Manipulating niche composition limits damage to haematopoietic stem cells during Plasmodium infection

Abstract

Severe infections are a major stress on haematopoiesis, where the consequences for haematopoietic stem cells (HSCs) have only recently started to emerge. HSC function critically depends on the integrity of complex bone marrow (BM) niches; however, what role the BM microenvironment plays in mediating the effects of infection on HSCs remains an open question. Here, using a murine model of malaria and combining single-cell RNA sequencing, mathematical modelling, transplantation assays and intravital microscopy, we show that haematopoiesis is reprogrammed upon infection, whereby the HSC compartment turns over substantially faster than at steady-state and HSC function is drastically affected. Interferon is found to affect both haematopoietic and mesenchymal BM cells and we specifically identify a dramatic loss of osteoblasts and alterations in endothelial cell function. Osteo-active parathyroid hormone treatment abolishes infection-triggered HSC proliferation and—coupled with reactive oxygen species quenching—enables partial rescuing of HSC function.

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Fig. 1: P. berghei infection alters HSC transcriptional identity and function.
Fig. 2: P. berghei affects population dynamics of early HSPCs.
Fig. 3: P. berghei-exposed HSPCs and BM stromal cells exhibit a strong IFN response.
Fig. 4: Osteolineage targeting inhibits P. berghei-induced HSC proliferation.
Fig. 5: P. berghei infection affects vascular integrity and function.
Fig. 6: Treating infected animals with NAC reduces HSPC intracellular ROS levels.
Fig. 7: Combining PTH treatment and sequestration of ROS during infection partially rescues HSC function.

Data availability

The scRNA-seq data that support the findings of this study have been deposited in the Gene Expression Omnibus under accession code GSE156410. Data are also available in processed form for interactive browsing at http://128.232.224.252/malaria_control/ and http://128.232.224.252/malaria_infected/. All other data supporting the findings of this study are available from the corresponding author on reasonable request. Source data are provided with this paper.

Code availability

The computational code used in this study can be obtained by request to B.G. (bg200@cam.ac.uk).

References

  1. 1.

    Morrison, S. J. & Scadden, D. T. The bone marrow niche for haematopoietic stem cells. Nature 505, 327–334 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Essers, M. A. G. et al. IFNα activates dormant haematopoietic stem cells in vivo. Nature 458, 904–908 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Esplin, B. L. et al. Chronic exposure to a TLR ligand injures hematopoietic stem cells. J. Immunol. 186, 5367–5375 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. 4.

    King, K. Y. & Goodell, M. A.Inflammatory modulation of HSCs: viewing the HSC as a foundation for the immune response. Nat. Rev. Immunol. 11, 685–692 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    MacNamara, K. C., Jones, M., Martin, O. & Winslow, G. M. Transient activation of hematopoietic stem and progenitor cells by IFNγ during acute bacterial infection. PLoS ONE 6, e28669 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Baldridge, M. T., King, K. Y., Boles, N. C., Weksberg, D. C. & Goodell, M. A.Quiescent haematopoietic stem cells are activated by IFN-γ in response to chronic infection. Nature 465, 793–797 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Rashidi, N. M. et al. In vivo time-lapse imaging shows diverse niche engagement by quiescent and naturally activated hematopoietic stem cells. Blood 124, 79–83 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Matatall, K. A. et al. Chronic infection depletes hematopoietic stem cells through stress-induced terminal differentiation. Cell Rep. 17, 2584–2595 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Vainieri, M. L. et al. Systematic tracking of altered haematopoiesis during sporozoite-mediated malaria development reveals multiple response points. Open Biol. 6, 160038 (2016).

    PubMed  PubMed Central  Google Scholar 

  10. 10.

    Phillips, M. A. et al. Malaria. Nat. Rev. Dis. Prim. 3, 17050 (2017).

    PubMed  Google Scholar 

  11. 11.

    Dörmer, P., Dietrich, M., Kern, P. & Horstmann, R. Ineffective erythropoiesis in acute human P. falciparum malaria. Blut 46, 279–288 (1983).

