2014). In
particular, spines are traditionally characterised by being split into three
different groups; mushroom, stubby and thin (Harris et al., 1992), along with
non-mature protrusions called filopodia (Figure 2.5 a). Mushroom spines are
considered be the most mature spines – they have a well defined bulbous head
and a thin neck of varying lengths. Stubby spines also have a bulbous head but no
neck and appear to be continuous with the dendrite. Thin spines have no obvious
width difference between the neck and head and are considered less mature.
Finally, the least mature are the filopodia, extremely long, often bent protrusions
which are the precursors to mature synapses, and as such do not connect to a
pre-synaptic partner.
The proportions of these different types of spines change through development
(De Simoni et al., 2003), and shifts to the relative abundance of different spine
66
types are also characteristic of various neurodevelopmental disorders such as
Fragile X syndrome (Irwin et al., 2000). We therefore investigated whether
inducing HSP changed the indistinguishable of the different types of spines. We
found that the proportions were identical between the control and the TTX
condition at the 48 hours timepoint (by which point structural changes to the
spine volume are already visible as reported in Figure 2.4). The proportions of
spines on each dendrite was extremely consistent, with mushroom being the
most populous (mean = 0.61 control, 0.64 TTX), followed by stubby (mean = 0.24
control, 0.23 TTX), thin (0.11 control, 0.08 TTX) and filopodia (0.03 control, 0.07
TTX) (Figure 2.5 b & c). We therefore conclude that although the volume of
spines changes after TTX treatment, the distributions of different spine
morphologies stays the same. This implies that the information content (believed
Figure 2.5 Distributions of spine morphologies are not affected by HSP at 48 hours
a) Spines were classified as either mushroom, stubby, thin or filopodia. Scale bar = 1µm. b)
Proportions of spines of different spine types were not affected by activity blockade. N of
dendrites = 17 control, 18 TTX. Lines represent mean values. c) Quantification of mean
proportions of spines of each type in the two different conditions
67
to be stored in mature mushroom spines) has not been disrupted by the
induction of HSP, so the system has still maintained its function as a memory
storage and encoding structure. This idea of how the information coding capacity
of the system is altered following HSP will be explored further in Chapter 3.
2.4.1.5 Activity blockade does not change spine density or dendrite
volume
Structural plasticity can refer to not only the volume but also the number of
spines along a dendrite. Previous studies have shown conflicting results for
whether activity blockade causes changes to spine density; with some reporting
an increase in number of spines (Stroemer et al., 1995; Arendt et al., 2013) and
others showing no change (Thiagarajan et al., 2005). We manually traced
dendrites and spines using the Neurolucida programme to measure spine
density and also dendrite thickness, which is a physiological indicator of slice
and cell condition (Figure 2.6 a). We observed that the density of spines
increased over the time-course of our experiment (Figure 2.6 b). However, there
were no differences in spine density between the control and TTX conditions at
any timepoint, meaning that the overall increase is likely to be a normal process
occurring over the development of the neurons and the slice. In agreement with
our observations, spine turnover is maintained after activity block (Yasumatsu et
al., 2008), suggesting that the normal maturation processes are not disturbed by
blocking cell firing. Our data therefore agree with previous studies which do not
show an increase in spine density induced by homeostatic plasticity. To ensure
that activity blockade over 72 hours did not compromise cell health (Schonfeld-
Dado and Segal, 2009), we measured some structural parameters which could
reflect the viability of the cells. In experience we have observed that unhealthy
cells undergoing apoptosis display membrane blebbing (i.e. changes to
membrane thickness and beading along the dendritic branch). We therefore
measured dendrite thickness following chronic treatment with TTX as an
68
indicator of neuronal health and found that this was constant throughout the
days of observation (Figure 2.6 c), showing that the TTX treatment was not
inducing visible dendritic damage.
Figure 2.6 Dendrite thickness and spine density are not affected by activity blockade
a) Dendrites and spines were manually traced in 3D using Neurolucida. Representative
images are from the 48 hour timepoint. Different colours of spines represent the different
classes described in section 2.4.1.4. Blue = mushroom; green = stubby; red = thin; yellow =
filopodia. b) Dendrite thickness remained constant over 72 hours of treatment and did not
differ between treated and control dendrites (p>0.05, ANOVA). c) Spine density increased
along the time-course of the experiment (p=0.01, ANOVA), but did not differ between
treated and control conditions at any timepoint (p>0.05, ANOVA). N of dendrites = 10, 9,
16, 19 control, 20, 9, 11, 21 TTX.
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2.4.2 Removal of activity block following HSP
For a system to be robust to changes in activity, it should maintain its ability to
re-adjust activity and synapse size following the induction of HSP. There are
some experimental indications that the changes wrought by HSP can be
reversed. HPS-dependent increased GluA1 receptor expression induced by AMPA
and NMDA receptor blockade starts to reverse by 24 hours out of inhibition, and
reaches control levels by 36 hours (O’Brien et al., 1998). However, this induction
protocol should induce the fast RA-dependent form of HSP (see section 1.5.1.2),
as opposed to our protocol which induces the slower non-RA dependent form. In
vivo, some of the structural and functional changes caused by dark rearing were
reversed after a brief re- exposure to light (Wallace and Bear, 2004). It is
remains unknown whether the structural changes induced by TTX blockade can
be reversed and, if so, the time-course over which this may happen. We therefore
subjected our slices to 48 hours of TTX treatment, and then removed slices from
the treated media and replaced them in normal, non-TTX containing media to
allow normal firing to recommence (Figure 2.7 a). We then examined the
functional and structural properties of the spines which had undergone removal,
as compared to control cells.
2.4.2.1 Firing rates return to control levels following removal of activity
block
In order to understand what kind of network activity occurs when activity
blockade is removed, we recorded firing rates of neurons at the 48 hours
timepoint (i.e. immediately after the removal of the TTX block) and at the 96
hour timepoint (48 hours after the removal of TTX) (Figure 2.7 b). It has been
reported that activity block causes an increase in firing rate once the blockade is
lifted (Turrigiano et al., 1998). To ensure that any firing rate effect we saw was
not due to a short-term rebound after lifting of the activity blockade, we added
an ‘acute TTX’ control to this experiment. For this condition, slices were kept in
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control conditions until the day of the experiment. At the beginning of the day
TTX was added to the media for 2-4 hours, to block all cell firing. At this point all
conditions (control, acute TTX and TTX) were treated equally. Slices were
removed from culture media and placed on the microscope whilst being
perfused with non-TTX containing aCSF. Activity could therefore take place
unchecked in all conditions. Firing rate was recorded between 20 - 300 minutes
Figure 2.7 Firing rates return to control levels after removal of activity block
a) Timeline for TTX removal experiments. Slices were prepared as in previous
experiments and incubated in TTX. After 48 hours, slices were removed from TTX-
containing media and placed in standard media. Controls were maintained in standard
media. b) Example traces of firing rates following 48 hours of activity block. Spikes are
demarked with black circles. Scale bar = 20 mV, 1 s. c) Quantification of firing rates. TTX
treated cells had a significantly higher firing rate than both controls at 48 hours (p<0.05),
directly after TTX removal. By 96 hours (i.e. 48 hours after TTX removal), firing rates had
become significantly smaller (p>0.01) and were indistinguishable from control levels. N =
17 control, 20 acute TTX, 25 TTX (48 hours); 15 control, 18 TTX (96 hours).
71
after this. If activity block removal per say caused increased cell firing, it should
be observed in both the TTX and acute TTX condition.
We observed firstly that there was no significant difference between the control
and the acute TTX firing rates (Figure 2.7 b) (Mann-Whitney, p > 0.05). We
could therefore attribute any difference we saw between the control and TTX
conditions to the effects of HSP alone. Compared to the control condition, there
was an increased firing rate for the 48 hour TTX condition (Figure 2.7 b),
showing that as expected the synaptic and network changes wrought by HSP
cause an increase in activity. Interestingly, not only was the average firing rate
increased but the distribution of firing rates was also altered. In the control
condition, the vast majority of the cells had an extremely low firing rate
(between 0 and 8 spikes per minute) whilst a subset had much higher rates (up
to 100). This is in agreement with the accepted model of network firing rates in
the hippocampus and cortical circuits, where firing rate dynamics are extremely
skewed so that the majority of the spikes come from a small subset of cells
(Mizuseki and Buzsáki, 2013). However, in the TTX condition, there were no cells
with a near-zero firing rate. Instead, the majority of values clustered around the
median value of 13.3 spikes/min, with only one value which was much higher
than this. By the 96 hour timepoint, the TTX values had returned to control
levels, both in terms of the median firing rate and the distribution of the spikes
(Figure 2.7 b).
2.4.2.2 Spine volumes reverse after HSP-induced growth upon removal of
activity block
Having seen that firing rates recover to control levels after removal of activity
blockade, we next examined the spine volume to see if the same trend was
observed. We measured the spine volumes at 96 hours and 120 hours following
the initiation of the stimulation (Figure 2.8 a). We saw that the changes induced
through HSP at 48 hours had started to reverse by 96 hours; however the spines
72
remain significantly bigger than control spines from the same developmental
time point (Figure 2.8 b). However, one day later at 120 hours post TTX
stimulation, the spines sizes have significantly decreased in size (Figure 2.8 b).
Closer examination of the distributions of spine sizes (rather than just the mean
and SEM) revealed that in fact the median at 120 hours post TTX was marginally
smaller than their control counterparts (p < 0.05) (Figure 2.8 c). Interestingly,
this finding supports findings from a previous in vivo study where prolonged
dark rearing caused spine head diameters to increase; but short re-exposure to
light caused sizes to shrink below the volumes of control animals which had
never experienced sensory deprivation (Wallace and Bear, 2004). We conclude
that the structural changes that we observe in response to HSP are indeed
reversible, although it takes up to 3 days after the removal of the activity block
before volumes are fully reversed. It is notable firing rates recover to control
levels by 96 hours (48 hours after removal of TTX), whilst spine structural
changes took until 120 hours (72 hours after the removal of TTX) to return to the
control sizes. This result, where the structural changes appear to ‘lag behind’ the
functional changes, echoes the data presented in Chapter 2, whereby linear
synaptic scaling was seen at 48 hours as measured functionally, but the linear
structural changes only manifested at 72 hours. Despite the slower time course
for structural changes to effects, it is clear that the circuit maintains its ability to
adapt to ongoing changes in overall activity after HSP, dynamically regulating its
synaptic strengths to optimise its function.
2.4.3 Tsc1 is not required for structural changes induced by HSP
Knowing that activity blockade causes structural changes to spines, we
wondered what signalling mechanisms could be involved in this phenomenon.
One avenue was suggested by the phenotype which we observed after activity
blockade – the emergence of large spines with bulbous heads (see Figure 2.4 for
example images). Interestingly, this same phenotype was observed in neurons
73
which lack the gene encoding the hamartin protein, Tsc1, which is involved in
control of the mTOR signalling pathway (Tavazoie et al., 2005). Harmartin, along
with tuberin protein (encoded by the gene Tsc2), make up the mTOR
(mammalian Target of Rapamycin) suppressing Tuberous Sclerosis Complex
(TSC). We reasoned that since in a ‘normal’ state (i.e. without activity blockade),
Figure 2.8 Removal of activity block reverses HSP-dependent spine volume increase
a) Representative images from dendrites at 48 hour and 120 hour timepoints. Scale bar =
5µm. b) Spine sizes are increased by 48 hours of activity block as reported in section 0
(p<0.001). By 96 hours, spines have shrunk significantly (p<0.001) but are still larger than
controls. By 120 hours, spines have shrunk down to be even smaller than control spines
(p<0.05). Graph is plotted as mean + SEM. Stats = Kruskal-Wallis, post-hoc Dunn. n of
spines per timepoint = 285, 514, 326 control. 208, 430, 251 TTX. c) All spine sizes with
medians indicated with black bars.
74
neurons lacking this gene phenocopy those displaying the structural effects of
HSP, the ability for the neurons themselves to further undergo structural scaling
may be impaired.
