The age-related mortality curve for modern humans is an outlier across the tree of life, with an abrupt increase in mortality after the average lifespan7, leading to surprisingly low variance in age at death8. Studies of ageing at the cellular level have demonstrated that accumulation of molecular damage across the lifespan is gradual and lifelong, including telomere attrition, somatic mutation, epigenetic change and oxidative or replicative stress9. It remains unresolved how such gradual accumulation of molecular damage can translate into an abrupt increase in mortality after 70 years of age.

Sequencing of blood samples from population cohorts has revealed an age-related increase in acquired mutations in genes that cause myeloid neoplasms4,5,10,11,12, known as driver mutations. This phenomenon is called clonal haematopoiesis, and reaches 10–20% prevalence4,5,10,11,12 or higher13 after 70 years of age, but the driver mutations typically account for only a small fraction of haematopoiesis (less than 5% of cells). Some elderly individuals show evidence of clonal expansions even in the absence of known driver mutations14,15,16. Deep sequencing of bulk blood samples, as used in these studies, struggles to elucidate clonal relationships among cells and is insensitive to mutations that are present in a low proportion (below 1–5%) of blood cells—as a result, we lack an unbiased, high-resolution model for the clonal dynamics of human haematopoiesis with ageing. Whole-genome sequencing of colonies grown from single cells circumvents these limitations of bulk sequencing17,18,19. We sequenced whole genomes of 3,579 single cell-derived haematopoietic colonies from 10 healthy individuals spanning the human lifespan, revealing an abrupt and universal loss of clonal diversity after the age of 70 years.

Whole-genome sequencing of HSC colonies

We obtained samples from 10 individuals, with no known haematological disease, aged between 0 and 81 years (Extended Data Fig. 1a, Supplementary Table 1). One subject (KX002, a 38-year-old male) had inflammatory bowel disease treated with azathioprine and another (SX001, a 48-year0old male) had selenoprotein deficiency20, a genetic disorder not known to affect haemapoietic stem cell (HSC) dynamics. Stem cells were obtained from cord blood for the two neonates, and from bone marrow and/or peripheral blood for adult donors (Fig. 1a). Bone marrow samples were obtained peri-mortem, enabling sampling of large volumes (50–80 ml) from multiple vertebrae.

Fig. 1: Mutational burden in normal HSC/MPPs.
figure 1

a, Bar plot showing the numbers of colonies sequenced from each tissue and cell type for each donor in the study. Age and sex (F, female; M, male) are indicated below the donor ID. b, Burden of single nucleotide variants across the donor cohort. The points represent individual HSC/MPP colonies (n = 3,361) and are coloured by donor as indicated in a. The grey line represents a regression of age with mutation burden, with shading indicating the 95% confidence interval. c, Burden of small indels across the donor cohort. d, Bar plot showing the number of independently acquired structural variants (SVs) per colony sequenced in each donor. The absolute number of structural variants is shown at the top of each bar. e, Bar plot showing the number of independently acquired autosomal copy number aberrations (CNAs) per colony sequenced in each donor. The absolute number of copy number aberrations is shown at the top of each bar. f, Bar plot showing the number of independently acquired Y chromosome copy number aberrations sequenced in each male donor. The absolute number of copy number aberrations is shown at the top of each bar. g, Telomere length across the donor cohort, including only those samples sequenced on the HiSeq X10 platform. Each point represents a single HSC/MPP colony. Two outlying points for CB001 are not shown (telomere lengths 16,037 bp and 21,155 bp). In b, c, g, boxes overlaid indicate the median and interquartile range and whiskers denote the minimum of either the range or 25th and 75th centile plus 1.5× interquartile range.

Single immunophenotypic HSC/MPPs (LinCD34+CD38CD45RA−) were sorted using flow cytometry and cultured (Extended Data Fig. 1b). Overall, 42–89% of sorted HSC/MPPs produced colonies (Extended Data Fig. 1c), suggesting that the sequenced colonies were a representative sample of the HSC/MPP population in each individual, as supported by analysis of cell surface markers (Extended Data Fig. 1d). Haematopoietic progenitor cells (HPCs) (LinCD34+CD38+) were also flow-sorted for four individuals.

We performed whole-genome sequencing at average sequencing depths of 14× on 224–453 colonies per individual. We excluded 17 colonies with low coverage, 34 technical duplicates and 7 colonies derived from more than a single cell (Extended Data Fig. 2a–c). The final dataset comprised whole genomes from 3,579 colonies, of which 3,361 were derived from HSC/MPPs and 218 were derived from HPCs. Raw mutation burdens were corrected for sequencing depth using asymptotic regression (Extended Data Fig. 2d). Single-base substitution spectra were consistent with previous results17,18,21 (Extended Data Fig. 2e, f). Phylogenetic trees for each adult individual were constructed—terminal branch lengths were then corrected for sequencing depth, followed by normalization to identical total root-to-tip branch lengths and scaling to chronological time (Extended Data Fig. 3). Benchmarking of the phylogenies included assessments of internal consistency, stability across phylogenetic inference algorithms and robustness to bootstrapping (Supplementary Methods). All code for variant filtering, phylogenetic reconstruction, benchmarking and downstream analyses are available in the Supplementary Code.

Mutation burden and telomere lengths

Consistent with previous data18,22, point mutations accumulated linearly in HSC/MPPs throughout life, at a rate of 16.8 substitutions per cell per year (95% confidence interval 16.5–17.1; Fig. 1b) and 0.71 insertion–deletion mutations (indels) per cell per year (95% confidence interval 0.65–0.77; Fig. 1c). We found no significant difference in mutation burden between HSC/MPPs and HPCs (Extended Data Fig. 4a). Structural variants were rare, with only 1–17 events observed in each individual, mostly deletions, correlating with age (Fig. 1d, Extended Data Fig. 4b, Supplementary Table 3). Autosomal copy number aberrations were rare at all ages and comprised either copy-neutral loss-of-heterozygosity events or tetrasomies (Fig. 1e). By contrast, loss of the Y chromosome was frequent in males, increasing with age as previously shown23 (Fig. 1f); no corresponding examples of loss of the inactive X chromosome were observed in older females.

