A harmonized atlas of mouse spinal cord cell types and their spatial organization

Daniel E. Russ, Ryan B. Patterson Cross, Li Li, Stephanie C. Koch, Kaya J. E. Matson, Archana Yadav, Mor R. Alkaslasi, Dylan I. Lee, Claire E. Le Pichon, Vilas Menon and Ariel J. Levine ()
Additional contact information
Daniel E. Russ: Division of Cancer Epidemiology and Genetics, Data Science Research Group, National Cancer Institute, NIH
Ryan B. Patterson Cross: Spinal Circuits and Plasticity Unit, National Institute of Neurological Disorders and Stroke, NIH
Li Li: Spinal Circuits and Plasticity Unit, National Institute of Neurological Disorders and Stroke, NIH
Stephanie C. Koch: Department of Neuroscience, Physiology and Pharmacology, Division of Biosciences, University College London
Kaya J. E. Matson: Spinal Circuits and Plasticity Unit, National Institute of Neurological Disorders and Stroke, NIH
Archana Yadav: Department of Neurology, Center for Translational and Computational Neuroimmunology, Columbia University
Mor R. Alkaslasi: Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH
Dylan I. Lee: Department of Neurology, Center for Translational and Computational Neuroimmunology, Columbia University
Claire E. Le Pichon: Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH
Vilas Menon: Department of Neurology, Center for Translational and Computational Neuroimmunology, Columbia University
Ariel J. Levine: Spinal Circuits and Plasticity Unit, National Institute of Neurological Disorders and Stroke, NIH

Nature Communications, 2021, vol. 12, issue 1, 1-20

Abstract: Abstract Single-cell RNA sequencing data can unveil the molecular diversity of cell types. Cell type atlases of the mouse spinal cord have been published in recent years but have not been integrated together. Here, we generate an atlas of spinal cell types based on single-cell transcriptomic data, unifying the available datasets into a common reference framework. We report a hierarchical structure of postnatal cell type relationships, with location providing the highest level of organization, then neurotransmitter status, family, and finally, dozens of refined populations. We validate a combinatorial marker code for each neuronal cell type and map their spatial distributions in the adult spinal cord. We also show complex lineage relationships among postnatal cell types. Additionally, we develop an open-source cell type classifier, SeqSeek, to facilitate the standardization of cell type identification. This work provides an integrated view of spinal cell types, their gene expression signatures, and their molecular organization.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-25125-1

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DOI: 10.1038/s41467-021-25125-1

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