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Fluctuating methylation clocks for cell lineage tracing at high temporal resolution in human tissues

Gabbutt, Calum, Schenck, Ryan O., Weisenberger, Daniel J., Kimberley, Christopher, Berner, Alison, Househam, Jacob, Lakatos, Eszter, Robertson-Tessi, Mark, Martin, Isabel, Patel, Roshani, Clark, Susan K., Latchford, Andrew, Barnes, Chris P., Leedham, Simon J., Anderson, Alexander R. A., Graham, Trevor A., Shibata, Darryl·2022·Nature Biotechnology
DOI 10.1038/s41587-021-01109-wPMC PMC9110299

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Abstract

Molecular clocks that record cell ancestry mutate too slowly to measure the short-timescale dynamics of cell renewal in adult tissues. Here, we show that fluctuating DNA methylation marks can be used as clocks in cells where ongoing methylation and demethylation cause repeated ‘flip–flops’ between methylated and unmethylated states. We identify endogenous fluctuating CpG (fCpG) sites using standard methylation arrays and develop a mathematical model to quantitatively measure human adult stem cell dynamics from these data. Small intestinal crypts were inferred to contain slightly more stem cells than the colon, with slower stem cell replacement in the small intestine. Germline APC mutation increased the number of replacements per crypt. In blood, we measured rapid expansion of acute leukemia and slower growth of chronic disease. Thus, the patterns of human somatic cell birth and death are measurable with fluctuating methylation clocks (FMCs).

