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

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, w...

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Bibliographic Details
Published inCell reports (Cambridge) Vol. 40; no. 2; p. 111040
Main Authors Goetz, Jillian, Jessen, Zachary F., Jacobi, Anne, Mani, Adam, Cooler, Sam, Greer, Devon, Kadri, Sabah, Segal, Jeremy, Shekhar, Karthik, Sanes, Joshua R., Schwartz, Gregory W.
Format Journal Article
LanguageEnglish
Published United States Elsevier Inc 12.07.2022
Subjects
Animals
Gene Expression
Mice
Retina - physiology
retina, retinal ganglion cell, transcriptomics, morphology, light responses, classification
Retinal Ganglion Cells - metabolism
CP: Neuroscience
retina, retinal ganglion cell, transcriptomics, morphology, light responses, classification
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Summary: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. [Display omitted] •Function, morphology, and gene expression jointly specify retinal ganglion cell type•42 classified types in mice comprise 89% of the total population•Online resource at rgctypes.org to explore and download the data Function, morphology, and gene expression are the most common criteria used to classify neurons. Goetz et al. use all three criteria to create a unified classification of mouse retinal ganglion cells and build an interactive online resource for exploring the data.
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AUTHOR CONTRIBUTIONS
J.G. and G.W.S. designed the study. J.G. and G.W.S. collected functionally identified cells for RNA sequencing. A.J. performed RNA sequencing experiments in the lab of J.R.S. Z.F.J., G.W.S., and A.M. wrote analysis code for quantifying and classifying RGC responses. Z.F.J., S.C., and G.W.S. built rgctypes.org. S.K. and J.S. helped design the molecular studies and analyzed transcriptomics data. K.S. matched transcriptomic data to the previously identified clusters. J.R.S., K.S., and G.W.S. led the molecular parts of the project. D.G. analyzed data for the dense RGC recording (Figure S6) and assembled the database of traced RGC images at rgctypes.org.. Z.F.J. built the machine learning RGC classifier. Z.F.J. and G.W.S. performed morphological analyses. G.W.S. performed analyses to align the classification modalities. J.G., Z.F.J., A.M., S.C., D.G., G.W.S., and additional members of the Schwartz Lab recorded RGCs for the dataset. J.R.S. and G.W.S. acquired funding and managed the project. J.G. and G.W.S. wrote the first draft of the paper. Z.F.J., J.R.S., K.S., and G.W.S. revised the paper.
ISSN:2211-1247
2211-1247
DOI:10.1016/j.celrep.2022.111040