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

Jillian Goetz, Zachary F. Jessen, Anne Jacobi, Adam Mani, Sam Cooler, Devon Greer, Sabah Kadri, Jeremy Segal, Karthik Shekhar, Joshua R. Sanes, Gregory William Schwartz*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Classification 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 use visually evoked responses to classify 1,859 mouse RGCs into 42 types. We also obtain morphological or transcriptomic data from subsets and use these measurements to align the functional classification to publicly available morphological and transcriptomic datasets. We create 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.

Original languageEnglish (US)
Article number111040
JournalCell reports
Volume40
Issue number2
DOIs
StatePublished - Jul 12 2022

Keywords

  • CP: Neuroscience
  • retina, retinal ganglion cell, transcriptomics, morphology, light responses, classification

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)

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