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Cited by 10Hematopoietic Stem Cell TransplantationAcute Myeloid Leukemia ResearchMesenchymal stem cell research

Human haematopoietic stem cells remember inflammatory stress

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Zeng, Andy G. X., Nagree, Murtaza S., Jakobsen, Niels Asger, Shah, Sayyam, Varesi, Angelica, Kang, Jasmine Ryu Won, Murison, Alex, Cheong, Jin-Gyu +29 more·2026·Nature
DOI 10.1038/s41586-026-10522-7PMC PMC13345971
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Abstract

Inflammation activates blood cells, contributing to ageing and malignancy 1–3 . Haematopoietic stem cells (HSCs) survive a lifetime of infection to sustain life-long haematopoiesis 1–9 , but how human HSCs respond and adapt to inflammatory stress is largely unknown. Here, to empirically understand this adaptation, we developed xenograft inflammation–recovery models and performed single-cell multiomics on xenografted human HSCs. Two transcriptionally and epigenetically distinct HSC subsets were identified with one, termed HSC inflammatory memory (HSC-iM), retaining a molecular memory of previous inflammatory treatments. The HSC-iM subset exhibited quiescence and restrained haematopoietic output. Molecularly, the HSC-iM program was enriched in HSCs from adult and paediatric samples across conditions ranging from COVID-19 recovery, sickle cell disease, ageing and clonal haematopoiesis, establishing both the validity of our xenograft models and the physiological relevance of HSC-iM. Clonal haematopoiesis mutations in HSC-iM attenuated the effects of inflammatory stress by promoting HSC activation and differentiation. Moreover, transmission of the pro-inflammatory HSC-iM transcriptional program to differentiated immune progeny was demonstrated in xenograft and physiological settings. Finally, HSC-iM program enrichment in circulating blood cells was associated with a heightened risk score for all-cause mortality in population cohort analyses, underscoring the clinical relevance of this newly identified HSC subset in characterizing heterogeneous health outcomes across a lifetime.

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  • There is a potential causal link between UPR and IFN activation.

    “supporting a potential causal link between UPR and IFN activation 62”

    From Type 1 interferon perturbates clonal competition by reshaping human blood development (2026) · cites this paper as [62]

    The sections on inflammatory response priming and HSC-II memory describe inflammatory signalling in haematopoietic stem cells, but do not connect it to a UPR. Searches for “UPR,” “unfolded protein response,” and interferon activation with endoplasmic-reticulum stress found no passage addressing the proposed causal link. The available copy therefore does not verify the claim. (LLM, conf=0.76)

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  1. [1]Caiado, F., Pietras, E. M. & Manz, M. G. Inflammation as a regulator of hematopoietic stem cell function in disease, aging, and clonal selection. J. Exp. Med. 218, e20201541 (2021). 34129016 PMC8210622 · DOI · Paper page
  2. [2]Essers, M. A. G. et al. IFNα activates dormant haematopoietic stem cells in vivo. Nature 458, 904–908 (2009). 19212321 · DOI · Paper page
  3. [3]Yamashita, M. & Passegué, E. TNF-α coordinates hematopoietic stem cell survival and myeloid regeneration. Cell Stem Cell 25, 357–372.e7 (2019). 31230859 PMC6733032 · DOI · Paper page
  4. [5]Meisel, M. et al. Microbial signals drive pre-leukaemic myeloproliferation in a Tet2-deficient host. Nature 557, 580–584 (2018). 29769727 PMC6238954 · DOI · Paper page
