Systems level identification of a matrisome-associated macrophage polarisation state in multi-organ fibrosis
- Centre for Computational Biology, Duke-NUS Medical School, Singapore
- Programme in Cardiovascular and Metabolic Disorders, Duke-NUS Medical School, Singapore
- Institute for Big Data and Artificial Intelligence in Medicine, School of Science, China Pharmaceutical University (CPU), China
- Department of Immunology and Inflammation, Centre for Inflammatory Disease, Imperial College London, United Kingdom
Figures
Figure 1 with 6 supplements
SPP1+ macrophages are increased during fibrotic disease across human tissues.
(A–F) Uniform Manifold Approximation and Projection (UMAP) dimensionality reduction of all monocytes and macrophages in single-cell RNA-sequencing (scRNA-seq) of liver (A), lung (B), heart (C), skin …
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Figure 1—source data 1
Marker genes for monocyte / macrophages.
- https://cdn.elifesciences.org/articles/85530/elife-85530-fig1-data1-v2.xlsx
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Figure 1—source data 2
Marker genes for TREM2+ macrophages.
- https://cdn.elifesciences.org/articles/85530/elife-85530-fig1-data2-v2.xlsx
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Figure 1—source data 3
GSEA analysis of DEG between SPP1+ macrophages and other macrophages in each tissue.
- https://cdn.elifesciences.org/articles/85530/elife-85530-fig1-data3-v2.xlsx
Figure 1—figure supplement 1
Data-driven clustering of liver macrophages.
(A) Scatterplot of Silhouette score (see Materials and methods) of single-cell unsupervised Louvain clustering results at different resolutions, which dictates the granularity of clustering. The …
Figure 1—figure supplement 2
Data-driven clustering of lung macrophages.
(A) Scatterplot of Silhouette score (see Materials and methods) of single-cell unsupervised Louvain clustering results at different resolutions, which dictates the granularity of clustering. The …
Figure 1—figure supplement 3
Data-driven clustering of heart macrophages.
(A) Scatterplot of Silhouette score (see Materials and methods) of single-cell unsupervised Louvain clustering results at different resolutions, which dictates the granularity of clustering. The …
Figure 1—figure supplement 4
Data-driven clustering of skin macrophages.
(A) Scatterplot of Silhouette score (see Materials and methods) of single-cell unsupervised Louvain clustering results at different resolutions, which dictates the granularity of clustering. The …
Figure 1—figure supplement 5
Data-driven clustering of endometrium macrophages.
(A) Scatterplot of Silhouette score (see Materials and methods) of single-cell unsupervised Louvain clustering results at different resolutions, which dictates the granularity of clustering. The …
Figure 1—figure supplement 6
Data-driven clustering of kidney macrophages.
(A) Scatterplot of Silhouette score (see Materials and methods) of single-cell unsupervised Louvain clustering results at different resolutions, which dictates the granularity of clustering. The …
Figure 2 with 4 supplements
Identification of a matrisome-associated macrophage (MAM) state within SPP1+ macrophages.
(A) Uniform Manifold Approximation and Projection (UMAP) dimensionality reduction of all SPP1+ macrophages merged from the liver, lung, heart, skin, endometrium, and kidney tissues following data …
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Figure 2—source data 1
Marker genes for subclusters within SPP1+ macrophages.
- https://cdn.elifesciences.org/articles/85530/elife-85530-fig2-data1-v2.xlsx
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Figure 2—source data 2
Signature genes of SPP1+MAM+ macrophages.
- https://cdn.elifesciences.org/articles/85530/elife-85530-fig2-data2-v2.xlsx
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Figure 2—source data 3
GSEA analysis of pathways upregulated/downregulated in SPP1+MAM+ macrophages (relative to SPP1+MAM- macrophages).
- https://cdn.elifesciences.org/articles/85530/elife-85530-fig2-data3-v2.xlsx
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Figure 2—source data 4
Signature genes of "ECM-remodeling processes" upregulated in SPP1+MAM+ macrophages.
- https://cdn.elifesciences.org/articles/85530/elife-85530-fig2-data4-v2.xlsx
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Figure 2—source data 5
Signature genes of "metabolic processes" upregulated in SPP1+MAM+ macrophages.
- https://cdn.elifesciences.org/articles/85530/elife-85530-fig2-data5-v2.xlsx
Figure 2—figure supplement 1
Data-driven clustering of SPP1+ macrophages identifies a matrisome-associated macrophage polarisation state.
