Measuring single cell divisions in human tissues from multi-region sequencing data

Quantifying somatic evolutionary processes in cancer and healthy tissue is a challenge. Here, the authors use single time point multi-region sampling of cancer and normal tissue, combined with evolutionary theory, to quantify in vivo mutation and cell survival rates per cell division.

Guardado en:

Detalles Bibliográficos
Autores principales:	Benjamin Werner, Jack Case, Marc J. Williams, Ketevan Chkhaidze, Daniel Temko, Javier Fernández-Mateos, George D. Cresswell, Daniel Nichol, William Cross, Inmaculada Spiteri, Weini Huang, Ian P. M. Tomlinson, Chris P. Barnes, Trevor A. Graham, Andrea Sottoriva
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/1cc8e57a38b3428da9bd484790ebf103
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:1cc8e57a38b3428da9bd484790ebf103
record_format	dspace
spelling	oai:doaj.org-article:1cc8e57a38b3428da9bd484790ebf1032021-12-02T17:32:11ZMeasuring single cell divisions in human tissues from multi-region sequencing data10.1038/s41467-020-14844-62041-1723https://doaj.org/article/1cc8e57a38b3428da9bd484790ebf1032020-02-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-14844-6https://doaj.org/toc/2041-1723Quantifying somatic evolutionary processes in cancer and healthy tissue is a challenge. Here, the authors use single time point multi-region sampling of cancer and normal tissue, combined with evolutionary theory, to quantify in vivo mutation and cell survival rates per cell division.Benjamin WernerJack CaseMarc J. WilliamsKetevan ChkhaidzeDaniel TemkoJavier Fernández-MateosGeorge D. CresswellDaniel NicholWilliam CrossInmaculada SpiteriWeini HuangIan P. M. TomlinsonChris P. BarnesTrevor A. GrahamAndrea SottorivaNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-9 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Benjamin Werner Jack Case Marc J. Williams Ketevan Chkhaidze Daniel Temko Javier Fernández-Mateos George D. Cresswell Daniel Nichol William Cross Inmaculada Spiteri Weini Huang Ian P. M. Tomlinson Chris P. Barnes Trevor A. Graham Andrea Sottoriva Measuring single cell divisions in human tissues from multi-region sequencing data
description	Quantifying somatic evolutionary processes in cancer and healthy tissue is a challenge. Here, the authors use single time point multi-region sampling of cancer and normal tissue, combined with evolutionary theory, to quantify in vivo mutation and cell survival rates per cell division.
format	article
author	Benjamin Werner Jack Case Marc J. Williams Ketevan Chkhaidze Daniel Temko Javier Fernández-Mateos George D. Cresswell Daniel Nichol William Cross Inmaculada Spiteri Weini Huang Ian P. M. Tomlinson Chris P. Barnes Trevor A. Graham Andrea Sottoriva
author_facet	Benjamin Werner Jack Case Marc J. Williams Ketevan Chkhaidze Daniel Temko Javier Fernández-Mateos George D. Cresswell Daniel Nichol William Cross Inmaculada Spiteri Weini Huang Ian P. M. Tomlinson Chris P. Barnes Trevor A. Graham Andrea Sottoriva
author_sort	Benjamin Werner
title	Measuring single cell divisions in human tissues from multi-region sequencing data
title_short	Measuring single cell divisions in human tissues from multi-region sequencing data
title_full	Measuring single cell divisions in human tissues from multi-region sequencing data
title_fullStr	Measuring single cell divisions in human tissues from multi-region sequencing data
title_full_unstemmed	Measuring single cell divisions in human tissues from multi-region sequencing data
title_sort	measuring single cell divisions in human tissues from multi-region sequencing data
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/1cc8e57a38b3428da9bd484790ebf103
work_keys_str_mv	AT benjaminwerner measuringsinglecelldivisionsinhumantissuesfrommultiregionsequencingdata AT jackcase measuringsinglecelldivisionsinhumantissuesfrommultiregionsequencingdata AT marcjwilliams measuringsinglecelldivisionsinhumantissuesfrommultiregionsequencingdata AT ketevanchkhaidze measuringsinglecelldivisionsinhumantissuesfrommultiregionsequencingdata AT danieltemko measuringsinglecelldivisionsinhumantissuesfrommultiregionsequencingdata AT javierfernandezmateos measuringsinglecelldivisionsinhumantissuesfrommultiregionsequencingdata AT georgedcresswell measuringsinglecelldivisionsinhumantissuesfrommultiregionsequencingdata AT danielnichol measuringsinglecelldivisionsinhumantissuesfrommultiregionsequencingdata AT williamcross measuringsinglecelldivisionsinhumantissuesfrommultiregionsequencingdata AT inmaculadaspiteri measuringsinglecelldivisionsinhumantissuesfrommultiregionsequencingdata AT weinihuang measuringsinglecelldivisionsinhumantissuesfrommultiregionsequencingdata AT ianpmtomlinson measuringsinglecelldivisionsinhumantissuesfrommultiregionsequencingdata AT chrispbarnes measuringsinglecelldivisionsinhumantissuesfrommultiregionsequencingdata AT trevoragraham measuringsinglecelldivisionsinhumantissuesfrommultiregionsequencingdata AT andreasottoriva measuringsinglecelldivisionsinhumantissuesfrommultiregionsequencingdata
_version_	1718380353535606784

Measuring single cell divisions in human tissues from multi-region sequencing data

Ejemplares similares