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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| Published in | Cell reports (Cambridge) Vol. 40; no. 2; p. 111040 |
|---|---|
| Main Authors | , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Elsevier Inc
12.07.2022
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| Subjects | |
| Online Access | Get full text |
| ISSN | 2211-1247 2211-1247 |
| DOI | 10.1016/j.celrep.2022.111040 |
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| 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.
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•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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| AbstractList | 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.
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. 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. 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.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. 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. |
| ArticleNumber | 111040 |
| Author | Segal, Jeremy Schwartz, Gregory W. Goetz, Jillian Cooler, Sam Kadri, Sabah Jessen, Zachary F. Sanes, Joshua R. Shekhar, Karthik Mani, Adam Greer, Devon Jacobi, Anne |
| AuthorAffiliation | 13 These authors contributed equally 5 Center for Brain Science and Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA 11 Present address: Department of Neurosurgery, Stanford University, Stanford, CA, USA 3 Medical Scientist Training Program, Northwestern University, Chicago, IL, USA 6 Department of Pathology, Pritzker School of Medicine, University of Chicago, Chicago, IL, USA 14 Lead contact 10 Present address: Department of Neuroscience, Brown University, Providence, RI, USA 4 F.M. Kirby Neurobiology Center, Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA 12 Present address: Department of Pathology and Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA 1 Department of Ophthalmology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA 8 Department of Physiology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA 2 Nor |
| AuthorAffiliation_xml | – name: 5 Center for Brain Science and Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA – name: 9 Department of Neurobiology, Weinberg College of Arts and Sciences, Northwestern University, Evanston, IL, USA – name: 3 Medical Scientist Training Program, Northwestern University, Chicago, IL, USA – name: 6 Department of Pathology, Pritzker School of Medicine, University of Chicago, Chicago, IL, USA – name: 14 Lead contact – name: 11 Present address: Department of Neurosurgery, Stanford University, Stanford, CA, USA – name: 8 Department of Physiology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA – name: 1 Department of Ophthalmology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA – name: 12 Present address: Department of Pathology and Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA – name: 4 F.M. Kirby Neurobiology Center, Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA – name: 2 Northwestern University Interdepartmental Neuroscience Program, Northwestern University, Evanston, IL, USA – name: 13 These authors contributed equally – name: 7 Department of Chemical and Biomolecular Engineering and Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, USA – name: 10 Present address: Department of Neuroscience, Brown University, Providence, RI, USA |
| Author_xml | – sequence: 1 givenname: Jillian surname: Goetz fullname: Goetz, Jillian organization: Department of Ophthalmology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA – sequence: 2 givenname: Zachary F. surname: Jessen fullname: Jessen, Zachary F. organization: Department of Ophthalmology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA – sequence: 3 givenname: Anne surname: Jacobi fullname: Jacobi, Anne organization: F.M. Kirby Neurobiology Center, Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA – sequence: 4 givenname: Adam surname: Mani fullname: Mani, Adam organization: Department of Ophthalmology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA – sequence: 5 givenname: Sam surname: Cooler fullname: Cooler, Sam organization: Northwestern University Interdepartmental Neuroscience Program, Northwestern University, Evanston, IL, USA – sequence: 6 givenname: Devon surname: Greer fullname: Greer, Devon organization: Department of Ophthalmology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA – sequence: 7 givenname: Sabah surname: Kadri fullname: Kadri, Sabah organization: Department of Pathology, Pritzker School of Medicine, University of Chicago, Chicago, IL, USA – sequence: 8 givenname: Jeremy surname: Segal fullname: Segal, Jeremy organization: Department of Pathology, Pritzker School of Medicine, University of Chicago, Chicago, IL, USA – sequence: 9 givenname: Karthik surname: Shekhar fullname: Shekhar, Karthik organization: Department of Chemical and Biomolecular Engineering and Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, USA – sequence: 10 givenname: Joshua R. surname: Sanes fullname: Sanes, Joshua R. organization: Center for Brain Science and Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA – sequence: 11 givenname: Gregory W. orcidid: 0000-0001-8909-4397 surname: Schwartz fullname: Schwartz, Gregory W. email: greg.schwartz@northwestern.edu organization: Department of Ophthalmology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35830791$$D View this record in MEDLINE/PubMed |
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| Keywords | CP: Neuroscience retina, retinal ganglion cell, transcriptomics, morphology, light responses, classification |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 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. |
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