Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Single-cell multi-omics of human clonal hematopoiesis reveals that DNMT3A R882 mutations perturb early progenitor states through selective hypomethylation

Abstract

Somatic mutations in cancer genes have been detected in clonal expansions across healthy human tissue, including in clonal hematopoiesis. However, because mutated and wild-type cells are admixed, we have limited ability to link genotypes with phenotypes. To overcome this limitation, we leveraged multi-modality single-cell sequencing, capturing genotype, transcriptomes and methylomes in progenitors from individuals with DNMT3A R882 mutated clonal hematopoiesis. DNMT3A mutations result in myeloid over lymphoid bias, and an expansion of immature myeloid progenitors primed toward megakaryocytic–erythroid fate, with dysregulated expression of lineage and leukemia stem cell markers. Mutated DNMT3A leads to preferential hypomethylation of polycomb repressive complex 2 targets and a specific CpG flanking motif. Notably, the hypomethylation motif is enriched in binding motifs of key hematopoietic transcription factors, serving as a potential mechanistic link between DNMT3A mutations and aberrant transcriptional phenotypes. Thus, single-cell multi-omics paves the road to defining the downstream consequences of mutations that drive clonal mosaicism.

This is a preview of subscription content, access via your institution

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Fig. 1: GoT demonstrates comingling of mutated and wild-type cells in DNMT3A R882-clonal hematopoietic differentiation.
Fig. 2: DNMT3A R882 mutated CH cells demonstrate distinct differentiation biases at key junctures.
Fig. 3: Differential gene expression analysis between mutated and wild-type cells reveals markers of lineage aberrancies and dysregulated MYC activity.
Fig. 4: DNMT3A R882 promotes selective hypomethylation of PRC2 targets in human hematopoiesis.
Fig. 5: DNMT3A R882 displays flanking sequence specificity associated with MYC binding motif.
Fig. 6: Bone marrow CH progenitor cells display megakaryocytic–erythroid differentiation bias, MYC target gene expression and enhanced accessibility for the R882 hypomethylated motif.

Data availability

All of the processed counts matrix data and raw fastq files for murine experiments are available via Gene Expression Omnibus (GEO) under the accession number GSE158067. The raw fastq files for human samples are available via European Genome-Phenome Archive under the accession number EGAS00001006364.

Code availability

IronThrone v.2.2 pipeline is available at https://github.com/landau-lab/IronThrone-GoT

References

  1. Martincorena, I. et al. Somatic mutant clones colonize the human esophagus with age. Science 362, 911–917 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  2. Yizhak, K. et al. RNA sequence analysis reveals macroscopic somatic clonal expansion across normal tissues. Science 364, eaaw0726 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  3. Yokoyama, A. et al. Age-related remodelling of oesophageal epithelia by mutated cancer drivers. Nature 565, 312–317 (2019).

    CAS  PubMed  Article  Google Scholar 

  4. Yoshida, K. et al. Tobacco smoking and somatic mutations in human bronchial epithelium. Nature 578, 266–272 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  5. Martincorena, I. et al. High burden and pervasive positive selection of somatic mutations in normal human skin. Science 348, 880–886 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  6. Mustjoki, S. & Young, N. S. Somatic mutations in ‘benign’ disease. N. Engl. J. Med. 384, 2039–2052 (2021).

    CAS  PubMed  Article  Google Scholar 

  7. Shlush, L. I. et al. Identification of pre-leukaemic haematopoietic stem cells in acute leukaemia. Nature 506, 328–333 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  8. Jaiswal, S. et al. Age-related clonal hematopoiesis associated with adverse outcomes. N. Engl. J. Med. 371, 2488–2498 (2014).

    PubMed  PubMed Central  Article  Google Scholar 

  9. Genovese, G. et al. Clonal hematopoiesis and blood-cancer risk inferred from blood DNA sequence. N. Engl. J. Med. 371, 2477–2487 (2014).

    PubMed  PubMed Central  Article  Google Scholar 

  10. Xie, M. et al. Age-related mutations associated with clonal hematopoietic expansion and malignancies. Nat. Med. 20, 1472–1478 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  11. Steensma, D. P. et al. Clonal hematopoiesis of indeterminate potential and its distinction from myelodysplastic syndromes. Blood 126, 9–16 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  12. Young, A. L., Challen, G. A., Birmann, B. M. & Druley, T. E. Clonal haematopoiesis harbouring AML-associated mutations is ubiquitous in healthy adults. Nat. Commun. 7, 12484 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  13. Zink, F. et al. Clonal hematopoiesis, with and without candidate driver mutations, is common in the elderly. Blood 130, 742–752 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  14. Champion, K. M., Gilbert, J. G. R., Asimakopoulos, F. A., Hinshelwood, S. & Green, A. R. Clonal haemopoiesis in normal elderly women: implications for the myeloproliferative disorders and myelodysplastic syndromes. Br. J. Haematol. 97, 920–926 (1997).

    CAS  PubMed  Article  Google Scholar 

  15. SanMiguel, J. M. et al. Cell-extrinsic stressors from the aging bone marrow (BM) microenvironment promote Dnmt3a-mutant clonal hematopoiesis. Blood 134 (Suppl), 5 (2019).

    Article  Google Scholar 

  16. Terao, C. et al. Chromosomal alterations among age-related haematopoietic clones in Japan. Nature 584, 130–135 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  17. Watson, C. J. et al. The evolutionary dynamics and fitness landscape of clonal hematopoiesis. Science 367, 1449–1454 (2020).

    CAS  PubMed  Article  Google Scholar 

  18. Teixeira, V. H. et al. Deciphering the genomic, epigenomic, and transcriptomic landscapes of pre-invasive lung cancer lesions. Nat. Med. 25, 517–525 (2019).