We estimated telomere lengths for the 1,505 HSC/MPP colonies from 7 individuals sequenced on Hiseq X10 (Fig. 1g). As previously reported24,25, telomere lengths decreased steadily with age, at an average attrition rate of 30.8 base pairs (bp) per year in adult life (95% confidence interval 13.2–48.4), close to published estimates of 39 bp per year from bulk granulocytes25. By sequencing single cell-derived colonies, we can estimate the variance and distribution in telomere lengths among cells with a greater resolution than is possible with bulk populations. In cord blood and young adults, a small proportion of HSC/MPPs had unexpectedly long telomeres, a proportion that decreased with age (Extended Data Fig. 4c). Given that telomeres shorten at cell division, these outlier cells have presumably undergone fewer historic cell divisions. A rare population of infrequently dividing dormant HSCs has been described in the mouse26,27 and our telomere data are consistent with an analogous population in humans, especially early in life.

HSC clonal dynamics after 70 years of age

The phylogenetic trees generated here depict the lineage relationships among ancestors of the sequenced stem and progenitor cells. Given the consistent, linear rate of mutation accumulation across the lifespan, we scaled the raw phylogenetic trees (Extended Data Fig. 3d) to chronological time to study clonal dynamics of HSC/MPPs across the lifespan (Figs. 2, 3, Extended Data Fig. 5). Branch points in the tree (‘coalescences’) define historic stem cell divisions. In taxonomy, a clade is defined as a group of organisms descended from a single common ancestor—in the context of somatic cells, this represents a clone, and its size can be estimated from the fraction of colonies derived from that ancestor. Here, we define an ‘expanded clade’ as a postnatal ancestral lineage whose descendants contributed more than 1% of colonies at the time of sampling.

Fig. 2: HSPC phylogenies for three young adult donors.
figure 2

ac, Phylogenies were constructed for a 29-year-old (a), a 38-year-old (b) and a 63-year-old (c) male donor using shared mutation data and the algorithm MPBoot (Methods). Branch lengths are proportional to the number of mutations assigned to the branch—terminal branches have been corrected for sequence coverage, and overall root-to-tip branch lengths have been normalized to the same total length (because all colonies were collected from a single time point). The y-axis is scaled to chronological time using the somatic mutation rate as a molecular clock, with age 0 (representing birth) set at 55 mutations (as estimated from our cord blood colonies). Each tip on a phylogeny represents a single colony, with the respective numbers of colonies of each cell and tissue type recorded at the top. Onto these trees, we have layered clone and colony-specific phenotypic information. We have highlighted branches on which we have identified known oncogenic drivers (solid line) and possible oncogenic drivers (dashed line) in one of 17 clonal haematopoiesis genes (Supplementary Table 4), coloured by gene. Branches with autosomal copy number alterations are highlighted with a black dashed line. A heat map at the bottom of each phylogeny highlights colonies from known driver clades coloured by gene, expanded clades (defined as those with a clonal fraction above 1%) in blue and colonies with loss of the Y chromosome in pink (males only). BM, bone marrow; PB, peripheral blood; CN_LOH, copy-neutral loss of heterozygosity. The phylogeny of the fourth young adult donor is shown in Extended Data Fig. 5a.

Fig. 3: HSPC phylogenies for three elderly adult donors.
figure 3

ac, Phylogenetic trees were constructed for a 76-year-old female donor (a), a 77-year-old female donor (b) and an 81-year-old male donor (c) and presented as described for Fig. 2. The phylogeny of the fourth elderly adult donor included in the study is shown in Extended Data Fig. 5b.

Phylogenetic trees of the 4 adults aged below 65 years showed that healthy haematopoiesis in young and middle-aged individuals is highly polyclonal (Fig. 2, Extended Data Fig. 5a). Despite sequencing 361–408 colonies per individual, we found at most a single expanded clade in each sample, and the two that we observed contributed less than 2% of all haematopoiesis in those individuals. Only four known or possible driver point mutations were identified, none of which occurred in the two expanded clades.

Phylogenetic trees for the 4 adults aged over 70 years were qualitatively different (Fig. 3, Extended Data Fig. 5b), with an oligoclonal pattern of haematopoiesis. In each elderly individual, we found 12–18 independent clones established between birth and 40 years of age that each contributed between 1% and 34% of colonies sequenced, most in the 1–3% range. Collectively, these clones summed to a significant proportion of all blood production in our elderly research subjects—between 32% and 61% of all colonies sequenced derived from the expanded clades.

Only a minority of clonal expansions in the elderly individuals carried known driver mutations. Although we identified mutations in DNMT3A, TET2 and CBL, mutations in the top 17 myeloid driver genes could explain only 10 out of 58 expanded clades with a clonal fraction above 1%. We identified only 3 additional mutations when we extended the analysis to a wider set of 92 genes implicated in myeloid neoplasms28 (Supplementary Tables 4, 5), leaving 45 clonal expansions unexplained.

HSC/MPP population size in young donors

The frequency of branch points in phylogenetic trees in a neutrally evolving, well-mixed population of somatic cells is determined primarily by the product of population size and time between symmetric self-renewal cell divisions ()—both smaller populations and more frequent symmetric divisions increase coalescences. In young adults, in whom clonal selection has had minimal impact on phylogenetic trees, we can exploit this property to estimate the lifelong trajectory of population size dynamics29 (Fig. 4a). There is a rapid increase in estimated in utero, reaching a plateau during infancy and early childhood. This is evidenced by a high density of coalescences in the first approximately 50 mutations along molecular time (Fig. 2, Extended Data Fig. 5a); the cord blood colonies we sequenced had a mean of 55 mutations.

