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

doi:10.1016/j.celrep.2022.111040

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 journal › Article › peer-review

66 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 language	English (US)
Article number	111040
Journal	Cell reports
Volume	40
Issue number	2
DOIs	https://doi.org/10.1016/j.celrep.2022.111040
State	Published - Jul 12 2022

Funding

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.). 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.

Keywords

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

ASJC Scopus subject areas

General Biochemistry, Genetics and Molecular Biology

Access to Document

10.1016/j.celrep.2022.111040

Cite this

@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",

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TY - JOUR

T1 - Unified classification of mouse retinal ganglion cells using function, morphology, and gene expression

AU - Goetz, Jillian

AU - Jessen, Zachary F.

AU - Jacobi, Anne

AU - Mani, Adam

AU - Cooler, Sam

AU - Greer, Devon

AU - Kadri, Sabah

AU - Segal, Jeremy

AU - Shekhar, Karthik

AU - Sanes, Joshua R.

AU - Schwartz, Gregory William

N1 - 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: © 2022 The Authors

PY - 2022/7/12

Y1 - 2022/7/12

N2 - 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.

AB - 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.

KW - CP: Neuroscience

KW - retina, retinal ganglion cell, transcriptomics, morphology, light responses, classification

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DO - 10.1016/j.celrep.2022.111040

M3 - Article

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SN - 2211-1247

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JO - Cell reports

JF - Cell reports

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ER -

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

Abstract

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