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Figures

Fig. 1 Fluctuating methylation status as a lineage tracing marker. a, Illustration of the three possible methylation states at a specific CpG locus within a particular cell. A cell can either be homozygously (de)methylated or heterozygously methylated at that CpG locus. It is the spontaneous transitions between these states that allow methylation to act as a lineage tracing marker. b, Illustration of the link between the methylation status of a given CpG locus within a particular cell and the β value (the fraction of methylated DNA at that locus) associated with that cell. c, Graphical representation of how the methylation status in a small population of five stem cells at a particular CpG locus can change over time due to (1) methylation, (2) demethylation or (3) cell replacement. d, Methylation (β) distributions from an individual crypt; the peaks near 0% and 100% correspond to a clonal methylated or unmethylated CpG locus, respectively, whereas the peak at 50% corresponds both to clonal heterozygous CpG loci and subclonal populations caught mid-sweep.
Fig. 1 Fluctuating methylation status as a lineage tracing marker. a, Illustration of the three possible methylation states at a specific CpG locus within a particular cell. A cell can either be homozygously (de)methylated or heterozygously methylated at that CpG locus. It is the spontaneous transitions between these states that allow methylation to act as a lineage tracing marker. b, Illustration of the link between the methylation status of a given CpG locus within a particular cell and the β value (the fraction of methylated DNA at that locus) associated with that cell. c, Graphical representation of how the methylation status in a small population of five stem cells at a particular CpG locus can change over time due to (1) methylation, (2) demethylation or (3) cell replacement. d, Methylation (β) distributions from an individual crypt; the peaks near 0% and 100% correspond to a clonal methylated or unmethylated CpG locus, respectively, whereas the peak at 50% corresponds both to clonal heterozygous CpG loci and subclonal populations caught mid-sweep.CC BY · PubMed Central
Fig. 2 Identification of selectively neutral fCpG loci. a, Workflow used to identify fCpG loci that exhibit high intraindividual heterogeneity. Input data were the ~850,000 CpG loci assayed by an Illumina EPIC array. We removed type I probes and probes that cross-hybridize highly homologous DNA regions. For each CpG locus, we calculated the standard deviation for each set of approximately four crypts per individual and then calculated the mean standard deviation across the cohort as a metric for the intraindividual heterogeneity. We selected the top 5% most highly variable CpG loci and then removed CpG loci that have a mean β value (across the entire cohort) less than 0.4 or greater than 0.6; kb, kilobases. b, Left: fCpGs are enriched for CpG loci not associated with any genes (P = 6.5 × 10−34, chi-squared test). Right: the set of genes associated with fCpG loci exhibit lower average RNA expression (P = 6.6 × 10−6, two-sided Welch’s t-test performed following the log-transformed data) in normal colon than those genes associated with non-fCpG loci (center line, median; box limits, upper and lower quartiles; whiskers, 1.5 interquartile range). ***P < 0.001. c, β values of fCpG loci are correlated between the bottom and top halves of a crypt.
Fig. 2 Identification of selectively neutral fCpG loci. a, Workflow used to identify fCpG loci that exhibit high intraindividual heterogeneity. Input data were the ~850,000 CpG loci assayed by an Illumina EPIC array. We removed type I probes and probes that cross-hybridize highly homologous DNA regions. For each CpG locus, we calculated the standard deviation for each set of approximately four crypts per individual and then calculated the mean standard deviation across the cohort as a metric for the intraindividual heterogeneity. We selected the top 5% most highly variable CpG loci and then removed CpG loci that have a mean β value (across the entire cohort) less than 0.4 or greater than 0.6; kb, kilobases. b, Left: fCpGs are enriched for CpG loci not associated with any genes (P = 6.5 × 10−34, chi-squared test). Right: the set of genes associated with fCpG loci exhibit lower average RNA expression (P = 6.6 × 10−6, two-sided Welch’s t-test performed following the log-transformed data) in normal colon than those genes associated with non-fCpG loci (center line, median; box limits, upper and lower quartiles; whiskers, 1.5 interquartile range). ***P < 0.001. c, β values of fCpG loci are correlated between the bottom and top halves of a crypt.CC BY · PubMed Central
Fig. 3 W-shaped methylation distributions are indicative of clonal dynamics. In silico evaluation of the accuracy of Bayesian inference on stem cell number (S), replacement rate (λ) and (de)methylation rates (μ,γ) as a function of input (de)methylation rates. Three regimens were evaluated: μ = γ = 0.0005 methylation events per allele per stem cell per year (‘too slow’), very high methylation rates (μ = γ = 0.5 per allele per stem cell per year (‘too fast’)) and intermediate methylation rates (μ = γ= 0.05 per allele per stem cell per year (‘just right’)). a, Simulated fCpG methylation distributions from individual crypts at each of three input (de)methylation rates. The characteristic W distribution is only evident for the just-right (de)methylation rate. b, Posterior distributions of inferred replacement and (de)methylation rates for each input (de)methylation rate. c, Posterior distributions of inferred stem cell number. The stem cell number posterior mean was calculated by taking the softmax of the log evidence, while the error bars were calculated from the estimated error (1 s.d.) on the log evidence. In b and c, red dashed lines indicate the true (inputted) value of the parameter. The simulated datasets each contained S = 5 stem cells, had a replacement rate of λ = 1.0 per stem cell per year, and the noise due to sampling was simulated with offsets due to background noise Δ=0.04,ϵ=0.92 and peak specific noise with sample size kz = 100. d, Independent validation of the inference method on a spatial representation of the single crypt with varying stem cells. Methylation distributions are noise adjusted (Methods) for the inferences on the stem pool only. Mean inferred stem cell numbers are shown for ten replicate simulations, and the red bar represents the mean of the ten replicates while the error bars denote 1 s.d.
Fig. 3 W-shaped methylation distributions are indicative of clonal dynamics. In silico evaluation of the accuracy of Bayesian inference on stem cell number (S), replacement rate (λ) and (de)methylation rates (μ,γ) as a function of input (de)methylation rates. Three regimens were evaluated: μ = γ = 0.0005 methylation events per allele per stem cell per year (‘too slow’), very high methylation rates (μ = γ = 0.5 per allele per stem cell per year (‘too fast’)) and intermediate methylation rates (μ = γ= 0.05 per allele per stem cell per year (‘just right’)). a, Simulated fCpG methylation distributions from individual crypts at each of three input (de)methylation rates. The characteristic W distribution is only evident for the just-right (de)methylation rate. b, Posterior distributions of inferred replacement and (de)methylation rates for each input (de)methylation rate. c, Posterior distributions of inferred stem cell number. The stem cell number posterior mean was calculated by taking the softmax of the log evidence, while the error bars were calculated from the estimated error (1 s.d.) on the log evidence. In b and c, red dashed lines indicate the true (inputted) value of the parameter. The simulated datasets each contained S = 5 stem cells, had a replacement rate of λ = 1.0 per stem cell per year, and the noise due to sampling was simulated with offsets due to background noise Δ=0.04,ϵ=0.92 and peak specific noise with sample size kz = 100. d, Independent validation of the inference method on a spatial representation of the single crypt with varying stem cells. Methylation distributions are noise adjusted (Methods) for the inferences on the stem pool only. Mean inferred stem cell numbers are shown for ten replicate simulations, and the red bar represents the mean of the ten replicates while the error bars denote 1 s.d.CC BY · PubMed Central
