Abstract
Haematopoietic ageing is marked by a loss of regenerative capacity and skewed differentiation from haematopoietic stem cells (HSCs), leading to impaired blood production. Signals from the bone marrow niche tailor blood production, but the contribution of the old niche to haematopoietic ageing remains unclear. Here we characterize the inflammatory milieu that drives both niche and haematopoietic remodelling. We find decreased numbers and functionality of osteoprogenitors at the endosteum and expansion of central marrow LepR+ mesenchymal stromal cells associated with deterioration of the sinusoidal vasculature. Together, they create a degraded and inflamed old bone marrow niche. Niche inflammation in turn drives the chronic activation of emergency myelopoiesis pathways in old HSCs and multipotent progenitors, which promotes myeloid differentiation and hinders haematopoietic regeneration. Moreover, we show how production of interleukin-1β (IL-1β) by the damaged endosteum acts in trans to drive the proinflammatory nature of the central marrow, with damaging consequences for the old blood system. Notably, niche deterioration, HSC dysfunction and defective regeneration can all be ameliorated by blocking IL-1 signalling. Our results demonstrate that targeting IL-1 as a key mediator of niche inflammation is a tractable strategy to improve blood production during ageing.
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Data availability
The RNA-seq and microarray data that support the findings of this study have been deposited in the Gene Expression Omnibus under accession code GSE169162. The M. musculus genome GRCm38.p4 is available from the National Center for Biotechnology Information (https://www.ncbi.nlm.nih.gov/assembly/GCF_000001635.24/). The chip annotation MSigDB.v7.2.chip is available from the Broad Institute (https://software.broadinstitute.org/cancer/software/gsea/wiki/index.php/MSigDB_v7.2_Release_Notes). Gene sets used for GSEA h.all.v7.2.symbols.gmt and c2.cp.reactome.v7.2.symbols.gmt are available from the Broad Institute (https://data.broadinstitute.org/gsea-msigdb/msigdb/release/7.2/). All other data are available from the corresponding author upon reasonable request. Source data are provided with this paper.
Code availability
All code and packages used to support the findings of this study are either publicly available or available from the corresponding author upon reasonable request.
References
Rossi, D. J., Jamieson, C. H. M. & Weissman, I. L. Stems cells and the pathways to aging and cancer. Cell 132, 681–696 (2008).
Morrison, S. J. & Scadden, D. T. The bone marrow niche for haematopoietic stem cells. Nature 505, 327–334 (2014).
Pinho, S. & Frenette, P. S. Haematopoietic stem cell activity and interactions with the niche. Nat. Rev. Mol. Cell Biol. 20, 303–320 (2019).
Verovskaya, E. V., Dellorusso, P. V. & Passegué, E. Losing sense of self and surroundings: hematopoietic stem cell aging and leukemic transformation. Trends Mol. Med. 25, 494–515 (2019).
Ho, Y.-H. & Méndez-Ferrer, S. Microenvironmental contributions to hematopoietic stem cell aging. Haematologica 105, 38–46 (2020).
Pietras, E. M. et al. Functionally distinct subsets of lineage-biased multipotent progenitors control blood production in normal and regenerative conditions. Cell Stem Cell 17, 35–46 (2015).
Olson, O. C., Kang, Y.-A. & Passegué, E. Normal hematopoiesis is a balancing act of self-renewal and regeneration. Cold Spring Harb. Perspect. Med. 10, a035519 (2020).
Schepers, K., Campbell, T. B. & Passegué, E. Normal and leukemic stem cell niches: insights and therapeutic opportunities. Cell Stem Cell 16, 254–267 (2015).
Kovtonyuk, L. V., Fritsch, K., Feng, X., Manz, M. G. & Takizawa, H. Inflamm-aging of hematopoiesis, hematopoietic stem cells, and the bone marrow microenvironment. Front. Immunol. https://doi.org/10.3389/fimmu.2016.00502 (2016).
Soysal, P., Arik, F., Smith, L., Jackson, S. E. & Isik, A. T. in Frailty and Cardiovascular Diseases (ed. Veronese, N.) 55–64 (Springer International Publishing, 2020).
Almeida, M. & O’Brien, C. A. Basic biology of skeletal aging: role of stress response pathways. J. Gerontol. A. Biol. Sci. Med. Sci. 68, 1197–1208 (2013).