  5. [7]Mitchell, E. et al. Clonal dynamics of haematopoiesis across the human lifespan. Nature 606, 343–350 (2022). 35650442 PMC9177428 · DOI
  6. [8]Weng, C. et al. Deciphering cell states and genealogies of human haematopoiesis. Nature 627, 389–398 (2024). 38253266 PMC10937407 · DOI
  7. [9]Lettera, E. et al. Molecular and phenotypic blueprint of human hematopoiesis links proliferation stress to stem cell aging. J. Exp. Med. 223, e20251805 (2026). 41474970 PMC13248933 · DOI
  8. [10]Laurenti, E. & Göttgens, B. From haematopoietic stem cells to complex differentiation landscapes. Nature 553, 418–426 (2018). 29364285 PMC6555401 · DOI
  9. [12]Lee-Six, H. et al. Population dynamics of normal human blood inferred from somatic mutations. Nature 561, 473–478 (2018). 30185910 PMC6163040 · DOI · Paper page
  10. [14]Xie, S. Z. et al. Sphingosine-1-phosphate receptor 3 potentiates inflammatory programs in normal and leukemia stem cells to promote differentiation. Blood Cancer Discov. 2, 32–53 (2021). 33458693 PMC7116590 · DOI
  11. [15]Pietras, E. M. et al. Chronic interleukin-1 exposure drives haematopoietic stem cells towards precocious myeloid differentiation at the expense of self-renewal. Nat. Cell Biol. 18, 607–618 (2016). 27111842 PMC4884136 · DOI · Paper page
  12. [16]Hammond, C. A., Wu, S. W., Wang, F., MacAldaz, M. E. & Eaves, C. J. Aging alters the cell cycle control and mitogenic signaling responses of human hematopoietic stem cells. Blood 141, 1990–2002 (2023). 36652668 · DOI
  13. [17]Dybedal, I., Bryder, D., Fossum, A., Rusten, L. S. & Jacobsen, S. E. Tumor necrosis factor (TNF)-mediated activation of the p55 TNF receptor negatively regulates maintenance of cycling reconstituting human hematopoietic stem cells. Blood 98, 1782–1791 (2001). 11535512 · DOI
  14. [18]Xie, S. Z. et al. Sphingolipid modulation activates proteostasis programs to govern human hematopoietic stem cell self-renewal. Cell Stem Cell 10.1016/j.stem.2019.09.008 (2019). 31631013 PMC6838675 · DOI
  15. [19]Takayama, N. et al. The transition from quiescent to activated states in human hematopoietic stem cells is governed by dynamic 3D genome reorganization. Cell Stem Cell 28, 488–501.e10 (2021). 33242413 · DOI
  16. [20]Kaufmann, K. B. et al. A latent subset of human hematopoietic stem cells resists regenerative stress to preserve stemness. Nat. Immunol. 22, 723–734 (2021). 33958784 · DOI · Paper page
  17. [21]García-Prat, L. et al. TFEB-mediated endolysosomal activity controls human hematopoietic stem cell fate. Cell Stem Cell 28, 1838–1850.e10 (2021). 34343492 · DOI
  18. [22]Laurenti, E. et al. CDK6 levels regulate quiescence exit in human hematopoietic stem cells. Cell Stem Cell 16, 302–313 (2015). 25704240 PMC4359055 · DOI
  19. [25]Weinstock, J. S. et al. Aberrant activation of TCL1A promotes stem cell expansion in clonal haematopoiesis. Nature 616, 755–763 (2023). 37046083 PMC10360040 · DOI
  20. [28]Van Egeren, D. et al. Reconstructing the lineage histories and differentiation trajectories of individual cancer cells in myeloproliferative neoplasms. Cell Stem Cell 28, 514–523.e9 (2021). 33621486 PMC7939520 · DOI · Paper page
  21. [29]Cai, Z. et al. Inhibition of inflammatory signaling in Tet2 mutant preleukemic cells mitigates stress-induced abnormalities and clonal hematopoiesis. Cell Stem Cell 23, 833–849.e5 (2018). 30526882 PMC6317370 · DOI · Paper page
  22. [30]de Laval, B. et al. C/EBPβ-dependent epigenetic memory induces trained immunity in hematopoietic stem cells. Cell Stem Cell 26, 657–674.e8 (2020). 32169166 · DOI
  23. [31]SanMiguel, J. M. et al. Distinct tumor necrosis factor alpha receptors dictate stem cell fitness versus lineage output in Dnmt3a-mutant clonal hematopoiesis. Cancer Discov. 12, 2763–2773 (2022). 36169447 PMC9716249 · DOI