    CAS  PubMed  Article  Google Scholar 

  19. Steensma, D. P. & Ebert, B. L. Clonal hematopoiesis as a model for premalignant changes during aging. Exp. Hematol. 83, 48–56 (2020).

    PubMed  Article  Google Scholar 

  20. Desai, P. et al. Somatic mutations precede acute myeloid leukemia years before diagnosis. Nat. Med. 24, 1015–1023 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  21. Papaemmanuil, E. et al. Clinical and biological implications of driver mutations in myelodysplastic syndromes. Blood 122, 3616–3627 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  22. Abelson, S. et al. Prediction of acute myeloid leukaemia risk in healthy individuals. Nature 559, 400–404 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  23. Buscarlet, M. et al. DNMT3A and TET2 dominate clonal hematopoiesis and demonstrate benign phenotypes and different genetic predispositions. Blood 130, 753–762 (2017).

    CAS  PubMed  Article  Google Scholar 

  24. Young, A. L., Spencer Tong, R., Birmann, B. M. & Druley, T. E. Clonal hematopoiesis and risk of acute myeloid leukemia. Haematologica 104, 2410–2417 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  25. Jeong, M. et al. Loss of Dnmt3a immortalizes hematopoietic stem cells in vivo. Cell Rep. 23, 1–10 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  26. Ostrander, E. L. et al. Divergent effects of Dnmt3a and Tet2 mutations on hematopoietic progenitor cell fitness. Stem Cell Rep. 14, 551–560 (2020).

    CAS  Article  Google Scholar 

  27. Koya, J. et al. DNMT3A R882 mutants interact with polycomb proteins to block haematopoietic stem and leukaemic cell differentiation. Nat. Commun. 7, 10924 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  28. Kim, S. J. et al. A DNMT3A mutation common in AML exhibits dominant-negative effects in murine ES cells. Blood 122, 4086–4089 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  29. Russler-Germain, D. A. et al. The R882H DNMT3A mutation associated with AML dominantly inhibits wild-type DNMT3A by blocking its ability to form active tetramers. Cancer Cell 25, 442–454 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  30. Nam, A. S. et al. Somatic mutations and cell identity linked by genotyping of transcriptomes. Nature 571, 355–360 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  31. Gaiti, F. et al. Epigenetic evolution and lineage histories of chronic lymphocytic leukaemia. Nature 569, 576–580 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  32. Mouhieddine, T. H. et al. Clonal hematopoiesis is associated with adverse outcomes in multiple myeloma patients undergoing transplant. Nat. Commun. 11, 2996 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  33. Smith, T., Heger, A. & Sudbery, I. UMI-tools: modeling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy. Genome Res. 27, 491–499 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  34. Tickle, T., Tirosh, I., Georgescu, C., Brown, M. & Haas, B. inferCNV of the Trinity CTAT Project (Broad Institute of MIT and Harvard, 2019).

  35. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 e21 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  36. Pellin, D. et al. A comprehensive single cell transcriptional landscape of human hematopoietic progenitors. Nat. Commun. 10, 2395 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  37. Challen, G. A. et al. Dnmt3a and Dnmt3b have overlapping and distinct functions in hematopoietic stem cells. Cell Stem Cell 15, 350–364 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  38. Challen, G. A. et al. Dnmt3a is essential for hematopoietic stem cell differentiation. Nat. Genet. 44, 23–31 (2011).

    PubMed  PubMed Central  Article  Google Scholar 

  39. Cao, J. et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature 566, 496–502 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  40. Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381–386 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  41. Guryanova, O. A. et al. DNMT3A mutations promote anthracycline resistance in acute myeloid leukemia via impaired nucleosome remodeling. Nat. Med. 22, 1488–1495 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  42. Velten, L. et al. Human haematopoietic stem cell lineage commitment is a continuous process. Nat. Cell Biol. 19, 271–281 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  43. Tirosh, I. et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189–196 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  44. La Manno, G. et al. RNA velocity of single cells. Nature 560, 494–498 (2018).

    PubMed  PubMed Central  Article  Google Scholar 

  45. Bergen, V., Lange, M., Peidli, S., Wolf, F. A. & Theis, F. J. Generalizing RNA velocity to transient cell states through dynamical modeling. Nat. Biotechnol. 38, 1408–1414 (2020).

    CAS  PubMed  Article  Google Scholar 

  46. Clay, D. et al. CD9 and megakaryocyte differentiation. Blood 97, 1982–1989 (2001).

    CAS  PubMed  Article  Google Scholar 

  47. Noetzli, L. J., French, S. L. & Machlus, K. R.New insights into the differentiation of megakaryocytes from hematopoietic progenitors. Arterioscler. Thromb. Vasc. Biol. 39, 1288–1300 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  48. Slupsky, J. R. et al. The platelet antigens CD9, CD42 and integrin alphaIIbbetaIIIa can be topographically associated and transduce functionally similar signals. Eur. J. Biochem. 244, 168–175 (1997).

    CAS  PubMed  Article  Google Scholar 

  49. Dai, Y. J. et al. Conditional knockin of Dnmt3a R878H initiates acute myeloid leukemia with mTOR pathway involvement. Proc. Natl Acad. Sci. USA 114, 5237–5242 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  50. Thol, F. et al. Incidence and prognostic influence of DNMT3A mutations in acute myeloid leukemia. J. Clin. Oncol. 29, 2889–2896 (2011).