Ho, T. T. et al. Aged hematopoietic stem cells are refractory to bloodborne systemic rejuvenation interventions. J. Exp. Med. 218, e20210223 (2021).
Baryawno, N. et al. A cellular taxonomy of the bone marrow stroma in homeostasis and leukemia. Cell 177, 1915–1932.e16 (2019).
Tikhonova, A. N. et al. The bone marrow microenvironment at single-cell resolution. Nature 569, 222–228 (2019).
Baccin, C. et al. Combined single-cell and spatial transcriptomics reveal the molecular, cellular and spatial bone marrow niche organization. Nat. Cell Biol. 22, 38–48 (2020).
Kokkaliaris, K. D. et al. Adult blood stem cell localization reflects the abundance of reported bone marrow niche cell types and their combinations. Blood 136, 2296–2307 (2020).
Kusumbe, A. P. et al. Age-dependent modulation of vascular niches for haematopoietic stem cells. Nature 532, 380–384 (2016).
Poulos, M. G. et al. Endothelial transplantation rejuvenates aged hematopoietic stem cell function. J. Clin. Invest. 127, 4163–4178 (2017).
Maryanovich, M. et al. Adrenergic nerve degeneration in bone marrow drives aging of the hematopoietic stem cell niche. Nat. Med. 24, 782–791 (2018).
Ho, Y.-H. et al. Remodeling of bone marrow hematopoietic stem cell niches promotes myeloid cell expansion during premature or physiological aging. Cell Stem Cell 25, 407–418.e6 (2019).
Guidi, N. et al. Osteopontin attenuates aging‐associated phenotypes of hematopoietic stem cells. EMBO J. 36, 840–853 (2017).
Shen, B. et al. A mechanosensitive peri-arteriolar niche for osteogenesis and lymphopoiesis. Nature 591, 438–444 (2021).
Ergen, A. V., Boles, N. C. & Goodell, M. A. Rantes/Ccl5 influences hematopoietic stem cell subtypes and causes myeloid skewing. Blood 119, 2500–2509 (2012).
Kovtonyuk, L. V. et al. IL-1 mediates microbiome-induced inflammaging of hematopoietic stem cells in mice. Blood 139, 44–58 (2022).
Grover, A. et al. Single-cell RNA sequencing reveals molecular and functional platelet bias of aged haematopoietic stem cells. Nat. Commun. 7, 11075 (2016).
Poscablo, D. M., Worthington, A. K., Smith-Berdan, S. & Forsberg, E. C. Megakaryocyte progenitor cell function is enhanced upon aging despite the functional decline of aged hematopoietic stem cells. Stem Cell Rep. 16, 1598–1613 (2021).
Glatt, V., Canalis, E., Stadmeyer, L. & Bouxsein, M. L. Age-related changes in trabecular architecture differ in female and male C57BL/6J Mice. J. Bone Miner. Res. 22, 1197–1207 (2007).
Schepers, K. et al. Myeloproliferative neoplasia remodels the endosteal bone marrow niche into a self-reinforcing leukemic niche. Cell Stem Cell 13, 285–299 (2013).
Xu, X. et al. Age-related impairment of vascular structure and functions. Aging Dis. 8, 590 (2017).
Ambrosi, T. H. et al. Aged skeletal stem cells generate an inflammatory degenerative niche. Nature 597, 256–262 (2021).
Nishikawa, K. et al. Maf promotes osteoblast differentiation in mice by mediating the age-related switch in mesenchymal cell differentiation. J. Clin. Invest. 120, 3455–3465 (2010).
Singh, L. et al. Aging alters bone-fat reciprocity by shifting in vivo mesenchymal precursor cell fate towards an adipogenic lineage. Bone 85, 29–36 (2016).
Frisch, B. J. et al. Aged marrow macrophages expand platelet-biased hematopoietic stem cells via interleukin-1B. JCI Insight 4, e124213 (2019).
Villeda, S. A. et al. The ageing systemic milieu negatively regulates neurogenesis and cognitive function. Nature 477, 90–94 (2011).
Mrak, R. Interleukin-1, neuroinflammation, and Alzheimer’s disease. Neurobiol. Aging 22, 903–908 (2001).
Woollard, K. J. Soluble bio-markers in vascular disease: much more than gauges of disease? Clin. Exp. Pharmacol. Physiol. 32, 233–240 (2005).