This idea was supported by the biology of Tsc1 function. The mTOR pathway
which Tsc1 modulates is an important signalling pathway involved in many
cellular processes, and which has multiple roles in neural functioning (Hoeffer
and Klann, 2010). One of the main functions of the mTOR pathway is to up-
regulate protein translation. In dendrites, this can occur when a strong plasticity-
inducing stimulus arrives at a synapse, activating the mTOR signalling pathway.
New protein synthesis occurs to generate the protein substrates for plasticity
processes, such as CaMKII and AMPARs. In baseline conditions, protein
translation is suppressed by TSC, which down-regulates mTOR signalling thus
stalling translation until the appropriate moment when the suppression is lifted.
Loss of either Tsc1 or Tsc2 makes the TSC complex dysfunctional, lifting the
brake on translation which leads to excessively high levels of proteins
(Kwiatkowski and Manning, 2005). In humans, mutations in Tsc1 or Tsc2 cause
the condition Tuberous Sclerosis, a disorder characterised by somatic and brain
tubers, mental retardation and a high incidence of epilepsy (Roach et al., 1998).
Relevantly for our study, loss of Tsc1 or Tsc2 leads to dendritic and spine
abnormalities, which have been suggested to be causal for the neuropsychiatric
phenotypes (Tavazoie et al., 2005; Zhou et al., 2006). The enlarged spine
phenotype previously mentioned could result from increased levels of protein in
the dendrite after loss of the TSC repressing effect, which allows for unchecked
plasticity. Loss of TSC suppression also causes pathologically high activity levels
in neural systems, likely for the same reasons. One characteristic feature of
Tuberous Sclerosis syndrome is epilepsy, which stems from hyperactivity and
overly correlated firing in neural systems (Roach et al., 1998). In studies on
cultured neurons, homozygous loss of Tsc1 caused the development of
hyperactive spontaneous activity levels (Bateup et al., 2013b), and induced
75
neonatal seizures caused up-regulation of the mTOR pathway (Talos et al., 2012).
Combining both this structural and functional evidence, we hypothesised that
mechanisms of homeostatic synaptic plasticity could be impaired after loss of
TSC functioning, so the neurons are unable to regulate activity and spine sizes in
an appropriate way.
To test this hypothesis, we generated mice with homozygous knock-out (KO) of
Tsc1 in cortical neurons, by combining floxed Tsc1 with Cre-recombinase under
the control of an Emx1 promoter which expresses in cortical excitatory neurons
and glia (Gorski et al., 2002). Neurons were always compared to those from wild-
type litter-mate controls (i.e. double floxed Tsc1 but lacking the expression of
Cre-recombinase), which were age-matched to the treated cells, to account for
any effects from the genetic background of the mice. We then performed activity
blockade experiments using the same protocol as detailed before in Figure 2.1,
by incubating slices for 72 hours in TTX to block all activity and induce HSP. We
found that spine sizes showed a trend to be smaller at the 0 hours timepoint in
Tsc1- spines compared to wild-type, although the effect did not reach significance
(Figure 2.9). This is at odds with previous reports that spines are larger after
knock-out of the Tsc1 gene (Tavazoie et al., 2005). One possible explanation for
this is that the aforementioned study was carried out on isolated Tsc1- neurons
in a background of normal wild-type neurons, whereas our study constitutively
knocked out Tsc1 in all hippocampal neurons. In a wild-type background, a single
cell with increased firing rates may induce a large amount of LTP, rather than the
global homeostatic down-scaling response which should occur when in the
constitutive Tsc1- case when all cells have pathologically high firing. However,
counter to our initial hypothesis, the Tsc1- spines did show a HSP-induced
volume increase following 72 hours of TTX exposure, which mirrored the
increase seen in the wild-type cells after activity blockade (Figure 2.9).
Therefore, despite lacking the TSC suppression of translation, neurons were still
able to homeostatically scale upwards after activity blockade. This result accords
76
with a study in primary cultured neurons which finds that Erk signalling but not
mTOR signalling were necessary for Arc-dependent synaptic scaling (Bateup et
al., 2013a). We therefore conclude that there is no evidence that removing the
brake on the mTOR pathway through loss of Tsc1 disrupts synaptic up-scaling in
these neurons.
Figure 2.9 Loss of Tsc1 does not impair structural HSP
Spines were imaged at 0 hours and at 72 hours. Tsc1- spines showed a trend to be
smaller than controls at timepoint 0, although this did not reach significance. By 72
hours, both Tsc1- and wild-type spines which had undergone TTX treatment were bigger
than control spines (ANOVA with post-hoc Bonferroni. n is shown as 0 hours and 72
hours. WT control = 138, 115. WT TTX = 149, 116. Tsc1- Control = 9, 182. Tsc1- TTX = 17,
60).
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2.4.4 GABAA antagonists fail to induce HSP in our system
Having seen structural and functional up-scaling after a chronic block of activity,
we next investigated if raising activity levels would induce a complementary
decrease of synaptic strength. One reported way to increase firing rates over
prolonged periods of time is to apply antagonists to GABAA receptors, which will
reduce inhibitory activity in the system and increase the excitatory drive onto
neurons. Due to this effect, GABAA blockade is used as a model for epilepsy
(Seeburg and Sheng, 2008). It has been reported that chronic application of
either one of the GABAA antagonists bicuculline or picrotoxin can cause
homeostatic decreases in firing rate and synaptic strength in primary neuronal
cell culture (Turrigiano et al., 1998). However, only one study has reported this
same effect in organotypic slices (Karmarkar and Buonomano, 2006); other
work in this model system has shown variable results, with mEPSC strengths
showing both decreases and increases depending on the timepoint at which they
were measured (Seeburg and Sheng, 2008). Since the anatomy and cellular
composition of monolayer primary cultures is extremely different to the more
physiologically conserved organotypic slices, it is quite possible that the systemic
response to manipulations could vary between the two models. We applied
bicuculline or picrotoxin to organotypic slices using the same timing protocol as
represented in Figure 2.1 b, for 0 – 72 hours. We did not observe either a
reduction in functional synaptic strength as measured by mEPSC amplitudes
(Figure 2.10 a & c), and as a likely consequence, nor in spine volume (Figure
2.10 b & d) following this manipulation. Instead, for both bicuculline and
picrotoxin mEPSC amplitudes were increased by the drug application at 48 hours
(although they had returned to control levels at 72 hours), mirroring the results
reported previously in organotypic slices (Seeburg and Sheng, 2008). We
conclude that in our organotypic slice preparation, the effect of decreasing
GABAA signalling is not enough to produce down-scaling of synaptic strengths.
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Figure 2.10 Chronic GABAA blockade does not induce downscaling through HSP
Chronic application of either of the GABAA antagonists Bicuculline (a,b) or Picrotoxin
(c,d) failed to induce homeostatic downscaling (i.e. reduced synaptic strength), as
measured either functionally by mEPSCs (a,c) or structurally by spine volume (b,d).
Bicuculline experiments: n of mEPSCs = 415, 971 control; 425, 912 bicuculline. n of
spines = 479, 847, 780, 690 control; 508, 704, 382, 618 Bicuculline. Picrotoxin
experiments: n of mEPSCs = 283, 412 contro; 480, 384 picrotoxin, n of spines = 81, 166,
149, 184 control; 111, 155, 217, 284 picrotoxin.
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2.5 Discussion
In this chapter, we showed that HSP induced by activity blockade causes
functional synaptic scaling in neurons (Figure 2.3), which is accompanied by a
corresponding increase in spine volume (Figure 2.4). The volumes show a
supra-linear scaling relationship by 48 hours, which returns to a linear scaling by
72 hours. Spine growth occurs without affecting other spine population
parameters such as distributions of morphology (Figure 2.5) or density (Figure
2.6). Both the structural and the functional changes are reversible, with firing
rates returning to control levels by 96 hours (or 48 hours out of activity
blockade, Figure 2.7), and spine volumes returning by 120 hours (Figure 2.8).
This structural scaling does not require the mTOR regulator Tsc1 (Figure 2.9).
2.5.1 Structural consequences of HSP
The elucidation of structural changes accompanying HSP adds a new facet to our
knowledge of this vital cellular process. Space within spines is extremely
restricted and so without physical adaptations such as increasing volume, there
would be very limited potential for a synapse to change its strength (Sala and
Segal, 2014). Some forms of synaptic adjustment can occur without removal or
insertion of receptors or other synaptic components – for example,
phosphorylation of surface receptors to maintain them in an open state for
longer – but these tend to be short-lived reversible states, and at the very longest
will not be maintained beyond the time it takes for that particular receptor to be
turned over. To produce adjustments with increased longevity, it is necessary to
introduce structural changes to spines, dendrites and cells.
It is therefore interesting that previous data is equivocal about the structural
consequences of homeostatic plasticity. Various studies, in a range of different
systems and using genetic or chemical protocols to block activity, have reported
that there is no consequent spine volume change (Yasumatsu et al., 2008; Béïque
80
et al., 2011). It is uncertain why our results differ from these, although it is
possible to speculate on some of the reasons. In one study which characterises
how shifts in activity alter the dynamics of spine volume change over time, the
authors follow the same spines over time, and see no change (Yasumatsu et al.,
2008). Their protocol uses both TTX and NMDAR blockade which induces a
mechanistically different type of RA-dependent HSP. It is possible that this form
does not induce the structural changes seen after induction of the non-RA
dependent form in our results. The topic would benefit from further research to
resolve this question. A second study which reported no structural differences is
easier to reconcile with my results. In this work, activity is blocked by the over-
expression of the potassium channel Kir2.1 at upstream cells, and the
downstream spines are followed (Béïque et al., 2011). A lack of structural
changes could therefore be attributable to the fact that downstream cell firing,
which is itself sufficient to modulate synaptic scaling (Burrone et al., 2002; Ibata
et al., 2008), was not blocked, since silenced synapses made up only a small
proportion of the post-synaptic sites. Alternatively, their synaptic blockade may
not produce such strong results because it only lowers, rather than completely
abolishes, upstream firing and evoked synaptic activity.
Our data indicated that very large spines were over-represented after TTX
treatment (Figure 2.4). Although we do not know the original size of the spines
prior to TTX treatment, it is accepted that in general large spines are older and
more mature, whereas newly formed spines are small. Thus we can make an
educated guess that these large spines are the older ones in the culture, rather
than for instance being newly generated whilst in TTX blockade, and thus spines
which already exist on the branch are the first to show a HSP response, and grow
more. This may be because they already contain some of the components
necessary to engage the growth mechanisms, such as endoplasmic reticulum or
other organelles, whereas newly generated spines do not.
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2.5.2 Decoupling of structure and function
One interesting feature of the results presented in this chapter is the apparent
decoupling of the structure and function of dendritic spines after HSP. It is
generally accepted that under normal circumstances, there is a linear
relationship between a spine’s volume and its conductance (Matsuzaki et al.,
2001; Smith et al., 2003), which is maintained after Hebbian synaptic plasticity
(Govindarajan et al., 2011). Here we show that at the 48 hour timepoint of
activity blockade, mEPSC amplitudes scaled linearly between the TTX and
control conditions, as is commonly reported (Figure 2.3). However, at this same
timepoint spine volumes scaled supra-linearly compared to controls, requiring a
further 24 hours before the scaling of the volumes reflected that seen in mEPSC
amplitudes (Figure 2.4). Examination of the distributions showed a
preponderance of large spines (which is not reflected in the electrophysiological
data), indicating either that these spines grow first before the small spines catch
up by 72 hours, or that there is an over-growth of these spines, which then have
shrunk back down by 72 hours. Similarly, when the activity block was lifted to
allow action potentials to resume, firing rates returned to control levels by 96
hours (48 hours following lifting of activity block) but the structure took a
further 24 hours to shrink. It appears therefore that the structural changes
induced by HSP lag behind the functional changes. This mismatch between the
times of expression of the different processes could reflect the timeframe
required to co-opt different mechanisms for HSP. Functional changes, possibly
related to receptor insertion into pre-defined slots in the membrane, or different
receptor compositions (Lee et al., 2010), may be induced initially as a ‘first-pass’
attempt to equalise the functioning of the cells and circuits. If this doesn’t correct
the problem, structural changes may be implemented as a more extreme
measure. Interestingly, there is a previous suggestion that decoupling of
structure and function in spine head volume may be related to the proportion of
silent synapses in a population, which is increased after activity deprivation
82
(Busetto et al., 2008; Ashby and Isaac, 2011). This topic will be explored in
greater detail in chapter 3 and in the general discussion in chapter 4.