    CAS  PubMed  Article  Google Scholar 

  51. Xu, J. et al. DNMT3A Arg882 mutation drives chronic myelomonocytic leukemia through disturbing gene expression/DNA methylation in hematopoietic cells. Proc. Natl Acad. Sci. USA 111, 2620–2625 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  52. Saito, Y. et al. Identification of therapeutic targets for quiescent, chemotherapy-resistant human leukemia stem cells. Sci. Transl. Med. 2, 17ra9 (2010).

    PubMed  PubMed Central  Article  Google Scholar 

  53. Tsuboi, N., Asano, K., Lauterbach, M. & Mayadas, T. N. Human neutrophil Fcgamma receptors initiate and play specialized nonredundant roles in antibody-mediated inflammatory diseases. Immunity 28, 833–846 (2008).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  54. Lanier, L. L., Corliss, B. C., Wu, J., Leong, C. & Phillips, J. H. Immunoreceptor DAP12 bearing a tyrosine-based activation motif is involved in activating NK cells. Nature 391, 703–707 (1998).

    CAS  PubMed  Article  Google Scholar 

  55. Bouchon, A., Hernández-Munain, C., Cella, M. & Colonna, M. A DAP12-mediated pathway regulates expression of CC chemokine receptor 7 and maturation of human dendritic cells. J. Exp. Med. 194, 1111–1122 (2001).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  56. Karulf, M., Kelly, A., Weinberg, A. D. & Gold, J. A. OX40 ligand regulates inflammation and mortality in the innate immune response to sepsis. J. Immunol. 185, 4856–4862 (2010).

    CAS  PubMed  Article  Google Scholar 

  57. Leoni, C. et al. Dnmt3a restrains mast cell inflammatory responses. Proc. Natl Acad. Sci. USA 114, E1490–E1499 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  58. Fuster, J. J. et al. Clonal hematopoiesis associated with TET2 deficiency accelerates atherosclerosis development in mice. Science 355, 842–847 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  59. Jaiswal, S. & Libby, P. Clonal haematopoiesis: connecting ageing and inflammation in cardiovascular disease. Nat. Rev. Cardiol. 17, 137–144 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  60. Jaiswal, S. et al. Clonal hematopoiesis and risk of atherosclerotic cardiovascular disease. N. Engl. J. Med. 377, 111–121 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  61. Vester, S. K. et al. Nucleolin acts as the receptor for C1QTNF4 and supports C1QTNF4-mediated innate immunity modulation. J. Biol. Chem. 296, 100513 (2021).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  62. Li, Q. et al. Identification of C1qTNF-related protein 4 as a potential cytokine that stimulates the STAT3 and NF-κB pathways and promotes cell survival in human cancer cells. Cancer Lett. 308, 203–214 (2011).

    CAS  PubMed  Article  Google Scholar 

  63. Ramalingam, P. et al. Chronic activation of endothelial MAPK disrupts hematopoiesis via NFKB dependent inflammatory stress reversible by SCGF. Nat. Commun. 11, 666 (2020).

  64. Shen, B. et al. Integrin alpha11 is an Osteolectin receptor and is required for the maintenance of adult skeletal bone mass. eLife 8, e42274 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  65. Ito, C. et al. Serum stem cell growth factor for monitoring hematopoietic recovery following stem cell transplantation. Bone Marrow Transpl. 32, 391–398 (2003).

    CAS  Article  Google Scholar 

  66. Wingender, E., Dietze, P., Karas, H. & Knüppel, R. TRANSFAC: a database on transcription factors and their DNA binding sites. Nucleic Acids Res. 24, 238–241 (1996).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  67. Ben-Porath, I. et al. An embryonic stem cell-like gene expression signature in poorly differentiated aggressive human tumors. Nat. Genet. 40, 499–507 (2008).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  68. Coller, H. A. et al. Expression analysis with oligonucleotide microarrays reveals that MYC regulates genes involved in growth, cell cycle, signaling, and adhesion. Proc. Natl Acad. Sci. USA 97, 3260–3265 (2000).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  69. Delgado, M. D. & León, J. Myc roles in hematopoiesis and leukemia. Genes Cancer 1, 605–616 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  70. Guo, Y. et al. c-Myc-mediated control of cell fate in megakaryocyte–erythrocyte progenitors. Blood 114, 2097–2106 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  71. Mayers, S. et al. Establishment of an erythroid progenitor cell line capable of enucleation achieved with an inducible c-Myc vector. BMC Biotech. 19, 21 (2019).

    Article  Google Scholar 

  72. Vaisvila, R. et al. Enzymatic methyl sequencing detects DNA methylation at single-base resolution from picograms of DNA. Genome Res. 31, 1280–1289 (2021).

    PubMed Central  Article  Google Scholar 

  73. Picelli, S. et al. Full-length RNA-seq from single cells using Smart-seq2. Nat. Protoc. 9, 171–181 (2014).

    CAS  PubMed  Article  Google Scholar 

  74. Spencer, D. H. et al. CpG island hypermethylation mediated by DNMT3A is a consequence of AML progression. Cell 168, 801–816 e13 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  75. Glass, J. L. et al. Epigenetic identity in AML depends on disruption of nonpromoter regulatory elements and is affected by antagonistic effects of mutations in epigenetic modifiers. Cancer Discov. 7, 868–883 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  76. Vaisvila, R. et al. Enzymatic methyl sequencing detects DNA methylation at single-base resolution from picograms of DNA. Genome Res. 31, 1280–1289 (2021).

    PubMed Central  Article  Google Scholar 

  77. Wang, J. et al. Double restriction-enzyme digestion improves the coverage and accuracy of genome-wide CpG methylation profiling by reduced representation bisulfite sequencing. BMC Genomics 14, 11 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  78. Andersson, R. et al. An atlas of active enhancers across human cell types and tissues. Nature 507, 455–461 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  79. Davis, C. A. et al. The encyclopedia of DNA elements (ENCODE): data portal update. Nucleic Acids Res. 46, D794–D801 (2018).

