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.

  • Article
  • Published:

Hypoxia sensing in resident cardiac macrophages regulates monocyte fate specification following ischemic heart injury

Nature Cardiovascular Research volume 3, pages 1337–1355 (2024)Cite this article

Subjects

Abstract

Myocardial infarction initiates cardiac remodeling and is central to heart failure pathogenesis. Following myocardial ischemia–reperfusion injury, monocytes enter the heart and differentiate into diverse subpopulations of macrophages. Here we show that deletion of Hif1α, a hypoxia response transcription factor, in resident cardiac macrophages led to increased remodeling and overrepresentation of macrophages expressing arginase 1 (Arg1). Arg1+ macrophages displayed an inflammatory gene signature and may represent an intermediate state of monocyte differentiation. Lineage tracing of Arg1+ macrophages revealed a monocyte differentiation trajectory consisting of multiple transcriptionally distinct states. We further showed that deletion of Hif1α in resident cardiac macrophages resulted in arrested progression through this trajectory and accumulation of an inflammatory intermediate state marked by persistent Arg1 expression. Depletion of the Arg1+ trajectory accelerated cardiac remodeling following ischemic injury. Our findings unveil distinct trajectories of monocyte differentiation and identify hypoxia sensing as an important determinant of monocyte differentiation following myocardial infarction.

Access through your institution
Buy or subscribe

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

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Hif1α deletion in monocytes and macrophages leads to accelerated LV remodeling and reduced cardiac function after MI.
Fig. 2: scRNAseq of monocytes, macrophages and dendritic-like cells 5 days after MI reveals overrepresentation of Arg1+ macrophages in macHif1aKO mice.
Fig. 3: Spatiotemporal dynamics of Arg1+ macrophages after ischemic heart injury.
Fig. 4: Hif1α deletion in cardiac resident macrophages is sufficient to increase the abundance of Arg1+ macrophages and accelerate LV remodeling after MI.
Fig. 5: The Arg1 macrophage differentiation trajectory is maintained through MI and differentiates into various macrophage subtypes.
Fig. 6: The Arg1 macrophage differentiation trajectory represents a distinct monocyte to macrophage differentiation trajectory during ischemic heart injury.
Fig. 7: Hif1α activity in resident cardiac macrophages regulates progression through the Arg1 differentiation trajectory.
Fig. 8: Macrophages downstream of the Arg1+ differentiation trajectory are protective following ischemic heart injury.

Similar content being viewed by others

Data availability

scRNAseq and snRNAseq data are available on the Gene Expression Omnibus (GSE251991). Publicly available datasets used in this study are available at EnrichR/Wikipathways/ChEA (https://mayyanlab.cloud/Enrichr/) and PMID 30582448 (available on https://www.immgen.org/), GSE119355, E-MTAB-7376, GSE135310, GSE197441 and GSE197853. Source data are provided with this paper.

Code availability

R and Python scripts are available via GitHub at https://github.com/fkadyrov/Kadyrov−Lavine-2024-Hypoxia.

References

  1. Roger, V. L. Epidemiology of heart failure. Circ. Res. 113, 646–659 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Timmers, L. et al. Toll-like receptor 4 mediates maladaptive left ventricular remodeling and impairs cardiac function after myocardial infarction. Circ. Res. 102, 257–264 (2008).

    Article  CAS  PubMed  Google Scholar 

  3. Lewis, E. F. et al. Predictors of late development of heart failure in stable survivors of myocardial infarction: the CARE study. J. Am. Coll. Cardiol. 42, 1446–1453 (2003).

    Article  PubMed  Google Scholar 

  4. Epelman, S., Liu, P. P. & Mann, D. L. Role of innate and adaptive immune mechanisms in cardiac injury and repair. Nat. Rev. Immunol. 15, 117–129 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Bajpai, G. et al. Tissue resident CCR2− and CCR2+ cardiac macrophages differentially orchestrate monocyte recruitment and fate specification following myocardial injury. Circ. Res. 124, 263–278 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Bajpai, G. et al. The human heart contains distinct macrophage subsets with divergent origins and functions. Nat. Med. 24, 1234–1245 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Lavine, K. J. et al. The macrophage in cardiac homeostasis and disease: JACC macrophage in CVD series (Part 4). J. Am. Coll. Cardiol. 72, 2213–2230 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Li, W. et al. Heart-resident CCR2+ macrophages promote neutrophil extravasation through TLR9/MyD88/CXCL5 signaling. JCI Insight 1, e87315 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Frantz, S. & Nahrendorf, M. Cardiac macrophages and their role in ischaemic heart disease. Cardiovasc. Res. 102, 240–248 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Javaheri, A. et al. TFEB activation in macrophages attenuates postmyocardial infarction ventricular dysfunction independently of ATG5-mediated autophagy. JCI Insight 4, e127312 (2019).