2.5.3 Network changes after HSP
The changing of synaptic strengths may not only affect the activity of a single cell
but also the connectivity and shape of the network. Indeed, the data presented in
Figure 2.7 on firing rate changes throughout activity blockade and reversal
could represent an interesting shift in network dynamics after HSP. After 48
hours of activity blockade, the average firing rate was increased but the variance
of rates had decreased. In a standard cortical or hippocampal network, a
minority of cells will have very high firing rates, whilst the majority are near-
silent (Mizuseki and Buzsáki, 2013). After the TTX treatment however, there
were no cells with extremely low-firing rates, and most clustered around the
median value. Instead of the optimal coding strategy adopted by networks in a
normal state, activity block seems to homogenise firing rates to bring all the cells
up to approximately similar values. This may sensitise the system as a whole to
any inputs that arrive by giving all cells similar weights in the network dynamics,
as opposed to the highly skewed distribution seen in normal networks. In such
cases, if after a period of inactivity new activity is applied (for instance, if after a
period of sensory deprivation, stimuli again arise), the network can relearn an
optimal state for the new type of activity it receives, reshaping itself over days to
form the normal network state of highly skewed firing rates, appropriate for the
new patterns of activity.
2.5.4 Tsc1 signalling and HSP
Some indication that synaptic down-scaling could be impaired is seen in the
necessity for mTOR control to regulate a rodent model for early-life seizures
(Talos et al., 2012). Our data however showed no requirement for the Tsc1 gene
for the expression of structural up-scaling (Figure 2.9). This data is preliminary
83
since the sample size is small. It would be interesting to know whether the loss of
Tsc1 affects down-scaling (instead of up-scaling), since it is the inability to
regulate activity downwards which characterises these systems, leading them to
exhibit pathologically hyperactive firing rates. It may be that down-scaling is in
fact impaired. On the other hand, it is possible that the decreased average spine
size we observed at the 0 hours timepoint is the result of maximal synaptic
downscaling, in an unsuccessful attempt to return activity levels to normal. This
would imply that structural scaling is intact but is not effective enough to
compensate for the loss of mTOR control. Since we were unable to reliably
induce down-scaling in our system (Figure 2.10) we did not pursue these
experiments. Whether or not Tsc1- neurons can bi-directionally regulate activity
levels therefore remains an interesting open question in the field. If not, loss of
homeostatic control could account for the mental retardation, epilepsy and spine
abnormality phenotypes seen in mouse models and human patients with this
disorder.
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2.6 Bibliography
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Chapter 3
3 Threshold modulation of LTP induction by Homeostatic Synaptic Plasticity
Threshold modulation of LTP
induction by Homeostatic
Synaptic Plasticity
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Contributions:
Anna F. Hobbiss and Inbal Israely designed experiments.
Anna F. Hobbiss performed experiments and analysed data. Ali Özgür Argunşah
provided Matlab code for analysis in Figure 3.3
Anna F. Hobbiss wrote the chapter.
Inbal Israely, Yazmín Ramiro Cortés, Ali Özgür Argunşah, Maria Royo and Inês Vaz
contributed with discussion about the work.
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3.1 Abstract
Homeostatic and Hebbian plasticity represent opposing forces for the strength of
a synapse. How these two types of plasticity interact, and which parts of the cell
or circuit are most susceptible to each, is still to be understood. Here we use TTX
to induce HSP through global block activity, and subsequently induce LTP at
single spines using glutamate uncaging. We find that LTP in single spines is
maintained after HSP, and has an increased longevity compared to LTP in control
spines. The induction of LTP is more efficacious after HSP, especially in small
spines, and can be induced by lower levels of glutamate. Neighbouring spines
also show slight growth after HSP. This led us to propose that the threshold to
induce LTP is reduced by HSP expression, sensitising the circuit to any new
activity it receives.
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3.2 Introduction
Homeostatic and Hebbian plasticity are utilised by neural systems for different
purposes, namely stability and information coding respectively. There is strong
evidence that both rely on calcium signalling to sense the activity changes of the
post synaptic cell (Sabatini et al., 2001; Pawlak et al., 2005; Ibata et al., 2008).
Hebbian plasticity will strengthen a synapse when post-synaptic activity is high,
and weaken it when activity is low. Homeostatic synaptic plasticity applies the
opposite logic, decreasing synaptic strength when post-synaptic activity is high
and strengthening it when it drops. Although these two types of plasticity have
opposing modes of expression, in vivo they will co-exist temporally and spatially.
How they interact is an open question in the field of synaptic plasticity.
Two different possibilities can be postulated for how HSP affects the expression
of Hebbian LTP. One theory is that since HSP has already strengthened the
synapses by enlarging them and increasing their AMPAR content, they will reach
a ceiling for plasticity and LTP will be occluded. Alternatively, since the goal of
HSP is increase input to a cell, it could act to sensitise the synapse, so allowing it
to express more LTP. Which of these two scenarios occurs is an empirically
addressable question. Some recent studies have sought to understand the effect
of HSP on LTP expression, using various different induction methods for the two
different types of plasticity (Lee et al., 2010; Arendt et al., 2013; Félix-Oliveira et
al., 2014). One approach was to induce both HSP and LTP at single synapses,
using genetic methods and glutamate uncaging respectively (Lee et al., 2010).
This allows for comparisons of the affected spines with non-affected neighbours
in the same slice. At silenced synapses NMDAR currents were enhanced and
structural and functional LTP was increased. It is not clear whether this local
method of synaptic silencing is functionally equivalent to global HSP; and indeed
the authors of this study refer to the phenomenon as ‘metaplasticity’ rather than
homeostasis. Alternatively, both HSP and LTP can be induced on a wider scale
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(Arendt et al., 2013). In this study, the authors globally induced HSP using TTX
application, and then used electrical stimulation to induce LTP at multiple
synapses. They observed enhanced LTP and an increased proportion of newly
formed silent synapses after activity blockade. Silent synapses lack AMPA
receptors, and so do not participate in an excitatory post-synaptic current (EPSC)
response. The electrical LTP stimulus activates NMDA receptors due to the co-
incidence of glutamate binding and post-synaptic depolarisation from a back
propagating action potential. This allows calcium influx to the spine leading to
AMPA receptor insertion, so unsilencing the synapse. It will now functionally
contribute to post-synaptic EPSCs, so the response to stimulation will be higher.
The relative magnitude of LTP will be greatly enhanced by an increased number
of silent synapses, because the potential for change is larger. An increase in
newly formed silent synapses is also supported by previous data on AMPA
insertion following activity blockade (Thiagarajan et al., 2005), where GluA1 is
inserted into pre-existing synapses but not new ones, leaving the newly formed
synapses without AMPARs and therefore silent. This mechanism will cause
‘priming’ of a circuit to increase the magnitude of LTP in response to many
stimuli arriving simultaneously – as indeed happens during early developmental
timepoints when silent synapses are common and contribute to circuit
refinement (Busetto et al., 2008). Normally however activity does not arrive
simultaneously at multiple synapses, but at individual ones. There are important
mechanistic differences between stimulating a single spine and multiple spines,
because synapses compete for the use of resources such as proteins when
stimulated in close succession (Fonseca et al., 2004; Govindarajan et al., 2011).
This affects the outcome of plasticity, where competing spines could achieve a
lower level of LTP or enter a ‘win-or-lose’ contest for plasticity, with winning
synapses expressing normal LTP but losing synapses none. It is unknown what
the response of a synapse stimulated individually will be after HSP, especially if
that synapse is initially silent.
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There is still much to be discovered about how HSP modulates the expression of
Hebbian plasticity, especially in the most physiologically realistic cases of a
globally induced HSP and a locally induced Hebbian plasticity. In this chapter, we
use single-spine induction of LTP on a background of homeostatic plasticity to
address whether there is a constructive or competitive interaction between the
two plasticity types. We find that after HSP, LTP at single spines is longer lasting,
and is enhanced in small spines as compared to large spines. Spines also respond
to lower levels of glutamate following HSP. Neighbouring spines close to the
stimulated spine show a short-lasting structural potentiation. This leads us to
propose that HSP decreases the threshold for LTP expression.
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3.3 Materials and Methods
3.3.1 Sample preparation
Slice cultures, transfection and homeostatic plasticity induction using TTX were
performed as described in the previous chapter. Unless otherwise specified, all
experiments in this chapter are from the time-point of 48 hours of TTX
application, or from age matched controls which have not undergone activity
block.
3.3.2 Two-Photon imaging
Imaging experiments were performed using the same set-up and methods as
described in the previous chapter. For uncaging experiments, imaging was
performed at 10x magnification, giving a field of view of 20.28 x 20.28 µm.
Imaged dendrites contained both stimulated spines (see below) and
neighbouring spines. Neighbouring spines could be anywhere in the field of view,
i.e. up to a maximum distance of ~30 µm on the dendrite. 2 – 10 neighbours were
measured per experiment.
3.3.3 Glutamate Uncaging
MNI-caged-L-glutamate (Tocris) was dissolved in aCSF lacking MgCl2 or CaCl2 in
the dark to make a stock concentration of 10 mM. Individual aliquots were
diluted to the working concentration of 2.5 mM in uncaging aCSF (see below), in
3 ml volumes. To test each individual stock of MNI-glutamate, whole cell patch
clamp was performed as described in the previous chapter and uncaging was
applied to single spines. Uncaging-evoked EPSCs were measured at the soma. For
imaging experiments, slices were incubated for 30-45 minutes in normal aCSF
(Materials and Methods, Chapter 2) containing 0.5 µM TTX. Imaging was
performed using the same system as in Chapter 2. Uncaging was performed
using a second Ti:sapphire laser (720 nm to uncage MNI-glutamate; Coherent),
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controlled by PrairieView software (Prairie Technologies, Bruker). All uncaging
stimuli were performed in the presence of uncaging-aCSF lacking MgCl2 and
containing 4 mM CaCl2. The uncaging train consisted of 30 pulses at 0.5 Hz, with
a pulse width of either 4 ms (supra-threshold) or 1 ms (sub-threshold), using
30mW power as measured at the back aperture. The uncaging point was
positioned 0.6 µm from the end of the spine head. To control for non-glutamate
dependent laser effects, ‘sham’ stimulations (Figure 3.10) were performed after
5 minutes of perfusion with the uncaging-aCSF without the addition of MNI-
glutamate. If no spine growth was seen, the perfusion was switched to
recirculation of uncaging-aCSF containing 2.5mM MNI-glutamate, and the ‘real’
stimulus was applied after 5 minutes. Fast imaging (approximately 20 Hz) of a
region of interest (ROI) of the stimulated spine head and neck was performed
throughout the 60 second stimulation, to record the growth dynamics for this
time period. The perfusion was then immediately switched back to the normal
aCSF for the remainder of the experiment (1-2 hours). The first image was taken
immediately, and is designated 0 minute post stimulation. In practice, due to the
time taken to record the imaging stack, this image will be approximately 1
minute after the end of the 60s stimulation.
3.3.4 Spine volume determination
Volume of spines was determined using the Matlab plug-in Spines as described in
the Chapter 2 (Argunsah et al., 2016).
3.3.5 Statistical analysis
All statistical analysis was done in Graphpad Prism 5. Stars represent the
degrees of significance, with 1 star (*) meaning p < 0.05; ** meaning p < 0.01; ***
meaning p < 0.001.
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3.4 Results
3.4.1 LTP following induction of HSP
In vivo, homeostatic normalisation of synaptic strengths must occur concurrently
with synapse-specific, information-encoding Hebbian plasticity. The interplay
between these two types of plasticity is yet to be understood. Previous
experiments exploring how global HSP affects the expression of LTP have used
induction protocols which stimulate multiple axon fibres, resulting in a multi-
spine LTP (Arendt et al., 2013; Félix-Oliveira et al., 2014). Realistically however,
activity will arrive at single inputs on a dendrite rather than many
simultaneously. This can have important consequences for the output of
plasticity, since synapses compete for the use of resources such as proteins when
stimulated at the same time (Fonseca et al., 2004; Govindarajan et al., 2011). We
therefore opted to induce LTP only at single inputs, to avoid the confound of
synaptic competition and understand more precisely how the global
phenomenon of HSP and the local phenomenon of LTP co-exist and interact.
