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XCR1+ type 1 conventional dendritic cells drive liver pathology in non-alcoholic steatohepatitis

Nature Medicine volume 27, pages 1043–1054 (2021)Cite this article

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A Publisher Correction to this article was published on 12 January 2022

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Abstract

Non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH) are prevalent liver conditions that underlie the development of life-threatening cirrhosis, liver failure and liver cancer. Chronic necro-inflammation is a critical factor in development of NASH, yet the cellular and molecular mechanisms of immune dysregulation in this disease are poorly understood. Here, using single-cell transcriptomic analysis, we comprehensively profiled the immune composition of the mouse liver during NASH. We identified a significant pathology-associated increase in hepatic conventional dendritic cells (cDCs) and further defined their source as NASH-induced boost in cycling of cDC progenitors in the bone marrow. Analysis of blood and liver from patients on the NAFLD/NASH spectrum showed that type 1 cDCs (cDC1) were more abundant and activated in disease. Sequencing of physically interacting cDC-T cell pairs from liver-draining lymph nodes revealed that cDCs in NASH promote inflammatory T cell reprogramming, previously associated with NASH worsening. Finally, depletion of cDC1 in XCR1DTA mice or using anti-XCL1-blocking antibody attenuated liver pathology in NASH mouse models. Overall, our study provides a comprehensive characterization of cDC biology in NASH and identifies XCR1+ cDC1 as an important driver of liver pathology.

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Fig. 1: scRNA-seq characterization of the liver immune niche during development of NASH.
Fig. 2: NASH-induced increase in conventional dendritic cell subtypes.
Fig. 3: cDCs in human NASH.
Fig. 4: NASH boosts DC-poiesis output in bone marrow.
Fig. 5: DCs in NASH induce more pro-inflammatory DC–T cell interaction signatures in liver lymph nodes.
Fig. 6: Depletion of Xcr1+ cDC1 protects the liver from NASH development and progression in diet-induced mouse models of NASH.

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Data availability

The scRNA-seq data generated in this study are available at the Gene Expression Omnibus under accession GSE169447.

Code availability

The Metacell package is available at https://github.com/tanaylab/metacell. The PIC-seq analysis package is available at https://github.com/aygoldberg/PIC-seq.

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Acknowledgements

We thank T. Wiesel and T. Bigdary from the Scientific Illustration unit of the Weizmann Institute for artwork, C. Raanan for histology, M. Guilliams, A. Schlitzer and members of the Amit laboratory for fruitful discussions. We thank A. Leshem, H. Keren-Shaul, F. Sheban, L. Geirsdottir, E. Tatirovsky, F. Müller, J. Hetzer and D. Heide for technical support. A.D. is a recipient of Short-Term EMBO Fellowship no. 7395 and is supported by Eden and Steven Romick. D.P. is supported by the Helmholtz Future; Inflammation and Immunology. M.H.W. is supported by an ERC Consolidator grant (HepatoMetaboPath), an EOS grant, SFBTR179, SFBTR 209, SFBTR1335, Horizon 2020, Research Foundation Flanders under grant 30826052 (EOS Convention MODEL-IDI), Deutsche Krebshilfe projects 70113166 and 70113167, German-Israeli Cooperation in Cancer Research (DKFZ-MOST) and the Helmholtz-Gemeinschaft, Zukunftsthema ‘Immunology and Inflammation’ (ZT-0027). E.E. is supported by the Leona M. and Harry B. Helmsley Charitable Trust; Adelis Foundation; Pearl Welinsky Merlo Scientific Progress Research Fund; Park Avenue Charitable Fund; The Hanna and Dr. Ludwik Wallach Cancer Research Fund; Daniel Morris Trust; The Wolfson Family Charitable Trust and The Wolfson Foundation; Ben B. and Joyce E. Eisenberg Foundation; White Rose International Foundation; Estate of Malka Moskowitz; Estate of Myron H. Ackerman; Estate of Bernard Bishin for the WIS-Clalit Program; Else Kroener Fresenius Foundation; Jeanne and Joseph Nissim Center for Life Sciences Research; Aliza Moussaieff; Miel de Botton; Vainboim Family; Alex Davidoff; the V. R. Schwartz Research Fellow Chair; the Swiss Society Institute for Cancer Prevention Research at the Weizmann Institute of Science, Rehovot, Israel and by grants funded by the European Research Council; Israel Science Foundation; Israel Ministry of Science and Technology; Israel Ministry of Health; the Helmholtz Foundation; Garvan Institute; European Crohn’s and Colitis Organization; Deutsch-Israelische Projektkooperation; IDSA Foundation; and Welcome Trust. E.E. is the incumbent of the Sir Marc and Lady Tania Feldmann Professorial Chair; a senior fellow, Canadian Institute of Advanced Research; and an international scholar, The Bill and Melinda Gates Foundation and Howard Hughes Medical Institute. I.A. is an Eden and Steven Romick Professorial Chair, supported by Merck KGaA, Darmstadt, Germany, the Chan Zuckerberg Initiative, the Howard Hughes Medical Institute International Scholar award, the European Research Council Consolidator Grant 724471-HemTree2.0, a Single Cell Analysis (SCA) award of the Wolfson Foundation and Family Charitable Trust, the Thompson Family Foundation, a Melanoma Research Alliance Established Investigator Award (509044), the Israel Science Foundation (703/15), the Ernest and Bonnie Beutler Research Program for Excellence in Genomic Medicine, the Helen and Martin Kimmel award for innovative investigation, the NeuroMac DFG/Transregional Collaborative Research Center Grant, an International Progressive MS Alliance/NMSS PA-1604 08459, the ISF Israel Precision Medicine Program (IPMP) 607/20 grant and an Adelis Foundation grant.