    Article  PubMed Central  Google Scholar 

  11. Molawi, K. et al. Progressive replacement of embryo-derived cardiac macrophages with age. J. Exp. Med. 211, 2151–2158 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. van Amerongen, M. J., Harmsen, M. C., van Rooijen, N., Petersen, A. H. & van Luyn, M. J. A. Macrophage depletion impairs wound healing and increases left ventricular remodeling after myocardial injury in mice. Am. J. Pathol. 170, 818–829 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Kaikita, K. et al. Targeted deletion of CC chemokine receptor 2 attenuates left ventricular remodeling after experimental myocardial infarction. Am. J. Pathol. 165, 439–447 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Lavine, K. J. et al. Distinct macrophage lineages contribute to disparate patterns of cardiac recovery and remodeling in the neonatal and adult heart. Proc. Natl Acad. Sci. USA 111, 16029–16034 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Dick, S. A. et al. Self-renewing resident cardiac macrophages limit adverse remodeling following myocardial infarction. Nat. Immunol. 20, 29–39 (2019).

    Article  CAS  PubMed  Google Scholar 

  16. Wang, J. et al. Effect of CCR2 inhibitor-loaded lipid micelles on inflammatory cell migration and cardiac function after myocardial infarction. Int. J. Nanomed. 13, 6441–6451 (2018).

    Article  CAS  Google Scholar 

  17. Wang, G. L., Jiang, B. H., Rue, E. A. & Semenza, G. L. Hypoxia-inducible factor 1 is a basic-helix–loop–helix–PAS heterodimer regulated by cellular O2 tension. Proc. Natl Acad. Sci. USA 92, 5510–5514 (1995).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Semenza, G. L. & Wang, G. L. A nuclear factor induced by hypoxia via de novo protein synthesis binds to the human erythropoietin gene enhancer at a site required for transcriptional activation. Mol. Cell. Biol. 12, 5447–5454 (1992).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Wang, G. L. & Semenza, G. L. General involvement of hypoxia-inducible factor 1 in transcriptional response to hypoxia. Proc. Natl Acad. Sci. USA 90, 4304–4308 (1993).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Yona, S. et al. Fate mapping reveals origins and dynamics of monocytes and tissue macrophages under homeostasis. Immunity 38, 79–91 (2013).

    Article  CAS  PubMed  Google Scholar 

  21. Ryan, H. E. et al. Hypoxia-inducible factor-1α is a positive factor in solid tumor growth. Cancer Res. 60, 4010–4015 (2000).

    CAS  PubMed  Google Scholar 

  22. Keith, B., Johnson, R. S. & Simon, M. C. HIF1α and HIF2α: sibling rivalry in hypoxic tumor growth and progression. Nat. Rev. Cancer 12, 9–22 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Rizzo, G. et al. Dynamics of monocyte-derived macrophage diversity in experimental myocardial infarction. Cardiovasc. Res. 119, 772–785 (2023).

    Article  CAS  PubMed  Google Scholar 

  24. Farbehi, N. et al. Single-cell expression profiling reveals dynamic flux of cardiac stromal, vascular and immune cells in health and injury. eLife 8, e43882 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Vafadarnejad, E. et al. Dynamics of cardiac neutrophil diversity in murine myocardial infarction. Circ. Res. 127, e232–e249 (2020).

    Article  CAS  PubMed  Google Scholar 

  26. Semenza, G. L. HIF-1 and human disease: one highly involved factor. Genes Dev. 14, 1983–1991 (2000).

    Article  CAS  PubMed  Google Scholar 

  27. Lavine, K. J. et al. Coronary collaterals predict improved survival and allograft function in patients with coronary allograft vasculopathy. Circ. Heart Fail. 6, 773–784 (2013).

    Article  PubMed  Google Scholar 

  28. Swirski, F. K. & Nahrendorf, M. Cardioimmunology: the immune system in cardiac homeostasis and disease. Nat. Rev. Immunol. 18, 733–744 (2018).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Koenig, A. L. et al. Genetic mapping of monocyte fate decisions following myocardial infarction. Preprint at bioRxiv https://doi.org/10.1101/2023.12.24.573263 (2023).

  31. Glass, C. K. & Saijo, K. Nuclear receptor transrepression pathways that regulate inflammation in macrophages and T cells. Nat. Rev. Immunol. 10, 365–376 (2010).

    Article  CAS  PubMed  Google Scholar 

  32. Kobayashi, E. H. et al. Nrf2 suppresses macrophage inflammatory response by blocking proinflammatory cytokine transcription. Nat. Commun. 7, 11624 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Gilchrist, M. et al. Systems biology approaches identify ATF3 as a negative regulator of Toll-like receptor 4. Nature 441, 173–178 (2006).

    Article  CAS  PubMed  Google Scholar 

  34. Dorrington, M. G. & Fraser, I. D. C. NF-κB signaling in macrophages: dynamics, crosstalk, and signal integration. Front. Immunol. 10, 705 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Alexanian, M. et al. Chromatin remodeling drives immune-fibroblast crosstalk in heart failure pathogenesis. Preprint at bioRxiv https://doi.org/10.1101/2023.01.06.522937 (2023).

