Characterizing the composition of iPSC derived cells from bulk transcriptomics data with CellMap

Abstract Induced pluripotent stem cell (iPSC) derived cell types are increasingly employed as in vitro model systems for drug discovery. For these studies to be meaningful, it is important to understand the reproducibility of the iPSC-derived cultures and their similarity to equivalent endogenous ce...
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Autor*in:	Zhengyu Ouyang [verfasserIn] Nathanael Bourgeois-Tchir [verfasserIn] Eugenia Lyashenko [verfasserIn] Paige E. Cundiff [verfasserIn] Patrick F. Cullen [verfasserIn] Ravi Challa [verfasserIn] Kejie Li [verfasserIn] Xinmin Zhang [verfasserIn] Fergal Casey [verfasserIn] Sandra J. Engle [verfasserIn] Baohong Zhang [verfasserIn] Maria I. Zavodszky [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Übergeordnetes Werk:	In: Scientific Reports - Nature Portfolio, 2011, 12(2022), 1, Seite 14
Übergeordnetes Werk:	volume:12 ; year:2022 ; number:1 ; pages:14

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1038/s41598-022-22115-1

Katalog-ID:	DOAJ083708936

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spelling	10.1038/s41598-022-22115-1 doi (DE-627)DOAJ083708936 (DE-599)DOAJbb8dfd950f2a43319b5fd7d678c3f265 DE-627 ger DE-627 rakwb eng Zhengyu Ouyang verfasserin aut Characterizing the composition of iPSC derived cells from bulk transcriptomics data with CellMap 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Induced pluripotent stem cell (iPSC) derived cell types are increasingly employed as in vitro model systems for drug discovery. For these studies to be meaningful, it is important to understand the reproducibility of the iPSC-derived cultures and their similarity to equivalent endogenous cell types. Single-cell and single-nucleus RNA sequencing (RNA-seq) are useful to gain such understanding, but they are expensive and time consuming, while bulk RNA-seq data can be generated quicker and at lower cost. In silico cell type decomposition is an efficient, inexpensive, and convenient alternative that can leverage bulk RNA-seq to derive more fine-grained information about these cultures. We developed CellMap, a computational tool that derives cell type profiles from publicly available single-cell and single-nucleus datasets to infer cell types in bulk RNA-seq data from iPSC-derived cell lines. Medicine R Science Q Nathanael Bourgeois-Tchir verfasserin aut Eugenia Lyashenko verfasserin aut Paige E. Cundiff verfasserin aut Patrick F. Cullen verfasserin aut Ravi Challa verfasserin aut Kejie Li verfasserin aut Xinmin Zhang verfasserin aut Fergal Casey verfasserin aut Sandra J. Engle verfasserin aut Baohong Zhang verfasserin aut Maria I. Zavodszky verfasserin aut In Scientific Reports Nature Portfolio, 2011 12(2022), 1, Seite 14 (DE-627)663366712 (DE-600)2615211-3 20452322 nnns volume:12 year:2022 number:1 pages:14 https://doi.org/10.1038/s41598-022-22115-1 kostenfrei https://doaj.org/article/bb8dfd950f2a43319b5fd7d678c3f265 kostenfrei https://doi.org/10.1038/s41598-022-22115-1 kostenfrei https://doaj.org/toc/2045-2322 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2022 1 14
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allfieldsSound	10.1038/s41598-022-22115-1 doi (DE-627)DOAJ083708936 (DE-599)DOAJbb8dfd950f2a43319b5fd7d678c3f265 DE-627 ger DE-627 rakwb eng Zhengyu Ouyang verfasserin aut Characterizing the composition of iPSC derived cells from bulk transcriptomics data with CellMap 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Induced pluripotent stem cell (iPSC) derived cell types are increasingly employed as in vitro model systems for drug discovery. For these studies to be meaningful, it is important to understand the reproducibility of the iPSC-derived cultures and their similarity to equivalent endogenous cell types. Single-cell and single-nucleus RNA sequencing (RNA-seq) are useful to gain such understanding, but they are expensive and time consuming, while bulk RNA-seq data can be generated quicker and at lower cost. In silico cell type decomposition is an efficient, inexpensive, and convenient alternative that can leverage bulk RNA-seq to derive more fine-grained information about these cultures. We developed CellMap, a computational tool that derives cell type profiles from publicly available single-cell and single-nucleus datasets to infer cell types in bulk RNA-seq data from iPSC-derived cell lines. Medicine R Science Q Nathanael Bourgeois-Tchir verfasserin aut Eugenia Lyashenko verfasserin aut Paige E. Cundiff verfasserin aut Patrick F. Cullen verfasserin aut Ravi Challa verfasserin aut Kejie Li verfasserin aut Xinmin Zhang verfasserin aut Fergal Casey verfasserin aut Sandra J. Engle verfasserin aut Baohong Zhang verfasserin aut Maria I. Zavodszky verfasserin aut In Scientific Reports Nature Portfolio, 2011 12(2022), 1, Seite 14 (DE-627)663366712 (DE-600)2615211-3 20452322 nnns volume:12 year:2022 number:1 pages:14 https://doi.org/10.1038/s41598-022-22115-1 kostenfrei https://doaj.org/article/bb8dfd950f2a43319b5fd7d678c3f265 kostenfrei https://doi.org/10.1038/s41598-022-22115-1 kostenfrei https://doaj.org/toc/2045-2322 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2022 1 14
