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Single-cell transcriptomics across 2,534 microbial species reveals functional heterogeneity in the rumen microbiome

Nature Microbiology volume 9, pages 1884–1898 (2024)Cite this article

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Abstract

Deciphering the activity of individual microbes within complex communities and environments remains a challenge. Here we describe the development of microbiome single-cell transcriptomics using droplet-based single-cell RNA sequencing and pangenome-based computational analysis to characterize the functional heterogeneity of the rumen microbiome. We generated a microbial genome database (the Bovine Gastro Microbial Genome Map) as a functional reference map for the construction of a single-cell transcriptomic atlas of the rumen microbiome. The atlas includes 174,531 microbial cells and 2,534 species, of which 172 are core active species grouped into 12 functional clusters. We detected single-cell-level functional roles, including a key role for Basfia succiniciproducens in the carbohydrate metabolic niche of the rumen microbiome. Furthermore, we explored functional heterogeneity and reveal metabolic niche trajectories driven by biofilm formation pathway genes within B. succiniciproducens. Our results provide a resource for studying the rumen microbiome and illustrate the diverse functions of individual microbial cells that drive their ecological niche stability or adaptation within the ecosystem.

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Fig. 1: Overall workflow of MscT.
Fig. 2: Reference pangenomes of the rumen microbiome with 13,572 non-redundant genomes (average nucleotide identity > 95%).
Fig. 3: Single-cell functional landscape of the rumen microbiome.
Fig. 4: Pathway heterogeneity of functional clusters.
Fig. 5: Metabolic insights into rumen classic carbohydrate pathways.
Fig. 6: Functional heterogeneity and cellular trajectories of B. succiniciproducens.

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Data availability

All of the raw sequencing data of the MscT have been deposited to the Genome Sequence Archive database with the accession number CRA012211. The genome files of MAGs in the BGMGM, gene annotation files and intermediate files resulting from quality control, benchmarking and other processes have been submitted to the Figshare database at https://figshare.com/articles/dataset/Microbiome_single-cell_transcriptomics_reveal_functional_heterogeneity_of_metabolic_niches_covering_more_than_2_500_species_in_the_rumen/24844344 (ref. 80). Source data are provided with this paper.

Code availability

The main codes and scripts from this study were uploaded to GitHub (https://github.com/J-MimgHui/MscT_codes).

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Acknowledgements

We thank all of the members of the Institute of Dairy Science, College of Animal Sciences, Zhejiang University for assistance with sample collection. This work was supported by the National Natural Science Foundation of China (grant no. 32322077 to H.-Z.S.), National Key R&D Program of China (grant no. 2022YFD1301700 and 2023YFE0123100 to H.-Z.S.), Natural Science Foundation of Zhejiang Province (grant no. LR23C170001 to H.-Z.S.) and Leading Innovative and Entrepreneur Team Introduction Program of Zhejiang (grant no. 2021R01012 to Y.W.).

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Author notes

  1. Ming-Yuan Xue

    Present address: Xianghu Laboratory, Hangzhou, China

  2. These authors contributed equally: Minghui Jia, Senlin Zhu, Ming-Yuan Xue.

Authors and Affiliations

  1. Institute of Dairy Science, College of Animal Sciences, Zhejiang University, Hangzhou, China

    Minghui Jia, Senlin Zhu, Ming-Yuan Xue, Hongyi Chen, Jinghong Xu, Yifan Tang, Xiaohan Liu, Jian-Xin Liu & Hui-Zeng Sun

  2. Key Laboratory of Molecular Animal Nutrition, Ministry of Education, Zhejiang University, Hangzhou, China

    Minghui Jia, Senlin Zhu, Ming-Yuan Xue, Hongyi Chen, Jinghong Xu, Yifan Tang, Xiaohan Liu, Jian-Xin Liu & Hui-Zeng Sun

