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Summary High‐quality microbiome research relies on the integrity, management and quality of supporting data. Currently biobanks and culture collections have different formats and approaches to data management. This necessitates a standard data format to underpin research, particularly in line with the FAIR data standards of findability, accessibility, interoperability and reusability. We address the importance of a unified, coordinated approach that ensures compatibility of data between that needed by biobanks and culture collections, but also to ensure linkage between bioinformatic databases and the wider research community.
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High-throughput microbial culturomics using automation and machine learning
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 betweenRuminococcaceae,Bacteroidaceae,CoriobacteriaceaeandBifidobacteriaceaefamilies 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.
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- Award ID(s):
- 2025515
- PAR ID:
- 10397992
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Nature Biotechnology
- Volume:
- 41
- Issue:
- 10
- ISSN:
- 1087-0156
- Format(s):
- Medium: X Size: p. 1424-1433
- Size(s):
- p. 1424-1433
- Sponsoring Org:
- National Science Foundation
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