Fig. 4 Tissue-specific differences in stem cell dynamics. The stem cell dynamics Bayesian inference framework was applied to 71 individual intestinal crypts originating from 17 individuals. a, Examples of the posterior predictive distributions, the discrete stem cell number posterior and the posterior for the replacement rate, methylation rate and demethylation rate in crypts derived from normal colon, small intestine and the colon of individuals with FAP (left to right). Error bars were calculated from the estimated error (1 s.d.) on the log evidence. b, Individual crypt and posterior mean per individual for the stem cell number; AFAP, attenuated familial adenomatous polyposis. c, Replacement rate per stem cell with the 95% credible range of the GLM expectation, accounting for age, sex, tissue, disease state and intra- and interindividual heterogeneity. d–f, Posterior distributions for the effect of age (per decade), sex (with female encoded as reference), tissue type and disease state on the relative number of stem cells (d), replacement rate per stem cell (e) and total number of replacements (f) compared to normal colon. A Bayesian parameter estimation hypothesis testing approach was taken, such that a difference was called significant if the 95% credible region did not overlap.
Fig. 4 Tissue-specific differences in stem cell dynamics. The stem cell dynamics Bayesian inference framework was applied to 71 individual intestinal crypts originating from 17 individuals. a, Examples of the posterior predictive distributions, the discrete stem cell number posterior and the posterior for the replacement rate, methylation rate and demethylation rate in crypts derived from normal colon, small intestine and the colon of individuals with FAP (left to right). Error bars were calculated from the estimated error (1 s.d.) on the log evidence. b, Individual crypt and posterior mean per individual for the stem cell number; AFAP, attenuated familial adenomatous polyposis. c, Replacement rate per stem cell with the 95% credible range of the GLM expectation, accounting for age, sex, tissue, disease state and intra- and interindividual heterogeneity. d–f, Posterior distributions for the effect of age (per decade), sex (with female encoded as reference), tissue type and disease state on the relative number of stem cells (d), replacement rate per stem cell (e) and total number of replacements (f) compared to normal colon. A Bayesian parameter estimation hypothesis testing approach was taken, such that a difference was called significant if the 95% credible region did not overlap.CC BY · PubMed Central
Fig. 5 The FMC method is generalizable to other glandular tissue. The stem cell dynamics Bayesian inference framework was applied to 32 individual endometrial glands originating from eight individuals. a, Measured methylation β values (blue bars) and posterior predictive distribution (salmon overlay) are shown for an example endometrial gland. The methylation patterns exhibit a similar W shape to that observed in intestinal crypts. b, Posterior distributions for the replacement rate per stem cell and (de)methylation rates for the gland shown in a. c, Posterior distribution for stem cell number for the gland shown in a. Error bars were calculated from the estimated error (1 s.d.) on the log evidence. d, Inferred relative replacement rate per stem cell, number of stem cells and total replacement rate in endometrium versus colon, indicating that there are significantly more stem cells per gland in endometrium than in colon. Bars show 95% credible intervals derived from a GLM. e, Inferred number of stem cells and replacement rate per stem cell for each individual gland from each individual (dots) with the 95% credible range of the GLM expectation (bars).
Fig. 5 The FMC method is generalizable to other glandular tissue. The stem cell dynamics Bayesian inference framework was applied to 32 individual endometrial glands originating from eight individuals. a, Measured methylation β values (blue bars) and posterior predictive distribution (salmon overlay) are shown for an example endometrial gland. The methylation patterns exhibit a similar W shape to that observed in intestinal crypts. b, Posterior distributions for the replacement rate per stem cell and (de)methylation rates for the gland shown in a. c, Posterior distribution for stem cell number for the gland shown in a. Error bars were calculated from the estimated error (1 s.d.) on the log evidence. d, Inferred relative replacement rate per stem cell, number of stem cells and total replacement rate in endometrium versus colon, indicating that there are significantly more stem cells per gland in endometrium than in colon. Bars show 95% credible intervals derived from a GLM. e, Inferred number of stem cells and replacement rate per stem cell for each individual gland from each individual (dots) with the 95% credible range of the GLM expectation (bars).CC BY · PubMed Central
Fig. 6 FMC dynamics can further be observed in chronic and acute leukemia. a, The variance of the fCpG methylation distribution experiences a gradual increase with age in healthy individuals. The confidence band was calculated via bootstrapping and represents 95% confidence intervals. b, Left: the variance (center line, median; box limits, upper and lower quartiles; whiskers, 1.5 interquartile range) of paired blood samples taken 10 years apart (1997 and 2007) also exhibits a small but marked increase (0.37 Cohen’s d, P = 2.8 × 10−5 two-sided paired t-test). Right: a scatterplot showing the matched variance per individual sample taken 10 years apart, demonstrating that the variance typically rises with age. ***P < 0.001. c, The variance of the fCpG methylation distribution (center line, median; box limits, upper and lower quartiles; whiskers, 1.5 interquartile range) is a proxy for the rapidity of the clonal expansion within the blood. In normal samples, the large stem cell population size leads to the methylation distribution being concentrated near 50% (as one would expect for uncorrelated oscillators). However, as a clonal cancerous population expands, clonal peaks begin to separate from the 50% peak. In the case of acute lymphoblastic leukemia (ALL), the large, well-separated peaks near 0% and 100% are indicative of a single clonal population making up the majority of the remaining stem cells following rapid growth; CMML, chronic myelomonocytic leukemia; MDS, myelodysplastic syndrome; ET, essential thrombocythemia; PRV, polycythemia vera; PMF, primary myelofibrosis; CML, chronic myeloid leukemia; AML, acute myeloid leukemias. d, Simulations confirm that a simple model of hematopoiesis can recapitulate the observed methylation distribution overserved in human data. Data represent mean ± s.e.m.; CHIP, clonal hematopoiesis of indeterminate potential.
Fig. 6 FMC dynamics can further be observed in chronic and acute leukemia. a, The variance of the fCpG methylation distribution experiences a gradual increase with age in healthy individuals. The confidence band was calculated via bootstrapping and represents 95% confidence intervals. b, Left: the variance (center line, median; box limits, upper and lower quartiles; whiskers, 1.5 interquartile range) of paired blood samples taken 10 years apart (1997 and 2007) also exhibits a small but marked increase (0.37 Cohen’s d, P = 2.8 × 10−5 two-sided paired t-test). Right: a scatterplot showing the matched variance per individual sample taken 10 years apart, demonstrating that the variance typically rises with age. ***P < 0.001. c, The variance of the fCpG methylation distribution (center line, median; box limits, upper and lower quartiles; whiskers, 1.5 interquartile range) is a proxy for the rapidity of the clonal expansion within the blood. In normal samples, the large stem cell population size leads to the methylation distribution being concentrated near 50% (as one would expect for uncorrelated oscillators). However, as a clonal cancerous population expands, clonal peaks begin to separate from the 50% peak. In the case of acute lymphoblastic leukemia (ALL), the large, well-separated peaks near 0% and 100% are indicative of a single clonal population making up the majority of the remaining stem cells following rapid growth; CMML, chronic myelomonocytic leukemia; MDS, myelodysplastic syndrome; ET, essential thrombocythemia; PRV, polycythemia vera; PMF, primary myelofibrosis; CML, chronic myeloid leukemia; AML, acute myeloid leukemias. d, Simulations confirm that a simple model of hematopoiesis can recapitulate the observed methylation distribution overserved in human data. Data represent mean ± s.e.m.; CHIP, clonal hematopoiesis of indeterminate potential.CC BY · PubMed Central