Fig. 4: Estimating in the human long-term HSC compartment.
figure 4

a, Trajectory of for human long-term (LT)-HSCs in the four adult donors aged over 65 years, estimated using Bayesian phylodynamics. The black line represents the estimated mean trajectory of LT-HSC , with the shaded grey area on either side representing the 95% credibility interval. The solid blue line is the time of birth. The dashed blue lines enclose the region of time in each individual where the trajectory is at the late childhood–young adult level. The shaded region of the plots represents the period of time before sampling over which it is likely that short-term (ST)-HSC/MPPs are contributing to the observed . The trajectory line is shaded dark grey in the time period where coalescent events are occurring and the trajectory probably represents the combined of both long-term HSC and short-term HSC/MPP compartments. The trajectory line is shaded light grey where there is a complete absence of coalescent events and the estimates are therefore inaccurate. The red line shows the Bayesian (maximum posterior density) estimate of . b, Results from approximate Bayesian inference of population size over the first (non-shaded) part of life for each individual. The blue line represents the prior density of and the red line represents the posterior density. The vertical grey line denotes the peak for each donor; values are shown above each plot.

Using both phylodynamic and approximate Bayesian computation (ABC) modelling, we estimate that steady-state haematopoiesis plateaus at an of about 100,000 HSC-years (Fig. 4). This was consistent across all 4 young adults, with individual estimates and 95% confidence intervals in the range 50,000–250,000, matching published estimates17,30. To estimate N (the HSC population size), we require an estimate of τ, the time between symmetric HSC self-renewal divisions. Previous estimates17,25,31 of τ for HSCs range between 0.6 and 6 years. Telomeres in HSCs shorten at mitosis by 30–100 bp per cell division32, and therefore provide an independent means to estimate the number of historic cell divisions an HSC has undergone. We estimated that HSC/MPP telomeres shorten at a rate of 30 bp per year (Fig. 1g)—this bounds the number of symmetric cell divisions to at most 1–2 divisions per year. Two recent studies in mice have shown that symmetric divisions predominate within the HSC pool, accounting for 80–100% of all HSC divisions33,34. Together, these data are most consistent with adult haematopoiesis being maintained by a population of 20,000–200,000 long-term HSCs.

In the young individuals, and indeed in the four elderly individuals, we noted a sparsity of branch points towards the tips of the phylogenies compared to earlier ages—this emerged consistently about 10–15 years of molecular time before sampling, irrespective of the subject’s age. We hypothesize that this pattern arises from a large, short-term HSC/MPP compartment that contributes to haematopoiesis for 10–15 years. Owing to its short-term contribution, the effects of this compartment would only be evident at the tips of the phylogeny, with earlier branches reflecting dynamics in the long-term HSC compartment. A substantially larger population size for these short-term HSC/MPPs would explain why the density of coalescences decreases towards the tips of the trees (Extended Data Fig. 6). The mouse short-term HSC/MPP compartment is known to sustain steady-state haematopoiesis without significant input from the long-term HSC compartment for many months35,36, a similar proportion of a mouse’s lifespan as 10–15 years is for a human.

HSC dynamics in the elderly

We observed a qualitative change in the population structure of HSCs in the elderly, with an abrupt increase in the frequency of expanded clones and the total fraction of haematopoiesis they generate (Fig. 5a). The Shannon index, which measures clonal diversity, showed a precipitous decline in diversity after age 70 (Fig. 5b). Although this change was evident only in the elderly, the branch points in the phylogenetic tree that underpinned the clonal expansions occurred much earlier in life, between childhood and middle age (Extended Data Fig. 7). There are several possible explanations for these observations, including age-related changes in population size, changes in spatial patterning of HSCs and cell-autonomous variation in selective fitness.

Fig. 5: Widespread positive selection in the HSC/MPP compartment of normal individuals.
figure 5

a, Stacked bar plot of number and size of clades with a clonal fraction above 1% per individual. Blue segments denote clones with no known driver and red ones denote those with a known myeloid gene driver. Numbers above each stack denote the total number of expanded clades in that individual. b, Shannon diversity index calculated for each phylogeny from the number of lineages present at 100 mutations of molecular time (equivalent to the first few years after birth) and their abundance (number of colonies derived from that lineage). c, Distribution of fitness effects for the driver mutations entering the HSC population, as determined using the ABC modelling approach. The point estimate for the shape and rate parameters of the gamma distribution were shape = 0.47 and rate = 34 (univariate marginal maximum posterior density estimates; Supplementary Fig. 14). The black line denotes the median, interquartile range is shown in dark grey and 95% posterior intervals are shown in light grey. d, Median, interquartile range, and 95% posterior intervals for Shannon diversity indices calculated yearly for 10,000 HSC population simulations run with the optimal parameter values from the ABC modelling. e, Fitness effects within the HSC/MPP compartment are estimated for clades with known driver mutations containing four or more HSC/MPP colonies (per cent additional growth per year). Fitness effects are also estimated for expanded clades without known drivers containing five or more HSC/MPP colonies. Error bars show the 95% credibility intervals of inferred fitness effects. Clade numbers are illustrated on the phylogenies in Extended Data Fig. 11b.

Using ABC, we explored whether changes in population size, most probably a population bottleneck occurring in mid-life, could explain the observed trees (Extended Data Fig. 7b). However, although they accurately recapitulated trees observed in subjects over 65 years of age, even the 1% most closely matching phylogenies from these simulations poorly replicated those observed in the elderly (Extended Data Fig. 8). Essentially, observed trees in the elderly showed marked asymmetry among clones, with a few large clades but also numerous ‘singleton’ branches. By contrast, simulations of variable population sizes generated trees with more evenly distributed coalescences among clades.

