Use of high-content analysis and machine learning to characterize complex microbial samples via morphological analysis

Autoři: Jennifer Petitte aff001;  Michael Doherty aff001;  Jacob Ladd aff001;  Cassandra L. Marin aff001;  Samuel Siles aff001;  Vanessa Michelou aff001;  Amanda Damon aff001;  Erin Quattrini Eckert aff001;  Xiang Huang aff001;  John W. Rice aff001
Působiště autorů: Novozymes North America, Inc., Durham, North Carolina, United States of America aff001
Vyšlo v časopise: PLoS ONE 14(9)
Kategorie: Research Article
doi: 10.1371/journal.pone.0222528


High Content Analysis (HCA) has become a cornerstone of cellular analysis within the drug discovery industry. To expand the capabilities of HCA, we have applied the same analysis methods, validated in numerous mammalian cell models, to microbiology methodology. Image acquisition and analysis of various microbial samples, ranging from pure cultures to culture mixtures containing up to three different bacterial species, were quantified and identified using various machine learning processes. These HCA techniques allow for faster cell enumeration than standard agar-plating methods, identification of “viable but not plate culturable” microbe phenotype, classification of antibiotic treatment effects, and identification of individual microbial strains in mixed cultures. These methods greatly expand the utility of HCA methods and automate tedious and low-throughput standard microbiological methods.

Klíčová slova:

Antibiotics – Cell staining – DAPI staining – Flow cytometry – Machine learning – Machine learning algorithms – Protein synthesis – Image analysis


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