3.4.1.1 Single spine LTP induction protocol
All the following experiments were performed after 48 hours of incubation in
TTX (Figure 3.1 a). We used glutamate uncaging (Figure 3.1 b - e) to stimulate
individual synapses (Pettit et al., 1997). The uncaging technique involves using
light to photolyse a molecule of caged MNI-glutamate – a glutamate molecule
bound to an MNI ‘cage’, which inactivates it – releasing the active glutamate
(Figure 3.1 b) (Palma-Cerda et al., 2012). In this study we use a 2-photon laser
to photolyse the glutamate, thus taking advantage of one of the great advantages
of 2-photon microscopy – its extremely small point of excitation (Denk et al.,
1990). Release of glutamate is localised to individual spines, allowing for the
induction of LTP at only one spine on a dendrite, whilst neighbouring spines
remain unaffected (Figure 3.1 d).
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The uncaging protocol used was based on previously described stimulation
protocols to induce early LTP (E-LTP), and consisted of 1 minute of stimulation
pulses at 0.5 Hz, with a stimulation strength of 30mW and a pulse width of 4 ms
Figure 3.1 Protocol for uncaging experiments
a) Timeline for uncaging experiments. Slices were cultured at p7-9 (DIV 0) and transfected
at DIV 4-7. Chemical manipulations of activity were started at DIV 7-9 (designated 0
hours). Uncaging was performed at the 48 hour time-point. b) MNI-Caged glutamate
photolysis. Glutamate is inactivated by a bound MNI ‘cage’. 720 nm 2-photon laser
excitation cleaves the MNI-group, releasing the active glutamate. c) Uncaging-induced
structural plasticity. Stimulation of a single spine (yellow arrowhead and inset yellow box)
causes localised spine growth, whilst neighbouring spines (blue arrowhead and inset blue
box) remain unchanged. d) Example of volume changes over an E-LTP experiment.
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(Harvey and Svoboda, 2007; Govindarajan et al., 2011). The stimulations were
performed in aCSF lacking Mg2+, to allow NMDA receptor activation. This
protocol has been shown to produce structural LTP lasting approximately 1
hour, but which will decay to baseline thereafter, known as early LTP or E-LTP
(Figure 3.1 d). Other stimulation protocols have also been developed which
produce longer-lasting (late) potentiation (L-LTP) (Govindarajan et al., 2011),
which combine the same uncaging stimulus with chemical activation of cAMP
activity. We chose to induce E-LTP rather than L-LTP in this study because then
rather than being at a ceiling for plasticity induction we would be in the middle
of a dynamic range, allowing us to detect any changes in magnitude which
occurred either upwards or downwards after HSP. To control for any physical
effects of laser stimulation (independent of glutamate) on the spine, we
performed a ‘sham’ uncaging stimulus using exactly the same stimulation
protocol but without the presence of MNI-glutamate on spines from a different
branch than the one selected for the ‘real’ stimulation (with MNI-glutamate), at
the beginning of the experiment. If any growth was detected in the spines after
the sham stimulation, the experiment was discarded. If there was no change in
the stimulated spines, the perfusion solution was switched to one containing
MNI-glutamate and the ‘real’ stimulation was performed (supplementary
Figure 3.10).
3.4.2 Single spine LTP has an increased longevity following HSP
We applied an E-LTP stimulus to single visually identified spines in CA1 cells of
the hippocampus, and compared the spine volume of the stimulated spine to the
neighbouring spines. We found that glutamate uncaging induced a structural
potentiation over the two hour time-course in both the control and the TTX
condition (Figure 3.2 a & b, p < 0.001, 2-way rm ANOVA). The maintenance of
LTP after HSP shows that the capacity of the system to encode information
through Hebbian plasticity remains intact. The LTP magnitudes of the two
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Figure 3.2 LTP magnitude and longevity following HSP
a) and b) Control and TTX-incubated stimulated and neighbouring spine volumes. Both
conditions show significant structural LTP (p < 0.001) of the stimulated spines compared to
neighbours (2 way repeated measures ANOVA, n = 17 stimulated spines 85 neighbours
(control), 18 stimulated spines, 102 neighbours (TTX). c) The magnitude of LTP is not
different between control and TTX conditions (p > 0.05, 2 way rm ANOVA). d) – f).
Quantifications of the time-courses in parts a) – c). d) Control stimulated spines are
significantly larger than neighbours at timepoint 35-45 minutes (p<0.001) but not at 110-
120 minutes (p>0.05). e) TTX stimulated spines are significantly larger than neighbours at
both 35-45 (p<0.001) and 110-120 minutes (p<0.01). f) No significant differences in LTP
magnitude are seen between stimulated spines in the two conditions. All statistics for parts
d) - e) are t-tests.
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conditions were statistically indistinguishable from one another over the whole
time-course (Figure 3.2 c, p > 0.05, 2-way repeated-measures ANOVA). There is
a trend for the TTX magnitude to be greater than the control, but this does not
reach significance. This result differs from the previously reported increase in
LTP magnitude following HSP. A possible explanation for this difference is that
our protocol induces LTP at the level of an individual spine, instead of
simultaneously at a population of synapses as in the previous studies. We can
therefore conclude that the LTP magnitude at single spines is equivalent
independently of HSP expression. We next verified whether the longevity of the
LTP expressed in both cases was the same, by comparing the magnitude of LTP
expressed at 45 minutes (at which growth from E-LTP should be present) and
120 minutes (by which time only spines which have undergone L-LTP will
remain larger) with the respective sizes of the neighbouring spines. As expected
for the E-LTP protocol, we saw that in the control condition there was significant
LTP at 45 minutes, but this had returned to the level of neighbouring spines by
120 minutes (Figure 3.2 d). Surprisingly, LTP in the TTX-stimulated spines was
maintained until the 120 hour timepoint (Figure 3.2 e). This implies that
although the magnitude of LTP is not different between the conditions, the
longevity of the LTP induced is greater after HSP.
3.4.3 Short-term induction of LTP is stronger after HSP
Since HSP is known to cause insertion of AMPA receptors into the spine, the
increased longevity of LTP may be caused by stronger signalling at the time of
stimulation, which therefore induces a mechanistically different and longer
lasting form of LTP. To further investigate this hypothesis, we concentrated on
the dynamics of the plasticity induction. The growth dynamics of stimulated
spines show two non-continuous phases. The first is a very fast, high magnitude
growth (seen by the large peak at timepoint 0 following stimulation, Figure 3.2 a
- c). The second, from the 5 minute timepoint onwards, is a slow relaxation of the
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Figure 3.3 Spine growth dynamics during stimulation
a) Spine volume at timepoint 0 shows a trend to be higher in TTX than control spines,
although the effect does not reach significance (p = 0.189, t-test) b) The spine volume at
timepoint 0 is correlated with the final spine volume (i.e. the final magnitude of LTP). TTX
spines showed a stronger correlation (R2 = 0.683) than the control spines (R2 = 0.175) N = 17
control, 18 TTX. c) Representative image of spine growth during the 60 seconds of glutamate
uncaging. d) Individual traces of spine growth during glutamate uncaging stimulation. The
time of individual uncaging pulses are represented in blue at the bottom of the graph. There
was no significant difference between the overall growth curves between control and TTX (p
> 0.05, 2-way rm ANOVA). N = 16 control, 18 TTX. e) Growth between volume as imaged at
the end of stimulation (i.e. at 60 seconds through the stimulation) and volume at the next
imaging point (timepoint 0), approximately 1 minute later. TTX spines show a greater
increase between these two points, meaning these spines continue to grow more after the
end of stimulation (p < 0.05, t-test). N = 16 Control, 18 TTX.
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LTP down to a steady state value at approximately 45 minutes. This initial
growth, similar to post-tetanic potentiation (PTP) as is reported for
electrophysiologically-induced LTP (Bao et al., 1997), is a reliable structural
consequence of uncaging which has been reported previously for LTP
stimulations (Matsuzaki et al., 2004). It is likely caused by short-term plasticity
effects, and is gradually lost over the next hour as the plasticity settles to a
steady-state value. We hypothesised that the size of the PTP induced by the
stimulation is an indicator of the success of the uncaging induction; and thus the
amount of growth seen at time-point 0 should be reflective of the final
magnitude of LTP. Indeed, in agreement with the finding that TTX-spines show a
longer-lasting plasticity, the magnitude of the PTP is larger in the TTX spines
than the control, although this result did not reach significance (Figure 3.3 a, p =
0.189).
We then compared the initial spine volume at timepoint 0 to the final volume, to
test our hypothesis that the magnitude of the PTP was predictive of the final LTP
outcome. Indeed, we observed that there was a positive correlation between
these factors for both the TTX-incubated and the control condition (Figure 3.3
b). Interestingly, the correlation was stronger for TTX slices than for control
slices (r2 = 0.687 for TTX, 0.178 for controls).
We also examined the dynamics of the growth throughout the 60 second
stimulation itself, by recording high-speed ROIs (at approximately 20 Hz)
throughout stimulus delivery (Figure 3.3 c). We saw that for spines in both
conditions, growth only started occurring after approximately 15 seconds
(equivalent to 7 or 8 uncaging pulses) and continued throughout the stimulation
period in most but not all of the spines (Figure 3.3 d). There was no significant
difference in the growth curves between the control and TTX spines (Figure 3.3
d, p > 0.05, 2-way repeated measures ANOVA), indicating that HPS expression
has not affected the immediate dynamics of LTP induction. However, the growth
104
producing the PTP may not end immediately with the stimulation but could
continue up until the next timepoint imaged (approximately 1 minute later),
which is designated timepoint 0. We therefore compared the size at the end of
the 60 seconds of stimulation to the size at timepoint 0 to see whether indeed
growth continued. In the control case, spines do not continue to grow after the
end of stimulation, but the TTX spines do indeed show growth between these
two timepoints (Figure 3.3 e, p < 0.05, t-test). This suggests that that the
stimulus is having a longer lasting effect in the TTX case than in the control,
which could be the result of changes in receptor composition or short-term
plasticity mechanisms, such as increased calcium influx, which prolong the signal
from the glutamate and lead to enhanced signalling.
3.4.4 The proportion of spines that show LTP is increased after HSP
After determining that LTP induction appears to be stronger in the HSP case, we
next looked at how successful the induction of LTP was. Our stimulus is not
100% efficacious but instead generates a certain amount of failures which do not
result in LTP. In line with current theories of LTP longevity we chose to use the
period between 35 - 45 minutes as the point where, if induced, LTP should be
apparent (regardless of whether it would prove to be E-LTP or L-LTP at later
timepoints). We classified a successful LTP induction as one where the spine size
was significantly different to the baseline values in the timepoints between 30 -
45 minutes, using a standard t-test (Figure 3.4 a & b). We discovered that in the
control condition we induced LTP in 59% of spines, which was increased to 78%
in the TTX condition (Figure 3.4 c). This suggests that, as we had hypothesised
from the data in Figure 3.3, the induction of LTP is more efficacious after HSP.
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Figure 3.4 LTP induction had increased efficacy following HSP
a) All control experiments and b) All TTX experiments for LTP induction, showing stimulated
spine (dark grey and red respectively) and neighbours (light grey and pink respectively).
Neighbours are plotted as mean + SEM. The timepoint used to determine successful LTP (30-
45 minutes) is marked with a line. Successful induction of LTP is marked by a dashed grey
line; unsuccessful induction is marked by a solid maroon line. 10 out of 17 (59%) of control
spines show LTP, compared to 14 out of 18 (78% of TTX spines). c) Percentage LTP induction
successes and failures for the 2 conditions.