Abdelmagid, S. M. et al. Mutation in osteoactivin decreases bone formation in vivo and osteoblast differentiation in vitro. Am. J. Pathol. 184, 697–713 (2014).
Villarroya, F., Cereijo, R., Villarroya, J. & Giralt, M. Brown adipose tissue as a secretory organ. Nat. Rev. Endocrinol. 13, 26–35 (2017).
Yue, R., Zhou, B. O., Shimada, I. S., Zhao, Z. & Morrison, S. J. Leptin receptor promotes adipogenesis and reduces osteogenesis by regulating mesenchymal stromal cells in adult bone marrow. Cell Stem Cell 18, 782–796 (2016).
Coppé, J.-P., Desprez, P.-Y., Krtolica, A. & Campisi, J. The senescence-associated secretory phenotype: the dark side of tumor suppression. Annu. Rev. Pathol. Mech. Dis. 5, 99–118 (2010).
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).
Gekas, C. & Graf, T. CD41 expression marks myeloid-biased adult hematopoietic stem cells and increases with age. Blood 121, 4463–4472 (2013).
Young, K. et al. Progressive alterations in multipotent hematopoietic progenitors underlie lymphoid cell loss in aging. J. Exp. Med. 213, 2259–2267 (2016).
Rodriguez-Fraticelli, A. E. et al. Clonal analysis of lineage fate in native haematopoiesis. Nature 553, 212–216 (2018).
Dong, F. et al. Differentiation of transplanted haematopoietic stem cells tracked by single-cell transcriptomic analysis. Nat. Cell Biol. 22, 630–639 (2020).
Flach, J. et al. Replication stress is a potent driver of functional decline in ageing haematopoietic stem cells. Nature 512, 198–202 (2014).
Gutierrez-Martinez, P. et al. Diminished apoptotic priming and ATM signalling confer a survival advantage onto aged haematopoietic stem cells in response to DNA damage. Nat. Cell Biol. 20, 413–421 (2018).
Hérault, A. et al. Myeloid progenitor cluster formation drives emergency and leukaemic myelopoiesis. Nature 544, 53–58 (2017).
Park, D. et al. Endogenous bone marrow MSCs are dynamic, fate-restricted participants in bone maintenance and regeneration. Cell Stem Cell 10, 259–272 (2012).
Yamashita, M. & Passegué, E. TNF-α coordinates hematopoietic stem cell survival and myeloid regeneration. Cell Stem Cell 25, 357–372.e7 (2019).
Furman, D. et al. Expression of specific inflammasome gene modules stratifies older individuals into two extreme clinical and immunological states. Nat. Med. 23, 174–184 (2017).
Zhou, B. O., Yue, R., Murphy, M. M., Peyer, J. G. & Morrison, S. J. Leptin-receptor-expressing mesenchymal stromal cells represent the main source of bone formed by adult bone marrow. Cell Stem Cell 15, 154–168 (2014).
Picelli, S. et al. Full-length RNA-seq from single cells using Smart-seq2. Nat. Protoc. 9, 171–181 (2014).
Bagnoli, J. W. et al. Sensitive and powerful single-cell RNA sequencing using mcSCRB-seq. Nat. Commun. 9, 2937 (2018).
Anders, S., Pyl, P. T. & Huber, W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).
Brennecke, P. et al. Accounting for technical noise in single-cell RNA-seq experiments. Nat. Methods 10, 1093–1095 (2013).
Olsson, A. et al. Single-cell analysis of mixed-lineage states leading to a binary cell fate choice. Nature 537, 698–702 (2016).
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).
Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).
Wolock, S. L., Lopez, R. & Klein, A. M. Scrublet: computational identification of cell doublets in single-cell transcriptomic data. Cell Syst. 8, 281–291.e9 (2019).
Hoffman, P. et al. Comprehensive Integration of Single-Cell Data. Cell 177, 1888–1902 (2019).
Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587.e29 (2021).
Tusher, V. G., Tibshirani, R. & Chu, G. Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl Acad. Sci. USA 98, 5116–5121 (2001).
