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Cell-type-specific densities in mouse somatosensory cortex derived from scRNA-seq and in situ RNA hybridization
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 hybr...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10055737/ https://www.ncbi.nlm.nih.gov/pubmed/37007642 http://dx.doi.org/10.3389/fnana.2023.1118170 |
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author | Keller, Daniel Verasztó, Csaba Markram, Henry |
author_facet | Keller, Daniel Verasztó, Csaba Markram, Henry |
author_sort | Keller, Daniel |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10055737 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-100557372023-03-30 Cell-type-specific densities in mouse somatosensory cortex derived from scRNA-seq and in situ RNA hybridization Keller, Daniel Verasztó, Csaba Markram, Henry Front Neuroanat Neuroscience 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. Frontiers Media S.A. 2023-03-02 /pmc/articles/PMC10055737/ /pubmed/37007642 http://dx.doi.org/10.3389/fnana.2023.1118170 Text en Copyright © 2023 Keller, Verasztó and Markram. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Keller, Daniel Verasztó, Csaba Markram, Henry Cell-type-specific densities in mouse somatosensory cortex derived from scRNA-seq and in situ RNA hybridization |
title | Cell-type-specific densities in mouse somatosensory cortex derived from scRNA-seq and in situ RNA hybridization |
title_full | Cell-type-specific densities in mouse somatosensory cortex derived from scRNA-seq and in situ RNA hybridization |
title_fullStr | Cell-type-specific densities in mouse somatosensory cortex derived from scRNA-seq and in situ RNA hybridization |
title_full_unstemmed | Cell-type-specific densities in mouse somatosensory cortex derived from scRNA-seq and in situ RNA hybridization |
title_short | Cell-type-specific densities in mouse somatosensory cortex derived from scRNA-seq and in situ RNA hybridization |
title_sort | cell-type-specific densities in mouse somatosensory cortex derived from scrna-seq and in situ rna hybridization |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10055737/ https://www.ncbi.nlm.nih.gov/pubmed/37007642 http://dx.doi.org/10.3389/fnana.2023.1118170 |
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