Theoretically, altered spatial patterning of HSCs could also explain the changes in phylogeny—if, for example, HSCs circulated less frequently with ageing, the bone marrow population would be less well mixed and therefore show increased density of coalescences in a bone marrow sample obtained from just a few vertebrae. To address this, we sequenced both marrow and peripheral blood HSCs from the 81-year-old subject. Peripheral blood colonies recaptured most of the expanded clades evident in marrow and at similar clonal fractions, suggesting that spatial segregation is not sufficient to explain the changes with ageing (Extended Data Fig. 7c).

Genetic evidence for positive selection

The asymmetry of population structure in the elderly suggests that clone-specific factors could lead to differential expansion among HSCs. Positively selected driver mutations are one, but not the only, possible cause of clone-specific variation in expansion rates; for example, epigenetic change, telomere shortening and microenvironment ageing could also contribute. Furthermore, it is unclear whether driver mutations acquired randomly throughout life could lead to such a qualitative change in population structure after the age of 70. To address these questions, we used two approaches: one based on genetic analysis of selection and one based on ABC models.

For the genetic analysis, we considered synonymous mutations as selectively neutral, and used their rate to quantify whether non-synonymous mutations occurred at equivalent or elevated rates (dN/dS ratio, with dN/dS > 1.0 denoting a tilt towards positive selection)—this was undertaken gene-by-gene and across all coding genes collectively37 (Supplementary Tables 6, 7). Estimating dN/dS gene by gene identified three genes under positive selection: DNMT3A (q = 2.7 × 10−11), ZNF318 (q = 1.2 × 10−6) and HIST2H3D (q = 0.086) (Extended Data Fig. 9a, Supplementary Table 8). DNMT3A is a well-known myeloid cancer gene with 23 mutations in our dataset, of which 13 were in expanded clades. The age of occurrence of expanded DNMT3A mutations could be estimated from the trees: 2 were acquired before 10 years of age, 3 were acquired before 20 years of age, and the remainder were acquired before 40 years of age. Screening 534 acute myeloid leukaemia (AML) genomes38,39 for variants in ZNF318 and HIST2H3D identified only one possible oncogenic mutation in ZNF318 (Supplementary Table 9), showing that although these variants are under selection in HSC/MPPs, they do not necessarily contribute to malignancy.

The genome-wide estimate of dN/dS was significantly higher, at 1.06 (95% confidence interval 1.03–1.09; Extended Data Fig. 9b), a value that remained almost unchanged after evaluating potential biases (Supplementary Methods). This value equates to 1 in 18 (range: 1 in 34 to 1 in 12) non-synonymous coding mutations in the dataset being drivers (defined as any mutation under positive selection). Estimated dN/dS ratios were almost identical in young and old individuals (1.06 and 1.05, respectively), suggesting that the fraction of non-synonymous mutations (approximately 5%) under positive selection does not change with age. Given that the overall mutation rate is constant, driver mutations therefore enter the HSC pool at a constant rate throughout life, and the expected number of driver mutations per cell increases linearly with age.

Converting dN/dS ratios to estimated numbers of driver mutations revealed that each adult studied typically had more than 100 driver mutations among the colonies sequenced (Extended Data Fig. 9c). These numbers are considerably higher than the number of non-synonymous mutations identified in known cancer genes. This implies that mutations under positive selection in normal HSCs affect a wider set of genes than usually assessed, as suggested by other studies15, and corroborates our observation that many clonal expansions in the elderly occur in the absence of mutations in known cancer genes.

Loss of chromosome Y (loss of Y) was a frequent occurrence in older males in our cohort. Loss of Y has been described in bulk blood samples from ageing men, correlating with all-cause mortality23. Notably, in our data, loss of Y showed frequent parallel evolution—the oldest male in our study, aged 81 years, had at least 62 independent loss of Y events across the phylogeny. Furthermore, loss of Y was significantly correlated with clonal expansion (P < 0.001; Extended Data Fig. 9d)—several expanded clades in our dataset exhibited loss of Y, often without known point mutation drivers, even in younger males (Figs. 2, 3, Extended Data Fig. 5). Negative selection acting on loss of Y is likely to be less stringent than on loss of autosomes; neverthelessa, that loss of Y generates significantly larger clones than euploid HSCs suggests that it is also under positive selection, corroborating mouse models showing Y-linked KDM6C suppresses leukaemogenesis40.

Modelling positive selection in HSCs

The genetic analysis suggests that positive selection is pervasive in HSCs, but whether positive selection can completely explain the observed phylogenies remains unclear. To address this, we used ABC modelling to infer HSC dynamics (Supplementary Note 1), with three aims: (1) to assess whether clone-specific positive selection can explain apparently abrupt loss in clonal diversity after age 70; (2) to estimate the rate at which driver mutations enter the HSC pool; and (3) to estimate the distribution of fitness effects, defined as the excess average growth rate per year of a clone with the driver over that of wild-type HSCs (denoted as s, with s = 0 indicating neutrality; Extended Data Fig. 10, Supplementary Methods). Simulations showed that mutations with fitness effects s < 5% are unlikely to expand to more than 1% of HSCs over a human lifespan (Extended Data Fig. 10d), so were not further considered.

ABC modelling showed that our observed trees could be closely emulated (Extended Data Figs. 11, 12) and carry sufficient information to estimate occurrence rate and fitness effects of driver mutations (Fig. 5c, Extended Data Fig. 13a). From the ABC modelling, we estimated that driver mutations enter the HSC compartment at 2.0 × 10−3 per HSC per year. The estimate obtained from the genetic analysis, based on dN/dS, was of drivers accruing at 3.6–10.0 × 10−3 per HSC per year, an estimate which includes drivers with s < 5% present in colonies. Thus, even though the ABC estimates derive only from branch structures of the phylogenies and the genetic analysis relies only on non-synonymous versus synonymous mutations, the two approaches concur in implying much higher rates of driver mutation occurrence than estimated from bulk sequencing studies.

