Large scale matching of function to the genetic identity of retinal ganglion cells

Filippo Pisano,Alexander Sher,Jennifer Roebber,Martin D Dawson,Keith Mathieson,Niall Mcalinden,Erin Zampaglione

doi:10.1038/s41598-017-15741-7

Filippo Pisano, Alexander Sher + Show 5 more

Open Access

https://doi.org/10.1038/s41598-017-15741-7

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Abstract

Understanding the role of neurons in encoding and transmitting information is a major goal in neuroscience. This requires insight on the data-rich neuronal spiking patterns combined, ideally, with morphology and genetic identity. Electrophysiologists have long experienced the trade-offs between anatomically-accurate single-cell recording techniques and high-density multi-cellular recording methods with poor anatomical correlations. In this study, we present a novel technique that combines large-scale micro-electrode array recordings with genetic identification and the anatomical location of the retinal ganglion cell soma. This was obtained through optogenetic stimulation and subsequent confocal imaging of genetically targeted retinal ganglion cell sub-populations in the mouse. With the many molecular options available for optogenetic gene expression, we view this method as a versatile tool for matching function to genetic classifications, which can be extended to include morphological information if the density of labelled cells is at the correct level.

Highlights

The mammalian retina is a highly organized and approachable part of the central nervous system, which contains more than 60 distinct neuron types[1] and provides some of the most elegant examples of how neural structure contributes to function[2]
We have developed a system capable of performing spatio-temporal stimulation of visual pathways and direct optogenetic stimulation of retinal ganglion cells (RGCs)
We have described a method to associate functional data from large-scale extracellular electrophysiological recordings with genetically labelled RGCs in mice and shown how spatio-temporal optogenetic stimulation can be employed to generate highly-localised receptive fields that unambiguously identify individual RGC somas

Summary

Introduction

The mammalian retina is a highly organized and approachable part of the central nervous system, which contains more than 60 distinct neuron types[1] and provides some of the most elegant examples of how neural structure contributes to function[2]. The mouse retina, a system in which the investigation of neural circuits is empowered by a wide variety of genetic tools[3], is an ideal platform to approach one of the fundamental goals of neuroscience; matching neuronal molecular composition and morphology with function Finding such a match is a demanding task that requires associating functional data with both high-resolution anatomical information and genetic identity. As the authors point out, this approach involves complex experimental procedures and success relies critically on the presence of a clear axonal image for each cell This condition significantly limits the applicability of the match based solely on the EI and has motivated us to develop an innovative and accessible method to reliably match genetic identity to function in the RGC layer. Epifluorescent images of the retina on the MEA were taken to get soma locations of the ChR2-tdTomato-positive cells

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