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High-throughput identification and quantification of single bacterial cells in the microbiota

Author

Listed:
  • Jianshi Jin

    (Laboratory for Prediction of Cell Systems Dynamics, RIKEN Center for Biosystems Dynamics Research (BDR))

  • Reiko Yamamoto

    (Laboratory for Prediction of Cell Systems Dynamics, RIKEN Center for Biosystems Dynamics Research (BDR))

  • Tadashi Takeuchi

    (Laboratory for Intestinal Ecosystem, RIKEN Center for Integrative Medical Sciences (IMS)
    Keio University School of Medicine)

  • Guangwei Cui

    (Kyoto University)

  • Eiji Miyauchi

    (Laboratory for Intestinal Ecosystem, RIKEN Center for Integrative Medical Sciences (IMS))

  • Nozomi Hojo

    (Laboratory for Prediction of Cell Systems Dynamics, RIKEN Center for Biosystems Dynamics Research (BDR))

  • Koichi Ikuta

    (Kyoto University)

  • Hiroshi Ohno

    (Laboratory for Intestinal Ecosystem, RIKEN Center for Integrative Medical Sciences (IMS)
    Intestinal Microbiota Project, Kanagawa Institute of Industrial Science and Technology
    Yokohama City University)

  • Katsuyuki Shiroguchi

    (Laboratory for Prediction of Cell Systems Dynamics, RIKEN Center for Biosystems Dynamics Research (BDR))

Abstract

The bacterial microbiota works as a community that consists of many individual organisms, i.e., cells. To fully understand the function of bacterial microbiota, individual cells must be identified; however, it is difficult with current techniques. Here, we develop a method, Barcoding Bacteria for Identification and Quantification (BarBIQ), which classifies single bacterial cells into taxa–named herein cell-based operational taxonomy units (cOTUs)–based on cellularly barcoded 16S rRNA sequences with single-base accuracy, and quantifies the cell number for each cOTU in the microbiota in a high-throughput manner. We apply BarBIQ to murine cecal microbiotas and quantify in total 3.4 × 105 bacterial cells containing 810 cOTUs. Interestingly, we find location-dependent global differences in the cecal microbiota depending on the dietary vitamin A deficiency, and more differentially abundant cOTUs at the proximal location than the distal location. Importantly, these location differences are not clearly shown by conventional 16S rRNA gene-amplicon sequencing methods, which quantify the 16S rRNA genes, not the cells. Thus, BarBIQ enables microbiota characterization with the identification and quantification of individual constituent bacteria, which is a cornerstone for microbiota studies.

Suggested Citation

  • Jianshi Jin & Reiko Yamamoto & Tadashi Takeuchi & Guangwei Cui & Eiji Miyauchi & Nozomi Hojo & Koichi Ikuta & Hiroshi Ohno & Katsuyuki Shiroguchi, 2022. "High-throughput identification and quantification of single bacterial cells in the microbiota," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-28426-1
    DOI: 10.1038/s41467-022-28426-1
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    References listed on IDEAS

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