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Single-cell atlas of human liver and blood immune cells across fatty liver disease stages reveals distinct signatures linked to liver dysfunction and fibrogenesis

Nature Immunology volume 26, pages 1596–1611 (2025)Cite this article

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Abstract

Immune cells play a central yet poorly understood role in metabolic dysfunction-associated steatotic liver disease and metabolic dysfunction-associated steatohepatitis (MASLD/MASH), a global cause of liver disease with limited treatment. Limited access to human livers and lack of studies across MASLD/MASH stages thwart identification of stage-specific immunological targets. Here we provide a unique single-cell RNA sequencing atlas of paired peripheral blood and liver fine-needle aspirates from a full-spectrum MASLD/MASH human cohort. Our findings included heightened immunoregulatory programs with MASH progression, such as enriched hepatic regulatory T cells, monocytic myeloid-derived suppressor cells, TREM2+S100A9+ macrophages and S100hiHLAlo type 2 conventional dendritic cells. Hepatic cytotoxic T cell functions increased with inflammation, but decreased with fibrosis, while acquiring an exhausted signature, whereas natural killer cell-driven toxicity intensified. Our dataset proposes immunological mechanisms for increased fibrogenesis and vulnerability to liver cancer and infections in MASH and provides a basis for a deeper understanding of human immunological dysfunction in chronic liver disease and a roadmap to new targeted therapies.

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Fig. 1: The landscape of liver and peripheral blood immune cells across the MASLD/MASH spectrum.
Fig. 2: Monocytes, monocyte-derived cells and their connectivity across the MASLD/MASH spectrum.
Fig. 3: Macrophage and pre-macrophage subsets and association with MASLD/MASH progression.
Fig. 4: DC subsets and association with MASLD/MASH progression.
Fig. 5: T cell subsets and association with MASLD/MASH progression.
Fig. 6: B cell subsets and association with MASLD/MASH progression.
Fig. 7: NK cell subsets and association with MASLD/MASH progression.
Fig. 8: Hepatic immune cell crosstalk and changes with MASLD/MASH progression.

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Data availability

The human RNA-seq data are deposited in the NIH dbGaP portal (accession no. phs004044) and can be used only for studying health, medical or biomedical conditions. Source data are provided with this paper.

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Acknowledgements

We thank the Akoya scientist, A. Gad, for his valuable input with the Opal 3-plex tissue immunostaining used for validation. We thank Z. Li for helping organize the raw sequencing data for database deposition. This work was financially supported by Bristol Myers Squibb and grants from the National Institutes of Health (NIH) and the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) (grant nos. R01DK098079 to R.T.C. and R56DK134251 to N.A. and R.T.C.). The content of this work, however, is solely the responsibility of the authors and does not necessarily represent the official views of the sponsors.

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Authors and Affiliations

  1. Division of Gastroenterology, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    Owen P. Martin, Michael S. Wallace, Christopher Oetheimer, Hailey B. Patel, Michael D. Butler, Joshua Elbaz, Charlotte Costentin, Shadi Salloum, Zoe Reinus, Adaeze Obinelo, Kathleen E. Corey, Raymond T. Chung & Nadia Alatrakchi

  2. Department of Molecular Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    Lai Ping Wong, Ulandt Kim & Ruslan I. Sadreyev

  3. Division of Gastroenterology, Hepatology and Nutrition, Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA

    Pinzhu Huang & Yury V. Popov

  4. Department of Hepato-Gastroenterology and Digestive Oncology, CHU Grenoble Alpes, Institute for Advanced Biosciences, CNRS UMR 5309-INSERM U1209, Univ. Grenoble Alpes, Grenoble, France

    Charlotte Costentin

  5. Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    Stuti Shroff

  6. Bristol Myers Squibb, Lawrenceville, NJ, USA

    Edgar D. Charles

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Contributions

Conceptualization: R.T.C. and N.A. Methodology: C.O., M.S.W., H.B.P., O.P.M., L.P.W., P.H., U.K., Y.V.P., R.I.S. and N.A. Software and formal analysis: O.P.M., M.S.W. and C.O. Investigation: O.P.M., M.S.W., C.O., H.B.P., R.I.S., R.T.C. and N.A. Resources: M.D.B., J.E., C.C., S. Salloum, Z.R., A.O., S. Shroff, K.E.C. and E.D.C. Writing—original draft: M.S.W., O.P.M., C.O. and N.A. Writing—review and editing: O.P.M., M.S.W., E.D.C., R.T.C., R.I.S. and N.A. Funding acquisition: R.T.C. and N.A. Supervision: N.A.

