Abstract
The liver is the main gateway from the gut, and the unidirectional sinusoidal flow from portal to central veins constitutes heterogenous zones, including the periportal vein (PV) and the pericentral vein zones1,2,3,4,5. However, functional differences in the immune system in each zone remain poorly understood. Here intravital imaging revealed that inflammatory responses are suppressed in PV zones. Zone-specific single-cell transcriptomics detected a subset of immunosuppressive macrophages enriched in PV zones that express high levels of interleukin-10 and Marco, a scavenger receptor that sequesters pro-inflammatory pathogen-associated molecular patterns and damage-associated molecular patterns, and consequently suppress immune responses. Induction of Marco+ immunosuppressive macrophages depended on gut microbiota. In particular, a specific bacterial family, Odoribacteraceae, was identified to induce this macrophage subset through its postbiotic isoallolithocholic acid. Intestinal barrier leakage resulted in inflammation in PV zones, which was markedly augmented in Marco-deficient conditions. Chronic liver inflammatory diseases such as primary sclerosing cholangitis (PSC) and non-alcoholic steatohepatitis (NASH) showed decreased numbers of Marco+ macrophages. Functional ablation of Marco+ macrophages led to PSC-like inflammatory phenotypes related to colitis and exacerbated steatosis in NASH in animal experimental models. Collectively, commensal bacteria induce Marco+ immunosuppressive macrophages, which consequently limit excessive inflammation at the gateway of the liver. Failure of this self-limiting system promotes hepatic inflammatory disorders such as PSC and NASH.
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Data availability
Visium and single-cell RNA sequencing data have been deposited into the NCBI Gene Expression Omnibus (GEO) database under the accession numbers GSE213388 and GSE213165, respectively. For the re-analysis of mouse liver single-cell RNA sequencing and Visium, we obtained the datasets from the GEO (accession number GSE192742)9. For the re-analysis of human single-cell RNA sequencing data, we obtained the data of human liver CD45+ cells from eight individuals (four unaffected livers and four cirrhotic livers) from the GEO (accession numbers GSM4041150, GSM4041153, GSM4041155, GSM4041160, GSM4041161, GSM4041166, GSM4041168 and GSM4041169 and URL https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE136103)15. For the re-analysis of mouse liver and intestine single-cell RNA sequencing data, we obtained the datasets from Mouse Cell Atlas80 (https://bis.zju.edu.cn/MCA/). All other data in this study are available from the corresponding author upon reasonable request. Source data are provided with this paper.
Code availability
All source codes for the Visium and single-cell analyses are available from the GitHub repository (https://github.com/OU-ICB/YMiyamoto2023).
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Acknowledgements
We thank R. N. Germain for critically reviewing the manuscript; Y. Yahara, S. Kameoka, F. Sugihara, T. Sudo, T. Ariyoshi, B. Li, M. Shirazaki, F. Okiji and A. Sakai for their instructive comments and technical assistance; and staff at Editage (https://www.editage.jp) for English language editing. This work was supported by CREST (JPMJCR15G1 to M.I.) from the Japan Science and Technology (JST) Agency; Grant-in-Aid for Scientific Research (S) (19H05657 to M.I.) for Transformative Research Areas (A) (20H05901 to M.I.), for International Leading Research (22K21354 to M.I.), for JSPS Fellows (21J13888 to Y.M.) and Research Activity Start-up (22K20760 to Y.M.) from the Japan Society for the Promotion of Science (JSPS); the Innovative Drug Discovery and Development Project (JP21am0401009 to M.I.) and the Program on the Innovative Development and the Application of New Drugs for Hepatitis B (JP23fk0310512 to M.I.) from the Japan Agency for Medical Research and Development (AMED); and Uehara Memorial Foundation (to M.I.). Schematics in Fig. 4b and 5a and Extended Figs. 2a,b, 6a,d, 7d, 8a, 9a and 11a,e were created using BioRender (https://biorender.com).
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Contributions
Y.M. conceived the original idea of this study. Y.M. and M.I. devised the concrete concept. Y.M., J. Kikuta and M.I. designed the experiments. Y.M. conducted all of the experiments and data analyses with assistance from J. Kikuta, T.M., T.H., K.F., Y.U. and E.Y. D.M. and D.O. processed the sequencing data. Y.-c.L., S.S. and D.O. established a new data processing method for spatial transcriptomics. S.K. and H.E. collected and provided the human liver samples, and T.M. and E.M. performed the immunofluorescence staining. K. Tryggvason generated Marco−/− mice. T.S. maintained them and assisted with experiments using Marco knockout mice. K.A. and K.H. isolated and provided Odoribacteraceae strain 21. T.Y. and J. Kunisawa measured concentrations of isoallo-LCA in faeces. H.K. and K. Takeda supervised the experiments and analyses pertaining to gut commensal microbes. Y.M. wrote the initial draft, and Y.M., J. Kikuta and M.I. revised the final draft.
