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Developmental hematopoietic stem cell variation explains clonal hematopoiesis later in life

Kreger, Jesse, Mooney, Jazlyn A., Shibata, Darryl, MacLean, Adam L.·2024·Nature Communications
DOI 10.1038/s41467-024-54711-2PMC PMC11599844

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Abstract

Clonal hematopoiesis becomes increasingly common with age, but its cause is enigmatic because driver mutations are often absent. Serial observations infer weak selection indicating variants are acquired much earlier in life with unexplained initial growth spurts. Here we use fluctuating CpG methylation as a lineage marker to track stem cell clonal dynamics of hematopoiesis. We show, via the shared prenatal circulation of monozygotic twins, that weak selection conferred by stem cell variation created before birth can reliably yield clonal hematopoiesis later in life. Theory indicates weak selection will lead to dominance given enough time and large enough population sizes. Human hematopoiesis satisfies both these conditions. Stochastic loss of weakly selected variants is naturally prevented by the expansion of stem cell lineages during development. The dominance of stem cell clones created before birth is supported by blood fluctuating CpG methylation patterns that exhibit low correlation between unrelated individuals but are highly correlated between many elderly monozygotic twins. Therefore, clonal hematopoiesis driven by weak selection in later life appears to reflect variation created before birth.

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Fig. 1 Clonal dynamics of HSCs are characterized by methylation profiles over lifetime. A Cartoon depicting HSC population dynamics from embryo development throughout a human lifespan for two twins. Colors represent different HSC clones. Created in BioRender. Kreger, J. (2024) BioRender.com/m16o751. B β distributions of two twins over time, see circled numbers in panel A. Colors represent the different individuals. The Pearson correlation coefficient between individuals over time is also shown (right panel). C Pearson correlation coefficients for average methylation profiles between MZ twins. Dots represent individual comparisons and hexagons represent means of datasets. D Pearson correlation coefficients between DZ twins. E Pearson correlation coefficients between unrelated individuals. F Pearson correlation coefficients for all of the three comparisons (compilation of the data presented in C–E), along with lines of best fit and corresponding 90% confidence intervals. The lines of best fit and confidence intervals are calculated using the means of datasets (hexagons).
Fig. 1 Clonal dynamics of HSCs are characterized by methylation profiles over lifetime. A Cartoon depicting HSC population dynamics from embryo development throughout a human lifespan for two twins. Colors represent different HSC clones. Created in BioRender. Kreger, J. (2024) BioRender.com/m16o751. B β distributions of two twins over time, see circled numbers in panel A. Colors represent the different individuals. The Pearson correlation coefficient between individuals over time is also shown (right panel). C Pearson correlation coefficients for average methylation profiles between MZ twins. Dots represent individual comparisons and hexagons represent means of datasets. D Pearson correlation coefficients between DZ twins. E Pearson correlation coefficients between unrelated individuals. F Pearson correlation coefficients for all of the three comparisons (compilation of the data presented in C–E), along with lines of best fit and corresponding 90% confidence intervals. The lines of best fit and confidence intervals are calculated using the means of datasets (hexagons).CC BY · PubMed Central
Fig. 2 Variation during development is needed in order for model to match initial Pearson coefficients at birth. A Schematic diagram of the mathematical model, with probability of cell replacement α and probability of change in methylation γ. B HSC population dynamics over a lifetime. C Parameter estimation using initial Pearson coefficients of twins and unrelated individuals. Given a choice for the initial number of clones, the number of cells at which the embryo splits (Nsplit) can be determined. Data points (dots) represent the mean Pearson coefficient for 103 independent simulations of the model with a given number of clones (Nclones) and number of shared cells (Nsplit). Shaded areas and error bars denote one standard deviation from the mean over the 103 simulations. The horizontal lines represent the data: initial Pearson coefficients at birth for MZ twins (red), DZ twins (blue) and unrelated individuals (yellow). D–E Effect of varying Nsplit. White striped bars represent the clonal distribution in the shared embryo when it reaches Nsplit cells and splits into two embryos. Yellow/blue bars represent individuals 1 or 2 at the point at which the embryo has reached Ncells cells and finished growing. D: Nsplit = 10. E: Nsplit = 500. All other parameter values can be found in Table 2.
Fig. 2 Variation during development is needed in order for model to match initial Pearson coefficients at birth. A Schematic diagram of the mathematical model, with probability of cell replacement α and probability of change in methylation γ. B HSC population dynamics over a lifetime. C Parameter estimation using initial Pearson coefficients of twins and unrelated individuals. Given a choice for the initial number of clones, the number of cells at which the embryo splits (Nsplit) can be determined. Data points (dots) represent the mean Pearson coefficient for 103 independent simulations of the model with a given number of clones (Nclones) and number of shared cells (Nsplit). Shaded areas and error bars denote one standard deviation from the mean over the 103 simulations. The horizontal lines represent the data: initial Pearson coefficients at birth for MZ twins (red), DZ twins (blue) and unrelated individuals (yellow). D–E Effect of varying Nsplit. White striped bars represent the clonal distribution in the shared embryo when it reaches Nsplit cells and splits into two embryos. Yellow/blue bars represent individuals 1 or 2 at the point at which the embryo has reached Ncells cells and finished growing. D: Nsplit = 10. E: Nsplit = 500. All other parameter values can be found in Table 2.CC BY · PubMed Central