  24. [32]Zeng, A. G. X. et al. Single-cell transcriptional atlas of human hematopoiesis reveals genetic and hierarchy-based determinants of aberrant AML differentiation. Blood Cancer Discov. 6, 307–324 (2025). 40294241 PMC12209776 · DOI
  25. [33]Skirecki, T. et al. Early severe impairment of hematopoietic stem and progenitor cells from the bone marrow caused by CLP sepsis and endotoxemia in a humanized mice model. Stem Cell Res. Ther. 6, 142 (2015). 26272069 PMC4536694 · DOI
  26. [34]Damas, P. et al. Tumor necrosis factor and interleukin-1 serum levels during severe sepsis in humans. Crit. Care Med. 17, 975–978 (1989). 2791581 · DOI
  27. [35]Karki, R. et al. Synergism of TNF-α and IFN-γ triggers inflammatory cell death, tissue damage, and mortality in SARS-CoV-2 infection and cytokine shock syndromes. Cell 184, 149–168.e17 (2021). 33278357 PMC7674074 · DOI · Paper page
  28. [37]Notta, F. et al. Isolation of single human hematopoietic stem cells capable of long-term multilineage engraftment. Science 333, 218–221 (2011). 21737740 · DOI · Paper page
  29. [38]Belluschi, S. et al. Myelo-lymphoid lineage restriction occurs in the human haematopoietic stem cell compartment before lymphoid-primed multipotent progenitors. Nat. Commun. 9, 4100 (2018). 30291229 PMC6173731 · DOI
  30. [40]Doré, L. C. & Crispino, J. D. Transcription factor networks in erythroid cell and megakaryocyte development. Blood 118, 231–239 (2011). 21622645 PMC3138678 · DOI
  31. [41]Naik, S. et al. Inflammatory memory sensitizes skin epithelial stem cells to tissue damage. Nature 550, 475–480 (2017). 29045388 PMC5808576 · DOI · Paper page
  32. [42]Kaufmann, E. et al. BCG educates hematopoietic stem cells to generate protective innate immunity against tuberculosis. Cell 172, 176–190.e19 (2018). 29328912 · DOI · Paper page
  33. [43]Larsen, S. B. et al. Establishment, maintenance, and recall of inflammatory memory. Cell Stem Cell 28, 1758–1774.e8 (2021). 34320411 PMC8500942 · DOI
  34. [45]Kurd, N. S. et al. Early precursors and molecular determinants of tissue-resident memory CD8 + T lymphocytes revealed by single-cell RNA sequencing. Sci. Immunol. 5, eaaz6894 (2020). 32414833 PMC7341730 · DOI
  35. [46]Smith, N. P. et al. Resident memory T cell development is gradual and shows AP-1 gene expression in mature cells. JCI Insight 10, e187381 (2025). 40548376 PMC12220954 · DOI
  36. [48]Intlekofer, A. M. et al. Effector and memory CD8 + T cell fate coupled by T-bet and eomesodermin. Nat. Immunol. 6, 1236–1244 (2005). 16273099 · DOI
  37. [49]Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587.e29 (2021). 34062119 PMC8238499 · DOI · Paper page
  38. [51]Franceschi, C., Garagnani, P., Parini, P., Giuliani, C. & Santoro, A. Inflammaging: a new immune-metabolic viewpoint for age-related diseases. Nat. Rev. Endocrinol. 14, 576–590 (2018). 30046148 · DOI · Paper page
  39. [52]Jakobsen, N. A. et al. Selective advantage of mutant stem cells in human clonal hematopoiesis is associated with attenuated response to inflammation and aging. Cell Stem Cell 10.1016/j.stem.2024.05.010 (2024). 38917807 PMC11512683 · DOI · Paper page
  40. [53]Ainciburu, M. et al. Uncovering perturbations in human hematopoiesis associated with healthy aging and myeloid malignancies at single-cell resolution. eLife 12, e79363 (2023). 36629404 PMC9904760 · DOI
  41. [54]Zhang, Y. et al. Temporal molecular program of human hematopoietic stem and progenitor cells after birth. Dev. Cell 57, 2745–2760.e6 (2022). 36493772 · DOI
  42. [55]Avagyan, S. et al. Resistance to inflammation underlies enhanced fitness in clonal hematopoiesis. Science 374, 768–772 (2021). 34735227 · DOI · Paper page
