Abstract
The ability to spatially resolve the cellular architecture of human cortical cell types over informative areas is essential to understanding brain function. We combined in situ sequencing gene expression data and single-nucleus RNA-sequencing cell type definitions to spatially map cells in sections of the human cortex via probabilistic cell typing. We mapped and classified a total of 59,816 cells into all 75 previously defined subtypes to create a first spatial atlas of human cortical cells in their native position, their abundances and genetic signatures. We also examined the precise within- and across-layer distributions of all the cell types and provide a resource for the cell atlas community. The abundances and locations presented here could serve as a reference for further studies, that include human brain tissues and disease applications at the cell type level.
Highlights
The ability to spatially resolve the cellular architecture of human cortical cell types over informative areas is essential to understanding brain function
Hybridization-based in situ sequencing (HybISS) is an imagebased multi-targeted gene expression profiling technique that allows the precise mapping of individual cells in human brain tissues[7]
Counting the cell type occurrences in our tissue sections shows that non-neuronal cell types outnumber neuronal cell types by 3.38, comparable to published results that measured a ratio of 3.76 over the entire human cortex[1]
Summary
The ability to spatially resolve the cellular architecture of human cortical cell types over informative areas is essential to understanding brain function. We combined in situ sequencing gene expression data and single-nucleus RNA-sequencing cell type definitions to spatially map cells in sections of the human cortex via probabilistic cell typing. Hybridization-based in situ sequencing (HybISS) is an imagebased multi-targeted gene expression profiling technique that allows the precise mapping of individual cells in human brain tissues[7]. Various analytical approaches can assign detected transcripts to segmented cells and subsequently, cells to cell types One such approach, probabilistic cell typing by in situ sequencing (pciSeq), leverages single-cell RNA-sequencing data to guide cell type assignment[8,9]. We implement pciSeq to map cell types across three human cortical sections as a proof of principle to show an efficient and robust method to accurately resolve anatomical organization of human tissue that is envisioned for such efforts as the Human Cell Atlas[10]
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