  36. Schulz, C. et al. A lineage of myeloid cells independent of Myb and hematopoietic stem cells. Science 336, 86–90 (2012).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Schneider, C. et al. Tissue-resident group 2 innate lymphoid cells differentiate by layered ontogeny and in situ perinatal priming. Immunity 50, 1425–1438.e5 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Naresh, N. K. et al. Monocyte and/or macrophage infiltration of heart after myocardial infarction: MR imaging by using T1-shortening liposomes. Radiology 264, 428–435 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Aronoff, L., Epelman, S. & Clemente-Casares, X. Isolation and identification of extravascular immune cells of the heart. J. Vis. Exp. https://doi.org/10.3791/58114 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Tamoutounour, S. et al. CD64 distinguishes macrophages from dendritic cells in the gut and reveals the Th1-inducing role of mesenteric lymph node macrophages during colitis. Eur. J. Immunol. 42, 3150–3166 (2012).

    Article  CAS  PubMed  Google Scholar 

  42. Reese, T. A. et al. Chitin induces accumulation in tissue of innate immune cells associated with allergy. Nature 447, 92–96 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Wong, N. R. et al. Resident cardiac macrophages mediate adaptive myocardial remodeling. Immunity 54, 2072–2088.e7 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Madisen, L. et al. A robust and high-throughput Cre reporting and characterization system for the whole mouse brain. Nat. Neurosci. 13, 133–140 (2010).

    Article  CAS  PubMed  Google Scholar 

  45. Kopecky, B. J. et al. Donor macrophages modulate rejection after heart transplantation. Circulation 146, 623–638 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Zhu, Y. et al. Targeting fatty acid β-oxidation impairs monocyte differentiation and prolongs heart allograft survival. JCI Insight 7, e151596 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Corry, R. J., Winn, H. J. & Russell, P. S. Primarily vascularized allografts of hearts in mice. The role of H-2D, H-2K, and non-H-2 antigens in rejection. Transplantation 16, 343–350 (1973).

    Article  CAS  PubMed  Google Scholar 

  48. Schreiber, H. A. et al. Intestinal monocytes and macrophages are required for T cell polarization in response to Citrobacter rodentium. J. Exp. Med. 210, 2025–2039 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Abe, H. et al. Macrophage hypoxia signaling regulates cardiac fibrosis via oncostatin M. Nat. Commun. 10, 2824 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  50. DeBerge, M. et al. Hypoxia-inducible factors individually facilitate inflammatory myeloid metabolism and inefficient cardiac repair. J. Exp. Med. 218, e20200667 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Heck-Swain, K. L. et al. Myeloid hypoxia-inducible factor HIF1A provides cardio-protection during ischemia and reperfusion via induction of netrin-1. Front. Cardiovasc. Med. 9, 97041 (2022).

    Article  Google Scholar 

  52. Clausen, B. E., Burkhardt, C., Reith, W., Renkawitz, R. & Förster, I. Conditional gene targeting in macrophages and granulocytes using LysMcre mice. Transgenic Res. 8, 265–277 (1999).

    Article  CAS  PubMed  Google Scholar 

  53. Gerlach, C. et al. The chemokine receptor CX3CR1 defines three antigen-experienced CD8 T cell subsets with distinct roles in immune surveillance and homeostasis. Immunity 45, 1270–1284 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Becher, U. M. et al. Inhibition of leukotriene C4 action reduces oxidative stress and apoptosis in cardiomyocytes and impedes remodeling after myocardial injury. J. Mol. Cell. Cardiol. 50, 570–577 (2011).

    Article  CAS  PubMed  Google Scholar 

  55. Nahrendorf, M. et al. The healing myocardium sequentially mobilizes two monocyte subsets with divergent and complementary functions. J. Exp. Med. 204, 3037–3047 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Viola, A., Munari, F., Sánchez-Rodríguez, R., Scolaro, T. & Castegna, A. The metabolic signature of macrophage responses. Front. Immunol. 10, 1462 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Yang, Z. & Ming, X.-F. Functions of arginase isoforms in macrophage inflammatory responses: impact on cardiovascular diseases and metabolic disorders. Front. Immunol. 5, 533 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  58. Mouton, A. J. et al. Mapping macrophage polarization over the myocardial infarction time continuum. Basic Res. Cardiol. https://doi.org/10.1007/s00395-018-0686-x (2018).

  59. Yadav, P. et al. Reciprocal inflammatory signals establish profibrotic cross-feeding metabolism. Preprint at bioRxiv https://doi.org/10.1101/2023.09.06.556606 (2023).

  60. Hoeft, K. et al. Platelet-instructed SPP1+ macrophages drive myofibroblast activation in fibrosis in a CXCL4-dependent manner. Cell Rep. 42, 112131 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Croxford, A. L. et al. The cytokine GM-CSF drives the inflammatory signature of CCR2+ monocytes and licenses autoimmunity. Immunity 43, 502–514 (2015).

    Article  CAS  PubMed  Google Scholar 

  62. Li, W. et al. Intravital 2-photon imaging of leukocyte trafficking in beating heart. J. Clin. Invest. 122, 2499–2508 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Li, W. et al. Ferroptotic cell death and TLR4/Trif signaling initiate neutrophil recruitment after heart transplantation. J. Clin. Invest. 129, 2293–2304 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  64. Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).

