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The mutational landscape of human somatic and germline cells

Abstract

Over the course of an individual’s lifetime, normal human cells accumulate mutations1. Here we compare the mutational landscape in 29 cell types from the soma and germline using multiple samples from the same individuals. Two ubiquitous mutational signatures, SBS1 and SBS5/40, accounted for the majority of acquired mutations in most cell types, but their absolute and relative contributions varied substantially. SBS18, which potentially reflects oxidative damage2, and several additional signatures attributed to exogenous and endogenous exposures contributed mutations to subsets of cell types. The rate of mutation was lowest in spermatogonia, the stem cells from which sperm are generated and from which most genetic variation in the human population is thought to originate. This was due to low rates of ubiquitous mutational processes and may be partially attributable to a low rate of cell division in basal spermatogonia. These results highlight similarities and differences in the maintenance of the germline and soma.

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Fig. 1: Summary of the experimental design and clonal structures across tissues.
Fig. 2: Mechanisms underlying the low rate of germline mutations.
Fig. 3: Mutational signatures in normal tissues.
Fig. 4: Comparison of mutational biases between the germline and the soma.

Data availability

Information on data availability for all samples is available in Supplementary Table 4. Sequencing data have been deposited in the European Genome-Phenome Archive under the dataset accession number EGAD00001006641 and are available for general research purposes. Substitutions, indels and SVs are available in Supplementary Tables 36.

Code availability

Pipelines to call SBSs, indels, SVs, CNVs, mutation burden analysis, signature extraction with HDP and SigProfiler, and mutation burden for different genomic contexts are available from https://github.com/Rashesh7/PanBody_manuscript_analyses.

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Acknowledgements

We thank the staff of WTSI Sample Logistics, Genotyping, Pulldown, Sequencing and Informatics facilities for their contribution; K. Roberts and the cgp-lab for their assistance; P. Robinson, M. Goddard, P. S. Tarpey and P. Scott for their assistance with sample collection and the LCM pipeline; and M. Hurles, A. Scally and Y. S. Ju for providing useful feedback. This research is supported by core funding from the Wellcome Trust. R.R. is funded by Cancer Research UK (CRUK; C66259/A27114). L.M. is a recipient of a CRUK Clinical PhD fellowship (C20/A20917) and the Jean Shank/Pathological Society of Great Britain and Ireland Intermediate Research Fellowship (grant reference no. 1175). T.J.M. is supported by CRUK and the Royal College of Surgeons (C63474/A27176). The laboratory of R.C.F. is funded by a Core Programme Grant from the Medical Research Council (RG84369). Funding for sample collection was through the ICGC and was funded by a programme grant from CRUK (RG81771/84119). R.H. is a recipient of a PCF Challenge Research Award (ID #18CHAL11). I.M. is funded by CRUK (C57387/A21777) and the Wellcome Trust. P.J.C. is a Wellcome Trust Senior Clinical Fellow.

Author information

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Authors

Contributions

M.R.S., R.R. and L.M. conceived the project. R.R. and M.R.S. supervised the project. A.C., L.M., M.R.S. and R.R. wrote the manuscript. All authors reviewed and edited the manuscript. L.M., A.C. and T.H.H.C. led the analysis of the data with help from M.D.C.N., R.S., M.A.S., T.R.W.O., D.L., T.M.B., A.M., K.J.D. and R.R. L.M., A.C. and T.R.W.O. performed LCM. L.M. performed the rapid autopsy with help from T.J.M. and M.T. P.E., Y.H., L.O. and C.L. processed samples. L.M., A.N., R.v.B., C.A.I.-D., R.H. and R.C.F. collected samples. L.M., T.R.W.O., M.J. and A.Y.W. reviewed the histological images and clinical reports. I.M., P.J.C., M.R.S. and R.R. helped with data interpretation and statistical analysis.

Corresponding authors

Correspondence to Michael R. Stratton or Raheleh Rahbari.

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The authors declare no competing interests.

Additional information

Peer review information Nature thanks Ziyue Gao, Nuria Lopez-Bigas and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 Number of somatic mutations per genome.

The number of somatic mutations per genome for the 47-year-old man (PD43851), a 54-year-old woman (PD43850) and a 78-year-old man (PD28690) is shown by each tissue type. a, Median VAF per sample. b, SBS burden. c, Indel burden.

Extended Data Fig. 2 Mutation burden across tissues.

Mutation burden was estimated on a subset of tissues that passed all filtering criteria. Minor clone mutations were identified and removed using a truncated binomial algorithm. Cell types with a minimum of three samples from more than one individual were included for mutation burden analysis. a, Proportion of SBS mutations that were assigned to the major clone by the truncated binomial method. b, Peak VAF of SBSs belonging to the major clone. c, Clonal SBS burden.

Extended Data Fig. 3 Summary of indel burden for all 13 individuals.

a, Indels from each sample were merged together by tissue type. Indel signatures were generated using MutationalPatterns. b, Age correlation of clonal indels per genome (corrected for callable genome) for the colon (top panel) and testes (bottom panel). The whiskers in panel b extend to the largest/lowest value within the 1.5 × IQR from Q3/Q1 of the data, respectively. The shaded region around the regression lines represents 95% CI.

Extended Data Fig. 4 Summary of different SV types per tissue per genome.

The number of different types of SVs identified, coded by colour, and the number of patches used to identify SV events per tissue per individual. Overall, colonic crypts across all three donors have the highest number of SV events. In particular, high numbers of retrotransposition events were identified in colonic crypts of the 78-year-old man (donor PD28690).

Extended Data Fig. 5 Chromosome arm or focal losses, encompassing either NOTCH1 or TP53 in the oesophagus.

