@article{8ff37a3d46fb4266ab5fdf568b24a53e,
title = "Unified classification of mouse retinal ganglion cells using function, morphology, and gene expression",
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.",
keywords = "CP: Neuroscience, retina, retinal ganglion cell, transcriptomics, morphology, light responses, classification",
author = "Jillian Goetz and Jessen, {Zachary F.} and Anne Jacobi and Adam Mani and Sam Cooler and Devon Greer and Sabah Kadri and Jeremy Segal and Karthik Shekhar and Sanes, {Joshua R.} and Schwartz, {Gregory William}",
note = "Funding Information: We would like to thank all the members of the Schwartz Lab past and present, each one of whom has contributed to this dataset over the past 8 years. We thank Kavin Suritharen and Shikhar Gupta for their help with tracing RGC dendrites, and we thank the countless colleagues with whom we have discussed this work over the years. Funding for this work was provided by NEI ( R01EY031029 , R01EY031329 , and DP2 EY026770 to G.W.S.; R00EY028625 to K.S.; F30EY031565 to Z.F.J.; F31EY029593 to S.C.; and EY022073 to J.R.S.), NIHM ( MH105960 to J.R.S. and T32MH067564 to D.G.), NIGMS ( T32GM008152 to Z.F.J.), the Karl Kirchgessner Foundation (to G.W.S.), the Chicago Biomedical Consortium Catalyst Award ( A2011-00985 to J.G., J.S., S.K., and G.W.S.), the VitreoRetinal Surgical Foundation fellowship award (to Z.F.J.), and the Knights Templar Eye Foundation (to A.M.). Funding Information: We would like to thank all the members of the Schwartz Lab past and present, each one of whom has contributed to this dataset over the past 8 years. We thank Kavin Suritharen and Shikhar Gupta for their help with tracing RGC dendrites, and we thank the countless colleagues with whom we have discussed this work over the years. Funding for this work was provided by NEI (R01EY031029, R01EY031329, and DP2 EY026770 to G.W.S.; R00EY028625 to K.S.; F30EY031565 to Z.F.J.; F31EY029593 to S.C.; and EY022073 to J.R.S.), NIHM (MH105960 to J.R.S. and T32MH067564 to D.G.), NIGMS (T32GM008152 to Z.F.J.), the Karl Kirchgessner Foundation (to G.W.S.), the Chicago Biomedical Consortium Catalyst Award (A2011-00985 to J.G. J.S. S.K. and G.W.S.), the VitreoRetinal Surgical Foundation fellowship award (to Z.F.J.), and the Knights Templar Eye Foundation (to A.M.). 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. The authors declare no competing interests. Publisher Copyright: {\textcopyright} 2022 The Authors",
year = "2022",
month = jul,
day = "12",
doi = "10.1016/j.celrep.2022.111040",
language = "English (US)",
volume = "40",
journal = "Cell reports",
issn = "2211-1247",
publisher = "Cell Press",
number = "2",
}