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.
This is a preview of subscription content, access via your institution
Relevant articles
Open Access articles citing this article.
-
De novo detection of somatic mutations in high-throughput single-cell profiling data sets
Nature Biotechnology Open Access 06 July 2023
-
Deep learning model accurately classifies metastatic tumors from primary tumors based on mutational signatures
Scientific Reports Open Access 30 May 2023
-
Widespread somatic L1 retrotransposition in normal colorectal epithelium
Nature Open Access 10 May 2023
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 51 print issues and online access
$199.00 per year
only $3.90 per issue
Rent or buy this article
Prices vary by article type
from$1.95
to$39.95
Prices may be subject to local taxes which are calculated during checkout
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 3–6.
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.
References
Martincorena, I. & Campbell, P. J. Somatic mutation in cancer and normal cells. Science 349, 1483–1489 (2015).
Rouhani, F. J. et al. Mutational history of a human cell lineage from somatic to induced pluripotent stem cells. PLoS Genet. 12, e1005932 (2016).
Coorens, T. H. H. et al. Extensive phylogenies of human development reveal variable embryonic patterns. Preprint at bioRxiv https://doi.org/10.1101/2020.11.25.397828 (2020).
Blokzijl, F. et al. Tissue-specific mutation accumulation in human adult stem cells during life. Nature 538, 260–264 (2016).
Bae, T. et al. Different mutational rates and mechanisms in human cells at pregastrulation and neurogenesis. Science 359, 550–555 (2018).
Osorio, F. G. et al. Somatic mutations reveal lineage relationships and age-related mutagenesis in human hematopoiesis. Cell Rep. 25, 2308–2316.e4 (2018).
Martincorena, I. et al. Somatic mutant clones colonize the human esophagus with age. Science 362, 911–917 (2018).
Lee-Six, H. et al. The landscape of somatic mutation in normal colorectal epithelial cells. Nature 574, 532–537 (2019).
Moore, L. et al. The mutational landscape of normal human endometrial epithelium. Nature 580, 640–646 (2020).
Zhu, M. et al. Somatic mutations increase hepatic clonal fitness and regeneration in chronic liver disease. Cell 177, 608–621.e12 (2019).
Schmitt, M. W. et al. Detection of ultra-rare mutations by next-generation sequencing. Proc. Natl Acad. Sci. USA 109, 14508–14513 (2012).
Abascal, F. et al. Somatic mutation landscapes at single-molecule resolution. Nature 593, 405–410 (2021).
Rahbari, R. et al. Timing, rates and spectra of human germline mutation. Nat. Genet. 48, 126–133 (2016).
Goldmann, J. M. et al. Parent-of-origin-specific signatures of de novo mutations. Nat. Genet. 48, 935–939 (2016).
Kong, A. et al. Rate of de novo mutations and the importance of father’s age to disease risk. Nature 488, 471–475 (2012).
Lynch, M. Rate, molecular spectrum, and consequences of human mutation. Proc. Natl Acad. Sci. USA 107, 961–968 (2010).
Milholland, B. et al. Differences between germline and somatic mutation rates in humans and mice. Nat. Commun. 8, 15183 (2017).
Visvader, J. E. & Clevers, H. Tissue-specific designs of stem cell hierarchies. Nat. Cell Biol. 18, 349–355 (2016).
Jónsson, H. et al. Parental influence on human germline de novo mutations in 1,548 trios from Iceland. Nature 549, 519–522 (2017).
Ozturk, S. Telomerase activity and telomere length in male germ cells. Biol. Reprod. 92, 53 (2015).
Demanelis, K. et al. Determinants of telomere length across human tissues. Science 369, eaaz6876 (2020).
Fryxell, K. J. & Zuckerkandl, E. Cytosine deamination plays a primary role in the evolution of mammalian isochores. Mol. Biol. Evol. 17, 1371–1383 (2000).
Alexandrov, L. B. et al. Clock-like mutational processes in human somatic cells. Nat. Genet. 47, 1402–1407 (2015).
Alexandrov, L. B. et al. The repertoire of mutational signatures in human cancer. Nature 578, 94–101 (2020).
Boot, A. et al. In-depth characterization of the cisplatin mutational signature in human cell lines and in esophageal and liver tumors. Genome Res. 28, 654–665 (2018).
Forbes, S. A. et al. COSMIC: somatic cancer genetics at high-resolution. Nucleic Acids Res. 45, D777–D783 (2017).
Pleguezuelos-Manzano, C. et al. Mutational signature in colorectal cancer caused by genotoxic pks+ E. coli. Nature 580, 269–273 (2020).
Li, X. C. et al. A mutational signature associated with alcohol consumption and prognostically significantly mutated driver genes in esophageal squamous cell carcinoma. Ann. Oncol. 29, 938–944 (2018).
Nik-Zainal, S. et al. Mutational processes molding the genomes of 21 breast cancers. Cell 149, 979–993 (2012).
Alexandrov, L. B. et al. Mutational signatures associated with tobacco smoking in human cancer. Science 354, 618–622 (2016).
Di Persio, S. et al. Spermatogonial kinetics in humans. Development 144, 3430–3439 (2017).
Scally, A. Mutation rates and the evolution of germline structure. Phil. Trans. R. Soc. B 371, 20150137 (2016).
Frigola, J. et al. Reduced mutation rate in exons due to differential mismatch repair. Nat. Genet. 49, 1684–1692 (2017).