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Figure 3.5 Small spines show greater LTP after HSP
a) Linear regression of spine sizes vs. the magnitude of growth at 45 minutes. The TTX
condition shows a significant negative correlation (r2 = 0.27, p = 0.027), whilst the control
shows a non-significant negative trend (r2 = 0.13, p = 0.158). b) Spine sizes vs. magnitude of
LTP at 120 minutes. The TTX case shows an increased negative correlation (r2 = 0.48, p =
0.002), whilst in the control case the negative trend has all but disappeared (r2 = 0.06, p =
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3.4.5 Small spines show a greater LTP than large spines, which is
enhanced by HSP
Knowing that there was a difference in the proportions of spines that potentiate
between TTX and control conditions, we sought to understand what parameters
determine whether or not a spine can express plasticity. One factor that has been
shown to influence the capacity for structural plasticity is the initial spine
volume. Small spines have a greater tendency to potentiate and grow, whereas
large spines express less structural LTP during weak forms of plasticity
(Matsuzaki et al., 2004; Paulin et al., 2016). This result is unsurprising from the
point of view of resource availability, since it would require many more absolute
cellular resources for a large spine to double its volume than for a small one. It
also raises the intriguing possibility that there is a ceiling for potentiation, above
which a spine can’t grow. This maximum set-point could be a biological
imperative. Alternatively, it could be a consequence of the mechanisms required
for plasticity, because when the volume is very large, it may be impossible for
plasticity-inducing signals such as calcium to reach the requisite concentration to
induce further LTP (Sabatini et al., 2001). We looked for a connection between
the initial size of the spine and the plasticity it expressed, at 45 minutes (as a
correlate of E-LTP) and 120 minutes (as a correlate of L-LTP), in our two
conditions. At 45 minutes, as predicted from previous studies, there was a
negative correlation between the initial spine volume and the expressed
plasticity in both conditions (Figure 3.5 a). Unexpectedly there was a stronger
0.346). c) Distribution of all initial volumes of stimulated spines, with example spine images.
The cut-off for defining ‘large’ spines was the median volume off all spines multiplied by 1.5.
Scale bar = 1 µm. d) Time-course of the experiment plotting only the small spines. TTX-
incubated spines show a significantly increased magnitude of LTP (p < 0.05, 2-way repeated
measures ANOVA) than the control. N = 13 control, 11 TTX. e) Time-course of LTP plotting
only large spines. There is no difference between the volumes of TTX and control spines. N = 4
control, 7 TTX.
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correlation for the TTX-incubated condition than for the control condition (r2 =
0.27 for TTX, p < 0.05, r2 = 0.13 for control, p > 0.05). This trend was amplified by
the 120 hour timepoint (Figure 3.5 b), where the correlation had grown even
tighter for the TTX case (r2 = 0.48, p < 0.01), but had disappeared for the control
(r2 = 0.06, p > 0.3). Since the size dependence of LTP was clearly modulated by
the expression of HSP, we divided the stimulated spines into either ‘small’ or
‘large’ spines. Spines were classified as ‘large’ if their initial volume was more
than 150% of the median initial volume of all the spines (Figure 3.5 c). We then
plotted the whole time-course of LTP separately for the small and large spines.
When only the small spines were considered, there was a significant increase in
the magnitude of LTP in the TTX condition as compared to the controls (Figure
3.5 d) (p < 0.05, 2-way rm ANOVA). Large spines showed a negligible amount of
LTP with no difference between the two conditions (Figure 3.5 e). Thus the
capacity for specifically small spines to express LTP after undergoing HSP is
increased.
Although small spines undoubtedly show more plasticity after HSP, we also
know from the data presented in Chapter 2 of this thesis that spines on average
grow after 48 hours of TTX application, so small spines will be rarer in this
condition than in the control. The spines selected for uncaging were not picked
at random, but were in fact chosen to span a wide range of sizes, to allow us to
understand how size affects plasticity. Could it be that in the typical distribution
of spines after TTX application, those we have designated ‘small’ are in fact
extremely rare, and so their enhanced plasticity will be near-to-irrelevant for the
functioning of the circuit under normal circumstances? To address this question,
we used the population data of all spines sizes presented in Figure 2.4 from
Chapter 2. From these measurements we calculated the 25th, 50t and 75th
percentiles for spine sizes in each of the two conditions (Figure 3.6 a). We
observed that none of the stimulated spines in either condition fell below
the25% percentile, so even the smallest of our stimulated spines are not
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Figure 3.6 Behaviour of different sized spines during LTP
a) Population data from Figure 2.4, which was used to define percentiles of population
volumes. Beneath the population data is plotted the initial sizes of the spines used for
uncaging experiments. b) – d) Time-courses of LTP experiments for stimulated spines
separated by initial volumes. b) Stimulated spines in the 25th – 50th percentile. N = 2 control,
6 TTX. c) Stimulated spines in the 50th – 75th percentile. N = 7 control, 4 TTX. d) Stimulated
spines in the 75th – 100th percentile. N = 8 control, 8 TTX.
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vanishingly rare in the whole population. We then plotted the LTP time-course
for the percentiles separately (Figure 3.6 b – e). We saw that, as suggested by
the data in Figure 3.5 d, there is a clear difference between the magnitude of
LTP for spines between the 25th and 50th percentiles (Figure 3.6 b). This trend
did not reach significance, likely because the N was very low (2 for the control, 6
for the TTX). For all of the other percentile graphs, there was no difference
between the control and TTX spines (Figure 3.6 c – e). We can therefore
conclude that up to half of the spines in the population, those below the 50th
percentile, show an enhanced LTP after HSP, and so this effect is not so rare as to
be irrelevant. When the different percentiles are plotted together, there is a clear
relationship between the spine volume and the eventual outcome of plasticity for
the TTX spines (supplementary Figure 3.11 b), but no obvious relationship in
the control case (supplementary Figure 3.11 a). This result reinforces the
conclusion from Figure 3.5 a & b, i.e. that spine size is extremely predictive of
the efficacy of LTP after HSP, but not in the control condition.
3.4.6 Proximal neighbouring spines show short-term structural
potentiation after HSP
As described earlier, the stimulated spines in both experimental conditions show
significant structural LTP in comparison to neighbouring non-stimulated spines.
The majority of studies involving single spine plasticity report that there is no
effect of glutamate uncaging at single spines on nearby neighbours (Matsuzaki et
al., 2004; Harvey and Svoboda, 2007; Govindarajan et al., 2011). However, a
recent study reported that LTP at multiple spines simultaneously caused
shrinkage of nearby non-stimulated spines, hinting that complex dynamics may
be at play with regards to resource use and plasticity expression at neighbouring
spines (Oh et al., 2015). We decided to investigate whether the HSP alters
neighbouring spine dynamics after the induction of single-spine LTP.
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The first indication that there might be an effect on neighbouring spines came
from ranking all of the spines by their distance from the stimulated spine (with 1
being the closest to the stimulated spine) and plotting their growth dynamics
over the 2 hour experiment (Figure 3.7 a). There was a small but noticeable
increase in the volumes of neighbouring spines in close proximity to the
stimulated spine in the TTX case, which is not observed in the control (compare
the first 20 spines in each case, from 0 minutes to 60 minutes after stimulation).
To examine this effect further, we divided neighbours into those closer than 5
µm from the stimulated spine (proximal), and those further away (distal). When
we plotted the time-course over the whole experiments in these two conditions,
we observed that whilst the control neighbours were indistinguishable in the
two conditions (Figure 3.7 b), the TTX proximal neighbours were significantly
different from the distal neighbours over the whole time-course (Figure 3.7 c),
(p < 0.05, 2-way rm ANOVA). This difference was especially prominent
immediately after stimulus delivery and for the succeeding 20-30 minutes. We
conclude that after HSP the neighbours are being affected by the glutamate
uncaging stimulus, even though they are not the spines being directly targeted.
Close examination of the neighbouring dynamics revealed that proximal TTX
neighbours showed a structural post-tetanic potentiation (PTP) at timepoint 0 in
a qualitatively similar manner to that which was observed in stimulated spines,
although with a smaller magnitude. Intriguingly, distal TTX neighbours showed a
slight depression at this timepoint and for the subsequent 5-10 minutes (Figure
3.7 d). This led us to propose that proximal spines may be positively correlated
to the growth dynamics of the stimulated spine; whereas distal spines may be
negatively correlated. To test this we calculated the correlation coefficient
between the dynamics of the stimulated spine and of the neighbouring spines. If
the spine dynamics were highly positively correlated the correlation coefficient
would be close to 1, whereas negative correlations would be close to -1, and
completely uncorrelated dynamics would yield 0. We indeed found that, whilst
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Figure 3.7 Neighbouring spines respond to stimulation after HSP
a) Heat maps of growth dynamics of neighbouring spines over time. Neighbours from all
experiments were pooled and ranked in order of how far they were from the stimulated
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there was no relationship between distance and correlation coefficient in the
control condition (Figure 3.7 d), the correlation coefficient for TTX neighbours
was highly dependent on the distance from it to the stimulated spine (Figure 3.7
f, p < 0.001). Neighbours proximal to the stimulated spine had a high correlation,
which then fell with increasing distance to reach negative values at > 10 µm from
the spine. This intriguing result implies there are some competitive mechanisms
at play which mean that growth (and therefore resource use) of the proximal
spines causes depletion of the resources for distal spines, causing them to shrink.
Other spine parameters were uncorrelated to distance, such as initial spine
volume (supplementary Figure 3.12 a & b) or final spine volume
(supplementary Figure 3.12 c). This shows that there was no bias to the spines
picked to be analysed which could explain the structural effects we see on
proximal neighbours.
spine. These were then plotted in order, with index 1 being the spine closest to its stimulated
spine, and the final index being the spine furthest away. Colours represent the normalised
volume of the spine, from 0 to 5. N = 85 spines, 17 dendrites (Control); 102 spines, 18
dendrites (TTX). b) & c) Spines were divided into proximal (< 5 µm from the stimulated
spine) and distal (> 5 µm). b) Control neighbouring spines show no difference between
proximal and distal spines. n = 22 proximal, 63 distal. c) TTX neighbouring spines are
significantly different between proximal and distal (p < 0.05, 2-way rm ANOVA. n = 32
proximal, 70 distal). d) The average volume over the first 10 minutes after stimulation for
the different neighbouring conditions. Control neighbours show no growth and no difference
to one another (p > 0.05), whilst TTX proximal neighbours are significantly larger than distal
(p < 0.001, Kruskal-Wallis post-hoc Dunn. n = 22 control proximal, 63 control distal, 32 TTX
proximal, 70 TTX distal). e) & f) Correlation coefficients were calculated between the
stimulated spine and the neighbouring spines. This was regressed against the distance from
the stimulated spine. e) There is no relationship between distance and correlation
coefficient for the control neighbours (p > 0.05). f) TTX neighbours show a strong
relationship between distance and correlation coefficient (p < 0.001).
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3.4.7 The threshold for plasticity induction is reduced following
HSP
To test the hypothesis that HSP sensitises spines to allow them to respond to
lower glutamate levels, we devised a new stimulus protocol with a reduced
strength. Based on reported protocols in the literature, this employs a shortened
laser pulse width of 1 ms (compared to 4 ms for the previous protocol), so
lowering the amount of glutamate released during the uncaging stimulus
(Harvey and Svoboda, 2007; Govindarajan et al., 2011). All other parameters of
the stimulus, i.e. stimulus power, frequency and duration, remained the same.