Raw ASCAT profile containing allele-specific copy number for all loci. The x axis depicts the genomic location and the y axis shows the DNA copy number. The purple and blue indicate the copy number of the minor allele and the estimated overall copy number, respectively. a, PD43851k_P52_OSPHG_H12: NOTCH1 missense mutation (Chr9: Pos139417476:G>T) and subclonal loss 9qter. b, PD43851k_P53_OSPHG_B2: TP53 and loss of single copy 17p. c, PD43851k_P53_OSPHG_E2: NOTCH1 missense mutation (Chr9: Pos139412332:C>T) and copy number neutral LOH of 9q. d, PD43851k_P53_OSPHG_G2: NOTCH1 missense mutation (Chr9: Pos139412332:C>T) and TP53 missense mutation (Chr17: Pos7579358:C>A) combined with copy number neutral LOH of 9qter (approximately 4.8 Mb).

Extended Data Fig. 6 Telomere length comparison between the testes and colon.

a, Absolute telomere length in seminiferous tubules (purple) and colonic crypts (orange) (n = 6 individuals). The centre dot represents the median, with 25% and 75% percentiles indicated as point range. b, Regression lines from the linear models comparing the effect of age on telomere length between colonic crypts (red) and seminiferous tubules (blue). The shaded region around the regression lines represents 95% CI. c, Correlation between absolute SBS burden and telomere length in the microbiopsies of the colonic crypts. d, Correlation between absolute SBS burden and telomere length in the microbiopsies of the seminiferous tubules. e, Correlation between absolute SBS1 burden and telomere length in the microbiopsies of the colonic crypts. f, Correlation between absolute SBS1 burden and telomere length in the microbiopsies of the seminiferous tubule. g, Correlation between absolute SBS5 burden and telomere length in the microbiopsies of the colonic crypts. h, Correlation between absolute SBS5 burden and telomere length in the microbiopsies of the seminiferous tubules. Correlation was tested using Spearman’s rank test and the respective coefficient (rho), and P values are stated on the plots in panels ch. The samples sequenced on NovaSeq were excluded from the analyses. SBS1 and SBS5 contributions estimated by SigProfiler were used to estimate the mutation burden associated with the respective signatures.

Extended Data Fig. 7 Comparison of mutational biases in the oesophagus between individuals.

Mutations in the oesophagus were compared between two individuals. a, The log2 ratio of SBSs on the transcribed to non-transcribed strands for the six mutation classes. The asterisks indicate significant transcriptional strand biases after accounting for multiple tests (P < 0.05, two-sided Poisson test). bd, Observed/expected mutation burden for intergenic, intronic and exonic regions (b), transcripts across four oesophagus-specific GTEx35 gene expression level bins (c), and early, intermediate and late replicating regions of the genome (d). The expected burden for a bin is calculated based on the trinucleotide counts of regions in that bin and the average trinucleotide mutation rates in that tissue. The error bars indicate the 95% CI. PD28690 (a 78-year-old man), with SBS16, shows outlier patterns.

Extended Data Fig. 8 Effect of gene expression and transcription strand bias on germline mutation rate.

a, Mutation burden in germline datasets across spermatogonia expression groups. Observed/expected mutation burden for deCODE trio DNMs, gnomAD population variants and seminiferous tubules (n = 13 individuals) in transcripts across eight expression groups of increasing expression level identified from single-cell sequencing of spermatogonia37. The expected burden for a bin is calculated based on the trinucleotide counts of regions in that bin and the average trinucleotide mutation rates in that dataset. The error bars indicate the 95% CI. b, Correlation between transcription strand bias and gene expression. Two SBS germline variant datasets were compared with 11 somatic tissues. The relative mutation rate of mutation classes on the transcribed and untranscribed strands across tissue-specific expression level bins. The relative mutation rate was calculated for each tissue bin as the mutation rate per base pair for each class divided by the total mutations per base pair.

Extended Data Fig. 9 Mutational signature contribution to mutational biases between the germline and the soma.

ac, Mutational signature contribution to observed/expected mutation burden for intergenic, intronic and exonic regions (a), transcripts across four tissue-specific GTEx35 gene expression level bins, and early, intermediate and late replicating regions of the genome. The expected burden for each bin is calculated based on the trinucleotide counts of regions in that bin and the average trinucleotide mutation rates in that tissue. The mutational signature breakdown is calculated using the probability of each variant belonging to each signature based on the fraction of signature in that tissue and the frequency of the mutation type with that signature.

Supplementary information

Supplementary Information

This file contains supplementary discussion, supplementary methods and supplementary figures 1 – 9.

Reporting Summary

Peer Review File

Supplementary Tables

This file contains Supplementary Tables 1–4 and 7–10.

Supplementary Table 1 | Information about the individuals recruited for this study

Supplementary Table 2 | Details of anatomical structures were sampled for this study

Supplementary Table 3 | Structural Variations identified per individual per cell type

Supplementary Table 4 | Sample Information

Supplementary Table 7 | List of known or suspected cancer drivers identified

Supplementary Table 8 | Mutational Signatures detected across individuals and tissues

Supplementary Table 9 | Pairing of PanBody and GTEx tissues

Supplementary Table 10 | Comparison of HDP signature extraction with the reference signatures.

Supplementary Table 5

Whole-genome SBS across all samples.

Supplementary Table 6

Whole-genome INDELs across all samples

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Moore, L., Cagan, A., Coorens, T.H.H. et al. The mutational landscape of human somatic and germline cells. Nature 597, 381–386 (2021). https://doi.org/10.1038/s41586-021-03822-7

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