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Cell. 2019 Jun 13;177(7):1873-1887.e17. doi: 10.1016/j.cell.2019.05.006. Epub 2019 Jun 6.

Single-Cell Multi-omic Integration Compares and Contrasts Features of Brain Cell Identity.

Author information

1
Broad Institute of Harvard and MIT, Stanley Center for Psychiatric Research, 450 Main Street, Cambridge, MA, USA. Electronic address: jwelch@broadinstitute.org.
2
Broad Institute of Harvard and MIT, Stanley Center for Psychiatric Research, 450 Main Street, Cambridge, MA, USA.
3
Broad Institute of Harvard and MIT, Stanley Center for Psychiatric Research, 450 Main Street, Cambridge, MA, USA; Massachusetts General Hospital, Department of Psychiatry, 55 Fruit Street, Boston, MA, USA. Electronic address: emacosko@broadinstitute.org.

Abstract

Defining cell types requires integrating diverse single-cell measurements from multiple experiments and biological contexts. To flexibly model single-cell datasets, we developed LIGER, an algorithm that delineates shared and dataset-specific features of cell identity. We applied it to four diverse and challenging analyses of human and mouse brain cells. First, we defined region-specific and sexually dimorphic gene expression in the mouse bed nucleus of the stria terminalis. Second, we analyzed expression in the human substantia nigra, comparing cell states in specific donors and relating cell types to those in the mouse. Third, we integrated in situ and single-cell expression data to spatially locate fine subtypes of cells present in the mouse frontal cortex. Finally, we jointly defined mouse cortical cell types using single-cell RNA-seq and DNA methylation profiles, revealing putative mechanisms of cell-type-specific epigenomic regulation. Integrative analyses using LIGER promise to accelerate investigations of cell-type definition, gene regulation, and disease states.

KEYWORDS:

bed nucleus of the stria terminalis; data integration; single-cell genomics; substantia nigra

PMID:
31178122
DOI:
10.1016/j.cell.2019.05.006

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