    PubMed  Google Scholar 

  12. 12.

    Maggio-Price, L., Brookoff, D. & Weiss, L. Changes in hematopoietic stem cells in bone marrow of mice with Plasmodium berghei malaria. Blood 66, 1080–1085 (1985).

    CAS  PubMed  Google Scholar 

  13. 13.

    Wickramasinghe, S., Looareesuwan, S., Nagachinta, B. & White, N. Dyserythropoiesis and ineffective erythropoiesis in Plasmodium vivax malaria. Br. J. Haematol. 72, 91–99 (1989).

    CAS  PubMed  Google Scholar 

  14. 14.

    Boehm, D., Healy, L., Ring, S. & Bell, A. Inhibition of ex vivo erythropoiesis by secreted and haemozoin-associated Plasmodium falciparum products. Parasitology 145, 1865–1875 (2018).

    CAS  PubMed  Google Scholar 

  15. 15.

    Orf, K. & Cunnington, A. J. Infection-related hemolysis and susceptibility to Gram-negative bacterial co-infection. Front. Microbiol. 6, 666 (2015).

    PubMed  PubMed Central  Google Scholar 

  16. 16.

    White, N. J. Anaemia and malaria. Malar. J. 17, 371 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Mamedov, M. R. et al. A macrophage colony-stimulating-factor-producing γδ T cell subset prevents malarial parasitemic recurrence. Immunity 48, 350–363.e7 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Weinreb, C., Wolock, S. & Klein, A. SPRING: a kinetic interface for visualizing high dimensional single-cell expression data. Bioinform. Oxf. Engl. https://doi.org/10.1093/bioinformatics/btx792 (2017).

  19. 19.

    Hamey, F. K. & Göttgens, B. Machine learning predicts putative haematopoietic stem cells within large single-cell transcriptomics datasets. Exp. Hematol. 78, 11–20 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Wilson, A. et al. Hematopoietic stem cells reversibly switch from dormancy to self-renewal during homeostasis and repair. Cell 135, 1118–1129 (2008).

    CAS  PubMed  Google Scholar 

  21. 21.

    Oguro, H., Ding, L. & Morrison, S. J. SLAM family markers resolve functionally distinct subpopulations of hematopoietic stem cells and multipotent progenitors. Cell Stem Cell 13, 102–116 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Cabezas-Wallscheid, N. et al. Identification of regulatory networks in HSCs and their immediate progeny via integrated proteome, transcriptome, and DNA methylome analysis. Cell Stem Cell 15, 507–522 (2014).

    CAS  PubMed  Google Scholar 

  23. 23.

    Akinduro, O. et al. Proliferation dynamics of acute myeloid leukaemia and haematopoietic progenitors competing for bone marrow space. Nat. Commun. 9, 519 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Kent, D. G. et al. Prospective isolation and molecular characterization of hematopoietic stem cells with durable self-renewal potential. Blood 113, 6342–6350 (2009).

    CAS  PubMed  Google Scholar 

  25. 25.

    Meding, S., Cheng, S., Simon-Haarhaus, B. & Langhorne, J. Role of gamma interferon during infection with Plasmodium chabaudi chabaudi. Infect. Immun. 58, 3671–3678 (1990).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26.

    John, C. C. et al. Gamma interferon responses to Plasmodium falciparum liver-stage antigen 1 and thrombospondin-related adhesive protein and their relationship to age, transmission intensity, and protection against malaria. Infect. Immun. 72, 5135–5142 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27.

    King, T. & Lamb, T. Interferon-γ: the Jekyll and Hyde of malaria. PLoS Pathog. 11, e1005118 (2015).

    PubMed  PubMed Central  Google Scholar 

  28. 28.

    Lelliott, P. M. & Coban, C. IFN-γ protects hepatocytes against Plasmodium vivax infection via LAP-like degradation of sporozoites. Proc. Natl Acad. Sci. USA 113, 6813–6815 (2016).

    CAS  PubMed  Google Scholar 

  29. 29.

