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Denoising sparse microbial signals from single-cell sequencing of mammalian host tissues

Nature Computational Science volume 3, pages 741–747 (2023)Cite this article

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A preprint version of the article is available at bioRxiv.

Abstract

Existing genomic sequencing data can be used to study host–microbiome ecosystems; however, distinguishing signals that originate from truly present microbes from contaminating species and artifacts is a substantial and often prohibitive challenge. Here we show that emerging sequencing technologies definitely capture reads from present microbes. We developed SAHMI, a computational resource to identify truly present microbial nucleic acids, as well as filter contaminants and spurious false-positive taxonomic assignments from standard transcriptomic sequencing of mammalian tissues. In benchmark studies, SAHMI correctly identifies known microbial infections present in diverse tissues, and we validate SAHMI’s enrichment for correctly classified, truly present species using multiple orthogonal computational experiments. The application of SAHMI to single-cell and spatial genomic data thus enables co-detection of somatic cells and microorganisms and joint analysis of host–microbiome ecosystems.

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Fig. 1: Filtering false positives with k-mer correlation tests.
Fig. 2: Filtering contaminants and false positives with the cell-line test.

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Data availability

The cell lines microbiome negative control dataset is available in Supplementary Table 4. Other data are available on our Github: https://github.com/sjdlabgroup/SAHMI and at Zenodo: https://doi.org/10.5281/zenodo.7017103 (ref. 24). The following infection datasets were analyzed in this manuscript: COVID-19 (GSE145926), M. leprae (GSE151528 and GSE167889), gastric samples (GSE134520), Salmonella (GSE79363), Candida (GSE111731), M. tuberculosis (GSE167232) and HIV (GSE111727). The following human reference genome was used: hg19 (PRJNA31257). Source Data are provided with this paper.

Code availability

The SAHMI pipeline is available on our Github (https://github.com/sjdlabgroup/SAHMI) and at Zenodo (https://doi.org/10.5281/zenodo.7017103)24.

References

  1. Davis, N. M., Proctor, D. M., Holmes, S. P., Relman, D. A. & Callahan, B. J. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome 6, 226 (2018).

    Article  Google Scholar 

  2. Poore, G. D. et al. Microbiome analyses of blood and tissues suggest cancer diagnostic approach. Nature 579, 567–574 (2020).

    Article  Google Scholar 

  3. Breitwieser, F. P., Baker, D. N. & Salzberg, S. L. KrakenUniq: confident and fast metagenomics classification using unique k-mer counts. Genome Biol. 19, 198 (2018).

    Article  Google Scholar 

  4. Wood, D. E., Lu, J. & Langmead, B. Improved metagenomic analysis with Kraken 2. Genome Biol. 20, 257 (2019).

    Article  Google Scholar 

  5. Liao, M. et al. Single-cell landscape of bronchoalveolar immune cells in patients with COVID-19. Nat. Med. 26, 842–844 (2020).

    Article  Google Scholar 

  6. Ma, F. et al. The cellular architecture of the antimicrobial response network in human leprosy granulomas. Nat. Immunol. 22, 839–850 (2021).

    Article  Google Scholar 

  7. Zhang, P. et al. Dissecting the single-cell transcriptome network underlying gastric premalignant lesions and early gastric cancer. Cell Rep. 27, 1934–1947.e5 (2019).

    Article  Google Scholar 

  8. Saliba, A. E. et al. Single-cell RNA-seq ties macrophage polarization to growth rate of intracellular Salmonella. Nat. Microbiol. 2, 16206 (2016).

    Article  Google Scholar 

  9. Muñoz, J. F. et al. Coordinated host-pathogen transcriptional dynamics revealed using sorted subpopulations and single macrophages infected with Candida albicans. Nat. Commun. 10, 1607 (2019).

    Article  Google Scholar 

  10. Pisu, D. et al. Single cell analysis of M. tuberculosis phenotype and macrophage lineages in the infected lung. J. Exp. Med. 218, e20210615 (2021).

    Article  Google Scholar 

  11. Golumbeanu, M. et al. Single-cell RNA-seq reveals transcriptional heterogeneity in latent and reactivated HIV-infected cells. Cell Rep. 23, 942–950 (2018).

    Article  Google Scholar 

  12. Wyler, E. et al. Single-cell RNA-sequencing of herpes simplex virus 1-infected cells connects NRF2 activation to an antiviral program. Nat. Commun. 10, 4878 (2019).

    Article  Google Scholar 

  13. Sims, D., Sudbery, I., Ilott, N. E., Heger, A. & Ponting, C. P. Sequencing depth and coverage: key considerations in genomic analyses. Nat. Rev. Genet. 15, 121–132 (2014).

