Unified classification of mouse retinal ganglion cells using function, morphology, and gene expression

Abstract

Classification and characterization of neuronal types is critical for understanding their function and dysfunction. Neuronal classification schemes typically rely on measurements of electrophysiological, morphological and molecular features, but aligning these data sets has been challenging. Here, we present a unified classification of retinal ganglion cells (RGCs), the sole retinal output neurons. We used visually-evoked responses to classify 1777 mouse RGCs into 42 types. We also obtained morphological or transcriptomic data from subsets and used these measurements to align the functional classification to publicly available morphological and transcriptomic data sets. We created an online database that allows users to browse or download the data and to classify RGCs from their light responses using a machine-learning algorithm. This work provides a resource for studies of RGCs, their upstream circuits in the retina, and their projections in the brain, and establishes a framework for future efforts in neuronal classification and open data distribution.

Retinal patterns and the cellular repertoire of neuropsin (Opn5) retinal ganglion cells

“…Many RGC types are known either through the use of other cre‐mouse lines (e.g., JamB‐CreERT2 [Kim et al., 2008]), transgenic reporters (e.g., W3 RGCs in the TYW3 line [Y. Zhang et al., 2012]), scRNA‐Seq (Rheaume et al., 2018; Tran et al., 2019), and through electrophysiological responses to visual stimuli (Jacoby & Schwartz, 2017; Mani & Schwartz, 2017). We sought to more comprehensively assess Opn5 ‐RGC types and their visual response properties through electrophysiological profiling and mapping responses to known functional types (Goetz et al., 2021; Parmhans et al., 2021). To this end, we virally labeled Opn5 ‐RGCs in Opn5 cre ; Ai9 mice (using either AAV2‐Flex‐eGFP or AAV9‐BbTagBY) and recorded from Ai9+ eGFP+ (active cre‐expressing) or Ai9+ eGFP‐ (lineage) cells (Figure 5i).…”

“…SmRF + UHD ( molecular = 18.5% Tusc5+ Foxp2‐; functional = 30.3%), and ooDSRGCs ( molecular = 13.8%; functional = 5.2%) in the Opn5 cre line, increasing our confidence in the proposed functional types. Beyond these listed types, however, it is difficult to make accurate comparisons between our two forms of fingerprinting as comprehensive studies that link functional RGC properties to gene expression patterns are ongoing (Goetz et al., 2021). Our molecular analysis of Opn5‐ RGCs revealed roughly 40% were Tusc5+.…”

“…Drifting gratings with a mean luminance of 1000 R*/rod/s were shown at 12 different directions to identify OFF Orientation Selective RGCs (Amurta Nath & Schwartz, 2017). For a detailed description of visual stimuli used to identify each type, refer to Goetz et al., 2021 ( bioRxiv ).…”

“…Zhang et al, 2012]), scRNA-Seq (Rheaume et al, 2018;Tran et al, 2019), and through electrophysiological responses to visual stimuli (Jacoby & Schwartz, 2017;Mani & Schwartz, 2017). We sought to more comprehensively assess Opn5-RGC types and their visual response properties through electrophysiological profiling and mapping responses to known functional types (Goetz et al, 2021;Parmhans et al, 2021).…”

A retinal circuit that vetoes optokinetic responses to fast visual motion

“…Among the more than three dozen types of mammalian retinal ganglion cells (RGCs) [4][5][6], only a very specific subset appear to participate in visually driven image stabilization. These are direction-selective ganglion cells (DSGCs), and mainly those of a specific subclass -the ON DSGCs.…”