Acknowledgements
We thank S. Villeda (UCSF) for providing some of the old C57BL/6 mice; D. Reynaud (UCSF) for initial analyses of the old mice; A. Valencia (UCSF) for technical assistance with various experiments; S. Kinston (Cambridge University) for scRNA-seq library preparations; A. Li (Bone Imaging Research Core, UCSF) and both E. Guo and P. T. Shyu (CUIMC) for microCT analyses; M. Lee (UCSF) and M. Kissner (CUIMC) for management of our Flow Cytometry Core facilities; and all members of the Passegué Laboratory for critical insights and suggestions. C.A.M. was supported by NIH F31HL160207 and a NYSTEM training grant; E.V.V. by a Rubicon Grant from The Netherlands Organization for Scientific Research, a Stem Cell Grant from BD Biosciences and a NYSTEM training grant; O.C.O by CRI/Margaret Dammann Eisner postdoctoral fellowship CRI3617; J.W.S. by long-term EMBO postdoctoral fellowship ALTF-2021-196; P.V.D. by NIH F31HL151140; E.M.P. by NIH F32HL106989 and K01DK09831; S.T.B. by a CIRM postdoctoral fellowship; and T.T.H. by an AHA and Hillblom Center for the Biology of Aging predoctoral fellowship. F.J.C-N., X.W. and B.G. were supported by grants from the Wellcome (206328/Z/17/Z), CRUK (C1163/A21762) and core funding by the Wellcome to the Cambridge Stem Cell Institute. This work was funded by NIH R01CA184014, NIH R35HL135763, Glenn Foundation Research Award and LLS Scholar Award to E.P., and supported in part through the NIH/NCI Cancer Center Support Grant P30CA013696 to CUIMC. The funders of this work had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. Clipart in figures was created using BioRender.
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E.V.V. initiated the studies and contributed to most of the experiments with help from A.H. for BM fluid isolation and immunophenotyping. S.Y.Z. performed GMP cluster analyses and ELISA assays. A.F.S. performed immunofluorescence and whole-mount staining. S.T.B. performed initial 5-FU analyses. T.T.H. performed cell collection and provided technical assistance. C.A.M. re-analysed all the generated data, contributed to various stromal and molecular analyses and, with help from O.C.O., J.W.S. and P.V.D, completed the chronic IL-1β exposure studies and performed all the analyses of Il1r1-deficient mice. F.J.C-N., X.W. and B.G. prepared and analysed the scRNA-seq samples. E.M.P. prepared the microarray samples. E.V.V. and E.P. designed the initial experiments, and C.A.M. and E.P. revised the experiments. E.V.V., C.A.M. and E.P. wrote and edited the manuscript. C.A.M. and E.P. handled the resubmission.
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Extended data
Extended Data Fig. 1 Gross analysis of the remodeled old BM cavity.
a, H&E staining of humeri and sterna of 2 individual young and old mice. Scale bar, 100 µm. b, TPO and TGF-β levels in young and old BM fluids. Results are from 2 independent cohorts. c, μCT analyses of young and old tibia with representative images of cortical and trabecular regions (left) and quantification of bone volume/total volume (BV/TV) and connectivity density (right). d, Representative image of bone lining ALCAM+ osteoblasts immunofluorescence staining in young and old mice. Representative of 3 independent experiments. Scale bar, 100 µm. e, Representative images and quantification of immunofluorescence staining of vascular volume (laminin) and vascular leakage by dragon-green beads (DGB) diffusion assay in young and old BM. Scale bar, 50 µm. f, Representative images and quantification by flow cytometry of DGB endocytosis in young and old marrow SEC; scale bar, 5 µm. Results are from 3 independent cohorts. g, Von Kossa staining in young and old endosteal MSC-S. h, Experimental scheme for the indicated co-culture experiments showing the effects of young or old BM cells on young MSC-S (top right), and young BM cells on young and old MSC-S (bottom). i, Frequency of endosteal and marrow mesenchymal populations in young and middle age (13-month-old) mice with changes in CFU-F from endosteal OLCs (bottom right). Data are means ± S.D; P-values were obtained by two-tailed Welch’s t-test without adjustment for multiple comparisons.