(A) Uniform Manifold Approximation and Projection (UMAP) projection of SPP1+ macrophages, coloured by their tissue of origin. (B) Scatterplot of Silhouette score (see Materials and methods) of …
Figure 2—figure supplement 2
Markers of SPP1+MAM+ as predicted by COMET.
Scatterplot ranks the number of occurrences of a gene in a four-marker panel of SPP1+MAM+ macrophages, as predicted by COMET (see Materials and methods). The Elbow method (dashed lines) was employed …
Figure 2—figure supplement 3
Derivation of disease-associated microglia (DAM) transcriptomic signature from the temporal lobe of epilepsy patients.
(A) t-SNE of immune cells isolated from the temporal lobe epilepsy patients (Kumar et al., 2022b; in the manuscript; n=4 patients). Microglial cells as defined in the original paper are coloured in …
Figure 2—figure supplement 4
Derivation of scar-associated macrophages (SAMs) from human liver data.
(A) Uniform Manifold Approximation and Projection (UMAP) of human liver macrophages coloured by a SAM signature score defined using the genes SPP1, TREM2, and CD9. (B) Applying a z-score cutoff of …
Figure 3 with 1 supplement
The differentiation trajectory of SPP1+MAM+ macrophages across human tissues.
(A–F) Slingshot differentiation trajectory analyses of macrophages in the liver (A), lung (B), heart (C), skin (D), endometrium (E), and kidney (F). The predicted trajectories are drawn on Uniform …
Figure 3—figure supplement 1
Monocle analysis recapitulates the conserved differentiation trajectory from FCN1+ monocytes to SPP1+ macrophages across multiple tissues.
Monocle differentiation trajectory analyses in the liver, lung, heart, skin, endometrium, and kidney (see Table 1 for information regarding the origin of human tissues and the number of sequenced …
Figure 4
A core set of regulon activity associate with SPP1+MAM+ differentiation.
(A) Rank plot for the 173 regulons active in SPP1+MAM+ macrophages, ordered by the diagnostic odd ratio (DOR) (y-axis), which calculates the odds of the regulon being activated in SPP1+MAM+ cells …
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Figure 4—source data 1
Member genes of regulons listed in Figure 4D.
- https://cdn.elifesciences.org/articles/85530/elife-85530-fig4-data1-v2.xlsx
Figure 5 with 2 supplements
SPP1+MAM- gene signature is associated with ageing in mice and humans.
(A) Scatterplot evaluating the pseudo-bulk expression of the homeostatic RNASE1+, SPP1+MAM-, SPP1+MAM+, SPP1+MAM+ extracellular matrix (ECM) remodelling, and SPP1+MAM+ metabolic processes signatures …
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Figure 5—source data 1
Signature genes of SPP1+MAM- macrophages.
- https://cdn.elifesciences.org/articles/85530/elife-85530-fig5-data1-v2.xlsx
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Figure 5—source data 2
Homeostatic signature regression model.
- https://cdn.elifesciences.org/articles/85530/elife-85530-fig5-data2-v2.xlsx
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Figure 5—source data 3
SPP1+MAM- signature regression model.
- https://cdn.elifesciences.org/articles/85530/elife-85530-fig5-data3-v2.xlsx
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Figure 5—source data 4
SPP1+MAM+ signature regression model.
- https://cdn.elifesciences.org/articles/85530/elife-85530-fig5-data4-v2.xlsx
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Figure 5—source data 5
SPP1+MAM+ ECM signature regression model.
- https://cdn.elifesciences.org/articles/85530/elife-85530-fig5-data5-v2.xlsx
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Figure 5—source data 6
SPP1+MAM+ Metabolic signature regression model.
- https://cdn.elifesciences.org/articles/85530/elife-85530-fig5-data6-v2.xlsx
Figure 5—figure supplement 1
Homeostatic macrophage signatures modelled against age and stratified by smoking status.
The homeostatic RNASE1+ signature, pseudo-bulk at the donor level, is further stratified according to the donor’s smoking status and linear regression is performed separately on each of the donor …
Figure 5—figure supplement 2
SPP1+MAM- signature is associated with ageing in mice kidneys.
Violin plots summarising the SPP1+MAM-, SPP1+MAM+, SPP1+MAM+ extracellular matrix (ECM) remodelling, and SPP1+MAM+ metabolic processes signatures in healthy mouse kidney macrophages taken from …
Tables
Table 1
Summary of scRNA-seq datasets analysed in this study.
