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

Jianshi Jin, Reiko Yamamoto, Tadashi Takeuchi, Guangwei Cui, Eiji Miyauchi, Nozomi Hojo, Koichi Ikuta, Hiroshi Ohno and Katsuyuki Shiroguchi ()
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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)
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)
Katsuyuki Shiroguchi: Laboratory for Prediction of Cell Systems Dynamics, RIKEN Center for Biosystems Dynamics Research (BDR)

Nature Communications, 2022, vol. 13, issue 1, 1-13

Abstract: 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.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-28426-1

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DOI: 10.1038/s41467-022-28426-1

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