Author information

Author notes

  1. Aleksandra Deczkowska

    Present address: Departments of Immunology and Neuroscience, Institut Pasteur, Paris, France

  2. These authors contributed equally: Assaf Weiner, Ziv Ben-Ari, Mathias Heikenwälder, Eran Elinav, Ido Amit.

Authors and Affiliations

  1. Department of Immunology, Weizmann Institute of Science, Rehovot, Israel

    Aleksandra Deczkowska, Eyal David, Baoguo Li, Amir Giladi, Diego Adhemar Jaitin, Oren Barboy, Merav Cohen, Ido Yofe, Chamutal Gur, Shir Shlomi-Loubaton, Assaf Weiner, Eran Elinav & Ido Amit

  2. Division of Chronic Inflammation and Cancer, German Cancer Research Center Heidelberg (DKFZ), Heidelberg, Germany

    Pierluigi Ramadori, Dominik Pfister, Shing Kam & Mathias Heikenwälder

  3. Liver Disease Center, Sheba Medical Center, Tel Hashomer, Israel

    Michal Safran, Gil Ben Yakov, Oranit Cohen-Ezra, Yana Davidov, Mariya Likhter & Ziv Ben-Ari

  4. Department of Clinical Microbiology and Immunology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel

    Merav Cohen

  5. Department of Medicine, Hadassah–Hebrew University Hospital, Jerusalem, Israel

    Chamutal Gur

  6. Centre d’Immunologie de Marseille-Luminy, Aix Marseille Université, INSERM, CNRS, Marseille, France

    Sandrine Henri & Bernard Malissen

  7. Chirurgische Klinik, Allgemein, Viszeral und Transplantationschirurgie, Universitätsklinikum Heidelberg, Heidelberg, Germany

    Yousuf Suhail, Mengjie Qiu & Susanne Roth

  8. Department of Surgery C, Sheba Medical Center, Tel Hashomer, Israel

    Hila Hermon & David Goitein

  9. Department of Surgery B, Sheba Medical Center, Tel Hashomer, Israel

    Eylon Lahat

  10. Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel

    David Goitein & Ziv Ben-Ari

  11. Department of Pathology and Molecular Pathology, University Hospital Zurich, Zurich, Switzerland

    Achim Weber

  12. Centre d’Immunophénomique, Aix Marseille Université, INSERM, CNRS, Marseille, France

    Bernard Malissen

  13. Division of Microbiome and Cancer, German Cancer Research Center Heidelberg (DKFZ), Heidelberg, Germany

    Eran Elinav

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  1. Aleksandra Deczkowska
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Contributions

A.D. and I.A. designed, analyzed and interpreted all experiments. A.D. wrote the manuscript. A.D., B.L., D.A.J., I.Y., M.C., O.B., S.S.-L. and C.G. performed experiments and analyzed the data. D.P., P.R. and K.S. under supervision of M.H., and Y.S. and M.Q. under the supervision of S.R. performed experiments and analyzed the data. A. Weiner and E.D. analyzed scRNA-seq data. A.G. analyzed PIC-seq data. C.G., D.G., H.H., E.L., G.B.-Y., O.C.-E., Y.D. and M.L. recruited the patients for the study. M.S. and Z.B.-A. prepared human blood for analysis and diagnosed liver pathology. A. Weber diagnosed liver pathology. S.H. and B.M. contributed resources. A.D., E.D. and A. Weiner prepared the figures. M.H., E.E. and I.A. supervised the study.