  3. Key Laboratory of Dairy Cow Genetic Improvement and Milk Quality Research of Zhejiang Province, Zhejiang University, Hangzhou, China

    Minghui Jia, Senlin Zhu & Hui-Zeng Sun

  4. Liangzhu Laboratory, Zhejiang University, Hangzhou, China

    Mengdi Song, Tianyu Zhang & Yongcheng Wang

  5. Department of Laboratory Medicine, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China

    Mengdi Song, Tianyu Zhang & Yongcheng Wang

  6. M20 Genomics, Hangzhou, China

    Mengdi Song & Tianyu Zhang

  7. Shanghai Biozeron Biotechnology Company, Shanghai, China

    Ye Tao

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Contributions

H.-Z.S. supervised the project and designed the research. H.-Z.S., M.J., S.Z., Y. Tang, X.L. and Y. Tao constructed the BGMGM. M.J., S.Z., Y. Tang and X.L. collected the rumen fluid samples. M.S. performed the RNA-seq experiments with assistance from Y.W. M.J., S.Z., T.Z. and Y. Tao performed the species annotation and gene alignment at the single-cell level. M.J. and S.Z. performed the cluster analysis, cell-type annotation, pathway analysis and pseudo-time analysis. M.J., S.Z., H.C. and H.-Z.S. interpreted the data. M.J., S.Z. and J.X. visualized the results. M.J., S.Z. and M.-Y.X. wrote the paper. H.-Z.S., Y.W. and J.-X.L. revised the paper. All authors read and approved the final version of the paper.

Corresponding authors

Correspondence to Yongcheng Wang or Hui-Zeng Sun.

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The authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Overall workflow of BGMGM construction.

Workflow for the construction of the Bovine Gastro Microbial Genome Map (BGMGM).

Extended Data Fig. 2 The venn plot of annotated genes and the phylogenic tree of 47 Archaea MAGs.

(A) The Venn plot of BGMGM genes annotated by KEGG database, GO database, CAZy database, and COG database. (B) The phylogenic tree of 47 Archaea MAGs. MAGs: metagenome assembled genomes. BGMGM, bovine gastro microbial genome map.

Extended Data Fig. 3 Microbiome single-cell transcriptomics computational analysis pipeline and performance.

(A) Computational analysis pipeline including microbial pan-genome mapping, taxonomic level-by-level annotation, and functional cluster-based atlas construction. (B) The UMI numbers and unique gene numbers in each sample. UMI, unique molecular identifiers. Each box represents the interquartile range (IQR), in which the middle line represents the median. The whiskers extend to 1.5 × IQR.

Source data

Extended Data Fig. 4 Heterogeneity among different functional clusters and species.

(A) The cell and gene filtering steps as well as the benchmarking processes to determine the dimension and resolution values. (B) The UMAP plots for cells of different samples. (C) The UMAP plots for cells of different genera. UMAP, Uniform Manifold Approximation and Projection.

Extended Data Fig. 5 Differences between six functional clusters in the same species analyzed by the Kruskal–Wallis test with Dunn post hoc tests.

(A) The P values of inter-cluster comparison for FGPs in eight species. The UMAP plot presented the functional clusters involved in the analysis. The heat map showed the P values. (B) Dunn post hoc test performed on the FGPs of carbohydrate transport and metabolism. (C) Dunn post hoc test performed on the FGPs of lipid transport and metabolism. (D) Dunn post hoc test performed on the FGPs of amino acid transport and metabolism. FGP: functional gene proportion (the number of functional genes in a certain pathway/the number of all annotated genes in single cell); UMAP, Uniform Manifold Approximation and Projection.

Extended Data Fig. 6 SPIEC-EASI analysis.

The interaction networks of 213 cell units and the interactions between the HSP90+ HMACs—Basfia_succiniciproducens and other associated cell units. SPIEC-EASI, Sparse Inverse Covariance Estimation for Ecological Association Inference.