    Morales-Mantilla, D. E. & King, K. Y. The role of interferon-gamma in hematopoietic stem cell development, homeostasis, and disease. Curr. Stem Cell Rep. 4, 264–271 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Ma, X., Ling, K. & Dzierzak, E. Cloning of the Ly‐6A (Sca‐1) gene locus and identification of a 3′ distal fragment responsible for high‐level γ‐interferon‐induced expression in vitro. Br. J. Haematol. 114, 724–730 (2001).

    CAS  PubMed  Google Scholar 

  31. 31.

    Boulais, P. E. et al. The majority of CD45Ter119CD31 bone marrow cell fraction is of hematopoietic origin and contains erythroid and lymphoid progenitors. Immunity 49, 627–639.e6 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Calvi, L. M. et al. Osteoblastic cells regulate the haematopoietic stem cell niche. Nature 425, 841–846 (2003).

    CAS  PubMed  Google Scholar 

  33. 33.

    Zhang, J. et al. Identification of the haematopoietic stem cell niche and control of the niche size. Nature 425, 836–841 (2003).

    CAS  PubMed  Google Scholar 

  34. 34.

    Arai, F. et al. Tie2/angiopoietin-1 signaling regulates hematopoietic stem cell quiescence in the bone marrow niche. Cell 118, 149–161 (2004).

    CAS  PubMed  Google Scholar 

  35. 35.

    Lo Celso, C. et al. Live-animal tracking of individual haematopoietic stem/progenitor cells in their niche. Nature 457, 92–96 (2009).

    CAS  PubMed  Google Scholar 

  36. 36.

    Kalajzic, I. et al. Use of type I collagen green fluorescent protein transgenes to identify subpopulations of cells at different stages of the osteoblast lineage. J. Bone Miner. Res. 17, 15–25 (2002).

    CAS  PubMed  Google Scholar 

  37. 37.

    Jilka, R. L. et al. Increased bone formation by prevention of osteoblast apoptosis with parathyroid hormone. J. Clin. Invest. 104, 439–446 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Jilka, R. L. Molecular and cellular mechanisms of the anabolic effect of intermittent PTH. Bone 40, 1434–1446 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Jilka, R. L. et al. Intermittent PTH stimulates periosteal bone formation by actions on post-mitotic preosteoblasts. Bone 44, 275–286 (2009).

    CAS  Google Scholar 

  40. 40.

    Kim, S. W. et al. Intermittent parathyroid hormone administration converts quiescent lining cells to active osteoblasts. J. Bone Miner. Res. 27, 2075–2084 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Balani, D. H., Ono, N. & Kronenberg, H. M. Parathyroid hormone regulates fates of murine osteoblast precursors in vivo. J. Clin. Invest. 127, 3327–3338 (2017).

    PubMed  PubMed Central  Google Scholar 

  42. 42.

    Li, J.-Y. et al. PTH expands short-term murine hemopoietic stem cells through T cells. Blood 120, 4352–4362 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Yao, H. et al. Parathyroid hormone enhances hematopoietic expansion via upregulation of cadherin‐11 in bone marrow mesenchymal stromal cells. Stem Cells 32, 2245–2255 (2014).

    CAS  PubMed  Google Scholar 

  44. 44.

    Terauchi, M. et al. T lymphocytes amplify the anabolic activity of parathyroid hormone through Wnt10b signaling. Cell Metab. 10, 229–240 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Duarte, D. et al. Inhibition of endosteal vascular niche remodeling rescues hematopoietic stem cell loss in AML. Cell Stem Cell 22, 64–77.e6 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Hooper, A. T. et al. Engraftment and reconstitution of hematopoiesis is dependent on VEGFR2-mediated regeneration of sinusoidal endothelial cells. Cell Stem Cell 4, 263–274 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Butler, J. M. et al. Endothelial cells are essential for the self-renewal and repopulation of notch-dependent hematopoietic stem cells. Cell Stem Cell 6, 251–264 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Kobayashi, H. et al. Angiocrine factors from Akt-activated endothelial cells balance self-renewal and differentiation of haematopoietic stem cells. Nat. Cell Biol. 12, 1046–1056 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Winkler, I. G. et al. Vascular niche E-selectin regulates hematopoietic stem cell dormancy, self renewal and chemoresistance. Nat. Med. 18, 1651–1657 (2012).