    Article  CAS  PubMed  Google Scholar 

  65. Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587.e29 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Chen, E. Y. et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics https://doi.org/10.1186/1471-2105-14-12 (2013).

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Xie, Z. et al. Gene set knowledge discovery with Enrichr. Curr. Protoc. 1, e90 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Pico, A. R. et al. WikiPathways: pathway editing for the people. PLOS Biol. 6, e184 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  71. Lachmann, A. et al. ChEA: transcription factor regulation inferred from integrating genome-wide ChIP-X experiments. Bioinformatics 26, 2438–2444 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. https://doi.org/10.1186/s13059-017-1382-0 (2018).

  73. Hao, Y. et al. Dictionary learning for integrative, multimodal, and massively scalable single-cell analysis. Nat. Biotechnol. 42, 293–304 (2024).

    Article  CAS  PubMed  Google Scholar 

  74. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Jin, S., Plikus, M. V. & Nie, Q. CellChat for systematic analysis of cell–cell communication from single-cell transcriptomics. Nat. Protoc. https://doi.org/10.1038/s41596-024-01045-4 (2024).

  76. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

K.J.L. is supported by the Washington University in St. Louis Rheumatic Diseases Research Resource-Based Center grant (National Institutes of Health (NIH) P30AR073752), the NIH (R01 HL138466, R01 HL139714, R01 HL151078, R01 HL161185 and R35 HL161185), Leducq Foundation Network (#20CVD02), Burroughs Wellcome Fund (1014782), the Children’s Discovery Institute of Washington University and St. Louis Children’s Hospital (CH-II-2015-462, CH-II-2017-628 and PM-LI-2019-829), Foundation of Barnes-Jewish Hospital (8038-88) and generous gifts from Washington University School of Medicine. F.F.K. was supported by the NIH (5T32GM007067-45, 5T32GM007067-46 and 5T32HL134635-05). We thank the Genome Technology Access Center at the McDonnell Genome Institute at Washington University School of Medicine for help with genomic analysis. The Center is partially supported by National Cancer Institute Cancer Center Support grant no. P30 CA91842 to the Siteman Cancer Center. We are grateful to R. Locksley (University of California San Francisco) for providing us with Arg1tdT-CreERT2 mice. This publication is solely the responsibility of the authors and does not necessarily represent the official view of the National Center for Research Resources or NIH.

Author information

Authors and Affiliations

  1. Center for Cardiovascular Research, Division of Cardiology, Department of Medicine, Washington University School of Medicine, Saint Louis, MO, USA

    Farid F. Kadyrov, Andrew L. Koenig, Junedh M. Amrute, Carla J. Weinheimer, Jessica M. Nigro, Attila Kovacs, Andrea L. Bredemeyer, Steven Yang, Shibali Das, Vinay R. Penna, Alekhya Parvathaneni, Lulu Lai, Niklas Hartmann, Benjamin J. Kopecky & Kory J. Lavine

  2. Division of Cardiothoracic Surgery, Department of Surgery, Washington University School of Medicine, Saint Louis, MO, USA

