Nature NeuroscienceDevelopmental BiologyPeer-reviewed journal article
Spatial and single-cell transcriptional landscape of human cerebellar development
Kimberly A. AldingeriD, Zachary ThomsoniD, Ian G. Phelps, Parthiv HaldipuriD, Mei Deng, Andrew E. TimmsiD, Matthew HiranoiD, Gabriel SantpereiD, Charles M. RocoiD, Alexander B. RosenbergiD, Belén Lorente-GaldósiD, Forrest O. GuldeniD, Diana R. O’DayiD, Lynne M. Overman, Steven LisgoiD, Paula AlexandreiD, Nenad ŠestaniD, Dan DohertyiD, William B. DobynsiD, Georg SeeligiD, Ian A. GlassiD, Kathleen J. MilleniD
Abstract
ABSTRACTCerebellar development and function require precise regulation of molecular and cellular programs to coordinate motor functions and integrate network signals required for cognition and emotional regulation. However, molecular understanding of human cerebellar development is limited. Here, we combined spatially resolved and single-cell transcriptomics to systematically map the molecular, cellular, and spatial composition of early and mid-gestational human cerebellum. This enabled us to transcriptionally profile major cell types and examine the dynamics of gene expression within cell types and lineages across development. The resulting ‘Developmental Cell Atlas of the Human Cerebellum’ demonstrates that the molecular organization of the cerebellar anlage reflects cytoarchitecturally distinct regions and developmentally transient cell types that are insufficiently captured in bulk transcriptional profiles. By mapping disease genes onto cell types, we implicate the dysregulation of specific cerebellar cell types, especially Purkinje cells, in pediatric and adult neurological disorders. These data provide a critical resource for understanding human cerebellar development with implications for the cellular basis of cerebellar diseases.Competing Interest StatementA.B.R, C.R., and G.Se. are shareholders of Split Bioscience.View Full Text
- organism: human
- assay: spatial transcriptomics
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: clustering
- research focus: differential expression
- approach: statistical model
- Citations
- 217
- FWCI
- 10.74
- Field percentile
- top 1%
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