Systems level identification of a matrisome-associated macrophage polarization state in multi-organ fibrosis

  1. John F Ouyang  Is a corresponding author
  2. Kunal Mishra
  3. Yi Xie
  4. Harry Park
  5. Kevin Y Huang
  6. Enrico Petretto  Is a corresponding author
  7. Jacques Behmoaras  Is a corresponding author
  1. SingHealth Duke-NUS Academic Medical Centre, Singapore

Abstract

Tissue fibrosis affects multiple organs and involves a master-regulatory role of macrophages which respond to an initial inflammatory insult common in all forms of fibrosis. The recently unravelled multi-organ heterogeneity of macrophages in healthy and fibrotic human disease suggests that macrophages expressing osteopontin (SPP1), associate with lung and liver fibrosis. However, the conservation of this SPP1+ macrophage population across different tissues, and its specificity to fibrotic diseases with different etiologies remain unclear. Integrating 15 single cell RNA-sequencing datasets to profile 235,930 tissue macrophages from healthy and fibrotic heart, lung, liver, kidney, skin and endometrium, we extended the association of SPP1+ macrophages with fibrosis to all these tissues. We also identified a subpopulation expressing matrisome-associated genes (e.g., matrix metalloproteinases and their tissue inhibitors), functionally enriched for ECM remodelling and cell metabolism, representative of a matrisome-associated macrophage (MAM) polarization state within SPP1+ macrophages. Importantly, the MAM polarization state follows a differentiation trajectory from SPP1+ macrophages and is associated with a core set of regulon activity. SPP1+ macrophages without the MAM polarization state (SPP1+MAM-) show a positive association with ageing lung in mice and humans. These results suggest an advanced and conserved polarization state of SPP1+ macrophages in fibrotic tissues resulting from prolonged inflammatory cues within each tissue microenvironment.

Data availability

The current manuscript is a computational study where we meta-analyze previously published data. No new primary datasets have been generated in this manuscript.The code used in the study is publicly available at https://github.com/the-ouyang-lab/mam-reproducibility.See also MethodsThe processed Seurat object for each of the six tissues and SPP1 macrophages can be downloaded at https://zenodo.org/record/8266711 (See also Methods)DATA SET information: Details on previously published datasets are provided and described in Table 1 within the manuscript.

The following previously published data sets were used

Article and author information

Author details

  1. John F Ouyang

    Centre for Computational Biology, SingHealth Duke-NUS Academic Medical Centre, Singapore, Singapore
    For correspondence
    john.ouyang@duke-nus.edu.sg
    Competing interests
    The authors declare that no competing interests exist.
  2. Kunal Mishra

    Centre for Computational Biology, SingHealth Duke-NUS Academic Medical Centre, Singapore, Singapore
    Competing interests
    The authors declare that no competing interests exist.
  3. Yi Xie

    Centre for Computational Biology, SingHealth Duke-NUS Academic Medical Centre, Singapore, Singapore
    Competing interests
    The authors declare that no competing interests exist.
  4. Harry Park

    Centre for Computational Biology, SingHealth Duke-NUS Academic Medical Centre, Singapore, Singapore
    Competing interests
    The authors declare that no competing interests exist.
  5. Kevin Y Huang

    Centre for Computational Biology, SingHealth Duke-NUS Academic Medical Centre, Singapore, Singapore
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2288-3620
  6. Enrico Petretto

    Centre for Computational Biology, SingHealth Duke-NUS Academic Medical Centre, Singapore, Singapore
    For correspondence
    enrico.petretto@duke-nus.edu.sg
    Competing interests
    The authors declare that no competing interests exist.
  7. Jacques Behmoaras

    Centre for Computational Biology, SingHealth Duke-NUS Academic Medical Centre, Singapore, Singapore
    For correspondence
    jacquesb@duke-nus.edu.sg
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5170-2606

Funding

Ministry of Education - Singapore (T2EP30221-0013)

  • Enrico Petretto

Ministry of Education - Singapore (2022-MOET1-0003)

  • Jacques Behmoaras

National Medical Research Council (OFLCG22may-0011)

  • Enrico Petretto
  • Jacques Behmoaras

National Medical Research Council (MOH-OFYIRG21nov-0004)

  • John F Ouyang

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

© 2023, Ouyang et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. John F Ouyang
  2. Kunal Mishra
  3. Yi Xie
  4. Harry Park
  5. Kevin Y Huang
  6. Enrico Petretto
  7. Jacques Behmoaras
(2023)
Systems level identification of a matrisome-associated macrophage polarization state in multi-organ fibrosis
eLife 12:e85530.
https://doi.org/10.7554/eLife.85530

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https://doi.org/10.7554/eLife.85530

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