This ‘sub-threshold’ protocol has been described as having no long-lasting
structural effects on spines when applied to one spine. However, when applied in
conjunction with other strong LTP stimuli, the spine can respond through either
a process of cross-talk or synaptic cooperation for proteins. We applied the 1 ms
protocol to individual spines in both control and TTX conditions, and followed
the resulting spine volume for 1 hour post stimulus. We observed that in the
control condition, there is a short-term PTP following the stimulus but no long
lasting plasticity. The growth dynamics are not significantly different from the
neighbouring spines over the entire time-course (Figure 3.8 a, p > 0.05, 2-way
repeated measures ANOVA, post hoc Bonferroni). The initial PTP, which is not
reported in previous data using this protocol, may be attributable to a variation
in our protocol by which we ensure that Mg2+ levels in the uncaging aCSF are
absolute zero, to allow for maximal stimulation of NMDA receptors. The
previously reported protocols, whilst having no added Mg2+ in the uncaging
aCSF, likely had low levels of residual Mg2+ from the preceding perfusion of the
normal aCSF (personal communication). This will have reduced the likelihood of
NMDA channel opening and so decreased the stimulus strength. In the TTX
condition, in contrast to the control, the stimulated spine grows significantly
when compared to neighbouring spines over the 1 hour observation period
(Figure 3.8 b, p < 0.01, 2-way rm ANOVA, post hoc Bonferroni). Thus as we
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hypothesised the threshold for the glutamate level needed to induce LTP was
lowered by the previous expression of HSP. We then compared the magnitudes
of growth after the 1 ms protocol compared to our previous data from the 4 ms
protocol. In the control condition as expected there was a significant difference
between the 1 ms and 4 ms conditions (Figure 3.8 c, p > 0.05). In the TTX case
Figure 3.8 Reduced threshold for LTP after HSP
a) A sub-threshold stimulus with 1 ms pulse width produces a PTP but no long lasting
potentiation in the control condition (p > 0.05, 2-way repeated measures ANOVA post-hoc
Bonferroni) N = 11 stimulated spines, 52 neighbouring spines. b) A 1 ms stimulus does
produce LTP after TTX incubation (p < 0.01). N = 11 stimulated spines, 52 neighbouring
spines. c) The structural changes are significantly reduced after the 1 ms stimulus as
compared to the usual 4 ms LTP stimulus in the control condition (4 ms data is the same as is
shown in Figure 3.2). (p < 0.05 2-way rm ANOVA). N = 11 for 1 ms, 17 for 4 ms. d) There is
no significant difference between the 4 ms and 1 ms traces after TTX incubation. (p > 0.05m
2-way rm ANOVA, although they are different at the 0 minutes timepoint, p < 0.001, post-hoc
Bonferroni). N = 11 for 1 ms, 18 for 4 ms.
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however the 4 ms and the 1 ms were not significantly different, showing that this
nominally sub-threshold stimulus is in fact supra-threshold after HSP (Figure
3.8 d, p > 0.05). The two traces did however differ at the 0 minute timepoint (p <
0.001), implying that the induction of plasticity was stronger after the 4 ms
stimulus. Since we previously described in Figure 3.3 that the induction size was
predictive of the final outcome of plasticity, it would be interesting to repeat the
1 ms experiments but follow the spines for 2 hours instead of 1 hour as we
present here. We would predict that lower magnitude of induction than in the 4
ms case would be translated into shorter-lasting plasticity, probably being lost
before the 2 hour timepoint.
3.4.8 LTP capacity is maintained after 72 hours of activity blockade
Although we chose to conduct the majority of our LTP experiments after 48
hours of activity blockade with TTX, we know from the electrophysiology data
presented in Chapter 2 that, even after 72 hours incubation with TTX, neurons
are still healthy and showing normal electrophysiological responses. We
therefore decided to conduct a set of LTP experiments at 72 hours, to verify
whether the capacity for LTP was maintained even after so long without activity.
Importantly, the experiments previously described which showed an increased
magnitude of functional LTP after HSP were performed after 60 hours of activity
blockade (Arendt et al., 2013). Conducting experiments at 72 hours would allow
us to span the timeframe of these previous experiments and verify that any
differences we observed between ours and their results were not due to a change
in the system responses occurring between 48 hours and 60 hours of activity
blockade. When we performed the uncaging experiments after 72 hours of
activity blockade, we saw that LTP was indeed induced at this timepoint, and
that the magnitude was indistinguishable from that of the control slices (Figure
3.9). We therefore concluded that we saw no evidence for increased magnitude
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of LTP at 72 hours, so the difference between our data and that seen in Arendt et
al is not due to the timepoint, but is instead more likely to be attributable to the
differences between field stimulation and single spine stimulation.
Figure 3.9 Capacity for LTP is maintained after 72 hours of activity block
The time-courses for the progression of LTP after 72 hours of activity blockade are
indistinguishable for the control and the TTX conditions. N = 6 control, 3 TTX
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3.5 Discussion
In this chapter, we have shown that single spine LTP is intact and has an
increased longevity following 48 hours of activity blockade (Figure 3.2). These
data demonstrate that the information-coding capacity of the circuit is not
compromised by the expression of HSP. Since many of the output mechanisms
are the same between HSP and LTP, we may have expected the opposite – that
the expression of HSP to occludes LTP. Instead, the cells and spines retain their
ability to further undergo plasticity. Indeed, not only is LTP still possible but it is
even enhanced, as seen by an increased efficacy of induction (Figure 3.3 &
Figure 3.4). Small spines in particular show a higher LTP magnitude after
activity blockade (Figure 3.5). An explanation for this may be that HSP lowers
the threshold for potentiation, shown by metaplasticity at neighbouring spines
after HSP (Figure 3.7) and an increased response to a stimulus with lower
glutamate levels (Figure 3.8).
3.5.1 Correlation between spine size and LTP after HSP
An interesting aspect of our results was that spine size is extremely predictive of
the eventual outcome of LTP after TTX-mediated activity blockade, but not in the
control condition. Although it is not possible to know from our data what causes
this, we can speculate on a possible explanation, which is that magnitude of LTP
is determined not just by the uncaging stimulation but also by the previous
history of the spine. Recent plasticity may not affect the immediate growth
during the stimulation, which is dependent on contemporaneous calcium
signalling caused by activation of receptors, but it may modulate the long-term
outcome of plasticity due to residual plasticity-related products. Indeed not only
plasticity events at the spine itself but those occurring at a different spine in the
same dendritic domain can also affect the long-term plasticity of the spine
through processes of cross-talk mediated by diffusion of the signalling molecule
Ras (Harvey and Svoboda, 2007; Harvey et al., 2008) or sharing of proteins
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through synaptic tagging and co-operation (Frey and Morris, 1997; Govindarajan
et al., 2011). In the control case for our experiments, the spines have been
undergoing activity (and so by extension, activity-dependent plasticity)
throughout their development, so the uncaging stimulus and the growth caused
by it are only partially instructive of the final magnitude of plasticity. In the TTX
case however activity has been blocked for the preceding 48 hours, meaning that
there are no residual effects from other plasticity events and the uncaging
stimulus is very predictive of the final plasticity expressed.
3.5.2 Volume dependence of LTP expression
Enhanced LTP in small spines after HSP is a novel and interesting result for the
field of homeostatic synaptic plasticity. It suggests that although synaptic
strengths scale up proportionally after HSP, the potential for plasticity may be
selectively enhanced at small spines as compared to large ones. A clue to why
this might happen could lie in the data presented in Chapter 2. There we saw that
at the 48 hour timepoint, spines did not scale linearly but instead there was an
over-representation of large spines (Figure 2.4). This suggests that large spines
undergo homeostatic potentiation before small spines, and thus increase supra-
linearly. By 72 hours however, the distribution has re-linearised, which could be
achieved by the small spines having ‘caught up’ and themselves undergone
potentiation. The timepoint we studied here, 48 hours after activity block, may
therefore be one in which small spines are especially primed to structurally
potentiate. Homeostatic mechanisms have already occurred to strengthen the
synapses (as shown by the linear functional scaling seen in Figure 2.3), but the
structure has not yet stabilised to match. The result is a ‘gain modulation’ of
Hebbian plasticity – small spines are in a state of readiness which allows an
easier induction and increased longevity of LTP when they receive the
appropriate stimulus. This could act to make the circuit more sensitive to any
inputs it does receive. Large spines will already have a high conductance so their
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potentiation will not greatly change the circuit. Small spines however will
initially contribute little to the activity of the post-synaptic cell, so increasing
their capacity to potentiate can allow a neuron to respond to any activity more
easily. This possibility coheres well with the hypothesis put forward in Chapter
2, whereby activity blockade equalises network firing rates, disrupting the
normal skewed firing distribution. In an extreme case of activity perturbation
such as total activity blockade, the normal circuit architecture is clearly
insufficient to provide the necessary input for the network. It therefore makes
sense to override the previously determined circuit level firing, to attempt to find
a new network shape which can maintain the correct levels of firing. The same
argument can be put forth for the synapses on a single cell. Whilst synaptic
scaling will preserve weights and thus the learned shape of the network, in an
extreme case that architecture is clearly unhelpful, since the spines that
previously transferred a lot of network activity are now failing to do so. In such
cases, a cell needs to acquire alternate sources of upstream activity to maintain
the correct levels. This activity may come from sources which previously it did
not have strong links to – i.e. the small synapses. By priming the small synapses
to potentiate for lower levels of activity and to a higher magnitude, the cell
increases its chances of re-entering a functional network and restoring its
optimal activity levels.
3.5.3 Mechanisms for enhanced LTP after HSP
Previous studies of LTP following HSP have provided possible mechanistic
explanations for the results we observe in this chapter. A likely candidate for
mediating the changes in plasticity capacity is the NMDA receptor, which is
highly involved in plasticity due to its functioning as a coincidence detector and
its calcium permeability. The affect of activity changes on the expression of
NMDA channels in not unanimously agreed upon, with some reports of no
change in NMDA expression following HSP (Lissin et al., 1998) and others
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showing increased NMDA expression (Watt et al., 2000). Despite this, there is a
weight of evidence to suggest that even though the number of NMDARs may not
change, their distribution and subunit composition is altered by activity blockade
(Lee et al., 2010; Arendt et al., 2013). Blockade of single synapses using a genetic
tool to prevent pre-synaptic vesicle fusion caused an increase in NMDA receptors
containing the GluR2B (as opposed to the GluR2A) subunit (Lee et al., 2010). This
is a particularly relevant change because the GluR2B subunit has an increased
calcium permeability, as well as having an increased affinity with other signalling
components important for structural plasticity such as CaMKII (Barria and
Malinow, 2005; Paoletti et al., 2013). An increased calcium influx and prolonged
signalling dynamics could be responsible for the data presented in Figure 3.3,
which showed that for the same amount of glutamate stimulation, the TTX-
incubated spines grew for longer periods of time (even after the stimulation had
finished) and reached higher levels than the control spines. It would be
interesting in the future to use a calcium sensor such as GCaMP6 to observe the
calcium dynamics during and immediately following the stimulation, to test the
hypothesis that calcium signalling is prolonged after HSP leading to a longer
lasting plasticity. This subunit change may also make the synapse responsive to
lower glutamate levels, since less NMDAR activation is required for the same
amount of calcium signalling, which could explain the data from Figure 3.7 and
Figure 3.8.
A related recent finding is the reported increase in silent (i.e. AMPAR-lacking)
synapses after HSP induction (Arendt et al., 2013). Since these synapses are
newly formed they are likely to be small, and so could plausibly be the ‘small’
synapses we observe showing enhanced LTP in our TTX-incubated slices
(although it’s worth noting that in control conditions there is no correlation
between occurrence of silent synapses and spine age (Zito et al., 2009)). It is
currently unknown how a silent synapse will respond to single-synapse LTP. In
conditions of physiological extra-cellular Mg2+, the activation of NMDA receptors
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requires post-synaptic depolarisation to relieve the Mg2+ block, so it is unlikely
that glutamate stimulation of a silent synapse alone could cause plasticity. In our
experimental system where an extracellular concentration of 0 mM Mg2+ releases
the Mg2+ block, NMDA receptors will be activated by single synapse stimulation,
and thus a silent synapse could still undergo structural and functional LTP. Using
glutamate uncaging it is possible to stimulate multiple spines pseudo-
synchronously to cause the intra-cellular depolarisation necessary for Mg2+ block
release whilst in the presence of physiological concentrations of Mg2+ (Losonczy
and Magee, 2006; Losonczy et al., 2008; Govindarajan et al., 2011). It would be
very interesting to repeat these experiments in this chapter in physiological
concentrations of Mg2+ but stimulating multiple synapses, to see whether the
same results are obtained for enhanced small spine growth. To directly test the
capacity for plasticity of silent synapses, one could use a voltage-dependent dye
to fill the neuron. Imaging a synapse while performing glutamate uncaging at it
would reveal whether the stimulation produced a depolarisation. If no
depolarisation could be observed, the synapse would be lacking AMPAR and
would therefore be silent. The plasticity-inducing experiments detailed in this
chapter could then be carried out as usual.