The ABC modelling generates simulations of HSC clonal dynamics across a lifetime, which enables us to track how phylogenetic trees for a given avatar would appear when sampled at different ages. In these avatars, we found that there was typically a sharp change towards oligoclonal haematopoiesis after 60 years of age (Fig. 5d, Extended Data Figs. 12, 13b), demonstrating that lifelong accumulation of drivers triggering slow but inexorable exponential expansions causes abrupt loss of clonal diversity in the elderly.

For fitness effects, the distribution most consistent with the data had a preponderance of moderate-effect drivers, with s in the range 5–10%, but a heavy tail of rare drivers conferring greater selective advantage (s > 10%) (Fig. 5c). For the 46 largest observed clones, we could directly estimate their fitness effects from the patterns of coalescences within their clade, resulting in estimates of s in the range of 10–30% (Fig. 5e, Extended Data Fig. 11b). This shows that clones without known drivers can evolve comparable selective advantages to those with classic driver mutations.


Our data suggest that age-associated loss of clonal diversity is orders of magnitude more pervasive than estimated from previous studies4,5,10,11,12: (1) the prevalence of clones at more than 1% variant allele fraction (VAF) is universal after age 70, not 10–20% prevalent as previously estimated; (2) the number of expanded clones per individual is 10–20, not 1–2; (3) the fraction of overall haematopoiesis accounted for by expanded clones is 30–60%, not 3–5%; and (4) clonal expansions have their origins in mutations that occurred decades earlier, and not in old age. Age-related phenotypes in blood include anaemia, loss of regenerative capacity, remodelling of the bone marrow microenvironment and increased risk of blood cancer—phenotypes that sharply increase in prevalence after 70 years of age. The link between known driver mutations and blood cancer risk is clear4,5,6, with some evidence that expanded clones with undiscovered drivers also increase leukaemia transformation14. However, cancer need not be the ineluctable end-point of positive selection41, and addressing whether expanded clones collectively driving a sizable fraction of haematopoiesis contribute to, for example, age-related loss of resilience to perturbation or microenvironment remodelling would be of interest in future studies.

From an evolutionary perspective, ageing arises because the force of natural selection rapidly reduces to zero at ages beyond which reproductive output is negligible—the ‘selective shadow’42. Our data illustrate how an abrupt, qualitative change in tissue composition can occur within this shadow as the result of lifelong, gradual accumulation of molecular damage. HSCs acquire driver mutations with moderate fitness benefits at a steady rate throughout life, so the clonal expansions they trigger, although exponential, take decades to gather momentum. Evolutionary pressure on germline variants in genes controlling these dynamics—HSC population size and turnover, somatic mutation rates and distribution of fitness benefits—has historically acted to maintain robust, polyclonal haematopoiesis until 60–70 years of age, but no further. Thus, the distribution of fitness effects that we infer (Fig. 5c) has precisely this shape because of that evolutionary pressure—a right-shifted distribution with stronger drivers would cause sufficiently frequent blood cancers (or other deleterious phenotypes) in reproductively active humans to exert evolutionary pressure, but the selective shadow means evolutionary pressure for driving the distribution further to the left peters out.

Clonal expansion with abrupt collapse of stem cell diversity may be a feature of ageing that generalizes beyond human blood. Many human organ systems studied to date, including skin43, bronchus44, endometrium45,46 and oesophagus47,48, show age-related expansions of clones with driver mutations. Spatial organization of these tissues shapes their clonal dynamics, but selection for the same drivers in many independent clones can generate convergent genotypes at similar overall burden to those reported here (Supplementary Note 2). Similarly, oligoclonality occurs in elderly haematopoiesis in other mammalian species, including mice49 and macaques50. With such ubiquity of driver mutations—selected purely for their competitive advantage within the stem cell compartment—and with the wholesale rewiring of cellular pathways they induce, it is feasible that they may contribute to phenotypes of human ageing beyond the risk of cancer.



In order to obtain representative data from across the whole human lifespan, samples were obtained from three sources: (1) Stem Cell Technologies provided frozen mononuclear cells (MNCs) from two cord blood samples that had been collected with informed consent, including for whole-genome sequencing (catalogue no. 70007). (2) Cambridge Blood and Stem Cell Biobank (CBSB) provided fresh peripheral blood samples taken with informed consent from two patients at Addenbrooke’s Hospital (NHS Cambridgeshire 4 Research Ethics Committee reference 07/MRE05/44 for samples collected before November 2019 and Cambridge East Ethics Committee reference 18/EE/0199 for samples collected from November 2019 onwards. (3) Cambridge Biorepository for Translational Medicine (CBTM) provided frozen bone marrow with or without peripheral blood MNCs taken with informed consent from six deceased organ donors. Samples were collected at the time of abdominal organ collection (Cambridgeshire 4 Research Ethics Committee reference 15/EE/0152). All samples were collected under the approved studies quoted, with informed consent to use the materials for the research undertaken here and publish the results. Details of the individuals studied and the samples they provided are illustrated in Fig. 1b, with additional information in Supplementary Table 1.

No statistical methods were used to predetermine sample size. The experiments were not randomized and the investigators were not blinded to allocation during experiments and outcome assessment.

Isolation of MNCs from peripheral blood

MNCs were isolated using Lymphoprep density gradient centrifugation (STEMCELL Technologies), after diluting whole blood 1:1 with PBS. The red blood cell and granulocyte fraction of the blood was then removed. The MNC fraction underwent red cell lysis using 1 incubation at 4 °C for 15 min with RBC lysis buffer (BioLegend). CD34 positive cell selection of peripheral blood and cord blood MNC samples was undertaken using the EasySep human whole blood CD34 positive selection kit (STEMCELL Technologies). The kit was used per the manufacturer’s instructions, but with only a single round of magnetic selection. Bone marrow MNCs did not undergo CD34 positive selection prior to cell sorting.

