Abstract
Microbial genome-wide association studies (GWAS) have uncovered numerous host genetic variants associated with gut microbiota. However, links between host genetics, the gut microbiome and specific cellular contexts remain unclear. Here we use a computational framework, scBPS (single-cell Bacteria Polygenic Score), to integrate existing microbial GWAS and single-cell RNA-sequencing profiles of 24 human organs, including the liver, pancreas, lung and intestine, to identify host tissues and cell types relevant to gut microbes. Analysing 207 microbial taxa and 254 host cell types, scBPS-inferred cellular enrichments confirmed known biology such as dominant communications between gut microbes and the digestive tissue module and liver epithelial cell compartment. scBPS also identified a robust association between Collinsella and the central-veinal hepatocyte subpopulation. We experimentally validated the causal effects of Collinsella on cholesterol metabolism in mice through single-nuclei RNA sequencing on liver tissue to identify relevant cell subpopulations. Mechanistically, oral gavage of Collinsella modulated cholesterol pathway gene expression in central-veinal hepatocytes. We further validated our approach using independent microbial GWAS data, alongside single-cell and bulk transcriptomic analyses, demonstrating its robustness and reproducibility. Together, scBPS enables a systematic mapping of the host–microbe crosstalk by linking cell populations to their interacting gut microbes.
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Data availability
The microbial GWAS summary data of the Dutch Microbiome Project were downloaded from https://dutchmicrobiomeproject.molgeniscloud.org. The Tabula Sapiens human single-cell transcriptome data were downloaded from https://tabula-sapiens-portal.ds.czbiohub.org/. The GWAS summary data of MiBioGen project were downloaded from https://www.mibiogen.org/. The GWAS summary data of 10 liver-associated diseases were downloaded from the FinnGen database at https://r8.risteys.finngen.fi/ (accession phenocodes: T2D, T2D_WIDE, NAFLD, K11_TOXLIV, K11_FIBROCHIRLIV, FIBROLIV, E4_HYPERCHOL, E4_FH, E4_FH_IHD, CHIRHEP_NAS and C3_LIVER_INTRAHEPATIC_BILE_DUCTS_EXALLC). The KEGG pathways were downloaded from https://www.genome.jp/kegg/. The snRNA-seq data of mice livers are deposited in the Gene Expression Omnibus (GEO) database under accession number GSE289267. Source data are provided with this paper.
Code availability
Codes used for the analyses are provided in Zenodo at https://doi.org/10.5281/zenodo.15073160 (ref. 97).
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Acknowledgements
We thank J. Chen and H. Liu for technical assistance, and W. Pan for support in molecular experiments. This study was funded by the National Natural Science Foundation of China (32200535 to Y.M.), the Zhejiang Provincial Natural Science Foundation of China (2025C02153 to J.S.), and the China Postdoctoral Science Foundation (2023M732679 to J.L.).
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J.S., J.L. and Y.M. designed the study and developed statistical methodologies. W.-H.C., Y.C., Q.R., Q.Z. and Y.L. designed the animal study and conducted wet-lab experiments. J.L., Y.M., G.Z., C.C., Y. Zhou, Y. Zhang and C.D. performed data analysis and visualization. Y.M., W.-H.C. and J.S. provided guidance on data analysis and biological interpretations. J.L., Y.M., W.-H.C. and J.S. wrote the paper and response letters.
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Extended data
Extended Data Fig. 1 Performance comparison for scBPS.
a, The quantile-quantile plot illustrates results from null simulations. We randomly selected 1,000 as putative disease genes with random GWAS gene weights that matching the MAGMA z-score distributions of the gut microbes. b, Power analysis for causal simulations. Expression levels of causal genes were increased by factors of 1.1, 1.2, 1.3, 1.4, and 1.5 in the target cell types. We assessed the power to identify these target cell types at an FDR of 0.05 across these various fold changes. c, Impact of GWAS power on results for scBPS, scDRS, scPagwas, LDSC-SEG and MAGMA Celltyping. We analyzed GWAS summary statistics of 12 UK Biobank traits across varying subsample sizes (5 K, 10 K, 20 K, 50 K, and 80 K samples) coupled with the human single-cell atlas using different methods. The median number of discovered cell-types for the 12 traits were grouped by GWAS sample size. Dots represent traits and error bars denote upper and lower quantiles. The 12 traits are ASM (Asthma), BMD-HT (Heel Test), CLC (Clinical LDL Cholesterol), ECOL (College Education), Eczema, HDL (Free Cholesterol in HDL), LDL (Free Cholesterol in LDL), RBC (Red Blood Cell Count), RDW (Red Cell Distribution Width), SBP (Systolic Blood Pressure), Smoking, VLDL (Free Cholesterol in VLDL).
