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Spatial cell type composition in normal and Alzheimers human brains is revealed using integrated mouse and human single cell RNA sequencing

Single-cell RNA sequencing (scRNA-seq) resolves heterogenous cell populations in tissues and helps to reveal single-cell level function and dynamics. In neuroscience, the rarity of brain tissue is the bottleneck for such study. Evidence shows that, mouse and human share similar cell type gene marker...

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Autores principales: Johnson, Travis S., Xiang, Shunian, Helm, Bryan R., Abrams, Zachary B., Neidecker, Peter, Machiraju, Raghu, Zhang, Yan, Huang, Kun, Zhang, Jie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7582925/
https://www.ncbi.nlm.nih.gov/pubmed/33093481
http://dx.doi.org/10.1038/s41598-020-74917-w
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author Johnson, Travis S.
Xiang, Shunian
Helm, Bryan R.
Abrams, Zachary B.
Neidecker, Peter
Machiraju, Raghu
Zhang, Yan
Huang, Kun
Zhang, Jie
author_facet Johnson, Travis S.
Xiang, Shunian
Helm, Bryan R.
Abrams, Zachary B.
Neidecker, Peter
Machiraju, Raghu
Zhang, Yan
Huang, Kun
Zhang, Jie
author_sort Johnson, Travis S.
collection PubMed
description Single-cell RNA sequencing (scRNA-seq) resolves heterogenous cell populations in tissues and helps to reveal single-cell level function and dynamics. In neuroscience, the rarity of brain tissue is the bottleneck for such study. Evidence shows that, mouse and human share similar cell type gene markers. We hypothesized that the scRNA-seq data of mouse brain tissue can be used to complete human data to infer cell type composition in human samples. Here, we supplement cell type information of human scRNA-seq data, with mouse. The resulted data were used to infer the spatial cellular composition of 3702 human brain samples from Allen Human Brain Atlas. We then mapped the cell types back to corresponding brain regions. Most cell types were localized to the correct regions. We also compare the mapping results to those derived from neuronal nuclei locations. They were consistent after accounting for changes in neural connectivity between regions. Furthermore, we applied this approach on Alzheimer’s brain data and successfully captured cell pattern changes in AD brains. We believe this integrative approach can solve the sample rarity issue in the neuroscience.
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spelling pubmed-75829252020-10-23 Spatial cell type composition in normal and Alzheimers human brains is revealed using integrated mouse and human single cell RNA sequencing Johnson, Travis S. Xiang, Shunian Helm, Bryan R. Abrams, Zachary B. Neidecker, Peter Machiraju, Raghu Zhang, Yan Huang, Kun Zhang, Jie Sci Rep Article Single-cell RNA sequencing (scRNA-seq) resolves heterogenous cell populations in tissues and helps to reveal single-cell level function and dynamics. In neuroscience, the rarity of brain tissue is the bottleneck for such study. Evidence shows that, mouse and human share similar cell type gene markers. We hypothesized that the scRNA-seq data of mouse brain tissue can be used to complete human data to infer cell type composition in human samples. Here, we supplement cell type information of human scRNA-seq data, with mouse. The resulted data were used to infer the spatial cellular composition of 3702 human brain samples from Allen Human Brain Atlas. We then mapped the cell types back to corresponding brain regions. Most cell types were localized to the correct regions. We also compare the mapping results to those derived from neuronal nuclei locations. They were consistent after accounting for changes in neural connectivity between regions. Furthermore, we applied this approach on Alzheimer’s brain data and successfully captured cell pattern changes in AD brains. We believe this integrative approach can solve the sample rarity issue in the neuroscience. Nature Publishing Group UK 2020-10-22 /pmc/articles/PMC7582925/ /pubmed/33093481 http://dx.doi.org/10.1038/s41598-020-74917-w Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Johnson, Travis S.
Xiang, Shunian
Helm, Bryan R.
Abrams, Zachary B.
Neidecker, Peter
Machiraju, Raghu
Zhang, Yan
Huang, Kun
Zhang, Jie
Spatial cell type composition in normal and Alzheimers human brains is revealed using integrated mouse and human single cell RNA sequencing
title Spatial cell type composition in normal and Alzheimers human brains is revealed using integrated mouse and human single cell RNA sequencing
title_full Spatial cell type composition in normal and Alzheimers human brains is revealed using integrated mouse and human single cell RNA sequencing
title_fullStr Spatial cell type composition in normal and Alzheimers human brains is revealed using integrated mouse and human single cell RNA sequencing
title_full_unstemmed Spatial cell type composition in normal and Alzheimers human brains is revealed using integrated mouse and human single cell RNA sequencing
title_short Spatial cell type composition in normal and Alzheimers human brains is revealed using integrated mouse and human single cell RNA sequencing
title_sort spatial cell type composition in normal and alzheimers human brains is revealed using integrated mouse and human single cell rna sequencing
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7582925/
https://www.ncbi.nlm.nih.gov/pubmed/33093481
http://dx.doi.org/10.1038/s41598-020-74917-w
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