Fluorescence-activated cell sorting

MNC or CD34 enriched samples were centrifuged and resuspended in PBS/3%FBS containing an antibody panel consisting of: CD3/FITC, CD90/PE, CD49f/PECy5, CD38/PECy7, CD33/APC, CD19/A700, CD34/APCCy7, CD45RA/BV421 and Zombie/Aqua. Cells were stained (30 min at 4 °C) in the dark before washing and resuspension in PBS/3%FBS for cell sorting. For all samples, HSC/MPP pool cells (LinCD34+CD38CD45RA) were sorted using either a BD Aria III or BD Aria Fusion cell sorter (BD Biosciences) at the NIHR Cambridge BRC Cell Phenotyping hub. The gating strategy is illustrated in Extended Data Fig. 1b. The HSC/MPP population was treated as a single entity and not further subclassified in the analysis. The immunophenotypic HSC/MPP population includes both long-term, intermediate-term and short-term HSCs as well as multipotent progenitors, as demonstrated functionally in xenotransplantation assays51,52,53,54.

In a subset of individuals, a small number of HPCs (LinCD34+CD38+) were also sorted. The antibody panel used is shown in Supplementary Table 2. The HPC cells were treated as a single entity in the analysis and not further subclassified. The immunophenotypic HPC compartment includes predominantly myeloid and erythroid progenitors.

Single-cell colony expansion in vitro

Single phenotypic HSC/MPP or HPC cells were index sorted, as above, into Nunc 96 flat-bottomed tissue culture plates (Thermofisher), containing 100 μl supplemented StemPro medium (Stem Cell Technologies) but no mouse cell feeder layer. The following supplements were added to promote proliferation and push differentiation toward granulocyte, monocyte, erythroid and natural killer cell types: StemPro Nutrients (0.035%, Stem Cell Technologies), l-glutamine (1%, ThermoFisher), penicillin-streptomycin (1%, ThermoFisher) and cytokines (SCF, 100 ng ml−1; FLT3, 20 ng ml−1; TPO, 100 ng ml−1; EPO, 3 ng ml−1; IL-6, 50 ng ml−1; IL-3, 10 ng ml−1; IL-11, 50 ng ml−1; GM-CSF, 20 ng ml−1; IL-2, 10 ng ml−1; IL-7, 20 ng ml−1; lipids, 50 ng ml−1). Cells were incubated at 37 °C and the colonies that formed were topped up with 50 μl StemPro medium plus supplements at 14 ± 2 days as necessary. At 21 ± 2 days, a visual size assessment of colonies was undertaken prior to collection of cells for DNA extraction. Larger colonies (≥3,000 cells in size) were transferred to fresh U bottomed 96 well plate (Thermofisher). The U bottomed plates were then centrifuged (500g for 5 min), medium was discarded, and the cells were resuspended in 50μl PBS prior to freezing at −80 °C. Smaller colonies (less than 3,000 but more than 200 cells in size) were collected into 96-well skirted LoBind plates (Eppendorf) and centrifuged (800g for 5 min). Supernatant was removed to 5–10 μl using an aspirator prior to DNA extraction on the fresh pellet. For larger colonies DNA extraction was performed using the DNeasy 96 blood and tissue plate kit (Qiagen). The Arcturus Picopure DNA Extraction kit (ThermoFisher) was used to extract DNA from the smaller colonies. Both kits were used per the manufacturer’s instructions. Overall, 42–89% of the sorted HSC/MPP population in each individual produced a colony. This is much more efficient than previously used methods of colonygrowth in semi-solid media, where it is has been estimated that 10% of single HSC/MPPs will produce a colony. In addition, analysis of cell surface markers showed there was no immunophenotypic difference between sorted HSC/MPPs that formed colonies and were sequenced, compared to those that did not (Extended Data Fig. 1c, d).

Whole-genome sequencing of colonies

A recently developed low-input enzymatic fragmentation-based library preparation method45,55 was used to generate whole-genome sequencing libraries from 1–5 ng extracted DNA from each colony. Whole-genome sequencing was performed at a mean sequencing coverage of 14× (8–35×) on either the Hiseq X or the NovaSeq platforms (Illumina). BWA mem was used to align 150 bp paired end reads generated to the human reference genome (NCBI build 37; GRCh37d5).

Variant calling

CaVEMan (used for calling SNVs) and Pindel (used for calling small indels) were run against an unmatched synthetic normal genome using in-house pipelines45,56,57,58. Outputs were then filtered using several strategies to exclude library, sequencing and mapping artefacts. Structural variants were called using GRIDDS59, with all variants confirmed by visual inspection and by checking if they fit the distribution expected based on the SNV-derived phylogenetic tree. Autosomal CNAs and X chromosome CNAs in females were called using allele-specific copy number analysis of tumours60 (ASCAT), which was run against a single sample selected from each individual. The matched sample was selected to have a coverage > 15×, no loss of Y and to be a singleton in the phylogenetic tree (no coalescences post-birth). The ASCAT output was manually interpreted through visual inspection. ASCAT was unable to accurately call copy number changes on the haploid sex chromosomes in males. Therefore, we also ran the breakpoint analysis algorithm BRASS61 to generate an intermediate file containing information on binned read counts across 500-bp segments of the genome. A comparison of the mean coverage of the X and Y chromosomes was used to call X or Y CNAs in individual samples, which were then validated by visual inspection of read depth.

Full details of the variant calling strategies and filtering approaches used are described in the Supplementary Information.

Filtering at the colony level

As outlined in the main text, we removed some colonies from the dataset due to low coverage (17 samples), being technical duplicates (34 samples) and for showing evidence of non-clonality or contamination (7 samples). We used a peak VAF threshold of < 0.4 (after the removal of in vitro variants) as well as visual inspection of the VAF distribution plots to identify colonies with evidence of non-clonality (Extended Data Fig. 2a–c). Visual inspection was particularly important in the cord blood samples where there was greater variability in the distribution of variant allele frequencies due to the lower mutation burden. In these samples the VAF threshold of < 0.4 was therefore less stringently applied.

