bioRxivNeurosciencePreprint
Single-cell in situ transcriptomic map of astrocyte cortical layer diversity
Omer Ali BayraktariD, Theresa BartelsiD, Damon Polioudakis, Staffan HolmqvistiD, Lucile Ben HaimiD, Adam M. H. Young, Kirti PrakashiD, Alexander P.Y. BrowniD, Mercedes F. ParedesiD, Riki KawaguchiiD, John H. StockleyiD, Khalida SabeuriD, Sandra Chang, Eric Jinsheng HuangiD, Peter J. HutchinsoniD, Erik M. UllianiD, Daniel H. GeschwindiD, Giovanni CoppolaiD, David H. RowitchiD
Abstract
During organogenesis, patterns and gradients of gene expression underlie organization and diversified cell specification to generate complex tissue architecture. While the cerebral cortex is organized into six excitatory neuronal layers, it is unclear whether glial cells are diversified to mimic neuronal laminae or show distinct layering. To determine the molecular architecture of the mammalian cortex, we developed a high content pipeline that can quantify single-cell gene expression in situ. The Large-area Spatial Transcriptomic (LaST) map confirmed expected cortical neuron layer organization and also revealed a novel neuronal identity signature. Screening 46 candidate genes for astrocyte diversity across the cortex, we identified grey matter superficial, mid and deep astrocyte identities in gradient layer patterns that were distinct from neurons. Astrocyte layers formed in early postnatal cortex and mostly persisted in adult mouse and human cortex. Mutations that shifted neuronal post-mitotic identity or organization were sufficient to alter glial layering, indicating an instructive role for neuronal cues. In normal mouse cortex, astrocyte layer patterns showed area diversity between functionally distinct cortical regions. These findings indicate that excitatory neurons and astrocytes cells are organized into distinct lineage-associated laminae, which give rise to higher order neuroglial complexity of cortical architecture.
- organism: mouse
- organism: human
- assay: spatial transcriptomics
- research focus: cell type annotation
- research focus: clustering
- research focus: differential expression
- approach: statistical model
- Citations
- 16
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