Corresponding authors

Correspondence to Aleksandra Deczkowska, Mathias Heikenwälder, Eran Elinav or Ido Amit.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Medicine thanks Frank Tacke, Matteo Iannacone, Scott Friedman and the other, anonymous reviewer(s) for their contribution to the peer review of this work. Joao Monteiro was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 scRNA-seq characterization of the liver immune niche during development of NASH.

a, Representative pictures of H&E staining of livers in wild type C57/bl mice fed with ND or MCDD for 2, 4 and 8 weeks. b, Flow cytometry plots illustrating gating strategy for CD45 + immune cell selection for scRNA-seq analysis. c, Shown are number of reads, number of UMIs and percentage of cells analyzed per batch of 380 cells (that were pooled for library construction) for all cells collected for scRNA-seq in experiments presented in Figs. 1, 2 and 4. Black dots indicate cells used for downstream analysis and grey dots indicate cells which were filtered out.

Extended Data Fig. 2 Immune cell composition of the liver in NASH development.

a, Heat map of differential gene expression for all cells colored by the assigned metacells as in Fig. 1. Top bar graph shows relative contribution of ND and MCDD liver immune cells to each metacell. b, Density plots depicting distribution of cells in ND and MCDD condition (downsampled to 6699 cells) and enrichment analysis of MCDD as compared to ND (all time-points analyzed together) projected on the kNN graph. Red areas represent enrichment, blue areas represent depletion. c, kNN graph of liver immune cells of wild-type mice kept on ND and MCDD, down-sampled to 1000 cells at each time-point analyzed. d, relative abundance of immune cell subtypes in livers of ND or MCDD-fed wild type mice at each analyzed time-point.

Extended Data Fig. 3 Hepatic cDC increase in NASH.

a, Flow cytometry plots illustrating gating strategy for cDCs, cDC1, cDC2 and mDC quantification and sorting. b, Heat map of differential gene expression of cDC sorted from Xcr1Cre-mTFP1 reporter mice. Top plots illustrate relative fluorescent intensity of indicated FACS markers in each cell, ordered as in the heat map. c, Representative flow cytometry contour-plots of CD11c + MHC-II + cells out of CD45 + Lin- gate in livers of mice fed ND or MCDD for two weeks. Population frequencies represent mean ± s.e.m. d, Representative immunofluorescence images of CD11c (red), MHC-II (green) and CD31 (vasculature marker; magenta) in liver section of mice fed ND or MCDD for two weeks. Cell nuclei are stained with DAPI (blue). Scale bar, 30 μm. Images representative for two independent experiments. e, Representative pictures of H&E staining of livers in wild type C57/bl mice fed with ND or WD for 3 and 6 months. f, Percentage of CD11c + MHC-II + DCs among CD45 + cells in the livers of mice fed WD for 3 months, n = 6 per group, data are presented as mean ± s.e.m.; p-value was determined by two-tailed Student’s t-test. g, Representative pictures of H&E staining of livers in wild type C57/bl mice fed with ND or CDHFD for 6 months.

Extended Data Fig. 4 Proportions of hepatic cDC subtypes change in murine NASH and NAFLD models.

a, Representative pictures of H&E staining of livers in wild type C57/bl mice fed with ND or HFD for 6 months, 9 week-old Ob/Ob mouse and Ob/het control, 12 week-old Db/Db mouse and Db/het control. b, Percentage of DCs among CD45 + cells in the livers of wild type mice fed ND or HFD for 6 months, 9 week-old Ob/Ob mouse and Ob/het control, 12 week-old Db/Db mouse and Db/het control. c, Representative flow cytometry contour-plots of cDC1 and cDC2 out of cDC gate in livers of mice fed ND or MCDD for two weeks. Population frequencies represent mean ± s.e.m. d, Representative immunofluorescence image of XCR1 (red), CD11c (magenta), MHC-II (green) and DAPI (nuclear stain, blue) in liver section of mouse fed MCDD for two weeks. Scale bar, 30 μm. Images representative for two independent experiments. e, Representative immunofluorescence image of CD103 (white), CD11c (red), MHC-II (green) and DAPI (nuclear stain, blue) in liver section of mouse fed CDHFD for six months. Scale bar, 50 μm. Images representative for two independent experiments. f, Representative flow cytometry contour-plots of CCR7 + mDCs out of cDC gate in livers of mice fed ND or MCDD for two weeks. Population frequencies represent mean ± s.e.m. g-h, Percentage of cDC1 (g) and cDC2 (h) among CD45 + cells in the livers of wild type mice fed ND or HFD for 6 months, 9 week-old Ob/Ob mouse and Ob/het control, 12 week-old Db/Db mouse and Db/het control. In all graphs points indicate individual mice, n = 3-4 per group. Data are presented as mean ± s.e.m.; p-values were determined by two-tailed Student’s t-test; ∗∗∗p < 0.001.