    CAS  PubMed  Google Scholar 

  50. 50.

    Ishitobi, H. et al. Flk1–GFP BAC Tg mice: an animal model for the study of blood vessel development. Exp. Anim. Tokyo 59, 615–622 (2010).

    CAS  Google Scholar 

  51. 51.

    Kirito, K., Fox, N., Komatsu, N. & Kaushansky, K. Thrombopoietin enhances expression of vascular endothelial growth factor (VEGF) in primitive hematopoietic cells through induction of HIF-1α. Blood 105, 4258–4263 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Passaro, D. et al. Increased vascular permeability in the bone marrow microenvironment contributes to disease progression and drug response in acute myeloid leukemia. Cancer Cell 32, 324–341.e6 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. 53.

    Itkin, T. et al. Distinct bone marrow blood vessels differentially regulate haematopoiesis. Nature 532, 323–328 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. 54.

    Boettcher, S. et al. Cutting Edge: LPS-induced emergency myelopoiesis depends on TLR4-expressing nonhematopoietic cells. J. Immunol. 188, 5824–5828 (2012).

    CAS  PubMed  Google Scholar 

  55. 55.

    Schürch, C. M., Riether, C. & Ochsenbein, A. F. Cytotoxic CD8+ T cells stimulate hematopoietic progenitors by promoting cytokine release from bone marrow mesenchymal stromal cells. Cell Stem Cell 14, 460–472 (2014).

    PubMed  Google Scholar 

  56. 56.

    Lee, M. S. et al. Plasmodium products persist in the bone marrow and promote chronic bone loss. Sci. Immunol. 2, eaam8093 (2017).

    PubMed  Google Scholar 

  57. 57.

    Terashima, A. et al. Sepsis-induced osteoblast ablation causes immunodeficiency. Immunity 44, 1434–1443 (2016).

    CAS  Google Scholar 

  58. 58.

    Prendergast, Á. M. et al. IFNα-mediated remodeling of endothelial cells in the bone marrow niche. Haematologica 102, 445–453 (2016).

    PubMed  Google Scholar 

  59. 59.

    Niz, M. D. et al. Plasmodium gametocytes display homing and vascular transmigration in the host bone marrow. Sci. Adv. 4, eaat3775 (2018).

    PubMed  PubMed Central  Google Scholar 

  60. 60.

    Joice, R. et al. Plasmodium falciparum transmission stages accumulate in the human bone marrow. Sci. Transl. Med. 6, 244re5 (2014).

    PubMed  PubMed Central  Google Scholar 

  61. 61.

    Batsivari, A. et al. Dynamic responses of the haematopoietic stem cell niche to diverse stresses. Nat. Cell Biol. 22, 7–17 (2020).

    CAS  PubMed  Google Scholar 

  62. 62.

    Muzumdar, M., Tasic, B., Miyamichi, K., Li, L. & Luo, L. A global double‐fluorescent Cre reporter mouse. Genesis 45, 593–605 (2007).

    CAS  PubMed  Google Scholar 

  63. 63.

    Huang, S. et al. Immune response in mice that lack the interferon-gamma receptor. Science 259, 1742–1745 (1993).

    CAS  PubMed  Google Scholar 

  64. 64.

    Ramakrishnan, C. et al. Laboratory maintenance of rodent malaria parasites. Methods Mol. Biol. 923, 51–72 (2013).

    CAS  PubMed  Google Scholar 

  65. 65.

    Sturm, A. et al. Alteration of the parasite plasma membrane and the parasitophorous vacuole membrane during exo-erythrocytic development of malaria parasites. Protist 160, 51–63 (2009).

    PubMed  Google Scholar 

  66. 66.

    Hawkins, E. D. et al. Measuring lymphocyte proliferation, survival and differentiation using CFSE time-series data. Nat. Protoc. 2, 2057–2067 (2007).