Extended Data Fig. 7 Carbohydrate metabolic activity analysis.

Average classic carbohydrate metabolic FGPs of 10 sub-functional clusters generated from HMACs. FGPs, functional gene proportions, the number of functional genes in a certain pathway/the number of all annotated genes; HMACs, high metabolic activity cells.

Extended Data Fig. 8 Active cell proportion analysis.

Active cell proportion of 10 sub-functional clusters generated from HMACs in each classic carbohydrate metabolic pathway. HMACs, high metabolic activity cells.

Extended Data Fig. 9 Marker genes and biofilm formation pathway activity analysis of 8 sub-population functional clusters form B. succiniciproducen cells.

(A) The UMAP plot of eiight sub-population functional clusters form B. succiniciproducen cells. (B) Marker genes of eight sub-population functional clusters form B. succiniciproducen cells. (C) Transformational relationships between clusters “Multi signal cells”, “Integrase+ cells”, and “Transposase+ formate/nitrite TCs”. (D) “Biofilm.formation_P” pathway activity and two key gene proportion, n = 200 and 121 biologically independent cells. Data are presented as mean values +/- SEM. Two-side Wilcoxon rank sum test was used for data analysis. UMAP, Uniform Manifold Approximation and Projection.

Source data

Supplementary information

Reporting Summary

41564_2024_1723_MOESM2_ESM.xlsx

Supplementary Table 1 Published ruminant metagenomics datasets used in this study. Supplementary Table 2 Quality of all 55,715 genomes of the BGMGM (54,403 public genomes and 1,312 in-house genomes). Supplementary Table 3 Quality and annotation information for the 47,241 filtered genomes of the BGMGM. Supplementary Table 4 Quality and annotation information for the 13,572 non-redundant genomes of the BGMGM. Supplementary Table 5 Marker gene information for 12 functional clusters and ten sub-functional clusters of HMACs and eight sub-functional clusters of B. succiniciproducens cells. Supplementary Table 6 Average FGPs of 12 functional clusters in each COG pathway. Supplementary Table 7 P values of inter-cluster difference analysis between six functional clusters in 89 species. A Benjamini–Hochberg adjustment was made for multiple comparisons. Supplementary Table 8 Functional gene numbers of 5,636 active cells of HMACs in the classic carbohydrate pathways. Supplementary Table 9 The results of KEGG enrichment analysis. A two-sided Fisher’s exact test was used for data analysis. A Benjamini–Hochberg adjustment was made for multiple comparisons. Supplementary Table 10 Functional gene numbers of B. succiniciproducens cells in the top 14 enriched KEGG pathways. Supplementary Table 11 Functional gene numbers of B. succiniciproducens cells in the classic carbohydrate pathways. Supplementary Table 12 Read number thresholds, cell numbers, average nFeatures and average nCounts of 14 samples. Supplementary Table 13 Annotation results of genes used in classic carbohydrate pathways. Supplementary Table 14 Cell metadata of pseudo-time analysis.

Source data

Source Data Fig. 1

Ingredients and nutrient composition of the total mixed ration fed to the cows.

Source Data Fig. 3

Cell proportion data of core species in Fig. 3c.

Source Data Fig. 5

Unprocessed statistical data of Fig. 5a.

Source Data Fig. 6

Unprocessed statistical data of Fig. 6b and a table of enrichment analysis formula.

Source Data Extended Data Fig. 3

Unprocessed data and summary data for Extended Data Fig. 3b.

Source Data Extended Data Fig. 9

Unprocessed statistical data for Extended Data Fig. 9d.

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Jia, M., Zhu, S., Xue, MY. et al. Single-cell transcriptomics across 2,534 microbial species reveals functional heterogeneity in the rumen microbiome. Nat Microbiol 9, 1884–1898 (2024). https://doi.org/10.1038/s41564-024-01723-9

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  • DOI: https://doi.org/10.1038/s41564-024-01723-9

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