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author	Zhengyu Ouyang
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title	Characterizing the composition of iPSC derived cells from bulk transcriptomics data with CellMap
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title_full	Characterizing the composition of iPSC derived cells from bulk transcriptomics data with CellMap
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author_browse	Zhengyu Ouyang Nathanael Bourgeois-Tchir Eugenia Lyashenko Paige E. Cundiff Patrick F. Cullen Ravi Challa Kejie Li Xinmin Zhang Fergal Casey Sandra J. Engle Baohong Zhang Maria I. Zavodszky
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title_sort	characterizing the composition of ipsc derived cells from bulk transcriptomics data with cellmap
title_auth	Characterizing the composition of iPSC derived cells from bulk transcriptomics data with CellMap
abstract	Abstract Induced pluripotent stem cell (iPSC) derived cell types are increasingly employed as in vitro model systems for drug discovery. For these studies to be meaningful, it is important to understand the reproducibility of the iPSC-derived cultures and their similarity to equivalent endogenous cell types. Single-cell and single-nucleus RNA sequencing (RNA-seq) are useful to gain such understanding, but they are expensive and time consuming, while bulk RNA-seq data can be generated quicker and at lower cost. In silico cell type decomposition is an efficient, inexpensive, and convenient alternative that can leverage bulk RNA-seq to derive more fine-grained information about these cultures. We developed CellMap, a computational tool that derives cell type profiles from publicly available single-cell and single-nucleus datasets to infer cell types in bulk RNA-seq data from iPSC-derived cell lines.
abstractGer	Abstract Induced pluripotent stem cell (iPSC) derived cell types are increasingly employed as in vitro model systems for drug discovery. For these studies to be meaningful, it is important to understand the reproducibility of the iPSC-derived cultures and their similarity to equivalent endogenous cell types. Single-cell and single-nucleus RNA sequencing (RNA-seq) are useful to gain such understanding, but they are expensive and time consuming, while bulk RNA-seq data can be generated quicker and at lower cost. In silico cell type decomposition is an efficient, inexpensive, and convenient alternative that can leverage bulk RNA-seq to derive more fine-grained information about these cultures. We developed CellMap, a computational tool that derives cell type profiles from publicly available single-cell and single-nucleus datasets to infer cell types in bulk RNA-seq data from iPSC-derived cell lines.
abstract_unstemmed	Abstract Induced pluripotent stem cell (iPSC) derived cell types are increasingly employed as in vitro model systems for drug discovery. For these studies to be meaningful, it is important to understand the reproducibility of the iPSC-derived cultures and their similarity to equivalent endogenous cell types. Single-cell and single-nucleus RNA sequencing (RNA-seq) are useful to gain such understanding, but they are expensive and time consuming, while bulk RNA-seq data can be generated quicker and at lower cost. In silico cell type decomposition is an efficient, inexpensive, and convenient alternative that can leverage bulk RNA-seq to derive more fine-grained information about these cultures. We developed CellMap, a computational tool that derives cell type profiles from publicly available single-cell and single-nucleus datasets to infer cell types in bulk RNA-seq data from iPSC-derived cell lines.
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title_short	Characterizing the composition of iPSC derived cells from bulk transcriptomics data with CellMap
url	https://doi.org/10.1038/s41598-022-22115-1 https://doaj.org/article/bb8dfd950f2a43319b5fd7d678c3f265 https://doaj.org/toc/2045-2322
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author2	Nathanael Bourgeois-Tchir Eugenia Lyashenko Paige E. Cundiff Patrick F. Cullen Ravi Challa Kejie Li Xinmin Zhang Fergal Casey Sandra J. Engle Baohong Zhang Maria I. Zavodszky
author2Str	Nathanael Bourgeois-Tchir Eugenia Lyashenko Paige E. Cundiff Patrick F. Cullen Ravi Challa Kejie Li Xinmin Zhang Fergal Casey Sandra J. Engle Baohong Zhang Maria I. Zavodszky
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