Validation of mutation calls

Mutation spectrums were compared between the set of shared mutations (those present in two or more colonies), which are those we have the greatest confidence in, and private mutations (present in only one sample), in which we have lower confidence (Extended Data Fig. 2e). The mutation spectrums are almost identical, providing evidence that the private mutation set does not contain excess artefacts.

Mutation burden analysis

SNV and indel burden analysis was performed by first correcting the mutation and indel burden to a sequencing depth of 30, by fitting an asymptotic regression to the data (function NLSstAsymptotic, R package stats) (Extended Data Fig. 2d). Subsequently, linear mixed effects models were used to test for a linear relationship between age and number of SNVs or number of indels (function lmer, R package lme4). Number of mutations or indels per colony was regressed using log-likelihood maximization and age as a fixed effect, with the interaction between age and donor as a random effect. Progenitor samples were excluded from this analysis.

We also performed linear regression of age and mutation burden as above broken down by age range. Using data from the two cord blood donors and the youngest adult age 29 gives a rate of mutation accumulation of 17.56 per year (95% confidence interval 17.32–17.78). Using data from just the 3 younger donors aged 29, 38, 48 and 63 gives a rate of mutation accumulation of 17.21 per year (95% confidence interval 16.12–18.3). Linear regression of age and mutation burden using the 5 older donors aged 63, 75, 76, 77 and 81 gives a rate of mutation accumulation of 18.84 per (95% confidence interval 16.82–20.86). These results are very consistent over different phases of life.

Telomere analysis

Telomere length for each colony sequenced on the Hiseq platform (corresponding to the telomere length in the founding HSC/MPP or HPC) was estimated from the ratio of telomeric to sub-telomeric reads using the algorithm Telomerecat62. Colonies sequenced on the Novaseq platform could not be used as telomeric reads were removed by a quality control step prior to bam file creation.

Linear mixed effects models were used to test for a linear relationship between age and telomere length across all the adults. Cord blood samples were excluded due to possible non-linearity of the relationship between age and telomere length in very early life63.

Normality testing of the telomere length distributions was performed in R using the Shapiro-Wilk normality test (function shapiro.test) and visualized using Q–Q plots and density plots (functions ggqqplot and ggdensity). The percentage of outlying HSC/MPPs per individual was calculated using the interquartile range criterion (all samples outside the interval [q0.25 − 1.5 × IQR, q0.75 + 1.5 × IQR] are considered as outliers; IQR is the interquartile range; q0.25 and q0.75 are the 25th and 75th centiles respectively) (function boxplot.stats). For all individuals, the only outliers in the data had longer than expected rather than shorter than expected telomeres.

Construction of phylogenetic trees

The key steps to generate the phylogenies shown in Figs. 2, 3 and Extended Data Fig. 5 are as follows:

  1. 1.

    Generate a ‘genotype matrix’ of mutation calls for every colony within a donor. Our protocol, based on whole-genome sequencing of single cell-derived colonies, generates consistent and even coverage across the genome, leading to very few missing values within this matrix (ranging from 0.005 to 0.034 of mutated sites in a given colony across different donors within our cohort). This generates a high degree of accuracy in the constructed trees.

  2. 2.

    Reconstruct phylogenetic trees from the genotype matrix. This is a standard and well-studied problem in phylogenetics. The low fraction of the genome that is mutated in a given colony (<1 per million bases) coupled with the highly complete genotype matrix mean that different phylogenetics methods produce reassuringly concordant trees. We used the MPBoot algorithm64 for the tree reconstruction, as it proved both accurate and computationally efficient for our dataset.

  3. 3.

    Correct terminal branch lengths for sensitivity to detect mutations in each colony. The trees generated in the previous step have branch lengths proportional to the number of mutations assigned to each branch. For the terminal branches, which contain mutations unique to that colony, variable sequencing depth can underestimate the true numbers of unique mutations, so we correct these branch lengths for the estimated sensitivity to detect mutations based on genome coverage.

  4. 4.

    Make phylogenetic trees ultrametric. After step 3, there is little more than Poisson variation in corrected mutation burden among colonies from a given donor. Since these colonies all derived from the same timepoint, we can normalize the branch lengths to have the same overall distance from root to tip (known as an ultrametric tree). We used an ‘iteratively reweighted means’ algorithm for this purpose.

  5. 5.

    Scale trees to chronological age. Since mutation rate is constant across the human lifespan, we can use it as a molecular clock to linearly scale the ultrametric tree to chronological age.

  6. 6.

    Overlay phenotypic and genotypic information on the tree. The tip of each branch in the resulting phylogenetic tree represents a specific colony in the dataset, meaning that we can depict phenotypic information about each colony underneath its terminal branch (the coloured stripes along the bottom of Figs. 2, 3). Furthermore, every mutation in the dataset is confidently assigned to a specific branch in the phylogenetic tree. This means that we can highlight branches on which specific genetic events occurred (such as DNMT3A or other driver mutations).

A complete explanation of the steps undertaken in each of these stages, comparisons of different methods available and notes on the different approaches to validate the phylogenetic trees are available in the Supplementary Information.

Inferring HSC population size trajectory

The R package phylodyn65 provides a well-established approach to inferring historic population size trajectories from the pattern of coalescent events (more specifically the density of these events in historic time blocks) in a phylogenetic tree created from a random sample of individuals in the population. Its use has been pioneered in pathogen epidemiology65 and has also been previously applied to HSPC data from a single individual66. Extended Data Figs. 6b, 7b show how phylodyn can accurately recover simulated population trajectories using sample sizes similar to those we have used and illustrates how the number of coalescent events in a given time window in the tree informs on population size through time (assuming a constant rate of HSC symmetric cell division and a neutrally evolving population). We used phylodyn to infer historic changes in LT-HSC from the ultrametric phylogenies of the four youngest adults in the cohort (Fig. 4a).