    Hao Dun, Wenjun Li & Daniel Kreisel

  3. German Center for Cardiovascular Research (DZHK), Partner Site Heidelberg, Heidelberg, Germany

    Niklas Hartmann

  4. Department of Cardiology, Internal Medicine III, Heidelberg University Hospital, Heidelberg, Germany

    Niklas Hartmann

  5. Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, MO, USA

    Daniel Kreisel & Kory J. Lavine

  6. Department of Developmental Biology, Washington University School of Medicine, Saint Louis, MO, USA

    Kory J. Lavine

Authors
  1. Farid F. Kadyrov
    View author publications

    Search author on:PubMed Google Scholar

  2. Andrew L. Koenig
    View author publications

    Search author on:PubMed Google Scholar

  3. Junedh M. Amrute
    View author publications

    Search author on:PubMed Google Scholar

  4. Hao Dun
    View author publications

    Search author on:PubMed Google Scholar

  5. Wenjun Li
    View author publications

    Search author on:PubMed Google Scholar

  6. Carla J. Weinheimer
    View author publications

    Search author on:PubMed Google Scholar

  7. Jessica M. Nigro
    View author publications

    Search author on:PubMed Google Scholar

  8. Attila Kovacs
    View author publications

    Search author on:PubMed Google Scholar

  9. Andrea L. Bredemeyer
    View author publications

    Search author on:PubMed Google Scholar

  10. Steven Yang
    View author publications

    Search author on:PubMed Google Scholar

  11. Shibali Das
    View author publications

    Search author on:PubMed Google Scholar

  12. Vinay R. Penna
    View author publications

    Search author on:PubMed Google Scholar

  13. Alekhya Parvathaneni
    View author publications

    Search author on:PubMed Google Scholar

  14. Lulu Lai
    View author publications

    Search author on:PubMed Google Scholar

  15. Niklas Hartmann
    View author publications

    Search author on:PubMed Google Scholar

  16. Benjamin J. Kopecky
    View author publications

    Search author on:PubMed Google Scholar

  17. Daniel Kreisel
    View author publications

    Search author on:PubMed Google Scholar

  18. Kory J. Lavine
    View author publications

    Search author on:PubMed Google Scholar

Contributions

F.F.K. performed IHC staining, imaging and quantification; FACS analysis and sorting; qPCR; scRNAseq/snRNAseq analysis; trajectory analysis; receptor–ligand analysis and mouse treatments. C.J.W. and J.M.N. performed closed-chest I/R surgeries. A.K. performed echocardiography. K.J.L. quantified echocardiography data. A.L.B. performed FACS sorting and 10X library preparation for macHif1aKO mice and contributed to the design and interpretation of FACS experiments. A.L.K. optimized the Ccr2ERT2Cre Rosa26lsl-tdT recombination strategy; prepared, analyzed and annotated the reference MI scRNAseq dataset and contributed to bioinformatics analyses and IHC. J.M.A. and L.L. contributed to bioinformatics analyses. W.L. performed cervical syngeneic heart transplant and two-photon intravital imaging. H.D. performed abdominal syngeneic heart transplant surgeries. B.J.K. and D.K. contributed to design and interpretation of heart transplant experiments. S.Y. performed IHC staining, imaging and quantification. V.R.P. contributed to the design and interpretation of Cytek FACS experiments. S.D., N.H. and S.Y. performed cytokine and chemokine assays and preparation of protein samples. V.R.P. and A.P. assisted with snRNAseq library preparation. F.F.K. made all figures. F.F.K. and K.J.L. drafted the manuscript. All authors contributed to the experimental design, analysis and interpretation as well as manuscript production. K.J.L. is responsible for all aspects of this manuscript including experimental design, data analysis and manuscript production. All authors approved the final version of the manuscript.

Corresponding author

Correspondence to Kory J. Lavine.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Cardiovascular Research thanks Stefan Frantz, 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 Hif1α deletion in monocytes and macrophages does not impact angiogenesis or the myeloid compartment after MI.

a, qPCR of Hif1α mRNA expression in sorted monocytes and macrophages from Control and macHif1aKO mice 5 days after I/R. N = 3. b, Representative 20x confocal images of CD34 (green) IHC staining within the border zone of Control and macHif1aKO mice 4 days (N = 6 vs 5) and 4 weeks (N = 5) after I/R. DAPI is in blue. c, Quantification of b, displayed as the percentage of CD34 signal per 20x field. d, Representative 20x confocal images of αSMA (green) IHC in the border zone of Control and macHif1aKO mice 4 weeks after I/R. DAPI is in blue. Quantification is displayed as the total number of αSMA+ vessels per 20x field. N = 6 vs 5. e, FACS from Control and macHif1aKO mice 5 days after I/R. Quantification shows cell numbers normalized to mg of heart tissue used for FACS. N = 5 vs 6. f, Representative 20x confocal images of macrophage IHC within the infarct of Control and macHif1aKO mice 4 days after I/R. CD68 staining is in green, DAPI is in blue. g, Quantification of f displayed as the total number of CD68+ cells per 20x field. N = 6 vs 5. h, Representative 20x confocal images of neutrophil IHC within the infarct of Control and macHif1aKO mice 4 days after I/R. Ly6G staining is in green and DAPI staining is in blue. i, Quantification of h displayed as the total number of Ly6G+ cells per 20x field. N = 6 vs 5. P-values are determined using a two tailed t-test assuming equal variance. For N, each data point is an individual mouse. Data are presented as mean ± SEM.

Source data

Extended Data Fig. 2 Single cell RNA sequencing and reference mapping of cardiac monocytes, macrophages, and dendritic-like cells 5 days after MI.

a, FACS gating strategy to isolate monocytes, macrophages, and dendritic-like cells from Control and macHif1aKO mice 5 days after I/R for use in scRNAseq. Cells were gated for leukocytes (SSC-A vs CD45), single cells (SSC-W vs SSC-H), debris exclusion (SSC-A vs FSC-A), neutrophil exclusion (Ly6C vs Ly6G), live cells (SSC-A vs DAPI) and monocytes/macrophages (Ly6C vs CD64). b, Violin plots representing quality control of scSEQ data post quality control. Cells with greater than 500 and less than 5000 genes (nFeature_RNA), read counts less than 20,000 (nCount_RNA), and proportion of transcripts mapping to mitochondrial genes less than 10 percent (prompt) were used for downstream analysis. c, UMAP projection and annotations from the reference MI scSEQ data set (monocytes, macrophages, and dendritic-like cells 3, 7, 13, and 28 days after I/R) and the query dataset (Control and macHif1aKO monocytes, macrophages, and dendritic-like cells 5 days after I/R) after reference mapping in Seurat. Query dataset is plotted on a reference UMAP (refUMAP) d, Mapping scores of each subpopulation projected onto refUMAP of the query data set (Control and macHif1aKO). e, Heatmap of the top 10 upregulated genes in each subpopulation across the entire Control and macHif1aKO query data set. f, Z-score profile genes of each subpopulation from the reference data set. g, Z-scores from f projected onto refUMAP of the query data set (Control and macHif1aKO. h, Z-scores from f compared to each subpopulation in the query data set represented as a dot-plot.