  43. [56]Sobrino, S. et al. Severe inflammation and lineage skewing are associated with poor engraftment of engineered hematopoietic stem cells in patients with sickle cell disease. Nat. Commun. 16, 3137 (2025). 40169559 PMC11961595 · DOI
  44. [57]Platt, O. S. et al. Mortality in sickle cell disease. Life expectancy and risk factors for early death. N. Engl. J. Med. 330, 1639–1644 (1994). 7993409 · DOI · Paper page
  45. [58]Horne, B. D. et al. Exceptional mortality prediction by risk scores from common laboratory tests. Am. J. Med. 122, 550–558 (2009). 19486718 · DOI
  46. [59]Gonzales, K. A. U. et al. Stem cells expand potency and alter tissue fitness by accumulating diverse epigenetic memories. Science 374, eabh2444 (2021). 34822296 PMC8896201 · DOI
  47. [60]McEwen, B. S. Interacting mediators of allostasis and allostatic load: towards an understanding of resilience in aging. Metabolism 52, 10–16 (2003). 14577057 · DOI
  48. [61]Greaves, M. A causal mechanism for childhood acute lymphoblastic leukaemia. Nat. Rev. Cancer 18, 471–484 (2018). 29784935 PMC6986894 · DOI
  49. [62]Zhang, X. et al. An immunophenotype-coupled transcriptomic atlas of human hematopoietic progenitors. Nat. Immunol. 25, 703–715 (2024). 38514887 PMC11003869 · DOI
  50. [63]Kirsh, V. A. et al. Cohort profile: the Ontario Health Study (OHS). Int. J. Epidemiol. 10.1093/ije/dyac156 (2022). 35962976 PMC10321700 · DOI
  51. [64]Awadalla, P. et al. Cohort profile of the CARTaGENE study: Quebec’s population-based biobank for public health and personalized genomics. Int. J. Epidemiol. 42, 1285–1299 (2013). 23071140 · DOI
  52. [65]Urquhart, R. et al. Harnessing the power of data linkage to enrich the cancer research ecosystem in Canada. Int. J. Popul. Data Sci.10.23889/ijpds.v7i3.1950 (2022). · DOI
  53. [66]Wagenblast, E. et al. Functional profiling of single CRISPR/Cas9-edited human long-term hematopoietic stem cells. Nat. Commun.10, 4730 (2019). PMC6802205 31628330
  54. [67]Notta, F. et al. Distinct routes of lineage development reshape the human blood hierarchy across ontogeny. Science 351, aab2116 (2016). 26541609 PMC4816201 · DOI · Paper page
  55. [68]Hu, Y. & Smyth, G. K. ELDA: extreme limiting dilution analysis for comparing depleted and enriched populations in stem cell and other assays. J. Immunol. Methods 347, 70–78 (2009). 19567251 · DOI
  56. [69]Nagree, M. S. et al. Autologous, lentivirus-modified, T-rapa cell ‘micropharmacies’ for lysosomal storage disorders. EMBO Mol. Med. 14, e14297 (2022). 35298086 PMC8988206 · DOI
  57. [70]Mulder, D. T. et al. CapTCR-seq: hybrid capture for T-cell receptor repertoire profiling. Blood Adv. 2, 3506–3514 (2018). 30530777 PMC6290103 · DOI
  58. [71]Brugman, M. H. et al. Development of a diverse human T-cell repertoire despite stringent restriction of hematopoietic clonality in the thymus. Proc. Natl. Acad. Sci. USA112, E6020–E6027 (2015). PMC4640754 26483497
  59. [72]Lun, A. T. L., Bach, K. & Marioni, J. C. Pooling across cells to normalize single-cell RNA sequencing data with many zero counts. Genome Biol. 17, 75 (2016). 27122128 PMC4848819 · DOI · Paper page
  60. [73]Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018). 29409532 PMC5802054 · DOI · Paper page
  61. [74]Kotliar, D. et al. Identifying gene expression programs of cell-type identity and cellular activity with single-cell RNA-Seq. eLife 8, e43803 (2019). 31282856 PMC6639075 · DOI · Paper page
  62. [75]Choudhary, S. & Satija, R. Comparison and evaluation of statistical error models for scRNA-seq. Genome Biol. 23, 27 (2022). 35042561 PMC8764781 · DOI · Paper page
  63. [76]Korotkevich, G. et al. Fast gene set enrichment analysis. Preprint at bioRxiv10.1101/060012 (2016).