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Extended data figures and tables
Extended Data Fig. 1 Spatial heterogeneity in neutrophil adhesion in the steady-state liver.
a, Representative intravital image [left, green: neutrophils, red: Qtracker655 (blood vessels), and blue: SHG (tissue collagens)] and neutrophil tracks (right, individual colours mean individual cell tracks). Tracks of neutrophils that adhered to the tissue for over 10 min are shown. Scale bar: 100 µm. b, Numbers of the neutrophil tracks within each zones (n = 7). The quantitative data are presented as means (asterisk) with medians, smallest observations, lower and upper quartiles, and largest observations. Statistical significance was determined using unpaired two-sided Mann–Whitney U test.
Extended Data Fig. 2 Spatial heterogeneity in monocyte/macrophage responses to the laser-induced tissue damage in the liver.
a, Timeline of neutrophil and monocyte responses post the laser-induced tissue damage. b, Experimental design to quantify monocyte/macrophage accumulations at the laser-induced damaged sites. c,d, Representative intravital images of in situ inflammatory responses by monocytes/macrophages upon the laser-induced damages under control (c, n = 13) and clodronate liposome-treated (resident macrophage-depleted) conditions (d, n = 10) [green: monocytes/macrophages, white: damaged sites (autofluorescence), and blue: SHG (tissue collagens)]. Scale bar: 100 µm. Quantified accumulation scores of monocyte/macrophage at the lesions are shown (right). e, PV/CV ratio of monocyte/macrophage accumulation at 24 h post-laser ablation under control (n = 13) and resident macrophage-depleted (n = 10) conditions, indicating spatial polarisation of accumulation: >1 and <1 indicate bias towards PV and CV zones, respectively. All quantitative data are presented as means (asterisk) with medians, smallest observations, lower and upper quartiles, and largest observations. Statistical significance was determined using paired (c, d) and unpaired (e) two-sided Mann–Whitney U tests.
Extended Data Fig. 3 Reanalysis to identify the Marco+ Kupffer cell subset (MP2) using public mouse and human databases.
a, Reproduction of Uniform Manifold Approximation and Projection (UMAP) depicting distinct myeloid cell clusters identified in the mouse liver cell atlas. The Kupffer cell cluster was further analysed for isolating subclusters. b, Density of Marco- (left) and Il10- (right) expressing cells in each Kupffer cell subcluster. Subclusters 8, 10 and 17 should be the MP2. c, Reproduction of UMAP depicting the liver zonation in the mouse liver cell atlas. d, Density of Marco+ Clec4f+ spots on the liver zonation plot (left). Violin plot showing quantification of the densities in each zone (right). Statistical significance was determined using one-sided Student’s t-test and resultant p-values were corrected using the Benjamini-Hochberg method. e, Summary of human sample information (left). tSNE plot depicting distinct immune cell clusters (right). All single-cell data from healthy and cirrhotic samples were integrated and represented on the same tSNE plot. Each cluster was assigned to the known cell types based on marker genes (Supplementary Table 3). The numbers in brackets indicate the cluster number. Resident macrophages include three clusters. f, Gene expression of CD68 (left), a human macrophage marker, MARCO (centre), and IL10 (right) was visualised with an R package ‘Nebulosa’ (Kernel Gene-Weighted Density Estimation). g, Percentage of MARCO+ IL10+ cells to total macrophages under healthy and cirrhotic conditions. Data are presented as means (asterisk) with medians, smallest observations, lower and upper quartiles, and largest observations. Statistical significance was determined using unpaired two-sided Mann–Whitney U test.
Extended Data Fig. 4 Relationship between Marco and IL-10 expressions in Kupffer cells.
a, Transcriptional activity of Il10 in Marco− (MP1) and Marco+ (MP2) Kupffer cells visualised using Il10-Venus mice (n = 7). To confirm the background noise, we used wild-type mice as the negative control (n = 5). Venus expression was detected using AlexaFluor647-conjugated anti-Venus antibody to avoid the influence of autofluorescence. Mean fluorescence intensity (MFI) of AlexaFluor647 (from Il10-venus) was measured for statistical comparison. b, Correlation between Marco and Il10-venus expressions. ‘R’ indicates the correlation coefficient. The error bands mean 95% confidence interval. c, Relative mRNA expression of Il10, Il1rn and Tgfb1 to Gapdh in total Kupffer cell fraction from Marco+/+ control (n = 7–9) and Marco−/− (n = 5–9) mice. All data are presented as means (asterisk) with medians, smallest observations, lower and upper quartiles, and largest observations. Statistical significance was determined using unpaired two-sided Mann–Whitney U test.
