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

Benjamin Werner, Jack Case, Marc Williams, Ketevan Chkhaidze, Daniel Temko, Javier Fernández-Mateos, George David Cresswell, Daniel Nichol, William Cross, Inmaculada Spiteri, Weini Huang, Tomlinson, I., Chris Barnes, Trevor Graham and Andrea Sottoriva 2020. Measuring single cell divisions in human tissues from multi-region sequencing data. Nature Communications . 11 1035. https://doi.org/10.1038/s41467-020-14844-6

TitleMeasuring single cell divisions in human tissues from multi-region sequencing data
TypeJournal article
AuthorsBenjamin Werner, Jack Case, Marc Williams, Ketevan Chkhaidze, Daniel Temko, Javier Fernández-Mateos, George David Cresswell, Daniel Nichol, William Cross, Inmaculada Spiteri, Weini Huang, Tomlinson, I., Chris Barnes, Trevor Graham and Andrea Sottoriva
Abstract

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.

Article number1035
JournalNature Communications
Journal citation11
ISSN2041-1723
Year2020
PublisherNature Publishing Group
Publisher's version
License
CC BY 4.0
File Access Level
Open (open metadata and files)
Digital Object Identifier (DOI)https://doi.org/10.1038/s41467-020-14844-6
Web address (URL)http://dx.doi.org/10.1038/s41467-020-14844-6
Publication dates
Published25 Feb 2020

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