A novel approach to the functional classification of retinal ganglion cells.
Gerrit HilgenEvgenia KartsakiViktoriia KartyshBruno CessacEvelyne SernagorPublished in: Open biology (2022)
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.
Keyphrases
- induced apoptosis
- gene expression
- cell cycle arrest
- machine learning
- deep learning
- oxidative stress
- signaling pathway
- cell death
- endoplasmic reticulum stress
- spinal cord
- spinal cord injury
- stem cells
- copy number
- optical coherence tomography
- cell proliferation
- chronic pain
- transcription factor
- high resolution
- pi k akt
- wild type
- subarachnoid hemorrhage