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

• DOI: 10.1016/j.celrep.2022.111040
• Publication Date: 2022-07-12T15:17:53Z
• Authors: Jillian Goetz; Zachary F. Jessen; Anne Jacobi; Adam Mani; Sam Cooler; Devon Greer; Sabah Kadri; Jeremy Segal; Karthik Shekhar; Joshua R. Sanes; Gregory W. Schwartz
• Keywords: Retinal Ganglion Cells; 0301 basic medicine; retina; Medical Physiology; Gene Expression; light responses; Eye; Article; Retina; transcriptomics; Mice; 03 medical and health sciences; morphology; Animals; retinal ganglion cell; Eye Disease and Disorders of Vision; 0303 health sciences; Neurosciences; Biological Sciences; Biological sciences; classification; CP: Neuroscience; Neurological; Biochemistry and Cell Biology

AbstractClassification and characterization of neuronal types are critical for understanding their function and dysfunction. Neuronal classification schemes typically rely on measurements of electrophysiological, morphological, and molecular features, but aligning such datasets has been challenging. Here, we present a unified classification of mouse retinal ganglion cells (RGCs), the sole retinal output neurons. We used visually-evoked responses to classify 1859 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.