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Cell-type-specific densities in mouse somatosensory cortex derived from scRNA-seq and in situ RNA hybridization

Authors :
Daniel Keller
Csaba Verasztó
Henry Markram
Source :
Frontiers in Neuroanatomy, Vol 17 (2023)
Publication Year :
2023
Publisher :
Frontiers Media S.A., 2023.

Abstract

Cells in the mammalian cerebral cortex exhibit layer-dependent patterns in their distribution. Classical methods of determining cell type distributions typically employ a painstaking process of large-scale sampling and characterization of cellular composition. We found that by combining in situ hybridization (ISH) images with cell-type-specific transcriptomes, position-dependent cortical composition in P56 mouse could be estimated in the somatosensory cortex. The method uses ISH images from the Allen Institute for Brain Science. There are two novel aspects of the methodology. First, it is not necessary to select a subset of genes that are particular for a cell type of interest, nor is it necessary to only use ISH images with low variability among samples. Second, the method also compensated for differences in soma size and incompleteness of the transcriptomes. The soma size compensation is particularly important in order to obtain quantitative estimates since relying on bulk expression alone would overestimate the contribution of larger cells. Predicted distributions of broader classes of cell types agreed with literature distributions. The primary result is that there is a high degree of substructure in the distribution of transcriptomic types beyond the resolution of layers. Furthermore, transcriptomic cell types each exhibited characteristic soma size distributions. Results suggest that the method could also be employed to assign transcriptomic cell types to well-aligned image sets in the entire brain.

Details

Language :
English
ISSN :
16625129
Volume :
17
Database :
Directory of Open Access Journals
Journal :
Frontiers in Neuroanatomy
Publication Type :
Academic Journal
Accession number :
edsdoj.f5ed4e1c370475fa0471fbed7965d27
Document Type :
article
Full Text :
https://doi.org/10.3389/fnana.2023.1118170