    CAS  PubMed  Article  Google Scholar 

  80. Adelman, E. R. et al. Aging human hematopoietic stem cells manifest profound epigenetic reprogramming of enhancers that may predispose to leukemia. Cancer Discov. 9, 1080–1101 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  81. Jeong, M. et al. Large conserved domains of low DNA methylation maintained by Dnmt3a. Nat. Genet. 46, 17–23 (2014).

    CAS  PubMed  Article  Google Scholar 

  82. Zhang, X. et al. Large DNA methylation nadirs anchor chromatin loops maintaining hematopoietic stem cell identity. Mol. Cell 78, 506–521 e6 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  83. Hawkins, R. D. et al. Distinct epigenomic landscapes of pluripotent and lineage-committed human cells. Cell Stem Cell 6, 479–491 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  84. Margueron, R. & Reinberg, D. The Polycomb complex PRC2 and its mark in life. Nature 469, 343–349 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  85. Mohn, F. et al. Lineage-specific polycomb targets and de novo DNA methylation define restriction and potential of neuronal progenitors. Mol. Cell 30, 755–766 (2008).

    CAS  PubMed  Article  Google Scholar 

  86. Xie, H. et al. Polycomb repressive complex 2 regulates normal hematopoietic stem cell function in a developmental-stage-specific manner. Cell Stem Cell 14, 68–80 (2014).

    CAS  PubMed  Article  Google Scholar 

  87. Cancer Genome Atlas Research Network. Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. N. Engl. J. Med. 368, 2059–2074 (2013).

    Article  Google Scholar 

  88. Emperle, M. et al. Mutations of R882 change flanking sequence preferences of the DNA methyltransferase DNMT3A and cellular methylation patterns. Nucleic Acids Res. 47, 11355–11367 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  89. Anteneh, H., Fang, J. & Song, J. Structural basis for impairment of DNA methylation by the DNMT3A R882H mutation. Nat. Commun. 11, 2294 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  90. Yin, Y. et al. Impact of cytosine methylation on DNA binding specificities of human transcription factors. Science 356, eaaj2239 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  91. Prendergast, G. C. & Ziff, E. B. Methylation-sensitive sequence-specific DNA binding by the c-Myc basic region. Science 251, 186–189 (1991).

    CAS  PubMed  Article  Google Scholar 

  92. Tate, P. H. & Bird, A. P. Effects of DNA methylation on DNA-binding proteins and gene expression. Curr. Opin. Genet Dev. 3, 226–231 (1993).

    CAS  PubMed  Article  Google Scholar 

  93. Grau, J., Schmidt, F. & Schulz, M.H. Widespread effects of DNA methylation and intra-motif dependencies revealed by novel transcription factor binding models. Preprint at bioRxiv https://doi.org/10.1101/2020.10.21.348193 (2020).

  94. Takubo, K. et al. Regulation of the HIF-1alpha level is essential for hematopoietic stem cells. Cell Stem Cell 7, 391–402 (2010).

    CAS  PubMed  Article  Google Scholar 

  95. Krock, B. L. et al. The aryl hydrocarbon receptor nuclear translocator is an essential regulator of murine hematopoietic stem cell viability. Blood 125, 3263–3272 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  96. Mariani, C. J. et al. TET1-mediated hydroxymethylation facilitates hypoxic gene induction in neuroblastoma. Cell Rep. 7, 1343–1352 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  97. Li, Y. et al. Setd1a and NURF mediate chromatin dynamics and gene regulation during erythroid lineage commitment and differentiation. Nucleic Acids Res. 44, 7173–7188 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  98. Huang, S., Li, X., Yusufzai, T. M., Qiu, Y. & Felsenfeld, G. USF1 recruits histone modification complexes and is critical for maintenance of a chromatin barrier. Mol. Cell. Biol. 27, 7991–8002 (2007).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  99. Arnaud, L. et al. A dominant mutation in the gene encoding the erythroid transcription factor KLF1 causes a congenital dyserythropoietic anemia. Am. J. Hum. Genet. 87, 721–727 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  100. Grosselin, K. et al. High-throughput single-cell ChIP-seq identifies heterogeneity of chromatin states in breast cancer. Nat. Genet. 51, 1060–1066 (2019).

    CAS  PubMed  Article  Google Scholar 

  101. Rotem, A. et al. Single-cell ChIP-seq reveals cell subpopulations defined by chromatin state. Nat. Biotechnol. 33, 1165–1172 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  102. Wang, Q. et al. CoBATCH for high-throughput single-cell epigenomic profiling. Mol. Cell 76, 206–216 e7 (2019).

    CAS  PubMed  Article  Google Scholar 

  103. Fu, L. et al. Predicting transcription factor binding in single cells through deep learning. Sci. Adv. 6, eaba9031 (2020).

    CAS  PubMed  Article  Google Scholar 

  104. Ugarte, F. et al. Progressive chromatin condensation and H3K9 methylation regulate the differentiation of embryonic and hematopoietic stem cells. Stem Cell Rep. 5, 728–740 (2015).