    Article  Google Scholar 

  14. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  Google Scholar 

  15. Camacho, C. et al. BLAST+: architecture and applications. BMC Bioinformatics 10, 421 (2009).

    Article  Google Scholar 

  16. Salter, S. J. et al. Reagent and laboratory contamination can critically impact sequence-based microbiome analyses. BMC Biol. 12, 87 (2014).

    Article  Google Scholar 

  17. Byrd, A. L., Belkaid, Y. & Segre, J. A. The human skin microbiome. Nat. Rev. Microbiol. 16, 143–155 (2018).

    Article  Google Scholar 

  18. Jin, H. et al. mBodyMap: a curated database for microbes across human body and their associations with health and diseases. Nucleic Acids Res. 50, D808–D816 (2022).

    Article  Google Scholar 

  19. Ghaddar, B. et al. Tumor microbiome links cellular programs and immunity in pancreatic cancer. Cancer Cell 40, 1240–1253.e5 (2022).

    Article  Google Scholar 

  20. Riquelme, E. et al. Tumor microbiome diversity and composition influence pancreatic cancer outcomes. Cell 178, 795–806.e12 (2019).

    Article  Google Scholar 

  21. Jia, Y. et al. Sequencing introduced false positive rare taxa lead to biased microbial community diversity, assembly, and interaction interpretation in amplicon studies. Environ. Microbiome 17, 43 (2022).

    Article  Google Scholar 

  22. Stuart, T. et al. Comprehensive Integration of single-cell data. Cell 177, 1888–1902.e21 (2019).

    Article  Google Scholar 

  23. Franzén, O., Gan, L.-M. & Björkegren, J. L. M. PanglaoDB: a web server for exploration of mouse and human single-cell RNA sequencing data. Database 2019, baz046 (2019).

    Article  Google Scholar 

  24. sjdlabgroup. sjdlabgroup/SAHMI: SAHMI v1.0 (v1.0). Zenodo https://doi.org/10.5281/zenodo.7017103 (2022).

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Acknowledgements

We acknowledge the Office of Advanced Research Computing (OARC) at Rutgers, The State University of New Jersey for providing access to the Amarel cluster (https://it.rutgers.edu/oarc). We also acknowledge grant support from National Institutes of Health grants R21CA248122, R01GM129066 and R35GM149224 (S.D.), National Institutes of Health grant U01AI22285 (M.J.B.), Sergei Zlinkoff Foundation (M.J.B.), Canadian Institute for Advanced Research (M.J.B.), and National Institutes of Health, National Center for Advancing Translational Sciences, Rutgers Clinical and Translational Science Award TL1TR003019 (B.G.).

Author information

Authors and Affiliations

  1. Center for Systems and Computational Biology, Rutgers Cancer Institute of New Jersey, Rutgers University, New Brunswick, NJ, USA

    Bassel Ghaddar & Subhajyoti De

  2. Center for Advanced Biotechnology and Medicine, Rutgers University, Piscataway, NJ, USA

    Martin J. Blaser

Authors
  1. Bassel Ghaddar
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Contributions

B.G. and S.D. conceived the study. B.G. designed and performed all data analyses. B.G., M.J.B. and S.D. interpreted the data, and wrote and revised the manuscript.

Corresponding author

Correspondence to Bassel Ghaddar.

Ethics declarations

Competing interests

M.J.B. declares that he serves on the Scientific Advisory Board of Micronoma, Inc. B.G. and S.D. have jointly filed PCT patent applications PCT/US2022/025829 and PCT/US2022/025832.

Peer review

Peer review information

Nature Computational Science thanks Anders B. Dohlman, Thomas S.B. Schmidt and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Ananya Rastogi, in collaboration with the Nature Computational Science team.

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Supplementary information

Supplementary Information

Supplementary Sections 1–3 and Figs. 1–3.

Reporting Summary

Supplementary Table 1

Summary of benchmark studies analyzed.

Supplementary Table 2

SAHMI results for benchmark datasets.

Supplementary Table 3

Summary of cell-line experiments analyzed.

Supplementary Table 4

Cell lines microbiome reference.

Source data

Source Data Fig. 1

Numerical source data for all panels in Fig. 1.

Source Data Fig. 2

Numerical source data for all panels in Fig. 2.

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Ghaddar, B., Blaser, M.J. & De, S. Denoising sparse microbial signals from single-cell sequencing of mammalian host tissues. Nat Comput Sci 3, 741–747 (2023). https://doi.org/10.1038/s43588-023-00507-1

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