Extended Data Fig. 5 cDC in human NASH.

a, Full gating strategy used to select cDC from human livers. b-c, Number of UMIs (b) and percentage of QC-positive single cells (c) used for analysis out of all cells detected in scRNA-seq pipeline. d, Heat map of differential gene expression among CD45 + Lin-CD11C + cells sorted from healthy and NASH human livers. e, Relative abundance of cell subtypes among CD45 + Lin-CD11C + cells sorted from healthy and NASH human livers. f, Full gating strategy used to select cDC, cDC1 and cDC2 from human blood.

Extended Data Fig. 6 NASH boosts DC-poiesis.

a, Flow cytometry plots illustrating gating strategy for assessing BrdU incorporation in MDP, CDPs and preDCs in the bone marrow, blood and liver. b, Percentage of BrdU+ cells among MDP in the bone marrow of mice fed ND or MCDD for two weeks and pulsed with BrdU 10 h prior to DC progenitor analysis. n = 3 per group, data are presented as mean ± s.e.m. c, kNN graph of bone marrow-isolated CDP, pre-DC isolated form the bone marrow, blood and liver and liver DC isolated from wild-type mice kept on ND or fed MCDD for two weeks. d, Plot representing expression of marker genes in annotated clusters in cells showed in c. Color intensity indicates log2 of the mean UMI count; circle size represents percentage of cells within cluster expressing indicated genes. e, kNN graphs showing distribution of single cells from different organs and diet-regiments matched with bar plots showing relative contribution of each annotated cell type among CDP sorted from the bone marrow and pre-DCs sorted from the bone marrow, blood and liver of wild-type mice kept on ND or on MCDD for two weeks. Plots were down-sampled to 680 cells for CDP, 1404 for bone marrow preDC, 601 for blood preDC and 622 for liver preDC.

Extended Data Fig. 7 Characterization of genetic signatures of DC-T pairs in liver lymph nodes in NASH.

a, Full gating strategy for T and DC, as well as PIC-sorting from murine liver lymph nodes. b, Number of reads, number of UMIs and percentage of cells analyzed per batch of 380 cells (that were pooled for library construction) in PIC-seq experiments. c, Heat map of differential gene expression for all single cells sorted as DC and T cells, annotated based on their gene expression, and used to create ‘expected PICs’ for PIC-seq analysis. Highlighted are marker genes for the annotated DC and T subtypes. d,e, Gene-expression levels in observed PICs assigned to the cDC1-T identity (d) or cDC2-T identity (e), plotted against their expected levels as determined by PICseq in the liver lymph nodes of mice kept on ND or MCDD for two weeks. Highlighted genes are expressed differentially between observed and expected PICs in one or both conditions.

Extended Data Fig. 8 Partial depletion of hepatic cDC1 infiltration using anti-XCL1 antibody in CDHFD-fed mice attenuates NASH progression.

