Peter Andras School of Computing and Mathematics Keele University Voltage – sensitive dye imaging and computational modelling of the crab stomatogastric ganglion Peter Andras School of Computing and Mathematics Keele University

Overview Crab stomatogastric ganglion (STG) Voltage-sensitive dye (VSD) imaging VSD imaging of the crab STG Computational analysis of the imaging data Computational modelling of STG neurons and networks Summary

Why study the STG ? Stomatogastric ganglion (STG) of crabs 26 neurons arranged in a relatively flat sheet Relatively isolated (one input nerve from higher ganglia) Complex behaviour – central pattern generators (CPG) – pyloric and gastric mill rhythms Ideal model system for studying neural activity patterns, neuromodulation, motor pattern generation, restorative re-wiring

Voltage – sensitive dye (VSD) imaging The dye molecules attach to the cell membrane, pick up photons (green), go through internal reorganisation (charge shift) and then return to the baseline state and release a photon (red fluorescence) – this is what we measure: the change in the intensity of the emitted fluorescent light; very small up to 10 % intensity change per 100mV change in the membrane potential The membrane potential difference represents an electric field across the membrane and this field may facilitate or may reduce the chance of return of the excited molecules to their baseline state, influencing the intensity of the generated fluorescent light The dye may be injected into selected neurons or the whole ganglion may be bathed in a dyed saline solution Red photon MiCAM 02 SciMedia Green photon Olympus BX 51 WI

VSD impact on the STG Do the neurons take up the dye ? Is the dye toxic or not ? Does it have a significant impact on what the neurons do or not ? Stein, W, Andras, P (2010). Journal of Neuroscience Methods, 188:290-294

VSD impact on the STG There is an impact on STG neurons with strong light on the neuropil or neuron cell bodies, but no impact if light is directed onto axons The impact is reversible following the stopping of illumination Low light level minimizes the light induced impact

Imaging of dye filled neurons Neurons are filled through micro-electrodes with di-8-aneppq dye using 1s duration steps of positive current (+10nA) Alternative: dye filling with pico-spritzer Current based dye filling takes around 20-30 min per neuron, much quicker with pico-spritzer Stein, W, Städele, C, Andras, P (2011). Journal of Neuroscience Methods, 194:224-234 Stein, W, Städele, C, Andras, P (2011). Journal of Visualized Experiments, doi: 10.3791/2567.

Imaging of dye filled neurons Can we see the temporal difference in the recorded signal that is due to the propagation of the signal within the neuron ?

Imaging of dye filled neurons Simultaneous imaging of a pair of interconnected neurons (PD and LP) Can we record the spikes reliably using averaging ?

Imaging of dye filled neurons Can we record the spikes reliably using single-sweep recording without averaging ? Can we record small membrane potential change events (e.g. the impact of inhibitory input) ?

Imaging of dye filled neurons Importance: The recording of interconnected neurons in their physiological environment is critical for the understanding how neurons interact to deliver the emergent functionality of the neural system formed by them Most other systems where simultaneous recording of neurons is possible and demonstrated it is either not know whether the recorded neurons are connected or not (it is also not known way how they are connected – e.g. mammalian cortex, vertebrate retina, large invertebrate ganglia) or the functionality of the system formed by the neurons is not known (e.g. neurons in cell culture)

Optical recording of many neurons Bath application of the dye to the whole ganglion using dyed saline with di-4-ANEPPS dye Takes around 30 minutes to bath the ganglion that is followed by 30 minutes of washing away of the excess dye Städele, C, Andras, P, Stein, W (2012), Journal of Neuroscience Methods, 203: 78-88

Optical recording of many neurons Typically we have can have around 20 neurons in the field of view of the camera (compare with max 4 neurons that can be recorded using intracellular electrodes) Averaged recording over many cycles

Optical recording of many neurons Can we record reliably individual spikes, general spiking activity and the impact of inhibitory event following the bath loading of the VSD ?

Optical recording of many neurons Can we identify and record spike bursts and individual spikes within a burst generated by neurons following bath loading of the dye ?

Optical recording of many neurons Recording of lobster STG neurons – 3D arrangement Can we record slow and fast activity of neurons in details ?

Optical recording of many neurons Can we record neural activity in axons within nerves ? Can we track the propagation of activity within the axon ?