    CAS  Article  Google Scholar 

  105. Martin, E. W. et al. Chromatin accessibility maps provide evidence of multilineage gene priming in hematopoietic stem cells. Epigenetics Chromatin 14, 2 (2021).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  106. Granja, J. M. et al. Single-cell multiomic analysis identifies regulatory programs in mixed-phenotype acute leukemia. Nat. Biotechnol. 37, 1458–1465 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  107. Setty, M. et al. Characterization of cell fate probabilities in single-cell data with Palantir. Nat. Biotechnol. 37, 451–460 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  108. Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  109. Zavidij, O. et al. Single-cell RNA sequencing reveals compromised immune microenvironment in precursor stages of multiple myeloma. Nat. Cancer 1, 493–506 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  110. Izzo, F. et al. DNA methylation disruption reshapes the hematopoietic differentiation landscape. Nat. Genet. 52, 378–387 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  111. Lessard, J. & Sauvageau, G. Bmi-1 determines the proliferative capacity of normal and leukaemic stem cells. Nature 423, 255–260 (2003).

    CAS  PubMed  Article  Google Scholar 

  112. Liu, Y. et al. Convergence of oncogenic cooperation at single-cell and single-gene levels drives leukemic transformation. Nat. Commun. 12, 6323 (2021).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  113. Lu, R. et al. A model system for studying the DNMT3A hotspot mutation (DNMT3A(R882)) demonstrates a causal relationship between its dominant-negative effect and leukemogenesis. Cancer Res. 79, 3583–3594 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  114. van Galen, P. et al. The unfolded protein response governs integrity of the haematopoietic stem-cell pool during stress. Nature 510, 268–272 (2014).

    PubMed  Article  Google Scholar 

  115. Rodriguez-Meira, A. et al. Unravelling intratumoral heterogeneity through high-sensitivity single-cell mutational analysis and parallel RNA sequencing. Mol. Cell 73, 1292–1305 e8 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  116. Petti, A. A. et al. A general approach for detecting expressed mutations in AML cells using single cell RNA-sequencing. Nat. Commun. 10, 3660 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  117. Ludwig, L. S. et al. Lineage tracing in humans enabled by mitochondrial mutations and single-cell genomics. Cell 176, 1325–1339 e22 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  118. Nam, A. S., Chaligne, R. & Landau, D. A. Integrating genetic and non-genetic determinants of cancer evolution by single-cell multi-omics. Nat. Rev. Genet. 22, 3–18 (2021).

    CAS  PubMed  Article  Google Scholar 

  119. ENCODE Project Consortium.An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

    Article  Google Scholar 

  120. Saxonov, S., Berg, P. & Brutlag, D. L. A genome-wide analysis of CpG dinucleotides in the human genome distinguishes two distinct classes of promoters. Proc. Natl Acad. Sci. USA 103, 1412–1417 (2006).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  121. Aibar, S. et al. SCENIC: single-cell regulatory network inference and clustering. Nat. Methods 14, 1083–1086 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  122. Gothert, J. R. et al. In vivo fate-tracing studies using the Scl stem cell enhancer: embryonic hematopoietic stem cells significantly contribute to adult hematopoiesis. Blood 105, 2724–2732 (2005).

    PubMed  Article  Google Scholar 

  123. Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  124. Tange, O. GNU Parallel 20200622. Computer Program. Zenodo https://doi.org/10.5281/zenodo.3956817 (2020).

  125. Yu, G., Wang, L.-G., Han, Y. & He, Q.-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  126. Granja, J. M. et al. ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis. Nat. Genet. 53, 403–411 (2021).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

Download references

Acknowledgements

The work was enabled by the Weill Cornell Epigenomics Core and Flow Cytometry Core. We thank A. Melnick (Weill Cornell Medicine) for a critical review of the manuscript. A.S.N. is supported by the Burroughs Wellcome Fund Career Award for Medical Scientists, the National Institutes of Health Director’s Early Independence Award (DP5 OD029619) and the Starr Cancer Consortium. N.D. is supported by a F30 Predoctoral Fellowship from the National Heart, Lung, and Blood Institute (NHLBI) of the National Institutes of Health (F30HL156496). N.D. and R.M.M. are supported by a Medical Scientist Training Program grant from the National Institute of General Medical Sciences of the National Institutes of Health under award number T32GM007739 to the Weill Cornell/Rockefeller/Sloan Kettering Tri-Institutional MD-PhD Program. F.I. is supported by the American Society of Hematology Fellow-to-Faculty Scholar Award. R.C. is supported by Lymphoma Research Foundation and Marie Skłodowska-Curie fellowships. I.G. is supported by a grant from the Dr Miriam and Sheldon G. Adelson Medical Research Foundation, and a Stand Up To Cancer Dream Team Research Grant (SU2C-AACR-DT-28-18). D.A.L. is supported by the Burroughs Wellcome Fund Career Award for Medical Scientists, Valle Scholar Award, the National Institutes of Health Director’s New Innovator Award (DP2-CA239065), the Chan Zuckerberg Initiative Award, the Leukemia Lymphoma Society Career Development Program Award and the Mark Foundation Emerging Leader Award. This work was also supported by the NHLBI (R01HL145283) and the National Human Genome Research Institute, Center of Excellence in Genomic Science (RM1HG011014).

Author information

Authors and Affiliations

Authors

Contributions

A.S.N., N.D., F.I., R.M., R.C., T.H.M., I.G. and D.A.L. conceived the project, designed the study and interpreted results. I.G. and L.A.G. provided clinical samples. R.C., R.M.M., J.S., S.B. and M.W. performed the experiments. A.S.N., N.D., F.I. and R.M. performed the analyses. A.S.N., N.D., F.I., R.M., R.M.M., R.C., I.G. and D.A.L. wrote the manuscript. A.S.N., N.D., F.I., R.M., T.H.M., J.S., S.B., F.G., S.T., L.A.G., R.C., R.L., O.A.-W., I.G. and D.A.L. helped interpret results. All authors reviewed and approved the final manuscript.