3.5.4 Heterosynaptic plasticity after HSP
We observed that spine close the stimulated spine exhibited a transient
structural LTP in the TTX condition, but not the control. There are two
mechanistic possibilities for why proximal TTX spines are affected by the
uncaging stimulus, whilst distal ones are not. The first is that glutamate is
spilling over from the stimulation site and reaching neighbouring spines. In this
case, HSP must have sensitised the spines to respond to extremely low levels of
glutamate, since we do not see any effect in the control condition. This is unlikely
given what is known about the extremely small excitation volume of glutamate
uncaging (Pettit et al., 1997; Smith et al., 2003). The second more likely
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possibility is that plasticity-induced signalling within the dendrite is spreading
from the stimulated spine to the regions of neighbouring spines and causing
structural plasticity without the neighbouring synapse itself receiving glutamate
stimulation. It is known that plasticity-related signalling molecules such as Ras
can indeed spread to nearby dendritic regions and lower the threshold for
plasticity, a process known as cross-talk (Harvey and Svoboda, 2007; Harvey et
al., 2008). With our current data it is very difficult to distinguish whether either
(or both) of these two possibilities is occurring, since any nearby spines will be
close both in dendritic distance and in space for glutamate diffusion. An
empirical way to distinguish between the two would be to stimulate a spine
which had visible neighbours very close by but on a separate branch, so the
physical distance for glutamate to diffuse is small but the dendritic distance is
very large. If glutamate spill-over was the reason for the increased neighbouring
growth, we would expect these spines to show plasticity. If a signal in the
dendrite causes the threshold lowering, these spines should not be affected. It
will be extremely interesting to know if one or both of these mechanisms are
playing a role in the effect on neighbours we have observed. I will discuss the
implications of this type of heterosynaptic plasticity in greater detail in Chapter
4, the general discussion.
3.5.5 Conclusion
It is clear that much more work remains to be done to fully understand how
Hebbian plasticity and HSP interact. We here have uncovered a feature of HSP
which confers an increased sensitivity to LTP on the neural circuit, therefore
allowing it to respond more strongly to any inputs it receives, which opens the
door to deeper investigation of the molecular and structural mechanisms which
allow this to occur.
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3.6 Supplementary figures
Figure 3.10 Post-stimulation growth for real stimulations and sham stimulations
Sham stimulations were delivered to all neurons at the beginning of the experiment. Only if
no growth was seen due to the sham stimulus was the experiment continued to deliver the
real stimulation. Spine volumes after the real stimulations are significantly higher than for
either the neighbouring case, or the sham stimulation (p < 0.001, ANOVA post-hoc
Bonferroni).
Figure 3.11 LTP magnitude grouped by spine volume
a) Control stimulated spines divided into percentiles of spine volume. There is no clear
relationship between the volume of the spine and the eventual magnitude of LTP which it
expresses. b) TTX stimulated spines. After activity blockade, the spine volume is very
predictive of the magnitude of the LTP throughout the whole experimental time-course.
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Figure 3.12 Neighbouring spine parameters
a) There was no relationship between the distance from the stimulated spine and the initial
spine volume, showing that there was no bias in the spines selected for analysis which could
account for the neighbouring dynamics seen in Figure 3.7. TTX neighbours are on average
slightly larger than control neighbours, as would be expected from the data presented in
Chapter 2 Figure 2.4. b) Linear regressions of the initial spine volume with the normalised
final spine volume. Although there is a slight negative trend, there was no significant
correlation for either the control or TTX condition (p > 0.05). c) There is no relationship
between the distance from the stimulated spine and the final spine volume for either
condition (p < 0.05), showing that any structural changes which occur due to the stimulus are
short-lasting.
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3.7 Bibliography
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Argunsah AO, Erdil E, Ghani M., Yagci AM, Ramiro-Cortes Y, Hobbiss AF, Kanik S., Cetin M, Israely I, Unay D (2016) SpineS: A Tool for Automatic Tracking of Dendritic Spines. “In prep.”
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h
Chapter 4
4 General Discussion
General discussion:
Extending HSP from
synapses to network-level
functioning
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4.1 Abstract
The need for stability is a universal one in biological systems. In this thesis we
have shown that neurons can co-opt structural mechanisms to prolong and
enhance efforts to normalise their activity. In this discussion we explore how the
structural changes, which seem to be decoupled from functional ones, will affect
the circuit functioning. In particular, we focus on how the selective sensitisation
of small spines will alter the arrangement of the network, and how this might be
reinforced by known plasticity interactions such as synaptic tagging and capture.
We also discuss how the heterosynaptic plasticity observed in neighbouring
spines might contribute to network level changes. Lastly, we discuss how HSP
might play a role in neurodevelopmental disorders, and its connection to sleep
mechanisms as proposed in the synaptic homeostasis hypothesis (SHY).
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Before exploring the themes of this thesis, a note about phraseology. When
writing this discussion, the intuitive method to write about the tendency for a
system – be it a cell, synapse or circuit – to stay within optimal bounds is as
‘wanting’ to return. This anthropomorphism is clearly inaccurate, seeing as it
attributes a sense of agency to an individual component of a circuit. Despite that
it is perhaps not misleading, in that it allows us a shorthand way to refer to the
driving force behind the circuit dynamics; even if the biological reasons for that
driving force are not yet entirely known. Thus, a certain number of the terms
‘wanting, ‘preferring’ or such similar phrases may have crept in. I request that
the reader treat these with a sense of leniency in the literal sense, and instead
ask that attention to be concentrated on the idea behind them.
4.2 Overview of work in this thesis
In this thesis, we describe the structural changes which accompany the induction
and expression of homeostatic synaptic plasticity (HSP). We induce HSP by
incubating organotypic slices for 0 – 72 hours in the Na+ channel blocker TTX,
and use electrophysiological methods and imaging to assess the functional and
structural changes which occur as a result of the activity blockade. In common
with previous observations for physiology, we discovered that synapses scaled
upwards after 48 – 72 hours activity blockade. With regards to the structure, and
contrary to predictions, we did not discover a linear scaling relationship after
homeostatic synaptic plasticity. Instead, some spines showed a higher
propensity to co-opt homeostatic mechanisms than others, leading to a
preponderance of large spines after 48 hours of activity blockade. However,
when stimulated individually using glutamate uncaging, these large spines
showed little capacity to further express LTP (as indeed was the case for the
control condition). The small spines, which had not been scaled up by a large
amount, were however highly sensitive to LTP, and grew more than those in the
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control condition. Neighbouring spines at nearby dendritic locations to the
stimulated spine also showed a short-term structural plasticity after HSP, but not
in the control condition. This led us to propose a ‘sensitisation’ mechanism,
whereby small spines have an increased capacity to express LTP following HSP.
4.3 Beyond synaptic scaling – silent synapses and network
function
The process of sensitisation at select synapses will benefit a neural system
undergoing HSP by making it able to respond to new sources of activity. In vivo
strong homeostatic mechanisms, such as induced in the experiments for this
thesis, will come into play when a system loses a large amount of input, for
instance after sensory deprivation or after the loss of a limb. The main sources of
activity, which would have been encoded by strong connections from upstream
cells, suddenly become silent. For the system to restore its activity levels it
requires new upstream providers of activation, since the old ones have been
proven to no longer carry the information they used to. This could potentially be
achieved by amplifying every source of information in a correlated manner, as
proposed in the classical theory of synaptic scaling. Our results indicate however
that although scaling may occur when HSP occurs in an isolated system, when it
is embedded in a dynamic network undergoing different types of plasticity, more
complex dynamics may be at play. Firstly, the structure of the synapse, which
will be highly influential for the long-term consequences of the plasticity, does
not immediately scale in a correlated manner to the physiology. Instead, large
spines seem to respond initially to the activity blockade by increasing in volume,
with small spines following behind. Secondly, the sensitisation of small inputs to
LTP provides an alternative manner of increasing activity to pure synaptic
scaling. It primes the system for activation but only potentiates the spines that
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actually receive the stimulus. This is more efficient than increasing everything
equally, since in such a system further Hebbian plasticity would need to occur to
refine the circuit.
In Chapter 3, we expounded the idea that the small, highly sensitive spines we
saw after HSP could be the newly formed silent synapses, which are known to
arise after activity blockade in systems both in vivo and in vitro (Ashby and Isaac,
2011; Arendt et al., 2013). If it were verified that these silent synapses do have
an enhanced potential for plasticity, then this would have interesting
implications for the functioning of the downstream circuit, because it would
hasten the formation of an activity-dependent network. In the barrel cortex in
vivo, whisker trimming increased the proportion of silent synapses (Ashby and
Isaac, 2011). Interestingly, there was also a decoupling of structure and function,
i.e. between spine volume and conductance, as we observed in our own data
presented in Chapter 2. This mismatch was due specifically to the silent
synapses, because if only non-silent synapses were considered there was a
strong positive correlation between volume and conductance, a similar finding to
previous reports (Busetto et al., 2008). Ashby and Isaac suggest that the template
for connectivity in sensory cortex is experience independent, and is formed
through AMPAR-lacking spines. These are then unsilenced by activity which
drives AMPAR insertion. This mechanism, combined with our data that spines
after HSP have a decreased threshold for activity and increased magnitude of
LTP expression, emphasises the circuit-level sensitisation to any activity which
occurs after homeostasis. Although the mechanisms discussed in this thesis are
purely synaptic and nominally cell-autonomous, they will therefore also have
important implications for the network-level activity and organisation.
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4.4 Heterosynaptic plasticity and synaptic clustering
As well as the sensitisation of the spine receiving the activity, we also observed
structural plasticity in spines neighbouring it as presented in Figure 3.7 from
Chapter 3. An original tenet of Hebbian plasticity theory was that it is input-
specific, meaning the change in strength happens only at the synapse to which
the input has arrived. Many results in previous years have however challenged
this assumption, showing that plasticity at nearby synapses can affect each other
(Harvey and Svoboda, 2007; Govindarajan et al., 2011; Oh et al., 2015). This
phenomenon is known as heterosynaptic plasticity. It is intriguing that our
results point towards the co-option of heterosynaptic signalling occurring after
HSP, an association which has been hinted at in recent modelling studies
(Chistiakova et al., 2015).
One important mechanism of heterosynaptic plasticity which has received much
attention over the past decades is synaptic tagging and capture (Frey and Morris,
1997). In this process, a synapse is ‘tagged’ by receiving activity. This tagging
process, which may constitute a molecular or structural change to the synapse
(with rearrangements to the actin cytoskeleton being a possible candidate
(Fonseca, 2012)) allows it to capture plasticity-related proteins synthesised
nearby, at timeframes shortly preceding or succeeding the setting of the tag
(Redondo and Morris, 2011). Behaviourally, this will allow the binding together
of relevant stimuli in time to form better associations with events occurring in
the outside world. A second effect it will achieve is to sensitise the circuit to
activity, because stimuli which were previously sub-threshold or caused only
short-lasting plasticity are boosted, allowing them to cause long-lasting changes
(Govindarajan et al., 2011). This mechanism of sharing plasticity-related
resources is known as cooperation. Synapses can also compete for plasticity
when in a regime of limited resources (e.g. if protein synthesis is inhibited),
restricting the spatial and temporal range over which synapses can facilitate one
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another (Fonseca et al., 2004). On a structural level, the effect of synaptic
cooperation and competition can be profound for the organisation of synapses
on a dendrite. In particular, the sharing of plasticity-related proteins along a
limited synaptic distance will result in the formation of clusters of synapses
(Govindarajan et al., 2006; Ramiro-Cortés et al., 2014) (Figure 4.1 a – e). Cluster
formation will increase the memory storage capacity and computation power of
a circuit (Poirazi and Mel, 2001; Chklovskii et al., 2004). This effect is due to non-
linear dendritic integration mechanisms caused by voltage sensitive channels in
the membrane. These allow dendritic spiking, meaning inputs which arrive close
together in time and space are more effective at causing somatic depolarisation
Figure 4.1 Structural clustering after cooperation and competition
(adapted from Ramiro-Cortés, Hobbiss and Israely, 2014)
(a) A dendritic region receives a pattern of activity. Some spines receive strong late-LTP
inputs, in this example, spine i. Others (ii, iii, iv, v) receive weaker early-LTP inputs, which
set a synaptic tag. (b) Signalling mechanisms including protein synthesis are initiated
around spine i, which travel outwards along the dendrite. Tagged spines in the surrounding
regions (ii, iii, iv) compete for the resources. Spine v is outside the range of the proteins so
will not enter the competition. (c) The winning spines (i, ii, iii) are selectively strengthened.
