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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| Vydané v: | Nature biotechnology Ročník 41; číslo 10; s. 1424 - 1433 |
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| Hlavní autori: | , , , , , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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New York
Nature Publishing Group US
01.10.2023
Nature Publishing Group |
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| ISSN: | 1087-0156, 1546-1696, 1546-1696 |
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| 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.
A machine learning isolation and genotyping platform enable high-throughput bacterial culture generation. |
|---|---|
| AbstractList | 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. 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. 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. 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. 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.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. |
| Author | Richardson, Miles Velez-Cortes, Florencia Zhao, Shijie Blazejewski, Tomasz Ricaurte, Deirdre Kaufman, Andrew Sheth, Ravi U. Cohen, Lucas A. Dabaghi, Kendall Wang, Harris H. Sun, Yiwei Huang, Yiming Moody, Thomas Ronda, Carlotta |
| Author_xml | – sequence: 1 givenname: Yiming orcidid: 0000-0003-1932-2803 surname: Huang fullname: Huang, Yiming organization: Department of Systems Biology, Columbia University – sequence: 2 givenname: Ravi U. orcidid: 0000-0002-9556-3441 surname: Sheth fullname: Sheth, Ravi U. organization: Department of Systems Biology, Columbia University – sequence: 3 givenname: Shijie surname: Zhao fullname: Zhao, Shijie organization: Department of Systems Biology, Columbia University – sequence: 4 givenname: Lucas A. orcidid: 0000-0001-5819-1872 surname: Cohen fullname: Cohen, Lucas A. organization: Department of Systems Biology, Columbia University – sequence: 5 givenname: Kendall surname: Dabaghi fullname: Dabaghi, Kendall organization: Department of Systems Biology, Columbia University – sequence: 6 givenname: Thomas surname: Moody fullname: Moody, Thomas organization: Department of Systems Biology, Columbia University – sequence: 7 givenname: Yiwei orcidid: 0000-0002-5974-6232 surname: Sun fullname: Sun, Yiwei organization: Department of Biomedical Informatics, Columbia University – sequence: 8 givenname: Deirdre surname: Ricaurte fullname: Ricaurte, Deirdre organization: Department of Systems Biology, Columbia University – sequence: 9 givenname: Miles orcidid: 0000-0003-3004-1084 surname: Richardson fullname: Richardson, Miles organization: Department of Systems Biology, Columbia University – sequence: 10 givenname: Florencia surname: Velez-Cortes fullname: Velez-Cortes, Florencia organization: Department of Systems Biology, Columbia University – sequence: 11 givenname: Tomasz surname: Blazejewski fullname: Blazejewski, Tomasz organization: Department of Systems Biology, Columbia University – sequence: 12 givenname: Andrew surname: Kaufman fullname: Kaufman, Andrew organization: Department of Systems Biology, Columbia University – sequence: 13 givenname: Carlotta orcidid: 0000-0002-7501-914X surname: Ronda fullname: Ronda, Carlotta organization: Department of Systems Biology, Columbia University – sequence: 14 givenname: Harris H. orcidid: 0000-0003-2164-4318 surname: Wang fullname: Wang, Harris H. email: hw2429@columbia.edu organization: Department of Systems Biology, Columbia University, Department of Pathology and Cell Biology, Columbia University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36805559$$D View this record in MEDLINE/PubMed |
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| DOI | 10.1038/s41587-023-01674-2 |
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| Title | High-throughput microbial culturomics using automation and machine learning |
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