Corresponding authors

Correspondence to Irene Ghobrial or Dan A. Landau.

Ethics declarations

Competing interests

O.A.-W. has served as a consultant for H3B Biomedicine, Foundation Medicine Inc., Merck Pfizer, and Janssen, and is on the scientific advisory board of Envisagenics Inc. and AIChemy; O.A.-W. has received prior research funding from H3B Biomedicine and LOXO Oncology unrelated to the current manuscript. I.G. serves on the advisory board of Bristol Myers Squibb, Takeda, Janssen, Sanofi and GlaxoSmithKline. D.A.L. has served as a consultant for Abbvie, AstraZeneca and Illumina, and is on the scientific advisory board of Mission Bio, Alethiomics, Pangea and C2i Genomics; D.A.L. has received prior research funding from BMS, 10x Genomics, Ultima Genomics, Abbvie and Illumina unrelated to the current manuscript.

Peer review

Peer review information

Nature Genetics thanks Michael Rehli, Vijay Sankaran and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

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

Extended data

Extended Data Fig. 1 GoT captures genotyping information of thousands of CD34+ cells in scRNA-seq.

a, Summary of Genotyping of Transcriptomes (GoT) data from clonal hematopoiesis (CH) patient samples with DNMT3A R882 mutations. b, Number of genes per cell (left) and number of unique molecular identifiers (UMIs) per cell (right) from CD34+ sorted hematopoietic progenitors by patient sample after quality control (QC) filters (CH01 n = 6133, CH02 n = 5372, CH03 n = 7379, CH04 n = 8440). c, DNMT3A R882 mutant fraction of single cells determined by GoT versus DNMT3A R882 mutation variant allele frequencies (VAF) in bulk sequencing of matched unsorted stem cell product31. d, Fraction of cells by number of DNMT3A UMIs in standard 10x Genomics data without genotyping information (left), DNMT3A UMIs with R882 locus coverage in standard 10x data (middle), and DNMT3A UMIs with R882 locus coverage in GoT amplicon library (right). e, Species-mixing experiment data in which mouse cells (Ba/F3) with a human mutant CALR transgene were mixed with human cells (UT-7) with a human wildtype CALR transgene29. Mouse and human genome alignment of 10x data with genotyping data from GoT pre (top) and post (bottom) implementation of UMI consensus assembly based on Levenshtein distance (methods). f, Number of duplicate reads supporting cell barcode-UMI pair in the GoT library that is identified in the 10x gene expression (GEX) library as a DNMT3A gene (left), no gene (middle), or a nonDNMT3A gene (right). P-values from two-sided Wilcoxon rank sum test. g, Heatmap of relative expression of genes ordered by chromosome/chromosomal position following copy number variation analysis using the InferCNV package33. Cells (y-axis) are stratified by patient and DNMT3A R882 genotype status. h, Heatmap of relative expression of Y-chromosome genes following copy number variation analysis and cell stratification as in g. WT, wildtype, MUT, mutant.

Extended Data Fig. 2 Integration of DNMT3A R882 mutation and assignment of progenitor subsets in clonal hematopoiesis patient samples.

a, Uniform manifold approximation and projection (UMAP) of CD34+ progenitor cells from samples CH01-CH04 after integration using the Seurat package (methods). b, Heatmap of top 10 differentially expressed genes for progenitor subsets. c, Lineage-specific genes (left) and modules from Velten et al.41 (right, Supplementary Table 2) are scored and projected onto the UMAP representation of CD34+ cells. d, UMAP of CD34+ cells overlaid with cluster assignments, split by patient sample. HSPC, hematopoietic stem progenitor cells; IMP, immature myeloid progenitors; IMP-ME, megakaryocytic-erythroid biased IMP; IMP-GM, granulo-monocytic biased IMP; LMPP, lympho-myeloid primed progenitors; CLP, common lymphoid progenitor; MEP, megakaryocytic-erythroid progenitors; E/B/M, eosinophil, basophil, and mast cell progenitors; EP, erythroid progenitor; MkP, megakaryocytic progenitor; NP, neutrophil progenitor.

Extended Data Fig. 3 Classification of IMPs showing lineage biases and pseudotime analysis between mutated and wildtype cells.

a, UMAP of CD34+ cells, overlaid with cluster assignment of the IMP subsets. b, Neutrophil and Megakaryocytic-Erythroid lineage specific gene module scores from Velten et al.41 compared across the three IMP clusters. P-value was calculated from two-sided Wilcoxon rank sum test. c, UMAP of CD34+ cells overlaid with mutation status for wildtype (WT), DNMT3A R882 mutant (MUT), or unassigned (NA), split by genotype for all samples (top) and by patient sample (bottom). d, UMAP with projected pseudotime values (top left). Pseudotime comparison between WT and MUT cells for all samples (top right) and for individual samples (bottom) as estimated by Monocle39. P-value was calculated from likelihood ratio test of linear mixed model (LMM) with/without mutation status for aggregate analysis (methods, top) and two-sided Wilcoxon rank sum test for individual samples (bottom).

Extended Data Fig. 4 Comparison between mutated and wildtype progenitor cells of cell cycle module expression and RNA velocity derived transition probabilities.

a, Cell cycle module score represents the union of S-phase and G2M-phase gene-module expression (Supplementary Table 2)42. P-value was calculated from likelihood ratio test of linear mixed model with/without mutation status (methods). Analysis was performed for clusters with at least 200 genotyped cells across all patient samples. b, Single cell mean IMP → IMP-ME and c, IMP → IMP-GM transition probabilities, as measured via RNA velocity34, between wildtype or DNMT3A R882 mutant IMPs for each sample. P-values from two-sided Wilcoxon rank-sum test.