Corresponding author

Correspondence to Nadia Alatrakchi.

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Competing interests

E.D.C. was formerly employed by Bristol Myers Squibb. R.T.C. received research grants to the institution from Abbvie, Gilead Sciences, Merck, Boehringer, Janssen and BMS. N.A. received a research grant to the institution from Boehringer for unrelated work. The other authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Serum levels of monocyte and macrophage activation marker, soluble (s)CD163.

Levels of sCD163 significantly correlated with (a) non-invasive markers of fibrosis, liver stiffness (kPa) and APRI score; and (b) liver injury enzymes, ALT and AST, two-sided Spearman correlation. (c) Levels of sCD163 were significantly higher in patients with histologically defined MASH, compared to healthy controls (n = 10), and in advanced MASH (n = 9), compared to NS-SS (n = 6); and (d) in patients with high NAS score (>4, n = 11) compared to low NAS score (<5, n = 14) (the box plots represent the median and interquartile ranges, and the whiskers depict the minimum and maximum of the data set), two-sided Mann–Whitney unpaired U test. *P < .05, **P < .01, ***P < .001, ****P < .0001. Abbreviations: kPa, kilopascal; APRI, aspartate aminotransferase–to-platelet ratio index; ALT, Alanine aminotransferase; AST, Aspartate aminotransferase; NAS; MASLD (former NAFLD) activity score. NS, no steatosis/inflammation/fibrosis.

Extended Data Fig. 2 Additional information on the quality of scRNA-seq dataset.

(a) Ridge plot of the housekeeping gene B2M expression per sample, per compartment. (b) Stacked bar chart of sample proportion in each cell type, per compartment. (c) Separate clustering UMAPs for liver FNAs (n = 55,234) and PBMCs (n = 123,753), color-coded by cell type (clustering settings: 20 PCs, Louvain Resolution=0.05).

Extended Data Fig. 3 Broad characterization of cell type enrichment per compartment and per liver disease stage.

(a) Box plots showing per-sample (n = 25) frequencies of CD16− NK cells, MAIT cells, CD8+ T cells and B cells, and the ratio of CD4+/CD8+ T cells, per compartment: data are presented as median values (horizontal line), 25th/75th quartile values (bounds of boxes), and non-outlier minimum/maximum (whiskers); two-sided Wilcoxon. (b) Box plot of the proportion of each cell type as a percentage of each sample in FNA (left) and PBMC (right): data are presented as median values (horizontal line), 25th/75th quartile values (bounds of boxes), and non-outlier minimum/maximum (whiskers); NS-SS (n = 6), early MASH (n = 10), advanced MASH (n = 9); two-sided permutation test; significance (*) determined by false-discovery rate (FDR) < 0.05 and log2 false discovery (log2FD)>0.58. (c) Scatter plot of average pathway enrichment score per patient (n = 25) of (left) ROS/RNS production in macrophages by controlled attenuation parameter (CAP reflects steatosis), (middle) collagen formation in hepatic stellate cells (HSCs) by MASLD/NAFLD activity score (NAS), and (right) insulin-growth factor binding protein (IGF) and IGF-binding protein (IGFBP) in hepatocytes by liver enzyme ALT: error bands represent 95% confidence interval; two-sided Pearson.

Extended Data Fig. 4 Interferon-induced protein validation and liver sources of interferons.

