Evidence map›Paper›PMID 36805559›Full record

ArticleNature biotechnology2023

High-throughput microbial culturomics using automation and machine learning.

Yiming Huang, Ravi U Sheth, Shijie Zhao, Lucas A Cohen, Kendall Dabaghi, Thomas Moody, Yiwei Sun, Deirdre Ricaurte, Miles Richardson, Florencia Velez-Cortes and 4 more

Abstract read
In one paragraph

Article in Nature biotechnology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 126 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
126citing papers in PubMed, 1 pooled it
–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

126 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
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  4. Bridging the gap:Gut microbes · 2026
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66 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

14 authors.

Yiming Huang *Department of Systems Biology, Columbia University, New York, NY, USA.ORCID 0000-0003-1932-2803
Ravi U Sheth *Department of Systems Biology, Columbia University, New York, NY, USA.ORCID 0000-0002-9556-3441
Shijie ZhaoDepartment of Systems Biology, Columbia University, New York, NY, USA.
Lucas A CohenDepartment of Systems Biology, Columbia University, New York, NY, USA.ORCID 0000-0001-5819-1872
Kendall DabaghiDepartment of Systems Biology, Columbia University, New York, NY, USA.
Thomas MoodyDepartment of Systems Biology, Columbia University, New York, NY, USA.
Yiwei SunDepartment of Biomedical Informatics, Columbia University, New York, NY, USA.ORCID 0000-0002-5974-6232
Deirdre RicaurteDepartment of Systems Biology, Columbia University, New York, NY, USA.
Miles RichardsonDepartment of Systems Biology, Columbia University, New York, NY, USA.ORCID 0000-0003-3004-1084
Florencia Velez-CortesDepartment of Systems Biology, Columbia University, New York, NY, USA.
Tomasz BlazejewskiDepartment of Systems Biology, Columbia University, New York, NY, USA.
Andrew KaufmanDepartment of Systems Biology, Columbia University, New York, NY, USA.
Carlotta RondaDepartment of Systems Biology, Columbia University, New York, NY, USA.ORCID 0000-0002-7501-914X
Harris H WangDepartment of Systems Biology, Columbia University, New York, NY, USA. hw2429@columbia.edu.ORCID 0000-0003-2164-4318

Funding

MEDICAL SCIENTIST TRAINING PROGRAMT32GM007367 · NIGMS · COLUMBIA UNIV NEW YORK MORNINGSIDE · PI REINER, STEVEN L · 1985 to 2021
$25.1M
Micron-scale Spatial Metagenomic Mapping of Microbial Biogeography in the Gastrointestinal TractR01AI132403 · NIAID · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI Harris H Wang · 2017 to 2026
$6.0M
Ecological dynamics and metabolic interactions in thegut microbiome across space and timeR01DK118044 · NIDDK · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI VITKUP, DENNIS, WANG, HARRIS H · 2018 to 2022
$3.4M
Discovery of immunomodulatory gut microbes with MAGICR21AI146817 · NIAID · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI IVANOV, IVAYLO IVANOV · 2019 to 2020
$437k
NIAID NIH HHS R01 AI132403NIAID NIH HHS R21 AI146817NIDDK NIH HHS R01 DK118044NIGMS NIH HHS T32 GM007367
6 · The paper itself

Abstract

Pure bacterial cultures remain essential for detailed experimental and mechanistic studies in microbiome research, and traditional methods to isolate individual bacteria from complex microbial ecosystems are labor-intensive, difficult-to-scale and lack phenotype-genotype integration. Here we describe an open-source high-throughput robotic strain isolation platform for the rapid generation of isolates on demand. We develop a machine learning approach that leverages colony morphology and genomic data to maximize the diversity of microbes isolated and enable targeted picking of specific genera. Application of this platform on fecal samples from 20 humans yields personalized gut microbiome biobanks totaling 26,997 isolates that represented >80% of all abundant taxa. Spatial analysis on >100,000 visually captured colonies reveals cogrowth patterns between Ruminococcaceae, Bacteroidaceae, Coriobacteriaceae and Bifidobacteriaceae families that suggest important microbial interactions. Comparative analysis of 1,197 high-quality genomes from these biobanks shows interesting intra- and interpersonal strain evolution, selection and horizontal gene transfer. This culturomics framework should empower new research efforts to systematize the collection and quantitative analysis of imaging-based phenotypes with high-resolution genomics data for many emerging microbiome studies.

Indexed as

GenomicsMicrobiotaAutomationBacteriaHumansMachine Learning

Identifiers

PMID36805559
PMCPMC10567565

What OpenQuestion holds

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Read underepoch 390

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.