Sources of variability in cytosolic calcium transients triggered by stimulation of homogeneous uro-epithelial cell monolayers.

Epithelial tissue structure is the emergent outcome of the interactions between large numbers of individual cells. Experimental cell biology offers an important tool to unravel these complex interactions, but current methods of analysis tend to be limited to mean field approaches or representation by selected subsets of cells. This may result in bias towards cells that respond in a particular way and/or neglect local, context-specific cell responses. Here, an automated algorithm was applied to examine in detail the individual calcium transients evoked in genetically homogeneous, but asynchronous populations of cultured non-immortalized normal human urothelial cells when subjected to either the global application of an external agonist or a localized scratch wound. The recorded calcium transients were classified automatically according to a set of defined metrics and distinct sub-populations of cells that responded in qualitatively different ways were observed. The nature of this variability in the homogeneous cell population was apportioned to two sources: intrinsic variation in individual cell responses and extrinsic variability due to context-specific factors of the environment, such as spatial heterogeneity. Statistically significant variation in the features of the calcium transients evoked by scratch wounding according to proximity to the wound edge was identified. The manifestation of distinct sub-populations of cells is considered central to the coordination of population-level response resulting in wound closure.

The role of multiple chemotactic mechanisms in a model of chemotaxis in C. elegans: different mechanisms are specialised for different environments.

Unlike simpler organisms, C. elegans possesses several distinct chemosensory pathways and chemotactic mechanisms. These mechanisms and pathways are individually capable of driving chemotaxis in a chemical concentration gradient. However, it is not understood if they are redundant or co-operate in more sophisticated ways. Here we examine the specialisation of different chemotactic mechanisms in a model of chemotaxis to NaCl. We explore the performance of different chemotactic mechanisms in a range of chemical gradients and show that, in the model, far from being redundant, the mechanisms are specialised both for different environments and for distinct features within those environments. We also show that the chemotactic drive mediated by the ASE pathway is not robust to the presence of noise in the chemical gradient. This problem cannot be solved along the ASE pathway without destroying its ability to drive chemotaxis. Instead, we show that robustness to noise can be achieved by introducing a second, much slower NaCl-sensing pathway. This secondary pathway is simpler than the ASE pathway, in the sense that it can respond to either up-steps or down-steps in NaCl but not both, and could correspond to one of several candidates in the literature which we identify and evaluate. This work provides one possible explanation of why there are multiple NaCl sensing pathways and chemotactic mechanisms in C. elegans: rather than being redundant the different pathways and mechanism are specialised both for the characteristics of different environments and for distinct features within a single environment.

Cell-type-specific modelling of intracellular calcium signalling: a urothelial cell model.

Calcium signalling plays a central role in regulating a wide variety of cell processes. A number of calcium signalling models exist in the literature that are capable of reproducing a variety of experimentally observed calcium transients. These models have been used to examine in more detail the mechanisms underlying calcium transients, but very rarely has a model been directly linked to a particular cell type and experimentally verified. It is important to show that this can be achieved within the general theoretical framework adopted by these models. Here, we develop a framework designed specifically for modelling cytosolic calcium transients in urothelial cells. Where possible, we draw upon existing calcium signalling models, integrating descriptions of components known to be important in this cell type from a number of studies in the literature. We then add descriptions of several additional pathways that play a specific role in urothelial cell signalling, including an explicit ionic influx term and an active pumping mechanism that drives the cytosolic calcium concentration to a target equilibrium. The resulting one-pool model of endoplasmic reticulum (ER)-dependent calcium signalling relates the cytosolic, extracellular and ER calcium concentrations and can generate a wide range of calcium transients, including spikes, bursts, oscillations and sustained elevations in the cytosolic calcium concentration. Using single-variate robustness and multivariate sensitivity analyses, we quantify how varying each of the parameters of the model leads to changes in key features of the calcium transient, such as initial peak amplitude and the frequency of bursting or spiking, and in the transitions between bursting- and plateau-dominated modes. We also show that, novel to our urothelial cell model, the ionic and purinergic P2Y pathways make distinct contributions to the calcium transient. We then validate the model using human bladder epithelial cells grown in monolayer cell culture and show that the model robustly captures the key features of the experimental data in a way that is not possible using more generic calcium models from the literature.

Functional expression of purinergic P2 receptors and transient receptor potential channels by the human urothelium.