Extended Data Fig. 2 Molecular features of old mesenchymal populations.
a, UMAP visualization of the entire plate-based scRNAseq dataset of niche populations of mesenchymal and endothelial populations shown in Fig. 2a. b, Global changes in MSC-L gene identity in clusters M2 vs. M1. c, Representative flow cytometry staining (top) and quantification of LepR and PDGF-Rα levels (bottom) in young and old MSC-L. Results are from 6 independently analyzed groups of 1 or 2 young or old mice. d, HSC niche factors with violin plots representation of Kitl and Cxcl12 expression in the indicated young and old mesenchymal populations (left) and SCF and SDF1α levels in young and old BM fluids (right). e, GSEA results for Hallmark biological processes significantly affected in old MSC-L-like, OPr-like and MSC-S-like groups (FDR < 0.05). Data are means ± S.D. except for (b) and (d); P-values were obtained by two-tailed Student’s t-test without adjustment for multiple comparisons (b), or by Welch’s t-test without adjustment for multiple comparisons (c,d).
Extended Data Fig. 3 Molecular features of old endothelial populations and further characteristics of the old niche.
a, ICGS output of young and old endothelial populations from the plate-based scRNAseq dataset shown in Fig. 2a with 7 clusters of cells (E1 to E7, horizontal) defined according to the expressing pattern of the 7 clusters of genes (a to g, vertical). Examples included in gene clusters a to g are shown. Star, contaminating mesenchymal/endothelial doublets; P, pericytes; A, arterioles; T, transition vessels.Color scheme is based on normalized gene expression level from the indicated minimum(blue) to maximum (yellow) value in the scale. b, Ingenuity Pathway Analysis (IPA) canonical pathways enriched in old AEC-like and SEC-like groups (Z-score > 1; p < 0.01). c, Characteristic expression patterns for the indicated genes in the droplet-based scRNAseq dataset of young and old endosteal and central marrow stromal fractions shown in Fig. 2c. Cells in the UMAP were colored according to the expression levels of the indicated genes. Color scheme is based on ln scale of normalized counts from 0 (gray) to the indicated maximum value in the scale (red).
Extended Data Fig. 4 Age-related changes in blood and BM populations and altered lineage bias in old MPPs.
a, Overlap between cytokines upregulated in old BM fluids and published SASP profile40. b, Representative SA-β-gal staining in control irradiated mouse embryonic fibroblasts (MEF) and isolated young and old MSC-S and OLC. Representative of 3 independent experiments. Scale bar, 20 µm. c, Absolute expression of Il1b in different mature hematopoietic cell types and unfractionated endosteal and central marrow stroma from pooled young and old samples. Results are expressed as -log(dCt) relative to gapdh. d, Complete blood count (CBC) parameters in young and old mice. Results are from 7 independent cohorts. WBC, white blood cell; My, myeloid; Ly, lymphocyte; RBC, red blood cell; Pt, platelet. e, Cellularity and quantification of mature populations in young and old BM. Results are from 3 independent cohorts. Gr, granulocyte. f, Quantification of progenitor populations in young and old BM. Results are from 3 independent cohorts. CMP, common myeloid progenitor; GMP, granulocyte-macrophage progenitor; MEP, megakaryocyte-erythrocyte progenitor; CLP, common lymphoid progenitor; CFU-E, erythroid colony-forming unit; Pre-GM, pre-granulocyte/macrophage; Pre-MegE, pre-megakaryocyte/erythrocyte; MkP, megakaryocyte progenitor. g, Representative staining and quantification of young and old HSC markers. Results are from 2 independent cohorts, with data represented as box and whiskers (min to max) and expressed as fold changes in mean fluorescence intensity (MFI) relative to young HSCs. h, Characteristic expression patterns of lineage determinant genes in the droplet-based scRNAseq young and old LK/LSK dataset. Cells in the UMAP were colored according to the expression levels of the indicated genes. i, IPA Upstream Regulators analysis of young and old populations from the droplet-based scRNAseq young and old LK/LSK dataset filtered on cytokines and growth factors. Data are means ± S.D. except for (g) where box ranges from 25th to 75th percentile with center line at the median, and whiskers range from minima to maxima; P-values were obtained by two-tailed Welch’s t-test without adjustment for multiple comparisons.