Extended Data Fig. 3 Palantir analysis of Control and macHif1aKO mice 5 days after MI.

a, PCA plot of cell cycle genes in Control and macHif1aKO scSEQ data 5 days after I/R before (Cell Cycle PCA) and after (Post Correction) regressing out cell cycle scores. b, Reference UMAP of Control and macHif1aKO scSEQ data 5 days after I/R highlighting the starting cell used. Starting cell was determined by selecting the Control monocyte with the highest monocyte Z-score (Plac8, Ly6c2). c, Force directed layout (FDL) output of Palantir analysis highlighting each cell subpopulation in Control and macHif1aKO scSEQ data. d, Terminal state probability scores from Palantir analysis indicating the likelihood that a cell becomes either Trem2 or cDC2.

Extended Data Fig. 4 Intravascular staining FACS of Arg1tdT-CreERT2 mice 2 days after MI.

a, FACS gating strategy for intravascular staining in the heart of Arg1tdt-CreERT2 mice 2 days after I/R. Mice were injected with CD45 antibody 5 minutes prior to tissue collection (CD45IV). Cells were gated for leukocytes (SSC-A vs CD45), single cells (SSC-W vs SSC-H), debris exclusion (SSC-A vs FSC-A), myeloid cells (SSC-A vs CD11b), neutrophil exclusion (SSC-A vs Ly6G) and monocytes/macrophages (Ly6C vs CD64). Intravascular (CD45IV+) and extravascular cells (CD45IV−) were identified (CD45 vs CD45IV). Arg1+ cells were identified via TDT expression in the CD45iv− and CD45iv+ gate (CD45 vs Arg1 (TDT)). b, Representative FACS plots of the peripheral blood of Arg1tdt-CreERT2 mice 2 days after I/R when performing intravascular staining.

Extended Data Fig. 5 Single nuclei RNA sequencing and clustering of resHif1aKO hearts 3 days after MI.

a, Representative FACS plot of single nuclei prep from Control and resHif1aKO hearts 3 days after I/R stained with DRAQ5 and used for snRNAseq. b, Violin plots representing quality control of snRNAseq data post quality control. Nuclei with greater than 1000 and less than 10000 read counts (nCount_RNA) and proportion of transcripts mapping to mitochondrial genes less than 5 percent (prompt) were used for downstream analysis. c, Violin plots of scrublet scores in each snRNAseq library (KO1/KO2 – resHif1aKO, WT1/WT2 – Control). Nuclei with scores less that 0.25 were used for downstream analysis. d, UMAP of snRNAseq data after quality control, normalization, clustering, and annotation. e, UMAP of cleaned and re-clustered data from d. Cardiomyocyte + Epicardium, Endocardium + Endothelium, Fibroblast, Myeloid, NKT, and Pericyte + SMC populations were subset and individually re-clustered to remove contaminating populations, and then recombined and re-clustered with Adipocyte, BCell, Lymphatic, Neuron, Neutrophil, and pDC populations to generate a final dataset. Stacked bar graph represents proportions of each annotated population per genotype. f, UMAP of Myeloid populations after re-clustering. g, Z-scores from reference MI scRNAseq data set (monocytes, macrophages, and dendritic-like cells 3, 7, 13, and 28 days after I/R) used to assist with annotating data from f represented as a dot plot. h, UMAP and stacked bar graph split by genotype of annotated Myeloid populations. i, CellChat analysis of resHif1aKO data 3 days after I/R represented as a chord diagram of upregulated and downregulated ligand receptor interactions between resident macrophages and other cell types. j, CellChat chord diagram representing the GAS signaling network in Control and resHif1aKO mice from resident macrophages to other cell types.

Extended Data Fig. 6 Validation, single cell RNA sequencing, and reference mapping of Arg1ZsGr mice after MI.