    CAS  PubMed  Google Scholar 

  67. 67.

    Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. 68.

    Chen, E. Y. et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics 14, 128 (2013).

    PubMed  PubMed Central  Google Scholar 

  69. 69.

    Kuleshov, M. V. et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 44, W90–W97 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. 70.

    Hawkins, E. D. et al. T-cell acute leukaemia exhibits dynamic interactions with bone marrow microenvironments. Nature 538, 518–522 (2016).

    PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This work was funded by the ERC, BBSRC and Wellcome Trust (ERC_STG 337066, BB/L023776/1 and IA 212304/Z/18/Z to C.L.C.; PhD studentship 105398/Z/14/Z to M.L.R.H.). A.M.B. was funded by the MRC (NIRG MR/N00227X/1). S.W. was funded by an MRC studentship. T.C.L. was funded by a Sir Henry Dale Fellowship (210424/Z/18/Z). Work by the Göttgens group is funded by the Wellcome Trust, Bloodwise and Cancer Research UK, with core funding from the Wellcome–MRC Cambridge Stem Cell Institute. C.L.C. and K.R.D. were supported in part by the Royal Irish Academy–Royal Society International Exchange Program (IEC\R1\180061). We thank M. Tunnicliff for passaging parasites and preparing mosquitos, F. Angrisano and K. Sala for technical assistance and advice with infections, I. Kucinski for support creating the interactive website, Imperial College DoLS Flow Cytometry and Central Biomedical Services and Crick Biological Research Facilities for support, and H. Fletcher, A. Cunnington and all of the Lo Celso group members for constructive discussions.

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Contributions

M.L.R.H., C.L.C., A.M.B. and R.E.S. conceived of the project. A.M.B. provided all of the mosquitos and support with infections. M.L.R.H. conducted the core experiments and data analysis. K.E., A.L., H.A., F.B., C.P., S.G.A. and N.R. performed the animal and flow cytometry experiments. S.W., N.K.W. and B.G. implemented the scRNA-seq and analysis. K.R.D. conducted the mathematical modelling. M.L.V. created the histological sections. C.P., T.C.L. and J.L. contributed to the experimental design and data analysis. M.L.R.H. and C.L.C. analysed the data and wrote the manuscript. All authors contributed to feedback.

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Correspondence to Cristina Lo Celso.

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The authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Transcriptomic analysis reveals rewiring of haematopoiesis and loss of HSC identity in response to P. berghei infection.

The expression of some exemplar genes used to determine the clustering of a, primitive HSPC, b, myeloid, c, erythroid, d, MEP, e, megakaryocyte and f, basophil cell populations plotted on the force-directed graph embedding for cells from control and infected mice at day 7 psi. g, Proportion (left panel) and number of cells (right panel) in each cluster shown in Fig. 1c. Each dot represents one mouse and bars represent the mean (n = 2 control, 2 infected mice). h, Violin plots quantifying HSC-scores calculated for the top 1% of highest scoring cells in the control and infected samples based on the fact that multi-potential HSCs occur on the order of approximately 1 out of every 100 cells found in the Lineage-c-Kit+ gate. n = 142 control cells and 140 infected cells. The centre dot shows the median and the box limits show the upper and lower quartiles. The whiskers extend from the box limit to the smallest and largest value to no further than 1.5x inter-quartile range from the hinge. Source data

Extended Data Fig. 2 Quantification of instantaneous proliferation rate and size of HSPC populations.