Extended Data Fig. 2 Associations between gut microbial clusters and human tissue modules.
a, Representative taxa at different taxonomic level are displayed for each tissue-related bacteria cluster. The color of the dots indicated the percentage of taxa within the taxonomic level that assigned to the respective bacteria clusters. b, Barplot summarizing the fraction of associations with FDR < 0.05 for each module. c, Representation of interaction strengths between the four tissue modules and the three bacteria clusters, highlighting the top three associations. The width of the line corresponded to the median value of BPSAUC of the extended-data-figure_fig2.tif organ-taxon pairs within the respective module pairs. d, The top ten microbial taxa within cluster T1 amd T2 in association with tissue module M1. e, Heatmap exhibiting the association strengths between the three bacteria clusters and the 24 tissues. f, Boxplot summarizing distribution of BPSAUC values for bacteria clusters T1, T2, and T3 in association with the 24 tissues. The central lines indicated the median values. The lower and upper hinges indicated the first and third quartiles. The lower and upper whiskers extended from the hinge to the smallest and largest values no further than 1.5× the interquartile range from the hinge. g, Histograms for the BPSAUC scores of all the organ-bacterial taxon associations. Vertical redline indicated cutoff for outliers of the distribution that identified by exploreThresholds function in AUCell R package. h, The associations of the organs with the bacterial taxa at all taxonomic levels that met the stringent threshold described in Fig. 1d. The colors of the boxes were corresponded to the BPSAUC values of respective organ-taxon pairs. i, Boxplot summarizing distribution of BPSAUC values for each tissue.
Extended Data Fig. 3 Associations of gut microbial taxa with human tissues from validation datasets.
a, Venn plot showing the overlap of microbial taxa identified to be associated with kidney, heart, pancreas, and eye, as identified by our computational framework, with that identified from the GTEx and the Franke lab dataset using LDSC-SEG. The names of the overlapped taxa were labeled. b, Dot plots showing correlation of the number of taxa (stringent criterion) for individual organs, as identified by our computational framework, with that identified from the GTEx dataset using a significance threshold of p < 0.05 (left) and p < 0.1 (right). Significance was measured by Spearman correlation analysis. c, Same as panel (b) for results from the Franke lab dataset. d, Independent validation using GWAS summary data from the MiBioGen project. Correlation between the BPSAUC values of all organ-microbe pairs using GWAS data from the Lifelines project and those using GWAS data from the MiBioGen project. P-values were calculated by Pearson correlation analysis e, Same as panel (d) for all cell type-microbe pairs.
Extended Data Fig. 4 Associations between gut microbiome and host cell types.
a, Representative taxa at the family levels were displayed for each cell-type-related bacteria cluster from Fig. 2a. The color of the dots indicated the percentage of taxa within the taxonomic level that assigned to the respective bacteria clusters. b, Overlap of the cell-type-related bacteria clusters (C1-C9, as shown in Fig. 3a) with the tissue-related bacteria clusters (T1-T3, as shown in Fig. 2a). The color of dots represented the percentage of taxa within cell-type-related clusters that belong to each tissue-related cluster. c, Comparison of BPSAUC values across four cell type compartments. Boxplots inside the violin plots showed distribution of the BPSAUC values. The central line indicated the median. The lower and upper hinges indicated the first and third quartiles. The lower and upper whiskers extended from the hinge to the smallest and largest values no further than 1.5× the interquartile range from the hinge. Significances were tested using Wilcoxon Rank-sum tests. d, e, Barplots summarizing the fraction of associations with FDR < 0.05 for each cell-type module (d) and for cell types from the target organs within the four compartments (e). f, The top 10 interactions between the five cell type modules and the nine bacteria clusters. g, The connectivity among the taxa in terms of their BPSAUC values at cell type level. Node size was relative to centrality of the taxon. The edges indicate strong correlation (coefficient > 0.8) between the connected taxa. h, Histograms for the BPSAUC scores of all the cell type-bacterial taxon associations. Vertical redline indicated cutoff for outliers of the distribution that identified by exploreThresholds function in AUCell R package. i, Bar plot summarized the cell type profiles of significant associations that reached the stringent threshold. The number of significant associations for individual cell type were shown in stacked bars. All taxonomic levels were summarized. j, Comparison of the bacterial taxa within the Stringent, Moderate and Nonsig groups regarding their significance of association with liver or hepatocyte terms in the Roadmap dataset and the EN-TEX dataset. P-values were determined using the Wilcoxon Rank-sum tests. k, Bar plots showing weighted degree, degree, and Pageranks scores of the 44 taxa in the network in Fig. 2h.