Optical recording of many neurons Importance: Bath loading of the dye reduces the time required to load many neurons We can record the simultaneous activity of many STG neurons (much more than what is possible using microelectrodes) The method allows the investigation of the emergence of joint activity of neurons in neural systems in their natural physiological context We can track the activity travelling in single axons within a bundle of axons in a nerve

Computational data analysis Recording of multiple PY neurons without and with exposure to dopamine Expectation: dopamine de-synchronises the activity of PY neurons

Computational data analysis Depolarized activity plateu Feature points: minimal slope maximal slope beginning of top zero slope end of top zero slope Trace features: length of depolarized activity plateu length of hyperpolarised inhibition period Joint activity features: length of temporal distance between matching feature points Hyperpolarised inhibition period Delay between matching feature points Steyn, J, Andras, P, (in preparation)

Computational data analysis The dopamine has differential effect on different PY neurons, shifting their feature points differently through the modulation of their activity  De-synchronisation of PY neurons

Computational data analysis 11 pairs of PY neurons compared using the measurements of 4 different feature points F-test used to compare variances of temporal differences for the with and without dopamine cases 22 out of 44 statistical test indicate that dopamine de-synchronises the PY neurons, only one indicates the opposite, while 21 tests show no significant change

Computational data analysis Importance: Novel method for reliable analysis of imaging data for the assessment of the dynamics of temporal relationships between neural activities First time to show in a physiologically valid context that the expected de-synchronisation of PY neurons following dopamine exposure actually does happen

Computational modelling Hodgkin – Huxley models of STG neurons Include equations for Calcium concentration Two compartments: axon and soma/dendrite Typically about 20 differential equations per neuron

Computational modelling Others have shown that many conductance parameter settings lead to very similar behaviours of model neurons The conductances obey correlation rules – these are maintained by exposure to neuromodulators and get relaxed in the absence of this Often published models are very finely tuned and small changes stop the proper working of these models Often joining model neurons that work well independently leads to non-functional network models

Computational modelling We built a large working simulation of the pyloric rhythm network of the STG – 1 AB, 2 PD, 1 LP, 5 PY neurons – about 180 differential equations Novelty: neurons belonging to the same class are simulated by multiple model neuron – others usually model these by a single model neuron Aim: model the de-synchronsation impact of dopamine on PY neurons

Computational modelling Measurements: Inter-Spike Interval (ISI) the distance between the temporally closest spikes considering pairs of PY neurons Spike Distance (SD) uses ISI to calculate a more precise measure of the synchronisation of two spike trains Both are implemented in the Spiky spike sequence data analysis tool developed by Thomas Kreuz

Computational modelling We simulated slightly different PY neurons by randomly setting their conductance parameters while respecting the correlational rules for conductances of appropriate ionic currents 100 simulations were run and we measured the ISI and SD spike synchronisation measures considering pairs of simulated PY neurons Dopamine exposure was simulated by reducing the strength of gap junction connections between simulated PY neurons – this effect is known from experimental data

Computational modelling Sufficient reduction in gap junction strength causes statistically significant change in the ISI and SD measures of PY synchronisation Steyn, J, Alderson, T, Andras, P, (in preparation)

Computational modelling Computational analysis of the impact of the variability of conductance parameters on model PD neurons Biological PD neurons are never fully synchronous and the delay between spikes changes and the joint spike patterns may change under the impact of neuromodulators

Computational modelling One PD has fixed conductance parameters the other has variable parameters The models reproduce the observed biological behaviour of PD neurons Dos Santos, Andras, P, (in preparation)

Computational modelling Importance: Computational modelling can lead to testable hypotheses about the explanations of observed biological phenomena Separate modelling of multiple neurons of the same kind can help in understanding and modelling of activity and modulation dependent changes in the roles of neurons

Other related work New voltage sensitive dyes based on the Bodipy molecule – in collaboration with Prof Andrew Benniston (Newcastle), current Leverhulme project Combined use of MEA stimulation and VSD recording to restore activity in damaged STG – recent project in collaboration with Prof Alex Yakovlev and Dr Patrick Degenaar (Newcastle)

Conclusions VSD recording of the STG provides a radically new neural system in which deciphering of how neural interactions lead to emergent system level behaviour becomes possible – the closest alternative systems (in terms of accessibility and functionality) are the enteric plexus of guinea pigs and snail & nudibranch ganglia, but none of them is known in comparable details as the STG The ability to record individual axons in nerves provides an opportunity for unprecedented insight into how complex control of the motor patterns is realised by higher neural centres in the context of the STG The reliable computational analysis of noisy optical imaging data is complicated and we developed novel methods that support this – we showed statistically convincingly the expected de-synchronisation of PY neurons in the STG The computational modelling of separate copies of neurons of the same kind allows the computational analysis of impacts of neuromodulation of neural activity synchronisation and also on other kinds of correlated neural activities in principle, potentially leading to new testable hypotheses about dynamic changes in the roles of neurons

Acknowledgements Filipa Dos Santos (Keele) Jannetta Steyn (Newcastle) Thomas Alderson (Newcastle / Ulster) David Fourie (Newcastle / Singapore) Wolfgang Stein, Carola Staedele (Illinois State)