Extended Data Fig. 5 Interleukin-10 signalling in PV zones suppressively regulates ICAM1-integrin interactions between endothelial cells and neutrophils.
a, Representative flow cytometry gating to identify the liver sinusoidal endothelial cell (LSEC) subsets. The histogram shows the ICAM-1 expression levels on each subset. b, Mean fluorescence intensity (MFI) from ICAM-1 on CD117+ and CD117− LSECs (n = 4). c, Representative immunofluorescence images of ICAM-1 in the liver tissue (n = 4, blue: E-cadherin+ PV zones, green: ICAM-1). PV, portal vein; CV, central vein. Scale bar: 100 µm. d, MFI from ICAM-1 on CD117+ and CD117– LSECs under anti-IL10R and isotype control antibody-treated conditions (n = 9 and 7, respectively). e, Fold changes of Cxcl1 and Cxcl2 mRNA expressions to Gapdh in CD117+ LSECs, CD117− LSECs, and Kupffer cells from anti-IL10R and isotype control antibody-treated mice (n = 7 and 6, respectively). Data were standardized to ensure a control group mean value of ‘1’. f–h, Analyses of infiltrating neutrophils in the liver under anti-IL10R and isotype control antibody-treated conditions (n = 4, respectively). Representative staining of integrin αM (Mac-1 or CD11b) on CD45+ Mac-1+ Ly-6G+ neutrophils (f), percentage of Mac-1high neutrophils (g), and absolute number of neutrophils (h). All data are presented as means (asterisk) with medians, smallest observations, lower and upper quartiles, and largest observations. Statistical significance was determined using unpaired two-sided Mann–Whitney U test.
Extended Data Fig. 6 In vitro and in vivo assays of E. coli-capturing activity of MP1 and MP2 Kupffer cells.
a, Experimental design for in vitro bacteria-capture assay. b, Representative flow cytometry gating for identifying Marco− (MP1) and Marco+ (MP2) Kupffer cells, and comparison of E. coli-derived fluorescence signals. c, Mean fluorescence intensity (MFI) of E. coli-derived GFP signals in MP1 and MP2 (n = 7). d, Experimental design for in vivo bacteria-capture assay. e, Representative immunofluorescence images (n = 3, 9 visual fields, white: E. coli, red: F4/80+ macrophages, blue: E-cadherin+ PV zones) showing E. coli localisation in the liver (left and centre). Scale bar: 100 µm. Percentage of E. coli numbers within each zone to total E. coli (right). The exact p-value is 4.114 × 10−5. f, Representative immunofluorescence images (n = 3, 15 visual fields, white: E. coli, blue: Marco− MP1, and red: Marco+ MP2) showing the E. coli-capturing capability of each subset (left). The raw images were processed using the Imaris software (centre, yellow: E. coli, blue: Marco− MP1, and red: Marco+ MP2). Scale bar: 100 µm. Percentage of E. coli-capturing Marco− MP1 and Marco+ MP2 to total E. coli-capturing cells (right). g, Percentage of cells engulfing more than two E. coli in each Kupffer cell subset. h, Representative images showing E. coli localisation in the Marco+/+ (n = 5, 25 visual fields) and Marco−/− (n = 4, 30 visual fields) livers (left, yellow: E. coli, blue: E-cadherin+ PV zones). E. coli are shown as spherical spots using the imaris. Scale bar: 100 µm. Percentage of E. coli numbers within each zone to total E. coli (right). The exact p-values are 1.376 × 10−6 (PV) and 1.376 × 10−6 (CV). Data are presented as means (asterisk) with medians, smallest observations, lower and upper quartiles, and largest observations. Statistical significance was determined using paired (c) and unpaired (e-h) two-sided Mann–Whitney U test.