Extended Data Fig. 5 Altered functionality of old HSCs and MPPs.
a, Colony formation in methylcellulose for young (Y) and old (O) HSCs, MPP3 and MPP4. Results are from 2 independent experiments. Mix: all lineages; GM: granulocyte/macrophage; G(or)M: granulocyte (or) macrophage; MegE: megakaryocyte/erythrocyte; CFU: colony-forming units. b, Myeloid differentiation in liquid culture for young and old MPP3 with quantification (right) of immature Sca-1+/c-Kit+ cells (left) and mature Mac-1+/FcγR+ macrophage (right). c, Representative flow cytometry staining of CD19+ lymphoid vs. Mac-1+ myeloid differentiation in OP9 + IL7 culture conditions for young and old HSCs, MPP3 and MPP4. Results are representative of 3 independent experiments. d, Representative histograms of CFSE staining of cultured young and old HSCs, MPP3 and MPP4. Results are representative of 3 independent experiments. e, Cleaved caspase 3/7 (CC3/7) activity in cultured young and old HSCs, MPP3 and MPP4. f, Short-term lineage tracking following transplantations of young and old HSCs, MPP3 and MPP4 in sub-lethally irradiated recipients with experimental scheme (left) and quantification of overall blood donor chimerism (top graphs) and myeloid chimerism among donor cells (bottom graphs). Results are from 3 independent cohorts. Data are means ± S.D. except for (f) (± S.E.M.); P-values were obtained by two-tailed Welch’s t-test without adjustment for multiple comparisons (a, d, f), or by two-tailed Student’s t-test without adjustment for multiple comparisons (b).
Extended Data Fig. 6 Improved aging features with IL-1 signaling blockade.
a–c, Short-term blockade of IL-1 signaling upon Anakinra (Ana) treatment in young (Y) and old (O) mice with: (a) experimental scheme; (b) changes in HPSC frequency; and (c) engraftment over time (left) and lineage reconstitution (right) at 4 months (4 mo) post-transplantation (Tplx) of the indicated HSC populations. Results are from 3 independent cohorts of young and old mice injected with either PBS or Anakinra, with HSCs isolated from the pooled BM of mice from the same treatment group and transplanted into 3 to 5 recipients, each. d, e, Additional characterization of the effects of Anakinra blockade of IL-1 signaling during 5FU-mediated regeneration in young and old mice with: (d) changes in IL-1α, IL-1β and MIP1α levels in BM fluids; and (e) platelet (Plt) levels in peripheral blood. Results are from 3 independent cohorts started with 15 young and 11 old mice treated once with 5FU, injected daily with either PBS or Anakinra, and analyzed at day 12 post-5FU treatment. f, GSEA results for Hallmark biological processes significantly enriched in MSC-L2 vs. MSC-L1 groups (FDR < 0.05). Data are from the droplet-based scRNAseq analyses of endosteal and central marrow stromal fractions in young (n = 2) and old (n = 2) Il1r1+/+ wild type (WT) mice and old (n = 1) Il1r1−/− mice shown in Fig. 7b. g, h, Droplet-based scRNAseq analyses of Lin−/c-Kit+ (LK) and Lin−/Sca-1+/c-Kit+ (LSK) BM fractions isolated from young (n = 2) and old (n = 2) Il1r1+/+ wild type (WT) and old (n = 1) Il1r1−/− mice with (g) UMAP visualization and (h) quantification of percent of HSPCs and progenitors. Data are means ± S.D. except for engraftment results shown in (c) (± S.E.M.); P-values were obtained by one-way Anova adjusted for multiple comparisons using the Holm-Šídák method.
Extended Data Fig. 7 Key role of IL-1 in the aging of both BM niche and blood system.
a, b, GSEA results in old Il1r1−/− HSPC population for the Gene Ontology pathways affected in either old WT HSPCs (a) or young WT HSPCs (b) identified by droplet-based scRNAseq analyses. n/a, non-available; ns, not significant; *nominal p value ≤ 0.05, ** nominal p value ≤ 0.01, *** nominal p value ≤ 0.001. c, Peripheral blood CD45.1+ donor chimerism (left) and number of donor-derived GMPs (right) in young or old WT and Il1r1−/− CD45.2+ recipient mice at 4 months (mo) after lethal irradiation and transplantation (Tplx) with 2 × 106 young WT CD45.1+ donor BM cells. d, Il1r1 expression in the droplet-based scRNAseq of young and old WT stroma and LK/LSK datasets. Cells in the UMAP were colored according to the expression levels of the indicated genes. Color scheme is based on ln scale of normalized counts from the indicated minimum (gray) to maximum (red) value in the scale. e–i, Unchanged aging features in old Tnf−/− mice with: (e) color scheme; (f) blood parameters; (g) endosteal (left) and central marrow (right) mesenchymal population frequencies; (h) BM hematopoietic population frequencies; and (i) engraftment over time (left) and lineage reconstitution (right) at 4 mo post-Tplx of the indicated HSC populations. Results are from 3 independent cohorts of young and old WT and age-matched Tnf−/− mice, with HSCs isolated from the pooled BM of mice of the same genotype and transplanted into 3 to 5 recipients, each. Data are means ± S.D. except for engraftment results shown in (i) (± S.E.M.); P-values were obtained by Kolmogorov-Smirnov test (a,b), by one-way Anova adjusted for multiple comparisons using the Holm-Šídák method (c,i), or by two-tailed Student’s t-test without adjustment for multiple comparisons (f, g, h).
