Journal
CELL
Volume 166, Issue 5, Pages 1308-+Publisher
CELL PRESS
DOI: 10.1016/j.cell.2016.07.054
Keywords
-
Categories
Funding
- Klarman Cell Observatory
- National Institute of Mental Health (NIMH) [U01MH105960, EY024833-02]
- Charles A. King Trust Postdoctoral Research Fellowship Program, Bank of America, N.A.
- imeon J. Fortin Charitable Foundation, Bank of America, N.A.
Ask authors/readers for more resources
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 similar to 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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available