Extended Data Fig. 7 Identification of gut commensal bacteria that induce MP2 Kupffer cells and involvement of gut commensals in MP2 induction by isoallo-lithocholic acids.
a, Relative abundance (%) of microbes at the family level (n = 6 and 5 for the SPF-A and SPF-B groups, respectively). The data were obtained by 16 S rRNA-sequencing. b, Relative abundance (%) of each bacterium significantly enriched in “SPF-A” colorectal contents (n = 6 and 5 for the SPF-A and SPF-B groups, respectively). c, Correlation between the relative abundance of bacterium and the percentage of Marco+ Kupffer cells (MP2). ‘R’ indicates the correlation coefficient. d, Graphical protocol for isoallo-lithocholic acid (isoalloLCA) and/or antibiotic treatments. e, Percentage of Marco+ cells to total Kupffer cells under DMSO (n = 10), isoalloLCA (n = 10), and antibiotic/isoalloLCA (n = 13) treatments. f, Fold changes of Il10 mRNA expression in total Kupffer cells under each condition [DMSO (n = 10), isoalloLCA (n = 10), and antibiotics/isoalloLCA (n = 9)]. Data were standardized to ensure a control group mean value of ‘1’. All data are presented as means (asterisk) with medians, smallest observations, lower and upper quartiles, and largest observations. Statistical significance was determined using unpaired two-sided Mann–Whitney U test.
Extended Data Fig. 8 Periportal immunosuppressive Kupffer cells protect against gut commensal-driven liver inflammation related to experimental colitis.
a, Experimental design; Marco+/+ and Marco−/− mice received 1% dextran sodium sulphate (DSS) via drinking water for 7 days to induce acute colitis, followed by drinking normal water for 4 days for recovery. On day 11, the livers were harvested for assays. b, Relative mRNA expression of anti-inflammatory cytokines Il10 and Il1rn in Kupffer cells from Marco+/+ and Marco−/− mice (n = 6 each). c, Representative intravital images of infiltrating inflammatory neutrophils in Marco+/+ (left) and Marco−/− (right) mice (n = 6 each, green: neutrophils, red: vascular structures visualised by Qtracker655). Scale bar: 100 µm. d, Quantification of neutrophil numbers in 100 µm3 tissues (n = 6, 12 visual fields per condition). Data contain two tissue sections from different lobes per mouse. e, Body weight change showing the percentage of body weight on day 11 to the original body weight (on day 0) (n = 6 each). All quantitative data are presented as means (asterisk) with medians, smallest observations, lower and upper quartiles, and largest observations. Statistical significance was determined using unpaired two-sided Mann–Whitney U test.
Extended Data Fig. 9 Periportal immunosuppressive Kupffer cells suppress progression of the non-alcoholic fatty liver disease.
a, Analysis schedule. b, Representative Marco and TIM-4 staining in CD45+ CX3CR1− F4/80+ CD64+-gated macrophages. c,d,f,h, Kinetics of the frequency of Marco+ TIM-4+ MP2 Kupffer cells (c), serum AST (d), serum ALT (f), and neutrophil abundance (h) [Healthy (n = 6-7), NAFLD/NASH 2 W (n = 9–12), 4 W (n = 11-12) and 6 W (n = 8)]. e,g,i, Correlation between MP2 frequency and AST (e), ALT (g), and neutrophil abundance (i) in NAFLD/NASH 2 W. ‘R’ indicates the correlation coefficient. The error bands mean 95% confidence interval. j, Serum AST and ALT levels in NAFLD/NASH-induced Marco+/+ (n = 12, 12, 8 for 2 W, 4 W, 6 W, respectively) and Marco−/− mice (n = 8, 10, 8 for 2 W, 4 W, 6 W, respectively). k, Representative Masson trichrome staining of healthy Marco+/+, NAFLD/NASH-induced Marco+/+ and Marco−/− livers. Scale bar: 200 µm. l, Percentage of area occupied by fat droplets around portal veins in NAFLD/NASH 6 W: Marco+/+ (n = 5, 8 visual fields) and Marco−/− (n = 5, 10 visual fields). m, AST/ALT ratio in NAFLD/NASH 6 W: Marco+/+ and Marco−/− mice (n = 8 each). n, Representative immunofluorescence images showing MARCO (green), CD68 (red), and CK19 (cyan) in human livers: NAFLD/NASH (n = 7, 21 visual fields) and normal controls (n = 9, 27 visual fields). Scale bar: 100 µm. o, Absolute numbers of CD68-positive cells (macrophages) per visual field. p, Percentage of Marco-positive cells to total macrophages. All curve graphs represent means ± standard error of the mean (SEM). All box plots represent means (asterisk) with medians, smallest observations, lower quartiles, upper quartiles, and largest observations. Statistical significance was determined using unpaired two-sided Mann–Whitney U test. The exact p-values are 3.969 × 10−5 (h), 7.693 × 10−5 (p, Normal vs NAFLD), 5.114 × 10−10 (p, Normal vs NASH).
