NatureNeurosciencePeer-reviewed journal article
Developmental diversification of cortical inhibitory interneurons.
Christian MayeriD, Christoph HafemeisteriD, Rachel C. Bandler, Robert P. MacholdiD, Renata Batista‐BritoiD, Xavier Hubert Jaglin, Kathryn C. AllawayiD, Andrew ButleriD, Gord FishelliD, Rahul SatijaiD
Abstract
Diverse subsets of cortical interneurons play a particularly important role in the stability of the neural circuits underlying cognitive and higher order brain functions, yet our understanding of how this diversity is generated is far from complete. We applied massively parallel single-cell RNA-seq to profile a developmental time course of interneuron development, measuring the transcriptomes of over 60,000 progenitors during their maturation in the ganglionic eminences and embryonic migration into the cortex. While diversity within mitotic progenitors is largely driven by cell cycle and differentiation state, we observed sparse eminence-specific transcription factor expression, which seeds the emergence of later cell diversity. Upon becoming postmitotic, cells from all eminences pass through one of three precursor states, one of which represents a cortical interneuron ground state. By integrating datasets across developmental timepoints, we identified transcriptomic heterogeneity in interneuron precursors representing the emergence of four cardinal classes (Pvalb, Sst, Id2 and Vip), which further separate into subtypes at different timepoints during development. Our analysis revealed that the ASD-associated transcription factor Mef2c discriminates early Pvalb-precursors in E13.5 cells, and removal of Mef2c confirms its essential role for Pvalb interneuron development. These findings shed new light on the molecular diversification of early inhibitory precursors, and suggest gene modules that may link developmental specification with the etiology of neuropsychiatric disorders.
- organism: mouse
- assay: scRNA-seq
- research focus: clustering
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: database / resource
- approach: statistical model
- approach: experimental assay
- Citations
- 492
- FWCI
- 22.29
- Field percentile
- top 1%
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