a, Representative flow cytometry plots showing the changing pattern of Sca-1 and c-Kit expression in Lineage BM haematopoietic cells. Boxes indicate the gating strategy used for LKS, LK, MPP and SLAM HSPC compartments throughout. b, A comparison of the absolute number of SLAM HSPCs obtained when gating with (LKS SLAM) and without (LK SLAM) the marker for Sca-1 in the BM of infected mice at day 7 psi. (n = 6 per group). c, Representative flow cytometry plots showing the gating strategy for EdU+, BrdU+ and EdU+BrdU+ Lineage-c-Kit+Sca-1- (LK) (top panel), MPP (middle panel) and SLAM HSPC (lower panel) populations in control and infected mice at day 7 psi. d, Proliferation rates and e, absolute numbers of LK, MPP and SLAM HSPC populations analysed by flow cytometry at day 1, 2, 3, 5 and 7 psi in control and infected mice. n numbers represent individual mice and are indicated as data points on the graph. f, Representative flow cytometry plots showing the gating strategy for E-SLAM cells (defined as CD48neg/lowCD150+CD45+EPCR++) in control BM. g, Representative flow cytometry plots showing the gating strategy for CD48neg HSCs in control and infected BM. Data pooled from up to 3 independent infection experiments. All data presented as mean ± s.e.m. P values determined by unpaired two-tailed Student’s t-tests. N.S, not significant. Source data

Extended Data Fig. 3 P. berghei triggers an inflammatory response driven primarily by IFN-γ.

a, Violin plots showing the expression of the 20 initial driving genes determined via scRNAseq analysis in control (black, left panels) and infected (maroon, right panels) mice. n = 14,193 control cells and 13,905 infected cells. The centre dot shows the median and the box limits show the upper and lower quartiles. The whiskers extend from the box limit to the smallest and largest value to no further than 1.5x inter-quartile range from the hinge. MA plots demonstrating the differential expression of genes in b, all cells analysed and c, the primitive HSPC cluster specifically. All significant differentially expressed genes are highlighted in red and the 109 driving genes are highlighted in green. d, IFN-α levels (pg/mL) measured by ELISA in the serum and BM supernatant of control and infected mice at day 7 psi (n = 9 control, 7 infected serum and 9 infected BM supernatant samples). e, TNF-α levels (pg/mL) measured by ELISA in the serum and BM supernatant of control and infected mice at day 7 psi (n = 6 control, 5 infected serum and 4 infected BM supernatant samples). Data pooled from 2 independent infection experiments (d and e). Data presented as mean ± s.e.m. P values determined by one-way ANOVA with post-hoc Bonferroni corrections. Source data

Extended Data Fig. 4 Evaluating the effects of IFN-γ on HSPCs and BM stroma of P. berghei-infected mice.

a, Absolute numbers and b, proliferation rates of MPPs and SLAM HSPCs in control and infected Ifngr1-/- mice at day 7 psi (n = 3 per group). IFN-γ levels (pg/mL) measured by ELISA in the serum and BM supernatant of control and infected c, Ifngr1-/-, d, WT → KO, e, KO + WT → WT and f, KO + WT → KO chimeric mice at day 7 psi (n = 5 per group). All data presented as mean ± s.e.m. P values determined by unpaired two-tailed Student’s t-tests. N.S, not significant. Source data

Extended Data Fig. 5 Targeting the osteolineage with PTH during P. berghei infection.

Representative images of haematoxylin and eosin stained sections of femurs from a, control and b, infected mice at day 7 psi. Black arrowheads indicate osteoblasts. Scale bars in control and infected images represent 50 μm. Scale bars in control and infected high magnification images represent 20 μm.c, Parasitaemia measured in peripheral blood calculated from Giemsa-stained blood films taken from PBS- (n = 14) and PTH-treated (n = 14), infected mice at day 7 psi - quantified by microscopic analysis. d, Representative flow cytometry plots of Lineage- BM haematopoietic cells in PTH-treated control and infected mice at day 7 psi. Boxes indicate the gating strategy used for LKS and LK populations throughout. e, Absolute number and f, proliferation rate of LK cells in PBS- and PTH-treated control and infected mice at day 7 psi. n numbers represent individual mice and are indicated as data points on the graph. g, Multilineage output of transplanted SLAM HSPCs from PBS- (maroon line) and PTH-treated (blue line), infected mT/mG donors into lethally irradiated CD45.2 recipient mice, assessed by flow cytometry up to 20 weeks after transplantation (n = 6 per group). h, Absolute number of CD3+ T-cells in peripheral blood of PBS- and PTH-treated mice. i, CD4+ and CD8+ T-cells as a proportion of CD3+ T-cells, j, proportion of activated CD8+ T-cells and k, proportion of CD4+ and CD8+ T-cells producing IFN-γ in peripheral blood of PBS- and PTH-treated, control and infected mice - quantified by flow cytometry. n = 4 control, 5 infected (h – k). Data pooled from up to 6 independent infection experiments. All data shown as mean ± s.e.m. P values determined by unpaired two-tailed Student’s t-tests (c, g), one-way (e, f, h, j) or two-way (i, k) ANOVA with post-hoc Bonferroni corrections. N.S, not significant. Source data