Extended Data Fig. 8 Schematic of the crosstalk between the BM niche and hematopoietic system during physiological aging.
In youth, HSCs reside primarily in the central marrow where they are maintained by peri-sinusoidal MSC-L and produce a balanced output of all mature cell lineages (Mk, megakaryocytes; Ery, erythrocytes, My, myeloid cells; Ly, lymphoid cells). Abundant peri-arteriolar MSC-S at the endosteum efficiently produce OPr cells that support osteoblast development, ECM deposition and bone formation. With age, numerical loss and functional decline of MSC-S and OPr leads to bone thinning, with the remaining OPr constitutively producing IL-1. Chronic IL-1, in turn, reinforces niche degradation at the endosteum and contributes to dysfunction of the sinusoidal vasculature. Chronic IL-1 also acts in trans on central marrow MSC-L and HSPCs, driving the appearance of an inflammatory iMSC-L subset and steady-state engagement of emergency myelopoiesis (EM) programs with GMP cluster (cGMP) formation. Strikingly, acute IL-1 blockade with Anakinra enables more youthful blood production during 5FU-mediated regeneration, and life-long removal of IL-1 signaling in Il1r1−/− mice maintains MSC-L in a more youthful cell state associated with improved blood production and HSC function.
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Supplementary Tables
Supplementary Table 1: ICGS of young and old niche cells. Plate-based scRNA-seq gene expression data of mesenchymal and ECs were separated and subjected to unsupervised single-cell population identification using ICGS. The relative expression of every guide gene in each cluster, as well as the relative expression of every guide gene in each pooled group of clusters corresponding to the indicated cell types, was calculated by averaging the relative expression of each gene across individual cells within a cluster or group. Supplementary Table 2: DEGs and pathway analyses of young versus old niche cells. Plate-based scRNA-seq gene expression data between young and old cells in pooled identity groups as defined by ICGS were analysed for DEGs using the DESeq2 package. Overlap with Hallmark gene sets was evaluated, and significant (FDR < 0.05) overlaps are indicated in bold for mesenchymal populations. As no significant overlaps were observed for endothelial populations, IPA software (Qiagen) was used to determine IPA canonical pathways with absolute z-score ≥ 1 and –log10(P value) ≥ 2 enriched in young and old AEC-like and SEC-like cells. Supplementary Table 3: Quantibody array-based measurements of cytokine concentration in young and old BM fluids. BM fluids isolated from young (10 weeks of age) and old (27–29 months of age) mice (n = 5) were analysed using Quantibody Testing Service (Raybiotech). Samples were diluted fourfold before analyses. Results are mean ± s.d. and are expressed as pg ml–1 concentration; *P < 0.05, **P < 0.05, ***P < 0.05. Supplementary Table 4: DEGs and pathway analyses of young versus old HSCs. SAM was performed on young and old cells within each population to determine SAM delta scores. The top 1,000 most highly DEGs for each group were collected. SAM scores for these genes were used for GSEA and overlap with Reactome gene sets was evaluated for each population. The top 5 statistically significant (FDR < 0.05) overlaps for each group are in bold.
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Mitchell, C.A., Verovskaya, E.V., Calero-Nieto, F.J. et al. Stromal niche inflammation mediated by IL-1 signalling is a targetable driver of haematopoietic ageing. Nat Cell Biol 25, 30–41 (2023). https://doi.org/10.1038/s41556-022-01053-0
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DOI: https://doi.org/10.1038/s41556-022-01053-0
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