  64. [77]Lehnertz, B. et al. HLF expression defines the human hematopoietic stem cell state. Blood 138, 2642–2654 (2021). 34499717 · DOI
  65. [78]Zheng, S., Papalexi, E., Butler, A., Stephenson, W. & Satija, R. Molecular transitions in early progenitors during human cord blood hematopoiesis. Mol. Syst. Biol. 14, e8041 (2018). 29545397 PMC5852373 · DOI
  66. [80]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). 29608179 PMC6700744 · DOI · Paper page
  67. [81]Stuart, T., Srivastava, A., Madad, S., Lareau, C. A. & Satija, R. Single-cell chromatin state analysis with Signac. Nat. Methods 18, 1333–1341 (2021). 34725479 PMC9255697 · DOI · Paper page
  68. [83]Aibar, S. et al. SCENIC: single-cell regulatory network inference and clustering. Nat. Methods 14, 1083–1086 (2017). 28991892 PMC5937676 · DOI · Paper page
  69. [84]Germain, P.-L., Lun, A., Garcia Meixide, C., Macnair, W. & Robinson, M. D. Doublet identification in single-cell sequencing data using scDblFinder. F1000Res. 10, 979 (2021). 35814628 PMC9204188 · DOI
  70. [85]Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019). 31740819 PMC6884693 · DOI · Paper page
  71. [86]Zhang, Y. et al. Model-based analysis of ChIP-seq (MACS). Genome Biol. 9, R137 (2008). 18798982 PMC2592715 · DOI · Paper page
  72. [87]Wang, C. X., Zhang, L. & Wang, B. One Cell At a Time (OCAT): a unified framework to integrate and analyze single-cell RNA-seq data. Genome Biol. 23, 102 (2022). 35443717 PMC9019955 · DOI
  73. [88]Schwartz, G. W. et al. TooManyCells identifies and visualizes relationships of single-cell clades. Nat. Methods 17, 405–413 (2020). 32123397 PMC7439807 · DOI
  74. [89]Schwartz, G. W., Zhou, Y., Petrovic, J., Pear, W. S. & Faryabi, R. B. TooManyPeaks identifies drug-resistant-specific regulatory elements from single-cell leukemic epigenomes. Cell Rep. 36, 109575 (2021). 34433064 PMC8409102 · DOI
  75. [90]Korsunsky, I., Nathan, A., Millard, N. & Raychaudhuri, S. Presto scales Wilcoxon and auROC analyses to millions of observations. Preprint at bioRxiv10.1101/653253 (2019).
  76. [91]Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014). 25516281 PMC4302049 · DOI · Paper page
  77. [95]Lawrence, M., Gentleman, R. & Carey, V. rtracklayer: an R package for interfacing with genome browsers. Bioinformatics 25, 1841–1842 (2009). 19468054 PMC2705236 · DOI
  78. [96]Ben-Kiki, O., Bercovich, A., Lifshitz, A. & Tanay, A. Metacell-2: a divide-and-conquer metacell algorithm for scalable scRNA-seq analysis. Genome Biol. 23, 100 (2022). 35440087 PMC9019975 · DOI · Paper page
  79. [97]Bravo González-Blas, C. et al. cisTopic: cis -regulatory topic modeling on single-cell ATAC-seq data. Nat. Methods 16, 397–400 (2019). 30962623 PMC6517279 · DOI · Paper page
  80. [98]Lambert, S. A. et al. The human transcription factors. Cell 172, 650–665 (2018). 29425488 PMC12908702 · DOI · Paper page
  81. [99]Hoffman, G. E. & Schadt, E. E. variancePartition: interpreting drivers of variation in complex gene expression studies. BMC Bioinformatics 17, 483 (2016). 27884101 PMC5123296 · DOI · Paper page
  82. [100]Hänzelmann, S., Castelo, R. & Guinney, J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics 14, 7 (2013). 23323831 PMC3618321 · DOI · Paper page
  83. [101]Tarashansky, A. J., Xue, Y., Li, P., Quake, S. R. & Wang, B. Self-assembling manifolds in single-cell RNA sequencing data. eLife 8, e48994 (2019). 31524596 PMC6795480 · DOI
