High-throughput microbial culturomics using automation and machine learning

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 describ...

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Veröffentlicht in:Nature biotechnology Jg. 41; H. 10; S. 1424 - 1433
Hauptverfasser: Huang, Yiming, Sheth, Ravi U., Zhao, Shijie, Cohen, Lucas A., Dabaghi, Kendall, Moody, Thomas, Sun, Yiwei, Ricaurte, Deirdre, Richardson, Miles, Velez-Cortes, Florencia, Blazejewski, Tomasz, Kaufman, Andrew, Ronda, Carlotta, Wang, Harris H.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Nature Publishing Group US 01.10.2023
Nature Publishing Group
Schlagworte:
631/1647/2234
631/326/2565/2134
631/326/41/2537
Agriculture
Automation
Bacteria
Biobanks
Bioinformatics
Biomedical and Life Sciences
Biomedical Engineering/Biotechnology
Biomedicine
Biotechnology
Colonies
Comparative analysis
Feces
Gene transfer
Genomics
Genomics - methods
Genotypes
Genotyping
Horizontal transfer
Humans
Image resolution
Intestinal microflora
Learning algorithms
Life Sciences
Machine Learning
Microbiomes
Microbiota - genetics
Microorganisms
Phenotypes
Spatial analysis
ISSN:1087-0156, 1546-1696, 1546-1696
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Zusammenfassung: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. A machine learning isolation and genotyping platform enable high-throughput bacterial culture generation.
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ISSN:1087-0156
1546-1696
1546-1696
DOI:10.1038/s41587-023-01674-2