a, Levels of liver enzymes ALT and AST in the serum of wild type CDHFD-fed mice before anti-XCL1 or IgG isotype control treatment. b, Quantification of CD103 + MHC-II + CD11c + cells from 5–10 randomly selected areas per mouse c, Levels of AST, a measure of tissue injury, including liver, in the serum of wild type mice before feeding with CDHFD (grey), after 5 months of CDHFD but before treatment (black), and following 4 weeks of anti-XCL1 or control IgG treatment (blue). In a-c n = 6–7 per group. Data are presented as mean ± s.e.m. P-values were determined by two-way ANOVA with Tukey’s multiple comparisons test; ∗∗∗p < 0.001, ∗∗∗∗p < 0.0001 d,e, Representative pictures of H&E staining (d) and pathological evaluation of NAFLD activity score (e) in anti-XCL1 or IgG isotype control-treated, CDHFD-fed mice. Images representative for two experiments. f,g, Representative pictures (f) and quantification (g) of Sirius Red staining in anti-XCL1 or IgG isotype control-treated, CDHFD-fed mice. Images representative for two independent experiments. In e, g, data are presented as mean ± s.e.m.; n = 7 per group, two-tailed Student’s t-test h. Correlation analysis between MHC-II+CD11c+CD103+ cDC1 count and NASH pathology parameters in anti-XCL1-treated and IgG-treated mice. n = 14 mice, two-tailed Spearman correlation (crossed out points p > 0.05; color and circle size indicate R2).

Extended Data Fig. 9 Depletion of cDC1 in Xcr1DTA mice prevents NASH-induced immune infiltration.

a, Flow cytometry plots illustrating specific depletion of cDC1 in Xcr1DTA mice, compared to control mice kept both on ND and after two week of MCDD feeding. b, Representative pictures of H&E staining control mice and Xcr1DTA mice (lacking Xcr1 + cDC1) fed with ND or MCDD for two weeks. Images representative for two independent experiments. c,d, FACS gating strategy and quantification of CD45 + immune cell percentage among all cells isolated from livers of control and Xcr1DTA mice kept on ND or MCDD for two weeks. n = 4 per group. Data are presented as mean ± s.e.m.; ∗∗∗∗p < 0.0001 by One-way ANOVA with Tukey’s multiple comparisons test.

Extended Data Fig. 10 cDC1 modulate hepatic immune cell populations in the MCDD model of NASH.

a,b, Representative flow cytometry plots (a) and flow cytometry-based quantification (b) of CD8 + cells in the livers of control and Xcr1DTA mice kept on ND or MCDD for two weeks. n = 4 per group. c,d, Representative microscopic images of IHC staining for CD8 (c), and image-based quantification (d) of the stained CD8 + cells in the livers of control and Xcr1DTA mice kept on ND or MCDD for two weeks. bar=100 μm. For quantification 5–10 randomly selected fields per mouse were averaged, n = 5 mice in control and Xcr1DTA-ND groups, n = 4 in the Xcr1DTA-MCDD group. e, FACS-based quantification of CD44 + CD62L + T central memory CD8 + T cells and naïve CD44-CD8 + T cells, separated as shown in (a) in the livers of control and Xcr1DTA mice kept on ND or MCDD for two weeks. n = 4 individual mice per group f, Flow cytometry plots representing gating strategy used to dissect CD4 + T cell subpopulations. g, Flow cytometry-based quantification of CD4 + T cell percentage among CD45 + immune cells in the livers of control and Xcr1DTA mice kept on ND or MCDD for two weeks. n = 4 mice per group h,i, Representative microscopic images of IHC staining for CD4 (h) and image based quantification (i) of CD4 + cells in the livers of control and Xcr1DTA mice kept on ND or MCDD for two weeks. bar = 100 μm. For quantification 5–10 randomly selected fields per mouse were averaged, n = 5 mice per group. j, Flow cytometry-based quantification of CD4 + T cell subsets in the livers of control and Xcr1DTA mice kept on ND or MCDD for two weeks. n = 4 mice per group k,l, Flow cytometry contour plots and flow cytometry-based quantification of NKT cells (k) and monocytes (l) in the livers of control and Xcr1DTA mice kept on ND or MCDD for two weeks. n = 4 mice per group m, Representative microscopic images of IHC staining for CD42b, a marker of platelets, and image based quantification of the stained cells in the livers of control and Xcr1DTA mice kept on ND or MCDD for two weeks. bar = 100 μm. For quantification 5–10 randomly selected fields per mouse were averaged, n = 5 mice per group. In all graphs data are presented as mean ± s.e.m. P-values were determined by one-way ANOVA with Tukey’s multiple comparisons test; ∗∗∗p < 0.001, ∗∗∗∗p < 0.0001.

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Supplementary Table 1

Gene lists used to perform PIC-seq.

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Deczkowska, A., David, E., Ramadori, P. et al. XCR1+ type 1 conventional dendritic cells drive liver pathology in non-alcoholic steatohepatitis. Nat Med 27, 1043–1054 (2021). https://doi.org/10.1038/s41591-021-01344-3

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