a, Representative 20x confocal images of ARG1 IHC in the infarct and remote zone of Arg1ZsGr mice 2, 7, 30 days after I/R. Quantification of IHC is displayed as the percentage of ZsGr+ cells that are ARG1+ZsGr+. N = 4 vs 4 vs 3. Sensitivity of Arg1ZsGr mice 2 days after I/R within the infarct, quantified as the percentage of ARG1+ cells that are ZsGr+ or ZsGr−. N = 4 vs 4. b, Representative 20x confocal images of VSIG4 IHC in the infarct and remote zone of Arg1ZsGr mice 2, 7, 30 days after I/R. Quantification of IHC is displayed as the percentage of ZsGr+ cells that are VSIG4+ZsGr+. N = 3 vs 4 vs 3. c, FACS gating strategy for isolation of ZsGr− and ZsGr+ monocytes and macrophages from Arg1ZsGr mice 2 and 30 days after I/R. d, Violin plots representing scRNAseq data post quality control. Cells with greater than 500 and less than 5000 genes (nFeature_RNA), read counts less than 20,000 (nCount_RNA), and proportion of transcripts mapping to mitochondrial genes less than 10 percent (prompt) were used. e, Arg1 expression in the ZsGr− and ZsGr+ libraries at day 2 and 30. f, Stacked bar graph representing the proportion of each cell population in the data split between ZsGr− and ZsGr+ cells 2 and 30 days after I/R. g, Heat map of mapping scores for each cell population in the Query (Arg1ZsGr) plotted against the Reference MI data set. h, Z-score profiles from the reference data set (Extended Data Fig. 2f) compared to each subpopulation in the Arg1ZsGr data set represented as a dot-plot. i, Reference UMAP plots split between ZsGr− and ZsGr+ libraries 2 and 30 days after I/R after mapping to reference MI data set. The reference MI scRNAseq data set was the same as the one displayed in Extended Data Fig. 2. P-values are determined using the ordinary one-way ANOVA using Tukey’s multiple comparisons test. For N, each data point is an individual mouse. Data are presented as mean ± SEM.

Source data

Extended Data Fig. 7 Validation of Arg1ZsGr mice in Injury and Non-Injury conditions.

a, Schematic of no injury in Arg1ZsGr mice. 60 mg/kg of tamoxifen was injected i.p. and tissues were collected for IHC Day 7 after not inducing injury. b, Schematic of Arg1ZsGr donor heart syngeneic transplant (HTX) into C57BL/6 mice. Arg1ZsGr donor mice were given 60 mg/kg tamoxifen via gavage 1 day prior to transplant. C57BL/6 recipient mice were given 60 mg/kg tamoxifen via gavage 0 and +1 days relative to HTX. Donor hearts were collected 7 days after HTX for IHC. c, Representative 20x confocal images of CD68 (red) IHC staining in Control, uninjured Arg1ZsGr, and donor Arg1ZsGr myocardium. Endogenous ZsGr is in green, and DAPI is in blue. d, Quantification of IHC in c. Data is displayed as total CD68+ZsGr+ cells per 20x field, and the percentage of CD68+ cells that are CD68+ZsGr+. N = 5 (each data point is an individual mouse). P-values are determined using a two tailed t-test assuming equal variance. Data are presented as mean ± SEM.

Source data

Extended Data Fig. 8 Single cell RNA sequencing of ZsGr− and ZsGr+ cells in Control>ZsGr and Hif1a>ZsGr syngeneic heart transplant mice.

a, FACS gating strategy for isolation of ZsGr− and ZsGr+ monocytes and macrophages from Control>ZsGr and Hif1a>ZsGr mice 5 days after syngeneic heart transplant (HTX) for use in scRNAseq. b, ZsGr positive and negative controls and TDT positive and negative controls which were used to determine gating for a. c, Violin plots representing scRNAseq data post quality control. Cells with greater than 500 and less than 5000 genes (nFeature_RNA), read counts greater than 500 and less than 20,000 (nCount_RNA), and proportion of transcripts mapping to mitochondrial genes less than 10 percent (prompt) were used for downstream analysis. d, Annotated UMAP of ZsGr− and ZsGr+ cells in Control>ZsGr and Hif1a>Zsgr conditions 5 days after HTX. Arg1 expression plotted on a UMAP projection. e, Violin plot of Arg1 expression split between ZsGr− and ZsGr+ cells in Control>ZsGr and Hifa1>ZsGr mice. f, Z-score profiles from of each subpopulation in the Control>ZsGr and Hifa1>ZsGr data set represented as a dot-plot. Z-score profile genes for each subpopulation are listed. g, Annotated UMAP of scRNAseq data 5 days after HTX split between ZsGr− and ZsGr+ cells in Control>ZsGr and Hifa1>ZsGr mice, and stacked bar graph representing the proportion of each cell population in the data set split between conditions.

Extended Data Fig. 9 Palantir analysis of ZsGr− and ZsGr+ cells in Control>ZsGr and Hif1a>ZsGr syngeneic heart transplant mice.

a, PCA plot of cell cycle genes in ZsGr- and ZsGr+ cells in Control>ZsGr and Hif1a>ZsGr 5 days after syngeneic heart transplant (HTX) before (Cell Cycle PCA) and after (Post Correction) regressing out cell cycle scores. b, UMAP projection of scRNAseq data 5 days after HTX highlighting the starting cell used. Starting cell was determined by selecting the monocyte with the highest monocyte Z-score (Plac8, Ly6c2). c, Force directed layout (FDL) output of Palantir analysis highlighting each cell subpopulation in the ZsGr- and ZsGr+ cells in Control>ZsGr and Hif1a>ZsGr 5 days after HTX. d, Plots of entropy vs pseudotime scores from Palantir analysis for every cell subpopulation in the scRNAseq data set.