In addition to its role as a physical barrier, the urothelium is considered to play an active role in mechanosensation. A key mechanism is the release of transient mediators that activate purinergic P2 receptors and transient receptor potential (TRP) channels to effect changes in intracellular Ca²âº. Despite the implied importance of these receptors and channels in urothelial tissue homeostasis and dysfunctional bladder disease, little is known about their functional expression by the human urothelium. To evaluate the expression and function of P2X and P2Y receptors and TRP channels, the human ureter and bladder were used to separate urothelial and stromal tissues for RNA isolation and cell culture. RT-PCR using stringently designed primer sets was used to establish which P2 and TRP species were expressed at the transcript level, and selective agonists/antagonists were used to confirm functional expression by monitoring changes in intracellular Ca²âº and in a scratch repair assay. The results confirmed the functional expression of P2Y4 receptors and excluded nonexpressed receptors/channels (P2X1, P2X3, P2X6, P2Y6, P2Y11, TRPV5, and TRPM8), while a dearth of specific agonists confounded the functional validation of expressed P2X2, P2X4, P2Y1, P2Y2, TRPV2, TRPV3, TRPV6 and TRPM7 receptors/channels. Although a conventional response was elicited in control stromal-derived cells, the urothelial cell response to well-characterized TRPV1 and TRPV4 agonists/antagonists revealed unexpected anomalies. In addition, agonists that invoked an increase in intracellular Ca²âº promoted urothelial scratch repair, presumably through the release of ATP. The study raises important questions about the ligand selectivity of receptor/channel targets expressed by the urothelium. These pathways are important in urothelial tissue homeostasis, and this opens the possibility of selective drug targeting.

Triphasic spike-timing-dependent plasticity organizes networks to produce robust sequences of neural activity.

Synfire chains have long been proposed to generate precisely timed sequences of neural activity. Such activity has been linked to numerous neural functions including sensory encoding, cognitive and motor responses. In particular, it has been argued that synfire chains underlie the precise spatiotemporal firing patterns that control song production in a variety of songbirds. Previous studies have suggested that the development of synfire chains requires either initial sparse connectivity or strong topological constraints, in addition to any synaptic learning rules. Here, we show that this necessity can be removed by using a previously reported but hitherto unconsidered spike-timing-dependent plasticity (STDP) rule and activity-dependent excitability. Under this rule the network develops stable synfire chains that possess a non-trivial, scalable multi-layer structure, in which relative layer sizes appear to follow a universal function. Using computational modeling and a coarse grained random walk model, we demonstrate the role of the STDP rule in growing, molding and stabilizing the chain, and link model parameters to the resulting structure.

A model of chemotaxis and associative learning in C. elegans.

The nematode C. elegans has attracted a great deal of interest from the neuroscience community due to the simplicity of its nervous system, which in the hermaphrodite is composed of just 302 neurons. C. elegans is known to engage in a number of sophisticated behaviours such as chemo- and thermotaxis. Experimental work has shown that these behaviours can be modified by experience and that C. elegans is capable of associative learning. In this paper, we focus on the chemotactic response of C. elegans to sodium chloride mediated by the ASE sensory neurons. We construct a biophysical model of the ASEL and ASER neurons that captures the time course of the ASE responses in response to up- and down-steps in NaCl concentration. We use this model to show that the time course of the ASE responses provide sufficient temporal resolution to successfully drive chemotaxis in C. elegans via steering, pirouettes and control of final turn angle. We show that these different locomotion strategies are individually capable of driving chemotaxis and that by working together they produce the best chemotactic response. We find that there is a separation into upward and downward drives mediated by the left and right ASE neurons. We show that the connectivity from ASEL and ASER must be of opposite polarity and that ASER, and the concomitant ability to sense when the worm is moving down the gradient, is more important for chemotaxis than ASEL, findings that are consistent with existing modelling studies in the literature. Finally, we examine associative learning in the network and show that experimental data can be explained by changes that occur at either the synaptic or sensory neuron level, the choice of which has distinct consequences for network function.

The role of additive neurogenesis and synaptic plasticity in a hippocampal memory model with grid-cell like input.