Box plot by cell type per patient of average (a) ISG score per disease stage (NS-SS (n = 6), early MASH (n = 10), advanced MASH (n = 9)) in liver FNA (left) and PBMCs (right), (b) IFNG expression in liver FNAs (n = 25), and (c) upstream gene expression for interferon-alpha (left) and interferon-beta (right) in liver FNAs (n = 25): data are presented as median values (horizontal line), 25th/75th quartile values (bounds of boxes), and non-outlier minimum/maximum (whiskers); two-sided Wilcoxon; upstream genes are shown in Extended Data Table 2. (d) Magnification of Extended Data Fig. 3a: box plot of the percentage of pDCs out of each sample in FNA (left) and PBMC (right): data presented as in 5a; two-sided permutation test; significance (*) determined by false-discovery rate (FDR) < 0.05 and log2 false discovery (log2FD)>0.58. (e) Comparative ISG expression in liver monocytes (left) and macrophages (right) between patients without steatosis or with steatosis (NS-SS), those with MASH and those with untreated HCV: data are presented as median values (horizontal line); two-sided Wilcoxon; ***P < .001. (f) Immuno-fluorescent (IF) validation of MX1 and IFIT3 in macrophages in human liver tissue biopsies. Representative image merged and per channel (left) and expression fractions of MX1 + , IFIT3 + , and MX1 + IFIT3+ out of macrophages (CD68 + ) for three disease stages (right): simple steatosis (SS, n = 1), early MASH (F1, n = 1), and advanced MASH (F2, n = 1).

Extended Data Fig. 5 Additional data on monocyte association with MASLD/MASH progression.

(a) Monocyte chemotaxis receptors: box plots of average CCRL2 (left) and CMKLR1 (right) expression in FNA by patient: data are presented as median values (horizontal line), 25th/75th quartile values (bounds of boxes), and non-outlier minimum/maximum (whiskers); NS-SS (n = 6), early MASH (n = 10), advanced MASH (n = 9); two-sided Wilcoxon (b) Monocyte chemotaxis ligands: scatter plot of average RARRES2 expression in hepatocytes per patient (n = 25) by CAP score: error bands represent 95% confidence interval; two-sided Pearson.

Extended Data Fig. 6 Investigation of the monocyte and monocyte-derived cell paths without specifying a starting point of cell progression.

(a) Velocity analysis of monocyte and monocyte-derived cells (macrophages and cDCs) shown by UMAP in early MASH (left) and advanced MASH (right) and (b) unbiased pseudotime via slingshot analysis, color-coded per cell type.

Extended Data Fig. 7 Investigation of T-cell activation marker ICOS, its ligand, and B-cell light chain kappa versus lambda expression.

(a) Immunofluorescent staining of ICOS in human liver biopsies for three disease stages: simple steatosis (SS, steatosis grade 1, n = 1), early MASH (F1, steatosis grade 1, n = 1), and advanced MASH (F2, steatosis grade 2, n = 1). Box plots of (b) average ICOS expression by T-cell subpopulation per patient FNA (n = 25), (c) average Treg ICOS expression by histological steatosis grade per patient FNA (0-1: n = 10; 2: n = 11, 3 n = 3), two-sided Wilcoxon, and (d) average ICOSLG expression by cell type per patient FNA (n = 25): data are presented as median values (horizontal line), 25th/75th quartile values (bounds of boxes), and non-outlier minimum/maximum (whiskers). (e) Bar chart showing the fraction of B cells that express kappa (IGKC) or lambda (IGLC2-7) light chain genes for liver FNA (left) and PBMC (right).

Extended Data Fig. 8 Additional data on immune cell crosstalk.

Changes in predicted individual ligand-receptor interactions between (a) SS versus early MASH and (b) early versus advanced MASH, from HSCs to immune cells (left) and from immune cells to HSCs (right). (c) Radar plot of the total number of cell-cell interactions (both incoming and outgoing) between macrophages and other cell types (left) and between pDCs and other cell types (right).

Extended Data Table 1 Patient characteristics
Full size table
Extended Data Table 2 Gene sets used for custom scoring
Full size table

Supplementary information

Source data

Source Data Fig. 3

Composite IF images for SS, MASH F1 and MASH F2 in Fig. 3h.

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Martin, O.P., Wallace, M.S., Oetheimer, C. et al. Single-cell atlas of human liver and blood immune cells across fatty liver disease stages reveals distinct signatures linked to liver dysfunction and fibrogenesis. Nat Immunol 26, 1596–1611 (2025). https://doi.org/10.1038/s41590-025-02255-y

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