Extended Data Fig. 10 FACS, validation, single cell RNA sequencing, and reference mapping of Arg1DTR mice after MI.

a, FACS gating strategy for the isolation of monocytes/macrophages/dendritic-like cells and comparison of Arg1-TDT expressing monocytes/macrophages between Control and Arg1DTR mice 3 days after I/R. b, Violin plots representing post-quality control scRNAseq data of monocytes/macrophages/dendritic-like cells in Control and Arg1DTR mice 3 days after I/R. Cells with between 500 and 5000 genes (nFeature_RNA), more than 500 and less than 20,000 read counts (nCount_RNA), and percentage of transcripts mapping to mitochondrial genes less than 10 percent (prompt) were used for downstream analysis. c, Reference UMAP plot of Control and Arg1DTR scRNAseq data after mapping to reference MI data set. The reference MI scRNAseq data set was the same as the one displayed in Extended Data Fig. 2. d, Heat map of mapping scores for each cell population inf the Query (Control and Arg1DTR) plotted against the Reference MI data set. e, Z-score profiles from the reference data set (Extended Data Fig. 2f) compared to each subpopulation in the Control and Arg1DTR data set represented as a dot-plot. f, IL-13, KC, LIX, and MIP2 protein levels in Control and Arg1DTR mice 3 days after I/R quantified via Luminex. Concentrations (pg/ml) are normalized to the amount of protein in the sample (µg). N = 6 vs 4 (each data point is an individual mouse). P-values are determined using a two tailed t-test assuming equal variance. Data are presented as mean ± SEM.

Source data

Supplementary information

Supplementary Information

Supplementary Figs. 1–10.

Reporting Summary

Supplementary Tables 1–6

Supplementary Table 1. Marker list of subpopulations in control and macHif1aKO data 5 days after MI. Markers were calculated using the FindAllMarkers function in Seurat with a minimum percentage of cells expressing a marker cut off at 0.1, a log fold change threshold of 0.25 and only returning positive markers. Statistics were performed using default parameters: Wilcoxon rank sum test to identify differentially expressed cells and P value adjusted using Bonferroni correction. Supplementary Table 2. Marker list of subpopulations identified in nuclei of control and resHif1aKO mice 3 days after MI. Markers were calculated using the FindAllMarkers function in Seurat with a minimum percentage of cells expressing a marker cut off at 0.1, a log fold change threshold of 0.25 and only returning positive markers. Statistics were performed using default parameters: Wilcoxon rank sum test to identify differentially expressed cells and P value adjusted using Bonferroni correction. Supplementary Table 3. Marker list of myeloid subpopulations identified in nuclei of control and resHif1aKO mice 3 days after MI. Markers were calculated using the FindAllMarkers function in Seurat with a minimum percentage of cells expressing a marker cut off at 0.1, a log fold change threshold of 0.25 and only returning positive markers. Statistics were performed using default parameters: Wilcoxon rank sum test to identify differentially expressed cells and P value adjusted using Bonferroni correction. Supplementary Table 4. Marker list of subpopulations in Arg1ZsGr mice 2 days and 30 days after MI. Markers were calculated using the FindAllMarkers function in Seurat with a minimum percentage of cells expressing a marker cut off at 0.1, a log fold change threshold of 0.25 and only returning positive markers. Statistics were performed using default parameters: Wilcoxon rank sum test to identify differentially expressed cells and P value adjusted using Bonferroni correction. Supplementary Table 5. Marker list of subpopulations in donor control and resHif1aKO hearts transplanted into Arg1ZsGr mice 5 days after transplant. Markers were calculated using the FindAllMarkers function in Seurat with a minimum percentage of cells expressing a marker cut off at 0.1, a log fold change threshold of 0.25 and only returning positive markers. Statistics were performed using default parameters: Wilcoxon rank sum test to identify differentially expressed cells and P value adjusted using Bonferroni correction. Supplementary Table 6. Marker list of subpopulations in control and Arg1DTR mice 3 days after MI. Markers were calculated using the FindAllMarkers function in Seurat with a minimum percentage of cells expressing a marker cut off at 0.1, a log fold change threshold of 0.25 and only returning positive markers. Statistics were performed using default parameters: Wilcoxon rank sum test to identify differentially expressed cells and P value adjusted using Bonferroni correction.

Source data

Source Data Fig. 1

Statistical source data.

Source Data Fig. 2

Statistical source data.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 4

Statistical source data.

Source Data Fig. 5

Statistical source data.

Source Data Fig. 6

Statistical source data.

Source Data Fig. 7

Statistical source data.

Source Data Fig. 8

Statistical source data.

Source Data Extended Data Fig. 1/Table 1

Statistical source data.

Source Data Extended Data Fig. 6/Table 6

Statistical source data.

Source Data Extended Data Fig. 7/Table 7

Statistical source data.

Source Data Extended Data Fig. 10/Table 10

Statistical source data.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) 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

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kadyrov, F.F., Koenig, A.L., Amrute, J.M. et al. Hypoxia sensing in resident cardiac macrophages regulates monocyte fate specification following ischemic heart injury. Nat Cardiovasc Res 3, 1337–1355 (2024). https://doi.org/10.1038/s44161-024-00553-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s44161-024-00553-6

This article is cited by

Search

Advanced 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