NeuronNeurosciencePeer-reviewed journal article
Developmental heterogeneity of microglia and brain myeloid cells revealed by deep single-cell RNA sequencing
Qingyun LiiD, Zuolin ChengiD, Lu ZhouiD, Spyros DarmanisiD, Norma F NeffiD, Jennifer Okamoto, Gunsagar Singh GulatiiD, Mariko L. BennettiD, Lu O. SuniD, Laura E. Clarke, Julia Marschallinger, Guoqiang Yu, Stephen R. QuakeiD, Tony Wyss‐CorayiD, Ben A. BarresiD
Abstract
Microglia are increasingly recognized for their major contributions during brain development and neurodegenerative disease. It is currently unknown if these functions are carried out by subsets of microglia during different stages of development and adulthood or within specific brain regions. Here, we performed deep single-cell RNA sequencing (scRNA-seq) of microglia and related myeloid cells sorted from various regions of embryonic, postnatal, and adult mouse brains. We found that the majority of adult microglia with homeostatic signatures are remarkably similar in transcriptomes, regardless of brain region. By contrast, postnatal microglia represent a more heterogeneous population. We discovered that postnatal white matter-associated microglia (WAM) are strikingly different from microglia in other regions and express genes enriched in degenerative disease-associated microglia. These postnatal WAM have distinct amoeboid morphology, are metabolically active, and phagocytose newly formed oligodendrocytes. This scRNA-seq atlas will be a valuable resource for dissecting innate immune functions in health and disease.\n\nHighlightsO_LIMyeloid scRNA-seq atlas across brain regions and developmental stages\nC_LIO_LILimited transcriptomic heterogeneity of homeostatic microglia in the adult brain\nC_LIO_LIPhase-specific gene sets of proliferating microglia along cell cycle pseudotime\nC_LIO_LIPhagocytic postnatal white matter-associated microglia sharing DAM gene signatures\nC_LI
- organism: mouse
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: clustering
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: deep learning
- approach: statistical model
- Citations
- 1,128
- FWCI
- 42.07
- Field percentile
- top 1%
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