Extended Data Fig. 5 Heterogeneity of hepatocytes in gene expression and metabolism profiles.
a, Results of gene set enrichment analysis (GSEA) on genes correlated with Collinsella BPS scores among the hepatocytes. Significant pathways that reached the threshold of FDR < 0.05 were displayed. The pathways involved in cholesterol biosynthesis and metabolism were highlighted in red. b, Correlation of cholesterol pathways with Collinsella compared to random pathways. P-values were calculated using a Monte Carlo simulation approach. c, Correlations of Collinsella BPS quintiles with mean pathway scores across all KEGG pathways. Significance was assessed using linear regression model. d, Comparsons of mean pathway scores between cells with top and bottom 20% (left panel), 5% (middle panel) and 1% (right panel) BPS Collinsella scores. Significance assessed using t-test. e, Marker genes for hepatocyte zonation and their expression levels in the three subpopulations. f, Dot plots showing differentially expressed genes for the three hepatocyte subpopulations. The number of up-regulated (red) and down-regulated (blue) genes were summarized. g, Differential functions among the three hepatocyte subpopulations. Gene set enrichment analysis (GSEA) was performed to identify distinctive functional profiles for the three subpopulations. h, Expression level of gene CYP7A1 (the rate-limiting step of the bile-acid biosynthetic pathway), HMGCR (rate-limiting enzyme of cholesterol synthesis), and NR1H4 (the gene encoding FXR receptor) in the subpopulations of hepatocytes. i, Rankings of top 30 marker metabolic reactions for each hepatocyte subpopulation. j, Mean level of metabolic flux for reaction “Cholesterol −> Chenodeoxycholate” in the three hepatocyte subpopulations. k, Correlation of the reaction flux for “Cholesterol −> Chenodeoxycholate” and the Collinsella BPS values among the hepatocytes, as stratified by zonation. Significances of correlations were estimated by Pearson correlation analyses.
Extended Data Fig. 6 Association of Collinsella with liver-related diseases.
a, Distribution of DPS values of the 10 liver-associated diseases among the 24 tissues. The central line indicated the median. The lower and upper hinges indicated the first and third quartiles. The lower and upper whiskers extended from the hinge to the smallest and largest values no further than 1.5× the interquartile range from the hinge. b, Distribution of DPS values of the 10 liver-associated diseases among the 16 liver cell types. The central line indicated the median. The lower and upper hinges indicated the first and third quartiles. The lower and upper whiskers extended from the hinge to the smallest and largest values no further than 1.5× the interquartile range from the hinge. c, Associations of all (upper) and top 1000 (bottom) magma z-scores derived from Collinsella with disease GWAS data. Association coefficient and p-values were generated by linear regression analyses. d, Mendelian Randomization analysis inferring causal relationships of Collinsella with both HYPERCHOL and FH. Forest plot showed the MR estimates and 95% CI values of the bi-directional causal effects between Collinsella and both HYPERCHOL and FH, as estimated using eight different two-sample MR methods. The P values calculated by each MR method were listed. e, Multivariate linear regression model estimated association between Collinsella and HYPERCHOL among the hepatocytes, stratified by zonation, while adjusting for the effect of cholesterol biosynthesis. f, g, Multivariate linear regression model estimating association between Collinsella and FH among the hepatocytes, stratified by zonation, while adjusting for effect of cholesterol metabolism pathway (f) and cholesterol biosynthesis pathway (g). h, Predicted PROSE distance of three Collinsella isolate proteins with key proteins involved in cholesterol metabolism. Collinsella proteins with both sequence and structure similarity are highlighted in black. Those with only structure similarity are shown in gray.
Extended Data Fig. 7 Normalized t-statistics between cells from the expected and unexpected cell types for the eight hepatocyte-associated bacteria.
a, Normalized t-statistics for differences of scBPS values between expected (hepatocytes) and unexpected (other) cells for each taxon applying different gene selection threshold. b, Normalized t-statistics for differences of BPSAUC values between hepatocyte to other cell types.
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Li, J., Ma, Y., Cao, Y. et al. Integrating microbial GWAS and single-cell transcriptomics reveals associations between host cell populations and the gut microbiome. Nat Microbiol 10, 1210–1226 (2025). https://doi.org/10.1038/s41564-025-01978-w
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DOI: https://doi.org/10.1038/s41564-025-01978-w
