Cargando…
Measuring single cell divisions in human tissues from multi-region sequencing data
Both normal tissue development and cancer growth are driven by a branching process of cell division and mutation accumulation that leads to intra-tissue genetic heterogeneity. However, quantifying somatic evolution in humans remains challenging. Here, we show that multi-sample genomic data from a si...
Autores principales: | , , , , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7042311/ https://www.ncbi.nlm.nih.gov/pubmed/32098957 http://dx.doi.org/10.1038/s41467-020-14844-6 |
_version_ | 1783501285017780224 |
---|---|
author | Werner, Benjamin Case, Jack Williams, Marc J. Chkhaidze, Ketevan Temko, Daniel Fernández-Mateos, Javier Cresswell, George D. Nichol, Daniel Cross, William Spiteri, Inmaculada Huang, Weini Tomlinson, Ian P. M. Barnes, Chris P. Graham, Trevor A. Sottoriva, Andrea |
author_facet | Werner, Benjamin Case, Jack Williams, Marc J. Chkhaidze, Ketevan Temko, Daniel Fernández-Mateos, Javier Cresswell, George D. Nichol, Daniel Cross, William Spiteri, Inmaculada Huang, Weini Tomlinson, Ian P. M. Barnes, Chris P. Graham, Trevor A. Sottoriva, Andrea |
author_sort | Werner, Benjamin |
collection | PubMed |
description | Both normal tissue development and cancer growth are driven by a branching process of cell division and mutation accumulation that leads to intra-tissue genetic heterogeneity. However, quantifying somatic evolution in humans remains challenging. Here, we show that multi-sample genomic data from a single time point of normal and cancer tissues contains information on single-cell divisions. We present a new theoretical framework that, applied to whole-genome sequencing data of healthy tissue and cancer, allows inferring the mutation rate and the cell survival/death rate per division. On average, we found that cells accumulate 1.14 mutations per cell division in healthy haematopoiesis and 1.37 mutations per division in brain development. In both tissues, cell survival was maximal during early development. Analysis of 131 biopsies from 16 tumours showed 4 to 100 times increased mutation rates compared to healthy development and substantial inter-patient variation of cell survival/death rates. |
format | Online Article Text |
id | pubmed-7042311 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-70423112020-03-04 Measuring single cell divisions in human tissues from multi-region sequencing data Werner, Benjamin Case, Jack Williams, Marc J. Chkhaidze, Ketevan Temko, Daniel Fernández-Mateos, Javier Cresswell, George D. Nichol, Daniel Cross, William Spiteri, Inmaculada Huang, Weini Tomlinson, Ian P. M. Barnes, Chris P. Graham, Trevor A. Sottoriva, Andrea Nat Commun Article Both normal tissue development and cancer growth are driven by a branching process of cell division and mutation accumulation that leads to intra-tissue genetic heterogeneity. However, quantifying somatic evolution in humans remains challenging. Here, we show that multi-sample genomic data from a single time point of normal and cancer tissues contains information on single-cell divisions. We present a new theoretical framework that, applied to whole-genome sequencing data of healthy tissue and cancer, allows inferring the mutation rate and the cell survival/death rate per division. On average, we found that cells accumulate 1.14 mutations per cell division in healthy haematopoiesis and 1.37 mutations per division in brain development. In both tissues, cell survival was maximal during early development. Analysis of 131 biopsies from 16 tumours showed 4 to 100 times increased mutation rates compared to healthy development and substantial inter-patient variation of cell survival/death rates. Nature Publishing Group UK 2020-02-25 /pmc/articles/PMC7042311/ /pubmed/32098957 http://dx.doi.org/10.1038/s41467-020-14844-6 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Werner, Benjamin Case, Jack Williams, Marc J. Chkhaidze, Ketevan Temko, Daniel Fernández-Mateos, Javier Cresswell, George D. Nichol, Daniel Cross, William Spiteri, Inmaculada Huang, Weini Tomlinson, Ian P. M. Barnes, Chris P. Graham, Trevor A. Sottoriva, Andrea Measuring single cell divisions in human tissues from multi-region sequencing data |
title | 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_short | 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 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7042311/ https://www.ncbi.nlm.nih.gov/pubmed/32098957 http://dx.doi.org/10.1038/s41467-020-14844-6 |
work_keys_str_mv | AT wernerbenjamin measuringsinglecelldivisionsinhumantissuesfrommultiregionsequencingdata AT casejack measuringsinglecelldivisionsinhumantissuesfrommultiregionsequencingdata AT williamsmarcj measuringsinglecelldivisionsinhumantissuesfrommultiregionsequencingdata AT chkhaidzeketevan measuringsinglecelldivisionsinhumantissuesfrommultiregionsequencingdata AT temkodaniel measuringsinglecelldivisionsinhumantissuesfrommultiregionsequencingdata AT fernandezmateosjavier measuringsinglecelldivisionsinhumantissuesfrommultiregionsequencingdata AT cresswellgeorged measuringsinglecelldivisionsinhumantissuesfrommultiregionsequencingdata AT nicholdaniel measuringsinglecelldivisionsinhumantissuesfrommultiregionsequencingdata AT crosswilliam measuringsinglecelldivisionsinhumantissuesfrommultiregionsequencingdata AT spiteriinmaculada measuringsinglecelldivisionsinhumantissuesfrommultiregionsequencingdata AT huangweini measuringsinglecelldivisionsinhumantissuesfrommultiregionsequencingdata AT tomlinsonianpm measuringsinglecelldivisionsinhumantissuesfrommultiregionsequencingdata AT barneschrisp measuringsinglecelldivisionsinhumantissuesfrommultiregionsequencingdata AT grahamtrevora measuringsinglecelldivisionsinhumantissuesfrommultiregionsequencingdata AT sottorivaandrea measuringsinglecelldivisionsinhumantissuesfrommultiregionsequencingdata |