Extended Data Fig. 5 Comparison of differential expression analysis between permutation test and linear mixed model and MYC gene expression.

a, P-values from permutation test and linear mixed model (methods) are plotted per gene. Correlation coefficient R calculated using Pearson’s Correlation. P-values derived from two-sided Student’s t-distribution. Shading denotes 95% confidence interval. b, Normalized MYC gene expression between mutated and wildtype cells in MEP and EP. P-value was calculated from likelihood ratio test of linear mixed model with/without mutation status (methods).

Extended Data Fig. 6 Multi-omics single cell methylome, transcriptomic, and somatic genotyping reveals hypomethylation of PRC2 targets in DNMT3A R882 CH.

a, UMAP dimensionality reduction (n = 528 cells) based on scRNA-seq data (Smart-seq2) after integration and batch correction of six plates (methods). b, UMAP dimensionality reduction showing cluster gene markers for the transcriptome data. c, Number of CpG sites captured per cell after quality filtering (methods). The metrics for each sample according to enzymatic digestion with Msp1 (Single) or Msp1 plus HaeIII (Double) are shown. d, Average single cell methylation at all regions (global, double digest), promoters, introns or exons. P-values from likelihood ratio test of LMM with/without mutation status (methods). e, Average single cell methylation at CpH (that is CpA, CpC or CpT) sites. P-values from likelihood ratio test of LMM with/without mutation status (methods). f, Average single cell methylation at 269 hypomethylated promoters identified with differentially methylated region (DMR) analysis (shown in Fig. 4e, promoters with P-value < 0.05 and at least −5% methylation change) in CH02 and CH04. P-values from two-sided Wilcoxon rank sum test. g, Average single cell methylation at SUZ12 (top panel) and EZH2 (bottom panel) ENCODE chromatin immunoprecipitation sequencing (ChIP-seq) peaks intersected with bivalently H3K27me3, H3K4me3-marked regions in CD34+ cells for CH02 and CH04. P-values from likelihood ratio test of LMM with/without mutation status. h, Normalized expression of PRC2 target genes with preferentially hypomethylated transcription start site (TSS) (from Fig. 4e) in GoT data of WT versus MUT cells by progenitor subtype. P-values from likelihood ratio test of LMM with/without mutation status. i, Comparison of average methylation values for TSS ± 1 kb regions in normal HSPCs79 and DNMT3A WT (n = 6) versus DNMT3A R882, NPM1 mutated acute myeloid leukemia (AML; n = 7) samples in regions without (left) or with (right) PRC2 ChIP-seq peaks, controlling for CpG content. P-values from two-sided Wilcoxon rank sum test. j, Comparison of average methylation values for promoter regions in WT (n = 122) versus DNMT3A R882 mutated AML (n = 9) samples from TCGA in regions without (left) or with (right) PRC2 ChIP-seq peaks, controlling for CpG content. P-values from two-sided Wilcoxon rank sum test.

Extended Data Fig. 7 Motif enrichment at hypomethylated CpGs and hypomethylated motif enrichment in regions around differentially expressed genes.

a, Base frequency odds ratio of hypo- versus hyper-methylated CpG flanking sequences at positions N-2, N-1, N + 1, and N + 2. The odds ratios were derived from base frequencies of flanking positions of the CpG sites hypo- or hyper-methylated in mutant versus wildtype cells above the thresholds shown in the x axis for minimum absolute CpG methylation difference (Pearson correlation, P-values derived from two-sided F-test, shading denotes 95% confidence interval). b, Reported motif logos derived from Emperle et al.87 for either hypomethylated (disfavored) or hypermethylated (favored) sites for DNMT3A R882 compared with its wildtype counterpart (left). c, Similarity scores between the reported and our de novo DNMT3A R882 hypo- and hypermethylated motifs as measured by correlation coefficients of the position weight matrices for the respective motifs excluding the CpG dinucleotide. d, Heatmap of expression of transcription factors with binding motif similarity >0.5 compared with hypomethylated motif of DNMT3A R882 (that do not meet the overall expression threshold, Fig. 5a,b). e, Frequencies of DNMT3A R882 hypomethylated motif within 30 kb of TSS of the differentially expressed genes between MUT and WT cells in progenitor subsets. P-values were calculated by two-sided Wilcoxon rank sum test. f, Frequencies of DNMT3A R882 hypomethylated motif within 10 kb, 30 kb or 50 kb of TSS of the differentially expressed genes between MUT and WT cells in HSPCs and EPs. P-values were calculated by two-sided Wilcoxon rank sum test. g, Ratio of frequencies of DNMT3A R882 hypomethylated motif to those of the control shuffled motif with CpG (Fig. 5e) within 10 kb of TSS of the differentially expressed genes between MUT and WT cells in HSPCs and EPs. P-values were calculated by two-sided Wilcoxon rank sum test. h, Average per-gene incidence of DNMT3A R882 hypomethylated motif within 50 kb of TSS by distance from TSS for differentially expressed genes between MUT and WT cells in HSPCs (top) and EPs (bottom).