Recently, we presented a study of adult neurogenesis in a simplified hippocampal memory model. The network was required to encode and decode memory patterns despite changing input statistics. We showed that additive neurogenesis was a more effective adaptation strategy compared to neuronal turnover and conventional synaptic plasticity as it allowed the network to respond to changes in the input statistics while preserving representations of earlier environments. Here we extend our model to include realistic, spatially driven input firing patterns in the form of grid cells in the entorhinal cortex. We compare network performance across a sequence of spatial environments using three distinct adaptation strategies: conventional synaptic plasticity, where the network is of fixed size but the connectivity is plastic; neuronal turnover, where the network is of fixed size but units in the network may die and be replaced; and additive neurogenesis, where the network starts out with fewer initial units but grows over time. We confirm that additive neurogenesis is a superior adaptation strategy when using realistic, spatially structured input patterns. We then show that a more biologically plausible neurogenesis rule that incorporates cell death and enhanced plasticity of new granule cells has an overall performance significantly better than any one of the three individual strategies operating alone. This adaptation rule can be tailored to maximise performance of the network when operating as either a short- or long-term memory store. We also examine the time course of adult neurogenesis over the lifetime of an animal raised under different hypothetical rearing conditions. These growth profiles have several distinct features that form a theoretical prediction that could be tested experimentally. Finally, we show that place cells can emerge and refine in a realistic manner in our model as a direct result of the sparsification performed by the dentate gyrus layer.

Additive neurogenesis as a strategy for avoiding interference in a sparsely-coding dentate gyrus.

Recently we presented a model of additive neurogenesis in a linear, feedforward neural network that performed an encoding-decoding memory task in a changing input environment. Growing the neural network over time allowed the network to adapt to changes in input statistics without disrupting retrieval properties, and we proposed that adult neurogenesis might fulfil a similar computational role in the dentate gyrus of the hippocampus. Here we explicitly evaluate this hypothesis by examining additive neurogenesis in a simplified hippocampal memory model. The model incorporates a divergence in unit number from the entorhinal cortex to the dentate gyrus and sparse coding in the dentate gyrus, both notable features of hippocampal processing. We evaluate two distinct adaptation strategies; neuronal turnover, where the network is of fixed size but units may be deleted and new ones added, and additive neurogenesis, where the network grows over time, and quantify the performance of the network across the full range of adaptation levels from zero in a fixed network to one in a fully adapting network. We find that additive neurogenesis is always superior to neuronal turnover as it permits the network to be responsive to changes in input statistics while at the same time preserving representations of earlier environments.

Multispike interactions in a stochastic model of spike-timing-dependent plasticity.

Recently we presented a stochastic, ensemble-based model of spike-timing-dependent plasticity. In this model, single synapses do not exhibit plasticity depending on the exact timing of pre- and postsynaptic spikes, but spike-timing-dependent plasticity emerges only at the temporal or synaptic ensemble level. We showed that such a model reproduces a variety of experimental results in a natural way, without the introduction of various, ad hoc nonlinearities characteristic of some alternative models. Our previous study was restricted to an examination, analytically, of two-spike interactions, while higher-order, multispike interactions were only briefly examined numerically. Here we derive exact, analytical results for the general n-spike interaction functions in our model. Our results form the basis for a detailed examination, performed elsewhere, of the significant differences between these functions and the implications these differences have for the presence, or otherwise, of stable, competitive dynamics in our model.

Stable competitive dynamics emerge from multispike interactions in a stochastic model of spike-timing-dependent plasticity.

In earlier work we presented a stochastic model of spike-timing-dependent plasticity (STDP) in which STDP emerges only at the level of temporal or spatial synaptic ensembles. We derived the two-spike interaction function from this model and showed that it exhibits an STDP-like form. Here, we extend this work by examining the general n-spike interaction functions that may be derived from the model. A comparison between the two-spike interaction function and the higher-order interaction functions reveals profound differences. In particular, we show that the two-spike interaction function cannot support stable, competitive synaptic plasticity, such as that seen during neuronal development, without including modifications designed specifically to stabilize its behavior. In contrast, we show that all the higher-order interaction functions exhibit a fixed-point structure consistent with the presence of competitive synaptic dynamics. This difference originates in the unification of our proposed "switch" mechanism for synaptic plasticity, coupling synaptic depression and synaptic potentiation processes together. While three or more spikes are required to probe this coupling, two spikes can never do so. We conclude that this coupling is critical to the presence of competitive dynamics and that multispike interactions are therefore vital to understanding synaptic competition.

Synaptic and temporal ensemble interpretation of spike-timing-dependent plasticity.

We postulate that a simple, three-state synaptic switch governs changes in synaptic strength at individual synapses. Under this switch rule, we show that a variety of experimental results on timing-dependent plasticity can emerge from temporal and spatial averaging over multiple synapses and multiple spike pairings. In particular, we show that a critical window for the interaction of pre- and postsynaptic spikes emerges as an ensemble property of the collective system, with individual synapses exhibiting only a minimal form of spike coincidence detection. In addition, we show that a Bienenstock-Cooper-Munro-like, rate-based plasticity rule emerges directly from such a model. This demonstrates that two apparently separate forms of neuronal plasticity can emerge from a much simpler rule governing the plasticity of individual synapses.