Format

Send to

Choose Destination
Cell. 2016 Aug 25;166(5):1308-1323.e30. doi: 10.1016/j.cell.2016.07.054.

Comprehensive Classification of Retinal Bipolar Neurons by Single-Cell Transcriptomics.

Author information

1
Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA.
2
Department of Genetics, Harvard Medical School, Boston, MA 02115, USA; Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA.
3
Center for Brain Science and Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02130, USA.
4
Department of Genetics, Harvard Medical School, Boston, MA 02115, USA; Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA.
5
Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA.
6
Department of Genetics, Harvard Medical School, Boston, MA 02115, USA; Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA.
7
Department of Genetics, Harvard Medical School, Boston, MA 02115, USA; Department of Ophthalmology, Harvard Medical School, Boston, MA 02115, USA; Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA. Electronic address: cepko@genetics.med.harvard.edu.
8
Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA; Department of Biology and Koch Institute, MIT, Cambridge, MA 02139, USA. Electronic address: aregev@broadinstitute.org.
9
Center for Brain Science and Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02130, USA. Electronic address: sanesj@mcb.harvard.edu.

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.

PMID:
27565351
PMCID:
PMC5003425
DOI:
10.1016/j.cell.2016.07.054
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Elsevier Science Icon for PubMed Central
Loading ...
Support Center