A novel approach to the functional classification of retinal ganglion cells

Hilgen, Gerrit, Kartsaki, Evgenia, Kartysh, Viktoriia, Cessac, Bruno and Sernagor, Evelyne (2022) A novel approach to the functional classification of retinal ganglion cells. Open Biology, 12 (3). p. 210367. ISSN 2046-2441

rsob.210367.pdf - Published Version
Available under License Creative Commons Attribution 4.0.

Download (2MB) | Preview
Official URL: https://doi.org/10.1098/rsob.210367


Retinal neurons are remarkedly diverse based on structure, function and genetic identity. Classifying these cells is a challenging task, requiring multimodal methodology. Here, we introduce a novel approach for retinal ganglion cell (RGC) classification, based on pharmacogenetics combined with immunohistochemistry and large-scale retinal electrophysiology. Our novel strategy allows grouping of cells sharing gene expression and understanding how these cell classes respond to basic and complex visual scenes. Our approach consists of several consecutive steps. First, the spike firing frequency is increased in RGCs co-expressing a certain gene (Scnn1a or Grik4) using excitatory DREADDs (designer receptors exclusively activated by designer drugs) in order to single out activity originating specifically from these cells. Their spike location is then combined with post hoc immunostaining, to unequivocally characterize their anatomical and functional features. We grouped these isolated RGCs into multiple clusters based on spike train similarities. Using this novel approach, we were able to extend the pre-existing list of Grik4-expressing RGC types to a total of eight and, for the first time, we provide a phenotypical description of 13 Scnn1a-expressing RGCs. The insights and methods gained here can guide not only RGC classification but neuronal classification challenges in other brain regions as well.

Item Type: Article
Additional Information: Funding information: This project was funded by the Leverhulme Trust (RPG-2016-315 to E.S. and B.C.), by Newcastle University (Faculty of Medical Sciences Graduate School and Pro-Vice Chancellor Discretionary Fund). We thank Matthias Hennig for help with the spike distance calculations and Chris Williams who worked on related, unpublished aspects of the project.
Uncontrolled Keywords: DREADD, Grik4, Scnn1a, retinal ganglion cells, multielectrode array, classification
Subjects: C100 Biology
C700 Molecular Biology, Biophysics and Biochemistry
Department: Faculties > Health and Life Sciences > Applied Sciences
Depositing User: Elena Carlaw
Date Deposited: 14 Mar 2022 15:59
Last Modified: 14 Mar 2022 16:00
URI: http://nrl.northumbria.ac.uk/id/eprint/48667

Actions (login required)

View Item View Item


Downloads per month over past year

View more statistics