Extended Data Fig. 10 Marco+ Kupffer cells (MP2) belong to the CD206− ESAM− KC1 subset.
a, Representative staining of CD206 and ESAM on Marco+ and Marco− Kupffer cells (CD45+ CX3CR1− F4/80+ CD64+ population). b, Percentage of Marco+ cells in KC1 and KC2 (n = 4). c, Percentage of total Marco+ Kupffer cells, Marco+ KC1, and Marco+ KC2 in all Kupffer cells (n = 4). d, Diagram illustrating the relationship between KC1/KC2 and MP1/MP2 classifications of Kupffer cells. All box plots represent means (asterisk) with medians, smallest observations, lower quartiles, upper quartiles, and largest observations. Statistical significance was determined using unpaired two-sided Mann–Whitney U test.
Extended Data Fig. 11 Marco+ immunosuppressive Kupffer cells are supplied by embryo-derived macrophages.
a, Experimental design; generating a parabiosis model to examine the differentiation from bone marrow-derived monocytes into Marco+ Kupffer cells (MP2). b, Representative flow cytometry gating of tdTomato+ cells on CD45+ CX3CR1− F4/80+ CD64+ cells in the liver from wild-type parabionts (left). Percentages of Marco− (MP1) and Marco+ (MP2) cells to Tomato+ Kupffer cells (right, n = 7). c, Representative immunofluorescence images of a wild-type parabiont liver [blue: E-cadherin (PV zones), white: tdTomato (bone marrow-derived macrophages), red: F4/80 (Kupffer cells), and green: Marco (MP2)]. Scale bar: 100 µm (large image) and 20 µm (zoomed images). d, Representative immunofluorescence images of a wild-type parabiont liver [white: tdTomato (bone marrow-derived macrophages), red: CD68 (Kupffer cells), and green: TIM-4 (resident Kupffer cells)]. Scale bar: 100 µm. e, Graphical protocol for analysing resident and bone marrow-derived (repopulated) Kupffer cells. f, Representative gating of Kupffer cells in clodronate liposome (CLL)-treated (on day 2) and untreated mice (left). Absolute number of TIM-4+ resident Kupffer cells [right, control (n = 7) and CLL-treated (n = 6)]. g, Representative staining of Marco and TIM-4 on Kupffer cells in CLL-treated (on week 6) and untreated control mice. h, Absolute numbers of TIM-4+ resident Kupffer cells (left) and TIM-4− bone marrow-derived Kupffer cells (right) in CLL-treated (on week 6, n = 12) and untreated control (n = 7) mice. The exact p-value is 3.969 × 10−5. i, Percentage of Marco+ cells in TIM-4+ and TIM-4− Kupffer cells in CLL-treated mice (on week 6, n = 12). j, Relative mRNA expression of Il10 to Gapdh in TIM-4+ and TIM-4− Kupffer cells from CLL-treated mice (on week 6, n = 8). All data are presented as means (asterisk) with medians, smallest observations, lower and upper quartiles, and largest observations. Statistical significance was determined using unpaired (b, f, h) and paired (i, j) two-sided Mann–Whitney U test.
Supplementary information
Supplementary Information
Supplementary Figs. 1–8, Supplementary Tables 1–4 and full descriptions for Supplementary Videos 1–9.
Supplementary Data 1
Source data for Supplementary Fig. 1.
Supplementary Data 2
Source data for Supplementary Fig. 2.
Supplementary Data 3
Source data for Supplementary Fig. 4.
Supplementary Data 4
Source data for Supplementary Fig. 5.
Supplementary Data 5
Source data for Supplementary Fig. 8.
Supplementary Video 1
Inflammatory responses of neutrophils after sterile laser-induced damage in PV and CV zones.
Supplementary Video 2
Neutrophil adhesion to PV and CV zones in the steady-state liver.
Supplementary Video 3
Comparison of neutrophil dynamics responding to laser-induced tissue damage under normal and macrophage-depleted conditions.
Supplementary Video 4
IL-10 signalling blockade abrogates immunosuppressive functions of periportal macrophages.
Supplementary Video 5
Marco deficiency attenuates immunosuppressive functions of periportal macrophages.
Supplementary Video 6
Gut commensal depletion diminishes immunosuppressive functions of periportal macrophages.
Supplementary Video 7
Intrahepatic inflammation after acute experimental colitis in Marco+/+ and Marco–/– mice.
Supplementary Video 8
Intrahepatic inflammation after chronic experimental colitis in Marco+/+ and Marco–/– mice.
Supplementary Video 9
Intravital imaging of sinusoidal blood flow to identify portal and central veins in the liver.
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Miyamoto, Y., Kikuta, J., Matsui, T. et al. Periportal macrophages protect against commensal-driven liver inflammation. Nature (2024). https://doi.org/10.1038/s41586-024-07372-6
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DOI: https://doi.org/10.1038/s41586-024-07372-6
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