Extended Data Fig. 6 Short-term PTH treatment and combined long-term PTH and NAC treatment.

a, Schematic of the short-term PTH treatment regime carried out post-infection. b, Parasitaemia measured in peripheral blood calculated from Giemsa-stained blood films taken from PBS- (n = 5) and PTH-treated (n = 5), infected mice at day 7 psi - quantified by microscopic analysis.Absolute number of c, MPPs and e, SLAM HSPCs measured by flow cytometry in PBS- and PTH-treated, control and infected mice at day 7 psi (n > 4 per group).Proportion of EdU+ d, MPPs and f, SLAM HSPCs in PBS- and PTH-treated, control and infected mice at day 7 psi, measured by flow cytometry. n numbers represent individual mice and are indicated as data points on the graph. Absolute number of CD3+ T-cells in the g, BM and k, peripheral blood of PBS- and PTH-treated mice. CD4+ and CD8+ T-cells presented as a proportion of CD3+ T-cells in the h, BM and l, peripheral blood of PBS- and PTH post-infection-treated mice. Proportion of activated (defined as CD44+PD1+) CD8+ T-cells in the i, BM and m, peripheral blood of PBS- and PTH-treated mice. IFN-γ levels (pg/mL) measured by ELISA in j, BM supernatant and n, serum from PBS- and PTH post-infection-treated mice. For (g – n), n numbers represent individual mice and are indicated as data points on the graph. Data pooled from 2 independent infection experiments. All data presented as mean ± s.e.m. P values determined by unpaired two-tailed Student’s t-tests (b - f), one-way (g, h, j, k l, n) or two-way (i, m) ANOVA with post-hoc Bonferroni corrections. N.S, not significant. Source data

Extended Data Fig. 7 Combined PTH+NAC treatment partially rescues HSC function.

a, Parasitaemia measured in peripheral blood calculated from Giemsa-stained blood films taken from PBS- (n = 3) and NAC-treated (n = 4), infected mice at day 7 psi - quantified by microscopic analysis. b, Parasitaemia measured in peripheral blood calculated from Giemsa-stained blood films taken from PBS- (n = 3) and PTH+NAC-treated (n = 4), infected mice at day 7 psi - quantified by microscopic analysis. c, Multilineage output of transplanted SLAM HSPCs from PBS-treated, control (black line) and infected (maroon line) or PTH+NAC-treated, infected (green line) CD45.2 donors into lethally irradiated mT/mG recipient mice, assessed by flow cytometry of tomato- cells up to 20 weeks after transplantation (n = 7 recipients of PBS-treated control cells, 6 recipients of PBS-treated, infected cells, 7 recipients of PTH+NAC-treated, infected cells). All data presented as mean ± s.e.m. P values determined by unpaired two-tailed Student’s t-tests (a, b) or two-way ANOVA with post-hoc Bonferroni corrections (c). N.S, not significant. In (c) PBS-treated, infected versus PTH+NAC-treated, infected T-cell comparison P = 0.0137; PBS-treated, infected versus PTH+NAC-treated, infected B-cell comparison P = 0.0427; PBS-treated, control versus PTH+NAC-treated, infected Myeloid comparison P = 0.0396 and PBS-treated, infected versus PTH+NAC-treated, infected Myeloid comparison P = 0.0437. Source data

Extended Data Fig. 8 Gating strategy for measuring lineage output in transplantation assays.