Fig. 3 Frequency-dependent growth produces clonal variation during development. A Uniform growth during development with Nsplit = 36 and no selection (si = 0) for all clones. Left panel: Clonal dynamics during development, colors denote different clones. Right panel: β distributions at the end of development, i.e. birth. The initial Pearson correlation coefficient at birth is 0.99. B Same as A for frequency-dependent growth model during development. The initial Pearson correlation coefficient at birth is 0.87. All other parameter values can be found in Table 2.
Fig. 3 Frequency-dependent growth produces clonal variation during development. A Uniform growth during development with Nsplit = 36 and no selection (si = 0) for all clones. Left panel: Clonal dynamics during development, colors denote different clones. Right panel: β distributions at the end of development, i.e. birth. The initial Pearson correlation coefficient at birth is 0.99. B Same as A for frequency-dependent growth model during development. The initial Pearson correlation coefficient at birth is 0.87. All other parameter values can be found in Table 2.CC BY · PubMed Central
Fig. 4 Variants with weak selection arise during development and explain FMC dynamics for monozygotic twins. Simulations shown for MZ twins (Nsplit = 36) with frequency-dependent growth during development. For plots of the data (middle column and bottom row): dots represent individual comparisons, hexagons represent means of datasets, and shaded bands represent 90% confidence intervals. The data plotted in each panel (A-C and D-F) are the same for purposes of comparison against different model simulations. A–C Clone growth frequency plots for both individuals during development and life (dashed vertical line represents Nsplit), Pearson correlation coefficient, and β distributions at 0, 50, and 100 years of life. A: No selection. B: Weak selection (a = 0.05 and θ = 0.01). C: Strong selection (a = 0.05 and θ = 0.05). D–F Results from 102 simulations are shown (dashed lines are individual simulations and solid lines are mean trajectories). D: No selection. E: Weak selection (a = 0.05 and θ = 0.01). F: Strong selection (a = 0.05 and θ = 0.05). All other parameter values can be found in Table 2.
Fig. 4 Variants with weak selection arise during development and explain FMC dynamics for monozygotic twins. Simulations shown for MZ twins (Nsplit = 36) with frequency-dependent growth during development. For plots of the data (middle column and bottom row): dots represent individual comparisons, hexagons represent means of datasets, and shaded bands represent 90% confidence intervals. The data plotted in each panel (A-C and D-F) are the same for purposes of comparison against different model simulations. A–C Clone growth frequency plots for both individuals during development and life (dashed vertical line represents Nsplit), Pearson correlation coefficient, and β distributions at 0, 50, and 100 years of life. A: No selection. B: Weak selection (a = 0.05 and θ = 0.01). C: Strong selection (a = 0.05 and θ = 0.05). D–F Results from 102 simulations are shown (dashed lines are individual simulations and solid lines are mean trajectories). D: No selection. E: Weak selection (a = 0.05 and θ = 0.01). F: Strong selection (a = 0.05 and θ = 0.05). All other parameter values can be found in Table 2.CC BY · PubMed Central
Fig. 5 Weak selection with variants arising in development explains lifetime dynamics of twins and unrelated individuals. Simulations shown for weak selection (a = 0.05 and θ = 0.01) with frequency-dependent growth during development. For plots of the data (middle column and bottom row): dots represent individual comparisons, hexagons represent means of datasets, and shaded bands represent 90% confidence intervals. The data plotted in each panel (A-C and D-F) are the same for purposes of comparison against different model simulations. A–C Clone growth frequency plots for both individuals during development and life (dashed vertical line represents Nsplit), Pearson correlation coefficient, and β distributions at 0, 50, and 100 years of life. A: MZ twins, Nsplit = 36. B: DZ twins, Nsplit = 15. C: Unrelated individuals, Nsplit = 10. D–F: Results from 102 simulations are shown (dashed lines are individual simulations and solid lines are mean trajectories). D: MZ twins. E: DZ twins. F: Unrelated individuals. All other parameter values can be found in Table 2.
Fig. 5 Weak selection with variants arising in development explains lifetime dynamics of twins and unrelated individuals. Simulations shown for weak selection (a = 0.05 and θ = 0.01) with frequency-dependent growth during development. For plots of the data (middle column and bottom row): dots represent individual comparisons, hexagons represent means of datasets, and shaded bands represent 90% confidence intervals. The data plotted in each panel (A-C and D-F) are the same for purposes of comparison against different model simulations. A–C Clone growth frequency plots for both individuals during development and life (dashed vertical line represents Nsplit), Pearson correlation coefficient, and β distributions at 0, 50, and 100 years of life. A: MZ twins, Nsplit = 36. B: DZ twins, Nsplit = 15. C: Unrelated individuals, Nsplit = 10. D–F: Results from 102 simulations are shown (dashed lines are individual simulations and solid lines are mean trajectories). D: MZ twins. E: DZ twins. F: Unrelated individuals. All other parameter values can be found in Table 2.CC BY · PubMed Central

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Metadata

Authors
Kreger, J., Mooney, J. A., Shibata, D., & MacLean, A. L.
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2024-11-26
Volume
15
Issue
1
ISSN
2041-1723
DOI
10.1038/s41467-024-54711-2
PMC
PMC11599844
Cited by
12
References
5
Language
en
Licence
Funders
U.S. Department of Health & Human Services | National Institutes of Health (R35GM143019)

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Kreger, J., Mooney, J. A., Shibata, D., & MacLean, A. L. (2024). Developmental hematopoietic stem cell variation explains clonal hematopoiesis later in life. Nature Communications. https://doi.org/10.1038/s41467-024-54711-2