Simulations of complete HSC populations from conception to the age of sampling were performed for each individual using the R package rsimpop67 ( Rsimpop utilizes a birth-to-death model with specified somatic mutation accumulation rate and symmetric cell division rate, to simulate a complete HSC population. Each cell within the population has a rate of symmetric division and a rate of symmetric differentiation (or death). Asymmetric divisions do not impact on the HSC phylogeny and are not accounted for in the model. Full details are available in the Supplementary Information.

Approximate Bayesian computation

We evaluated several ABC models of population dynamics, including both models of selectively neutral genetic drift within the HSC compartment (assessing the effects of changes in population size on phylogenetic trees) and models of positive selection among the HSCs. An additional motivation for performing the Bayesian inferences on neutral models was to enable us to perform posterior predictive checks (PPC), with the aim of deciding whether the observed phylogenies are compatible with neutral models. Note that a separate donor-specific posterior distribution was generated (sampled) for each donor (donor-specific ABC), and a separate donor-specific posterior predictive p-value was computed for each donor (donor-specific PPC). Each donor-specific ABC for the neutral model was performed using the ABC rejection method (R package abc)68,69,70. Further details, together with detailed mathematical exposition, are provided in Supplementary Information.

We used the population trajectory from phylodyn to identify the time period prior to the increase related to a ST-HSC/MPP contribution, and the timing of the midlife and late-life fold-change in (Fig. 4a, Supplementary Fig. 12). We used our data to inform our choices for the time between symmetric cell divisions, which was set at one year (after the initial population growth phase in the first few years of life). We set the rate of mutation accumulation at 15 mutations per year with an additional 1 mutation for every cell division (both of these were drawn from a Poisson distribution centred on the input value).

Analysis of driver variants

Variants identified were annotated with Variation Annotation Generator (VAGrENT) ( to identify protein coding mutations and putative driver mutations in each dataset71,72,73,74,75,76,77. Supplementary Table 4 lists the 17 genes we have used as our top clonal haematopoiesis genes (those identified by Fabre et al. as being under positive selection in a targeted sequencing dataset of 385 older individuals78), whose ‘oncogenic’ and ‘possible oncogenic’ mutations (as assessed independently by E.M. and P.J.C.) are shown in Figs. 2, 3 and Extended Data Fig. 5. In the same table, we also list a more extensive list of 92 clonal haematopoiesis genes derived from the literature37, 71, 72 that were interrogated. In order to explore a wider set of cancer gene mutations we used the 723 genes listed in Cosmic’s Cancer Gene Census (

dN/dS analysis

We used the R package dndscv37 ( to look for evidence of positive selection in our dataset ( The dndscv package compares the observed ratio of missense, truncating and nonsense to synonymous mutations with that expected under a neutral model. It incorporates information on the background mutation rate of each gene and uses trinucleotide-context substitution matrices. The approach provides a global estimate of selection in the coding variant dataset (Supplementary Table 7), from which the number of excess protein coding, or driver mutations can be estimated. In addition, it identifies specific genes that are under significant positive selection. Further details are provided in Supplementary Information.

Chromosome Y loss analysis

We observe a series of phylogenetic trees from male individuals in which some clades have lost the Y chromosome. By eye, these clades seem to be larger than clades that have not lost Y. To test this formally, we use a randomization or Monte Carlo test to define the null expected distribution of clade size. For each Monte Carlo iteration, we draw branches of the phylogenetic tree at random—one random branch for each observed instance of Y loss. These branches are sampled (with replacement) from the set of all extant branches at the matched time-point in that individual, and the eventual clade size of that draw measured. For each simulation, the geometric mean (to allow for the log-normality of observed clade sizes) of clade sizes is calculated. We can then compare the distribution of geometric means from the Monte Carlo draws with the observed geometric mean.

Analysis of AML

Mutations in ZNF318 and HIST2H3D were identified in recently described tumour whole-genome sequencing data from 263 patients with AML and myelodysplastic syndromes seen at Washington University School of Medicine in St Louis38. Sequencing, initial processing using the hg38 human reference genome, and full variant calling details for this dataset were described previously38. Briefly, identification of SNVs and indels in ZNF318 and HIST2H3D was performed with Varscan2 run in SNV and indel mode using custom parameters to enhance sensitivity (--min-reads2 = 3, --min-coverage = 6, --min-var-freq = 0.02, and --p-value = 0.01), along with indel callers Pindel and Manta using default parameters. Variant calls identified via these approaches were merged and harmonized using a custom python script and annotated with VEP using Ensembl version 90. Only one potentially pathogenic variant was found in ZNF318. All identified variants are listed in Supplementary Table 8, the majority are likely to be rare germline variants as the sequencing strategy did not incorporate a matched normal sample.

In addition, there were no variants in ZNF318 or HIST2H3D reported in 200 cases of AML in the TCGA dataset79, compared to 51 cases with DNMT3A mutations. Similarly of 71 AML cases in the JAMA study39 23 cases had DNMT3A mutations but none had ZNF318 or HIST2H3D mutations. Both studies used a tumour/normal WGS approach and reported all somatic variants identified as part of their supplementary datasets.


Programs and software used in the data analyses: R: version 3.6.1, BWA-MEM: version 0.7.17 (, cgpCaVEMan: version 1.11.2/1.13.14/1.14.1 (, cgpPindel: version 2.2.5/3.2.0/3.3.0 (, Brass: version 6.1.2/6.2.0/6.3.0/6.3.4 (, ASCAT NGS: version 4.2.1/4.3.3 (, VAGrENT: version 3.5.2/3.6.0/3.6.1 (, GRIDSS: version 2.9.4 (, MPBoot: version 1.1.0 (, cgpVAF: version 2.4.0 (, dNdScv: version 0.0.1 (, Telomerecat: version 3.4.0 (, Rsimpop: version 2.0.4 (, Phylodyn: version 0.9.02 ( and FlowJo: version 10.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this paper.