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Cited by

See all 76

Metadata

Authors
Gabbutt, C., Schenck, R. O., Weisenberger, D. J., Kimberley, C., Berner, A., Househam, J., Lakatos, E., Robertson-Tessi, M., Martin, I., Patel, R., Clark, S. K., Latchford, A., Barnes, C. P., Leedham, S. J., Anderson, A. R. A., Graham, T. A., & Shibata, D.
Venue
Nature Biotechnology
Publisher
Springer Science and Business Media LLC
Published
2022-01-03
Volume
40
Issue
5
Pages
720-730
ISSN
  • 1087-0156
  • 1546-1696
DOI
10.1038/s41587-021-01109-w
PMC
PMC9110299
Cited by
76
References
12
Language
en
Licence
Funders
  • U.S. Department of Health & Human Services | NIH | National Cancer Institute (U54 CA217376, U01CA232382, U54CA143970, U54CA217376)
  • Wellcome Trust (108861/7/15/7, 209409/Z/17/Z, 206314/Z/17/Z)
  • Cancer Research UK (A19771)

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This page
CoChat. (2026). Citation check: Fluctuating methylation clocks for cell lineage tracing at high temporal resolution in human tissues. https://app.cochat.ai/papers/10.1038/s41587-021-01109-w
The paper
Gabbutt, C., Schenck, R. O., Weisenberger, D. J., Kimberley, C., Berner, A., Househam, J., Lakatos, E., Robertson-Tessi, M., Martin, I., Patel, R., Clark, S. K., Latchford, A., Barnes, C. P., Leedham, S. J., Anderson, A. R. A., Graham, T. A., & Shibata, D. (2022). Fluctuating methylation clocks for cell lineage tracing at high temporal resolution in human tissues. Nature Biotechnology. https://doi.org/10.1038/s41587-021-01109-w