  84. [102]Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015). 25605792 PMC4402510 · DOI · Paper page
  85. [103]van Galen, P. et al. Single-cell RNA-seq reveals AML hierarchies relevant to disease progression and immunity. Cell 176, 1265–1281.e24 (2019). 30827681 PMC6515904 · DOI · Paper page
  86. [104]Beneyto-Calabuig, S. et al. Clonally resolved single-cell multi-omics identifies routes of cellular differentiation in acute myeloid leukemia. Cell Stem Cell 30, 706–721.e8 (2023). 37098346 · DOI · Paper page
  87. [106]Granja, J. M. et al. Single-cell multiomic analysis identifies regulatory programs in mixed-phenotype acute leukemia. Nat. Biotechnol. 37, 1458–1465 (2019). 31792411 PMC7258684 · DOI · Paper page
  88. [108]Jakobsen, N. A., Turkalj, S. & Vyas, P. Protocol for high-quality RNA sequencing, cell surface protein analysis, and genotyping in single cells using TARGET-seq. STAR Protoc. 6, 103832 (2025). 40408252 PMC12149610 · DOI
  89. [109]Anderson, J. L. et al. Usefulness of a complete blood count-derived risk score to predict incident mortality in patients with suspected cardiovascular disease. Am. J. Cardiol. 99, 169–174 (2007). 17223413 · DOI
  90. [110]Horne, B. D., Anderson, J. L., Muhlestein, J. B., Ridker, P. M. & Paynter, N. P. Complete blood count risk score and its components, including RDW, are associated with mortality in the JUPITER trial. Eur. J. Prev. Cardiol. 22, 519–526 (2015). 24403296 · DOI

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Authors
Zeng, A. G. X., Nagree, M. S., Jakobsen, N. A., Shah, S., Varesi, A., Kang, J. R. W., Murison, A., Cheong, J.-G., Turkalj, S., Zhang, X., Radtke, F. A., Abera, T.-A., Lim, I. N. X., Jin, L., Araújo, J., Aguilar-Navarro, A. G., Parris, D., McLeod, J., Kim, H., Lee, H. S., Zhang, L., Boulanger, M., Bader, E., Gbeha, E., Parkhurst, C. N., Wagenblast, E., Flores-Figueroa, E., Wang, B., Schwartz, G. W., Shultz, L. D., Nam, A. S., Grimes, H. L., Josefowicz, S. Z., Awadalla, P., Vyas, P., Dick, J. E., & Xie, S. Z.
Affiliations
  • University Health Network (CA)
  • University of Oxford (GB)
  • Ontario Institute for Cancer Research (CA)
  • Cornell University (US)
  • Boston Children's Hospital (US)
  • Cincinnati Children's Hospital Medical Center (US)
  • Weill Cornell Medicine (US)
  • Universidade do Porto (PT)
  • Jackson Laboratory (US)
Corresponding author
Xie, Stephanie Z.
ORCID
Venue
Nature
Publisher
Springer Science and Business Media LLC
Published
2026-05-27
Volume
655
Issue
8122
Pages
458-467
ISSN
  • 0028-0836 (ISSN-L)
  • 1476-4687
DOI
10.1038/s41586-026-10522-7
PMC
PMC13345971
OpenAlex
W7162495308
Cited by
10
References
20
Citations by year
2026: 10
FWCI
48.90
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • PubMed Central (publishedVersion, cc-by)
Topics
  • Health Sciences › Medicine › Hematology › Hematopoietic Stem Cell Transplantation
  • Health Sciences › Medicine › Hematology › Acute Myeloid Leukemia Research
  • Health Sciences › Medicine › Genetics › Mesenchymal stem cell research
Keywords
inflammatory memory, inflammatory stress, single-cell multi-omics, HSC quiescence, clonal hematopoiesis, sickle cell disease, COVID-19 recovery, aging, epigenetic memory
MeSH terms
Clonal Hematopoiesis, COVID-19, Adult, Aging, Anemia, Sickle Cell, Animals, Cell Differentiation, Child, Female, Hematopoiesis, Hematopoietic Stem Cells, Humans, Inflammation, Male, Stress, Physiological, Mice, Single-Cell Analysis, Heterografts

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