Nature NeuroscienceDevelopmental BiologyPeer-reviewed journal article
Decoding spatiotemporal gene expression of the developing human spinal cord and implications for ependymoma origin
Xiaofei LiiD, Žaneta AndrusivováiD, Paulo CzarnewskiiD, Christoffer Mattsson LangsethiD, Alma AnderssoniD, Yang LiuiD, Daniel GyllborgiD, Emelie BrauniD, Ludvig LarssoniD, Lijuan HuiD, Zhanna AlekseenkoiD, Hower LeeiD, Christophe AveneliD, Helena Kopp KallneriD, Elisabet ÅkessoniD, Igor I. AdameykoiD, Mats F. NilssoniD, Sten LinnarssoniD, Joakim LundebergiD, Erik SundströmiD
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Abstract
The human spinal cord contains diverse cell types, governed by a series of spatiotemporal events for tissue assembly and functions. However, the spatiotemporal regulation of cell fate specification in the human developing spinal cord remains largely unknown. Single-cell RNA sequencing and spatial transcriptomics techniques have advanced the understanding of human organ development considerably. By performing integrated analysis of single-cell and spatial multi-omics methods, we created a comprehensive developmental cell atlas of the first trimester human spinal cord. Our data revealed that the cell fate commitment of neural progenitor cells and their spatial positioning are spatiotemporally regulated by specific gene sets. Beyond this resource, we unexpectedly discovered unique events in human spinal cord development compared to rodents, including earlier quiescence of active neural stem cells, different regulation of stem cell differentiation, and distinct spatiotemporal genetic regulations of cell fate choices. In addition, using our atlas we identified specific gene expression in cancer stem cells in ependymomas. Thus, we demonstrate spatiotemporal genetic regulation of human spinal cord development as well as its potential to understand novel disease mechanisms and to inspire new therapies.
- organism: human
- assay: scRNA-seq
- assay: spatial transcriptomics
- research focus: cell type annotation
- research focus: clustering
- research focus: gene regulatory network inference
- approach: statistical model
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