Evidence map›Paper›PMID 35194029›Full record

ArticleNature communications2022

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, Katsuyuki Shiroguchi

Abstract read
In one paragraph

Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

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  11. Single-cell analysis and spatial resolution of the gut microbiome.Frontiers in cellular and infection microbiology · 2023
    Review
  12. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Jianshi JinLaboratory for Prediction of Cell Systems Dynamics, RIKEN Center for Biosystems Dynamics Research (BDR), 6-2-3, Furuedai, Suita, Osaka, 565-0874, Japan.ORCID http://orcid.org/0000-0003-4697-8760
Reiko YamamotoLaboratory for Prediction of Cell Systems Dynamics, RIKEN Center for Biosystems Dynamics Research (BDR), 6-2-3, Furuedai, Suita, Osaka, 565-0874, Japan.
Tadashi TakeuchiLaboratory for Intestinal Ecosystem, RIKEN Center for Integrative Medical Sciences (IMS), 1-7-22, Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan.ORCID http://orcid.org/0000-0003-1693-7490
Guangwei CuiLaboratory of Immune Regulation, Department of Virus Research, Institute for Frontier Life and Medical Sciences, Kyoto University, 53 Shogoin Kawahara-cho, Sakyo-ku, Kyoto, 606-8507, Japan.
Eiji MiyauchiLaboratory for Intestinal Ecosystem, RIKEN Center for Integrative Medical Sciences (IMS), 1-7-22, Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan.ORCID http://orcid.org/0000-0002-8202-684X
Nozomi HojoLaboratory for Prediction of Cell Systems Dynamics, RIKEN Center for Biosystems Dynamics Research (BDR), 6-2-3, Furuedai, Suita, Osaka, 565-0874, Japan.
Koichi IkutaLaboratory of Immune Regulation, Department of Virus Research, Institute for Frontier Life and Medical Sciences, Kyoto University, 53 Shogoin Kawahara-cho, Sakyo-ku, Kyoto, 606-8507, Japan.
Hiroshi OhnoLaboratory for Intestinal Ecosystem, RIKEN Center for Integrative Medical Sciences (IMS), 1-7-22, Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan.ORCID http://orcid.org/0000-0001-8776-9661
Katsuyuki ShiroguchiLaboratory for Prediction of Cell Systems Dynamics, RIKEN Center for Biosystems Dynamics Research (BDR), 6-2-3, Furuedai, Suita, Osaka, 565-0874, Japan. katsuyuki.shiroguchi@riken.jp.ORCID http://orcid.org/0000-0003-4025-8789

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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 × 10

Indexed as

High-Throughput Nucleotide SequencingMicrobiotaAnimalsBacteriaDNA, BacterialMiceRNA, Ribosomal, 16SSequence Analysis, DNADNA, BacterialRNA, Ribosomal, 16S

Identifiers

PMID35194029
PMCPMC8863893

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.