Extended Data Fig. 8 Single nucleus ATAC-seq of Dnmt3a R878H Lin-, c-Kit + progenitors reveals enhanced accessibility of R882 hypomethylated motif and TF motifs with high similarity scores to the hypomethylated motif.

a, Distribution of fragment size in single-nucleus assay for transposase-accessible chromatin using sequencing (snATAC-seq) data of Dnmt3a R878H and wildtype Lin-, c-Kit+ progenitors (n = 3 in each cohort). b, TSS enrichment of accessible fragments as a function of unique fragments per cell. c, UMAP of integrated datasets Dnmt3a R878H and wildtype Lin-, c-Kit+ progenitors, displayed per sample (n = 3 in each cohort). d, Heatmap of gene accessibility scores for differentially accessible progenitor identity marker genes across progenitor subsets. e, Scatterplot of similarity scores of mouse transcription factor (TF) motifs versus human TF motifs to the R882-hypomethylated motif (Pearson’s correlation, P-value derived from two-sided F-test). f, Binding motifs of mouse and human TFs with high similarity score to the R882-hypomethylated motif and expression in HSPCs (Fig. 5b, HOCOMOCO v11). g, Family-wise error rate (FWER) adjusted P-values calculated by two-sided Wilcoxon rank sum test for accessibility changes between wildtype and Dnmt3a R878H cells by progenitor identities for hypo-methylated motif and shuffled motifs controls (with and without CpG), as well as motif accessibility deviation of the TFs identified Fig. 5b (related to Fig. 5f). h, Accessibility of PRC2 targets between wildtype and Dnmt3a R878H and wildtype Lin-, c-Kit+ progenitor subsets. P-values from likelihood ratio test of LMM with/without mutation status.

Extended Data Fig. 9 Integration of CH05 and control bone marrow CD34+ scRNA-seq data, assignment of progenitor subsets, and differentiation skews of CH cells.

a, UMAP of CD34+ progenitor cells from samples CH05 and control bone marrow samples BM01-05 after integration using the Seurat package (methods). b, Number of genes per cell (top) and number of UMIs per cell (bottom) from CD34+ hematopoietic progenitors by patient sample after QC filters and down-sampling to equivalent geometric means of UMIs per patient (BM1 n = 6,683, BM2 n = 7,254, BM3 n = 16,759, BM4 n = 2,200, BM5 n = 5,690, CH05 n = 5,952). c, Heatmap of top 10 differentially expressed genes for progenitor subsets. d, UMAP representation of CD34+ cells showing cell marker gene expressions. e, Modules from Velten et al.41 (Supplementary Table 2) are scored and projected onto the UMAP representation of CD34+ cells. f, Per-patient comparison of megakaryocytic-erythroid module scores in control bone marrow versus CH05 IMPs (Supplementary Table 2). Cell number downsampled to the same number (n = 132 cells per sample). P-values were calculated from likelihood ratio test of LMM with/without CH status. g, Per-patient comparison of granulocytic-monocytic module scores in control versus CH IMPs (Supplementary Table 2). Cell number downsampled to the same number (n = 132 cells per sample). P-values were calculated from likelihood ratio test of LMM with/without CH status. h, Fraction of IMP-ME cells out of all biased IMP (IMP-ME + IMP-GM) cells in control versus CH populations. P-value was calculated from one-sample t-test. Bar represents mean values ± SEM.

Extended Data Fig. 10 Bone marrow clonal hematopoiesis patient sample confirms results from CH01-CH04 and Dnmt3a R878H mouse model.

a, Per-patient comparison of module scores for differentially down- or up-regulated genes in mutant DNMT3A HSPCs (identified in GoT data, Fig. 3a,c) in control versus CH HSPCs. P-values were calculated from likelihood ratio test of LMM with/without CH status. b, Per-patient comparison of module scores for differentially down- or up-regulated genes in mutant DNMT3A EPs (identified in GoT data, Fig. 3a,c) in control versus CH EPs. P-values were calculated from likelihood ratio test of LMM with/without CH status. c, Module scores for genes upregulated in at least 2 cell types (identified in GoT data, Fig. 3b) in control versus CH cells of major cell types. P-values from likelihood ratio test of LMM with/without CH status. d, Fraction of control BM or CH05 cells in EP1 versus EP2 cell clusters. e, UMAP of CH05 cells (clustered independently of the control BM samples) with progenitor cell assignments. f, UMAP of CH05 cells with genotyping data for WT (n = 397 cells) and DNMT3A R882 mutant (MUT; n = 290 cells). g, Normalized expression of differentially upregulated genes in at least 2 cell types, highlighted in Fig. 3b in wildtype versus mutated cells in CH05. P-values from two-sided Wilcoxon rank sum test. h, UMAP of CH05 cells with protein expression (CITE-seq) and gene expression for CD38 and CD9. i, UMAP of CH05 cells highlighting HSPCs, IMP-ME, and MkPs (left) included in the comparison of CD9 expression in wildtype versus mutated cells (right). P-values from likelihood ratio test of LMM with/without mutation status, and cell type as random effect. j, Distribution of fragment size in snATAC-seq data of patient CH05 with DNMT3A R882 CH. k, TSS enrichment of accessible fragments as a function of unique fragments per cell. l, Heatmap of the gene accessibility scores for cluster marker genes (false discovery rate (FDR) < 0.01 and Log2(Fold-Change) (Log2FC) > 1) by cell cluster. m, Pseudotime trajectories for either erythroid (left, n = 1,843 cells) or lymphoid (right, n = 1,740 cells) differentiation. n, Difference between hypomethylated and shuffled motif accessibility z-scores across either erythroid (n = 1,843 cells) or lymphoid (n = 1,740 cells) pseudotime trajectory quartiles. P-values were calculated by two-sided Wilcoxon rank sum test.

Supplementary information

Supplementary Information

Supplementary Methods.

Reporting Summary

Supplementary Tables

Supplementary Tables 1−13.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Nam, A.S., Dusaj, N., Izzo, F. et al. Single-cell multi-omics of human clonal hematopoiesis reveals that DNMT3A R882 mutations perturb early progenitor states through selective hypomethylation. Nat Genet (2022). https://doi.org/10.1038/s41588-022-01179-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41588-022-01179-9

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing