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scREAD: A Single-Cell RNA-Seq Database for Alzheimer's Disease

Alzheimer's disease (AD) is a progressive neurodegenerative disorder of the brain and the most common form of dementia among the elderly. The single-cell RNA-sequencing (scRNA-Seq) and single-nucleus RNA-sequencing (snRNA-Seq) techniques are extremely useful for dissecting the function/dysfunct...

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Detalles Bibliográficos
Autores principales: Jiang, Jing, Wang, Cankun, Qi, Ren, Fu, Hongjun, Ma, Qin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7674513/
https://www.ncbi.nlm.nih.gov/pubmed/33241205
http://dx.doi.org/10.1016/j.isci.2020.101769
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author Jiang, Jing
Wang, Cankun
Qi, Ren
Fu, Hongjun
Ma, Qin
author_facet Jiang, Jing
Wang, Cankun
Qi, Ren
Fu, Hongjun
Ma, Qin
author_sort Jiang, Jing
collection PubMed
description Alzheimer's disease (AD) is a progressive neurodegenerative disorder of the brain and the most common form of dementia among the elderly. The single-cell RNA-sequencing (scRNA-Seq) and single-nucleus RNA-sequencing (snRNA-Seq) techniques are extremely useful for dissecting the function/dysfunction of highly heterogeneous cells in the brain at the single-cell level, and the corresponding data analyses can significantly improve our understanding of why particular cells are vulnerable in AD. We developed an integrated database named scREAD (single-cell RNA-Seq database for Alzheimer's disease), which is as far as we know the first database dedicated to the management of all the existing scRNA-Seq and snRNA-Seq data sets from the human postmortem brain tissue with AD and mouse models with AD pathology. scREAD provides comprehensive analysis results for 73 data sets from 10 brain regions, including control atlas construction, cell-type prediction, identification of differentially expressed genes, and identification of cell-type-specific regulons.
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spelling pubmed-76745132020-11-24 scREAD: A Single-Cell RNA-Seq Database for Alzheimer's Disease Jiang, Jing Wang, Cankun Qi, Ren Fu, Hongjun Ma, Qin iScience Article Alzheimer's disease (AD) is a progressive neurodegenerative disorder of the brain and the most common form of dementia among the elderly. The single-cell RNA-sequencing (scRNA-Seq) and single-nucleus RNA-sequencing (snRNA-Seq) techniques are extremely useful for dissecting the function/dysfunction of highly heterogeneous cells in the brain at the single-cell level, and the corresponding data analyses can significantly improve our understanding of why particular cells are vulnerable in AD. We developed an integrated database named scREAD (single-cell RNA-Seq database for Alzheimer's disease), which is as far as we know the first database dedicated to the management of all the existing scRNA-Seq and snRNA-Seq data sets from the human postmortem brain tissue with AD and mouse models with AD pathology. scREAD provides comprehensive analysis results for 73 data sets from 10 brain regions, including control atlas construction, cell-type prediction, identification of differentially expressed genes, and identification of cell-type-specific regulons. Elsevier 2020-11-05 /pmc/articles/PMC7674513/ /pubmed/33241205 http://dx.doi.org/10.1016/j.isci.2020.101769 Text en © 2020 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Article
Jiang, Jing
Wang, Cankun
Qi, Ren
Fu, Hongjun
Ma, Qin
scREAD: A Single-Cell RNA-Seq Database for Alzheimer's Disease
title scREAD: A Single-Cell RNA-Seq Database for Alzheimer's Disease
title_full scREAD: A Single-Cell RNA-Seq Database for Alzheimer's Disease
title_fullStr scREAD: A Single-Cell RNA-Seq Database for Alzheimer's Disease
title_full_unstemmed scREAD: A Single-Cell RNA-Seq Database for Alzheimer's Disease
title_short scREAD: A Single-Cell RNA-Seq Database for Alzheimer's Disease
title_sort scread: a single-cell rna-seq database for alzheimer's disease
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7674513/
https://www.ncbi.nlm.nih.gov/pubmed/33241205
http://dx.doi.org/10.1016/j.isci.2020.101769
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