Representative flow cytometry plots from the peripheral blood of transplantation assay recipient mice allowing the analysis of multilineage output after transplantation of SLAM HSPCs from control or infected donor mice (presented in Fig. 1i, Extended Data Figs. 5g and 7c). Black boxes indicate the gates used after excluding debris, doublets and dead cells to gate on T cells, B cells and the non-lymphoid compartment in order to isolate myeloid cells.

Extended Data Fig. 9 Gating strategy for analysing stroma, endothelial cells and osteoblasts.

Representative flow cytometry plots of the bone marrow of control and infected mice at day 7 psi demonstrating the gating strategy used to isolate haematopoietic cells and stroma (Fig. 3), (i) osteoblasts (Fig. 4) and (ii) endothelial cells (Fig. 5). Black boxes indicate the gates used after excluding debris, doublets and dead cells to gate on the populations labelled.

Extended Data Fig. 10 Gating strategy for analysing T-cell subsets.

Representative flow cytometry plots of the bone marrow of PBS-treated control and infected mice at day 7 psi demonstrating the gating strategy used to isolate CD3+ T-cells, identify CD4+ and CD8+ subsets and look at activated (defined as CD44+PD1+) CD8+ T-cells (presented in Fig. 4m–p, Extended Data Fig. h - k and Extended Data Fig. 6g–i,k–m). Black boxes indicate the gates used after excluding debris, doublets and dead cells to gate on the populations labelled. Note: the same gating strategy was used in PTH-treated mice as well as for the peripheral blood analysis of T cells.

Supplementary information

Supplementary Information

Supplementary Note. Mathematical modelling reveals strikingly altered HSPC dynamics upon P. berghei infection. Here, we describe in detail the reasoning behind the phenotypic analysis of cells used in this study, provide an in-depth discussion about the mathematical modelling carried out using our experimental data, and report the methods used to generate the models presented in Fig. 2.

Reporting Summary

Supplementary Tables 1–3

Supplementary Table 1: Driving genes identified by scRNA-seq. The 109 driving genes identified by scRNA-seq analysis of control and infected Lineagec-Kit+ BM at day 7 psi. The initial 20 driving genes from the analysis are highlighted in bold. Supplementary Table 2: Gene Ontology analysis of biological processes enriched in control and infected mice. Table of all data exported for the Gene Ontology analysis of biological processes enriched in samples sequenced from control and infected mice. The P values were determined by Fisher’s exact test and adjusted for multiple comparisons using post-hoc Bonferroni corrections. Supplementary Table 3: Antibodies used in this study.

Supplementary Video 1

Increased dynamism of endothelial cells in P. berghei-infected mice. Representative maximum projection of three-dimensional time-lapse data (shown at three frames per second) of an area from a control (left panel) and infected (right panel) Flk1–GFP mouse imaged at day 7 psi (shown in Fig. 5f). An image was recorded every 3 min for 120 min. Flk1–GFP+ endothelial cells are shown in black. Red numbered arrowheads show migratory GFP+ cells. Blue arrowheads show endothelial cells shifting their centre of mass. This video is representative of four control mice and seven infected mice imaged.

Supplementary Video 2

P. berghei induces an increase in vascular permeability. Representative maximum projection of three-dimensional time-lapse data (shown at two frames per second) of an area from a control (left panel) and infected (right panel) Flk1–GFP mouse imaged at day 7 psi, injected with 3 mg 65–80 kDa TRITC–dextran (shown in Fig. 5i). An image was recorded every minute for 10 min immediately after injecting the vascular dye to assess vascular leakiness. Flk1–GFP+ endothelial cells are shown in green. TRITC–dextran is shown in magenta. White lines delineate the bone and red boxes highlight regions of interest within the parenchyma where measurements were taken. This video is representative of four control and five infected mice imaged.

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Haltalli, M.L.R., Watcham, S., Wilson, N.K. et al. Manipulating niche composition limits damage to haematopoietic stem cells during Plasmodium infection. Nat Cell Biol 22, 1399–1410 (2020). https://doi.org/10.1038/s41556-020-00601-w

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