Comprehensive Classification of Retinal Bipolar Neurons by Single-Cell Transcriptomics
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nihms807574.pdf
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Author(s)
Shekhar, Karthik
Lapan, Sylvain W.
Whitney, Irene E.
Tran, Nicholas M.
Macosko, Evan Z.
Kowalczyk, Monika
Adiconis, Xian
Levin, Joshua Z.
Nemesh, James
Goldman, Melissa
Date Issued
August 2016
Journal
Cell
Publisher
Elsevier
Citation
Shekhar, Karthik et al. “Comprehensive Classification of Retinal Bipolar Neurons by Single-Cell Transcriptomics.” Cell 166, 5 (August 2016): 1308–1323 © 2016 Elsevier Inc
Version
Author's final manuscript
Abstract
Patterns of gene expression can be used to characterize and classify neuronal types. It is challenging, however, to generate taxonomies that fulfill the essential criteria of being comprehensive, harmonizing with conventional classification schemes, and lacking superfluous subdivisions of genuine types. To address these challenges, we used massively parallel single-cell RNA profiling and optimized computational methods on a heterogeneous class of neurons, mouse retinal bipolar cells (BCs). From a population of ∼25,000 BCs, we derived a molecular classification that identified 15 types, including all types observed previously and two novel types, one of which has a non-canonical morphology and position. We validated the classification scheme and identified dozens of novel markers using methods that match molecular expression to cell morphology. This work provides a systematic methodology for achieving comprehensive molecular classification of neurons, identifies novel neuronal types, and uncovers transcriptional differences that distinguish types within a class.
MIT Department
Massachusetts Institute of Technology. Department of Biology
Koch Institute for Integrative Cancer Research at MIT
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1016/J.CELL.2016.07.054