For each study, the sample size (number of single-cell datasets), age/gender, disease status, the number (and percentage) of tissue resident macrophages (Mφ), and the total number of live cells …
| Tissue | Study(ref in article) | Repository | Disease status | Sample size (n) | Sex (%M) | Age (years)± SD | TissueMφ (%) | Total cells (#) |
|---|---|---|---|---|---|---|---|---|
| Liver | Ramachandran et al., 2019 | GSE136103 | Cirrhosis | 5 | 80.0 | 56.6±5.8 | 8332 (13.9%) | 60,094 CD45+ cells1 |
| Normal | 5 | 60.0 | 57.4±7.9 | |||||
| Liver | Fred et al., 2022 | Author provided data | NASH | 10 | 50.0 | 47.0±7.5 | 2069 (12.1%) | 17,154 |
| Lung | Morse et al., 2019 | GSE128033 | IPF | 3 | 33.3 | 69.3±0.6 | 17,570 (39.3%) | 44,652 |
| Normal | 4 | 50.0 | 38.3±20.6 | |||||
| Lung | Reyfman et al., 2019 | GSE122960 | IPF | 4 | 75 | 66.5±5.0 | 32,136 (41.7%) | 77,079 |
| Normal | 8 | 25 | 42.9±15.5 | |||||
| SSC | 2 | 0 | 46.0±9.9 | |||||
| Lung | Adams et al., 2020 | GSE136831 | IPF | 32 | 81.3 | 65.4±5.4 | 117,184 (37.4%) | 312,928 |
| Normal | 17 | 58.8 | 44.4±18.9 | |||||
| Lung | Valenzi et al., 2019 | GSE128169 GSE156310 | IPF | 1 | 100.0 | 68.0 | 17,407 (44.3%) | 39,252 |
| SSC | 4 | 75.0 | 56.8±9.5 | |||||
| Heart | Koenig et al., 2022 | GSE183852 | DCM | 5 | 60.0 | 50.0±19.2 | 3,922 (7.9%) | 49,665 |
| Normal | 2 | 100.0 | 50.5±17.7 | |||||
| Heart | Rao et al., 2021 | GSE145154 | DCM | 2 | 100.0 | 61.0±1.4 | 20,539 (30.0%) | 68,516 CD45+ cells |
| ICM | 4 | 100.0 | 47.3±10.6 | |||||
| Normal | 1 | 100.0 | 53.0 | |||||
| Skin2 | Gur et al., 2022 | GSE195452 | Normal | 22 | 22.7 | 44.8±10.4 | 2456 (15.6%) | 15,700 CD45+ cells |
| SSC | 55 | 5.4 | 50.1±13.2 | |||||
| Skin | Deng et al., 2021 | GSE163973 | Keloid | 3 | 66.7 | 25.6±7.3 | 320 (0.7%) | 45,094 |
| Normal | 3 | 66.7 | 31.0±7.0 | |||||
| Endo- metrium | Tan et al., 2022 | GSE179640 | Endometriosis | 9 | 0.0 | 35.8±5.8 | 3079 (8.6%) | 35,941 |
| Normal | 3 | 0.0 | 33.3±10.3 | |||||
| Endo- metrium | Fonseca et al., 2023 | GSE213216 | Endometriosis | 22 | 0.0 | 34.1±7.6 | 2951 (1.8%) | 163,882 |
| Normal | 8 | 0.0 | 37.9±10.1 | |||||
| Kidney | Kuppe et al., 2021 | Zenodo 4059315 | CKD | 6 | 66.7 | 70.8±12.5 | 3596 (7.5%) | 48,096 CD10- cells |
| Normal | 4 | 100.0 | 65.3±11.3 | |||||
| Kidney | Lake et al., 2023 | atlas.kpmp.org | AKI | 11 | 72.7 | 50.2±19.1 | 1597 (3.5%) | 46,249 |
| CKD | 15 | 66.7 | 61.9±12.7 | |||||
| Normal | 14 | 50.0 | 45.9±10.3 | |||||
| Kidney | Malone et al., 2020 | GSE145927 | AKI | 3 | 100.0 | 49.3±18.2 | 2772 (4.6%) | 60,080 |
| Normal | 2 | 50.0 | 54.0±1.0 |