The losing spine (iv) does not express long-lasting plasticity. At longer time scales: (d) new
spine growth can be initiated around the site of plasticity (dashed spines), while previously
existing spines that were not strengthened, are removed (faint spines). (e) A structural
cluster is formed, reflecting the summation of activity which came into the dendrite.
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(Branco and Häusser, 2009, 2011). There is emerging evidence for plasticity
clusters following Hebbian plasticity, implying that indeed this is a mechanism
that neurons employ to increase the capacity and stability of the circuit
(Kleindienst et al., 2011; Makino and Malinow, 2011; Fu et al., 2012; Lee et al.,
2016). The effect that synaptic clustering will have on HSP-mediated circuit
recovery following loss of input, and conversely that the expression of HSP will
have on the maintenance of pre-existing clusters, is still to be determined.
One likely effect supported by the data presented in this thesis is that
heterosynaptic mechanisms will cause a sensitisation of the circuit to new points
of activity. This could be an invaluable way to regain the requisite sources of
activation required to re-normalise network activity. If new synapses are formed
in response to activity blockade as is hinted at in previous data (Ashby and Isaac,
2011; Arendt et al., 2013), then these could be formed preferentially close to any
sites where activity still arrives (Figure 4.1 d). In a regime of global activity
blockade there will be no upstream activity so synaptogenesis may occur
randomly, which is why this effect may not have been reported in previous work.
However, in a more realistic system whereby some small amount of residual
activity remains, there could be a bias to form new synapses around these sites.
It is known that glutamate application alone can cause new synaptogenesis at
dendritic sites although this was discovered in an immature culture where
dendritic spines are as of yet unformed (Kwon and Sabatini, 2011). Since our
data points to a sensitisation of spines and dendritic sites to lower levels of
activation following HSP, it is plausible that any synaptic activation will cause
plasticity and synaptogenesis surrounding any sites of activity. The data
presented in Chapter 3 showing that small spines have an increased capacity for
potentiation would also enhance a clustering effect in this scenario, since the
newly formed small spines will be more easily potentiated and so will be
stabilised more easily. If HSP can indeed facilitate synaptic cluster formation, it
would accelerate the process of regaining the optimal activity level because the
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aforementioned non-linear dendritic integration would allow the cell to achieve
the same amount of output firing for less synaptic activation.
A tangible example of this type of circuit level change could be the re-wiring of
networks known to occur in the human cortex after a major perturbation, for
example going blind. The visual cortex is deprived of its major sources of input
and so its activity level will initially drop (Keck et al., 2013). Over time invasion
of the visual areas will occur from other sensory areas which still receive input
(Bavelier and Neville, 2002; Butz and van Ooyen, 2011). I will use the auditory
cortex as an example, although the somatosensory cortex is also commonly
involved (Dormal and Collignon, 2011; Lane et al., 2015). This new source of
innervations from auditory areas can result in increased auditory acuity due to a
larger neural space dedicated to it. Conversely loss of ‘real’ visual input can lead
to visual hallucinations (Reichert et al., 2013). These well-described phenomena
could be the result of the HSP-mediated re-wiring and cluster formation which
has been discussed (Butz and van Ooyen, 2013). It would fit the criteria of a large
activity change which can re-normalise not just by synaptic scaling, because the
previously reliable sources of upstream activity (in this case those carrying
visual information from the eyes) have become permanently silent. Instead, new
sources, those from the auditory areas which before were not important for
visual cortex functioning, suddenly become major players in providing synaptic
activation and so these synapses will become strengthened. Similarly, stroke-
induced lesions are known to induce neural remapping which could also be
accounted for by HSP mechanisms (Wittenberg, 2010; Butz et al., 2014).
4.5 Homeostatic Synaptic Plasticity in health and disease
The main focus of this thesis has been to investigate how a ‘normal’ (i.e. wild-
type) system responds to perturbations of activity. The in vivo analogies which
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have been drawn are with systems suddenly losing input, such as in sensory
deprivation or limb loss. A separate field however has started to take great
interest in the ideas of homeostasis; that of neurological disorders. There is
emerging evidence that homeostatic processes in the brain might be
compromised in some common disorders. Studies of homeostatic processes in
these conditions give us information about the pathophysiology of the disease.
Additionally, we can also gain insight into the wild-type signalling processes by
studying what modifications (normally genetic mutations) interrupt
homeostasis, and therefore which signalling pathways and molecular players are
involved.
We began to address this theory in section 2.4.3, by investigating whether the
loss of the mTOR regulator Tsc1, which in humans causes the
neurodevelopmental disorder Tuberous Sclerosis, affected HSP. We found that
despite structural differences between Tsc1- and wild-type spines, with those
bearing the Tsc1 mutation being smaller, structural up-scaling was intact in this
model. It may be that other types of homeostatic plasticity, in particular synaptic
down-scaling, are impaired by loss of this signalling mechanism, since the
resultant circuit has pathologically high activity rates (Talos et al., 2012; Bateup
et al., 2013b). There are many other mental retardation disorders which have
been suggested to have aberrant homeostatic plasticity, including Fragile X
disorder (Soden and Chen, 2010; Sarti et al., 2013), Rett syndrome (Blackman et
al., 2012; Qiu et al., 2012; Zhong et al., 2012) and numerous others (reviewed in
(Wondolowski and Dickman, 2013)). Interestingly, what these diseases have in
common is a high propensity to autism – in fact, all of the aforementioned
diseases can be classified as autism spectrum disorders (ASDs) (Toro et al.,
2010). ASDs constitute a complex, heterogeneous set of developmental disorders
characterised by a set of clinical symptoms including deficits in social
behavioural and impaired communication skills (Newschaffer et al., 2007). One
promising line of investigation into the causes of ASDs has emerged from the
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findings that aberrant protein translation occurs frequently (Kelleher III and
Bear, 2008). Loss of normal levels of protein – or as we could say, loss of protein
homeostasis – causes diverse defects to synapses and cells, including changes to
spine density, spine volume, soma size and capacity for plasticity. Although
occurring in many different points in signalling pathways, there is a convergence
of the downstream effects with similar behavioural phenotypes being exhibited.
Since HSP relies on fine-tuning levels of receptors and other signalling molecules
through processes of transcription and translation, the disregulation of these
processes (either upwards or downwards) will have serious consequences for
the ability to correctly express homeostatic processes. It is interesting to
postulate that when homeostasis goes awry, it could result in not only the most
obvious disorder of pathological activity rates, i.e. epilepsy, but also in the
impairment of many other cognitive functions. The relevance of HSP to neural
functioning is not therefore limited to extreme cases such as limb loss or sensory
deprivation, but is vital throughout the whole lifetime of an animal and for the
maintenance of many if not all of the ‘normal’ processes taking place in the brain.
As well as neurodevelopmental disorders, other neurological diseases such as
Alzheimer’s Disease have been associated with loss of homeostasis, in this case
as a consequence of the ageing process (Jang and Chung, 2016). Furthermore,
neuropsychiatric disorders such as depression and schizophrenia are associated
with global structural and functional changes to cells and dendrites (Dickman
and Davis, 2009; Castrén and Hen, 2013). Chronic stress, a known risk factor for
developing depression, causes reduced dendritic length and spine density in
striatal neurons (Dias-Ferreira et al., 2009). These tantalising clues to aberrant
signalling and plasticity process may prove to be the first steps in learning that
homeostasis is vital for keeping the brain in a healthy state, and that a multitude
of different disorders can arise if it is disrupted.
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4.6 The role of sleep in homeostasis
The homeostatic processes so far referred to have been concurrent with the
normal functioning of the circuit. It has been proposed however that sleep, a
neural state found in a majority of animal species and characterised by loss of
consciousness and highly altered patterns of correlated neural activity, functions
as a specific time for the enactment of synaptic homeostasis. This theory, known
as the synaptic homeostasis hypothesis (SHY), proposes that during wakefulness
the majority of learning that takes place is through strengthening of excitatory
connections. The function of sleep, therefore, is to restore synaptic homeostasis
by allowing synaptic weakening (Tononi and Cirelli, 2003, 2014). The SHY
theory has been recently supported by experimental evidence (Liu et al., 2010;
Bushey et al., 2011; Grosmark et al., 2012), although some of its predictions and
assumptions have proved controversial (Frank, 2012; Durkin and Aton, 2015;
Hengen et al., 2016). As well as specific sleep/wake states, the circadian cycle has
been implicated in setting the timing of homeostatic synaptic changes
(Appelbaum et al., 2010).
From the point of view of the data presented in this thesis, the differentiation
between timeframes for Hebbian and homeostatic plasticity may help to
delineate which mechanism is promoted at a specific time (i.e. high calcium
causes LTP during wakefulness, and down-scaling during sleep). If this were
true, it would imply some further signalling, such as by neuromodulators, which
differs between the sleep-state and the wake-state and will promote one form of
plasticity over the other. In our reduced system, we lack neuromodulatory input
which may explain why homeostatic changes take so long (48 hours) to emerge,
whereas homeostatic processes are theorised to occur on much shorter
timescales in vivo (Zenke et al., 2013; Zenke and Gerstner, 2016). Many of the
same mechanisms are apparent however, which include structural modifications
and functional synaptic scaling (Liu et al., 2010; Bushey et al., 2011), suggesting
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that whilst the mechanisms of HSP may take longer to develop in our system, the
same pathways might be co-opted in the end.
In an interesting link to the previous section on health and disease, disrupted
sleep, and the ensuing implication of disrupted synaptic homeostasis, have been
explicitly implicated in serious mood disorders such as depression and suicidal
thoughts (Sher, 2009). If indeed sleep performs the vital role of promoting HSP
to counteract the plasticity from a day’s wakefulness, it is unsurprising that
serious psychological disorders could arise from lack of it, again reinforcing the
vital role homeostasis plays in maintaining our neural system in a healthy state.
4.7 Conclusion
Homeostatic mechanisms occurring at the synapse are induced by global shifts in
activity levels. These mechanisms encompass functional changes, due to receptor
changes at the synapse, and structural ones, likely to be the result of changes to
the cytoskeleton and other scaffolding molecules. The result is global changes to
synaptic strengths across the neuron in a correlated manner. The end result
however does not tally with a simplistic concept of synaptic scaling, since not
only do important structural components such as the volume not scale linearly,
but the sensitivity to future plasticity is modulated depending on spine size.
Therefore HSP will not just alter the activity level of the individual cell but by
adjusting the sensitivity to different inputs will allow the circuit to change shape
as well. This will be enhanced by metaplasticity mechanisms which are co-opted
at neighbouring synapses to allow them to express plasticity. The result could be
that new clusters of synapses will arise which carry correlated information, to
restore the cell to a functional position within the neural circuit. It would be
fascinating to understand in vivo whether new strengthening of small inputs
occurs and whether it does lead to circuit re-arrangement in this manner. If so
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this could a major mechanism for well-described homeostatic re-wiring
phenomena such as innervations of sensory cortices by other cortical areas after
sensory deprivation. Disruptions to HSP are also becoming increasingly
prominent as a cause for at least a subset of the cognitive impairments seen in
many neurodevelopmental and autism spectrum disorders.
These results emphasise the critical role HSP plays in maintaining neural circuits
in a healthy and high functioning state. Future work will serve to further
investigate these intriguing links and furnish our knowledge of the dynamics and
mechanisms of homeostatic synaptic plasticity.
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Neuronal Structure and Function lab, April 2016
Champalimaud Neuroscience Programme, June 2012
Neuronal Structure and Function lab, April 2016