Rodriguez-Galindo, M., Casillas, S., Weghorn, D. & Barbadilla, A. Germline de novo mutation rates on exons versus introns in humans. Nat. Commun. 11, 3304 (2020).
GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369, 1318–1330 (2020).
Karczewski, K. J. et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature 581, 434–443 (2020).
Xia, B. et al. Widespread transcriptional scanning in the testis modulates gene evolution rates. Cell 180, 248–262.e21 (2020).
Koren, A. et al. Differential relationship of DNA replication timing to different forms of human mutation and variation. Am. J. Hum. Genet. 91, 1033–1040 (2012).
Francioli, L. C. et al. Genome-wide patterns and properties of de novo mutations in humans. Nat. Genet. 47, 822–826 (2015).
Chen, C., Qi, H., Shen, Y., Pickrell, J. & Przeworski, M. Contrasting determinants of mutation rates in germline and soma. Genetics 207, 255–267 (2017).
.Walsh, C. et al. Somatic mutations in single human cardiomyocytes demonstrate accelerated age-related DNA damage and cell fusion. Preprint at Research Square https://doi.org/10.21203/rs.3.rs-84503/v1 (2020).
Kirkwood, T. B. Evolution of ageing. Nature 270, 301–304 (1977).
Sakkas, D., Ramalingam, M., Garrido, N. & Barratt, C. L. R. Sperm selection in natural conception: what can we learn from Mother Nature to improve assisted reproduction outcomes? Hum. Reprod. Update 21, 711–726 (2015).
Brunner, S. F. et al. Somatic mutations and clonal dynamics in healthy and cirrhotic human liver. Nature 574, 538–542 (2019).
Lawson, A. R. J. et al. Extensive heterogeneity in somatic mutation and selection in the human bladder. Science 370, 75–82 (2020).
Olafsson, S. et al. Somatic evolution in non-neoplastic IBD-affected colon. Cell 182, 672–684.e11 (2020).
Ellis, P. et al. Reliable detection of somatic mutations in solid tissues by laser-capture microdissection and low-input DNA sequencing. Nat. Protoc. 16, 841–871 (2021).
Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. Preprint at arXiv https://arxiv.org/abs/1303.3997v2 (2013).
Tischler, G. & Leonard, S. biobambam: tools for read pair collation based algorithms on BAM files. Source Code Biol. Med. 9, 13 (2014).
Jones, D. et al. cgpCaVEManWrapper: simple execution of CaVEMan in order to detect somatic single nucleotide variants in NGS data. Curr. Protoc. Bioinformatics 56, 15.10.1–15.10.18 (2016).
Coorens, T. H. H. et al. Lineage-independent tumors in bilateral neuroblastoma. N. Engl. J. Med. 383, 1860–1865 (2020).
Yoshida, K. et al. Tobacco smoking and somatic mutations in human bronchial epithelium. Nature 578, 266–272 (2020).
Raine, K. M. et al. cgpPindel: identifying somatically acquired insertion and deletion events from paired end sequencing. Curr. Protoc. Bioinformatics 52, 15.7.1–15.7.12 (2015).
Slater, G. S. C. & Birney, E. Automated generation of heuristics for biological sequence comparison. BMC Bioinformatics 6, 31 (2005).
Suzuki, M. et al. Late-replicating heterochromatin is characterized by decreased cytosine methylation in the human genome. Genome Res. 21, 1833–1840 (2011).
Coorens, T. H. H. et al. Embryonal precursors of Wilms tumor. Science 366, 1247–1251 (2019).
Blokzijl, F., Janssen, R., van Boxtel, R. & Cuppen, E. MutationalPatterns: comprehensive genome-wide analysis of mutational processes. Genome Med. 10, 33 (2018).
Stamatoyannopoulos, J. A. et al. Human mutation rate associated with DNA replication timing. Nat. Genet. 41, 393–395 (2009).
Potten, C. S., Kellett, M., Rew, D. A. & Roberts, S. A. Proliferation in human gastrointestinal epithelium using bromodeoxyuridine in vivo: data for different sites, proximity to a tumour, and polyposis coli. Gut 33, 524–529 (1992).
Heller, C. G. & Clermont, Y. Spermatogenesis in man: an estimate of its duration. Science 140, 184–186 (1963).
Farmery, J. H. R., Smith, M. L., NIHR BioResource - Rare Diseases & Lynch, A. G. Telomerecat: a ploidy-agnostic method for estimating telomere length from whole genome sequencing data. Sci. Rep. 8, 1300 (2018).
Martincorena, I. et al. Universal patterns of selection in cancer and somatic tissues. Cell 171, 1029–1041.e21 (2017).
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
Authors and Affiliations
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
Ethics declarations
Competing interests
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 c–h. 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). b–d, 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.
a–c, 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.
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
Rights and permissions
About this article
Cite this article
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
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41586-021-03822-7
This article is cited by
-
FBXW7 loss of function promotes esophageal squamous cell carcinoma progression via elevating MAP4 and ERK phosphorylation
Journal of Experimental & Clinical Cancer Research (2023)
-
Widespread somatic L1 retrotransposition in normal colorectal epithelium
Nature (2023)
-
Mutational signatures in human small intestinal crypts indicate APOBEC mutagenesis
Nature Genetics (2023)
-
De novo detection of somatic mutations in high-throughput single-cell profiling data sets
Nature Biotechnology (2023)
-
Deep learning model accurately classifies metastatic tumors from primary tumors based on mutational signatures
Scientific Reports (2023)
Comments
By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.
