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 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…

• NS
  National Science Foundation

  Awards: 1R21AI146817, 1R01DK118044, 1644869, 1R01AI132403, 2025515, DGE-1644869, MCB-2025515
• BW
  Burroughs Wellcome Fund

  Award: 1016691
• HF
  Hertz Foundation
• IT
  Irma T. Hirschl Trust
• NI
  National Institutes of Health

  Awards: DGE-1644869, 1R01DK118044, 1R01AI132403, 1R21AI146817, 1016691, MCB-2025515, T32GM007367, 1644869
• OO
  Office of Naval Research

  Awards: N00014-18-1-2237, N00014-17-1, N00014-17-1-2353, N00014