Cell
Volume 166, Issue 5, 25 August 2016, Pages 1308-1323.e30
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Comprehensive Classification of Retinal Bipolar Neurons by Single-Cell Transcriptomics

https://doi.org/10.1016/j.cell.2016.07.054Get rights and content
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Highlights

  • Unsupervised clustering of 25,000 single-cell transcriptomes reveals 15 bipolar types

  • Molecularly defined types correspond 1:1 to morphologically defined types

  • One previously undescribed bipolar cell type has hybrid bipolar-amacrine features

  • Shallow sequencing of large cell numbers facilitates comprehensive classification

Summary

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.

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