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

Abstract

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.

A Inteligência Artificial Na Medicina Clínica: Principais Avanços E Aplicações

“…Os autores demonstram que a combinação de abordagens de aprendizado de máquina com dados gerados por HCA, uma abordagem agora comumente usada na descoberta de medicamentos, pode ser feita de maneira semelhante com conjuntos de dados de amostras microbianas. Este método permite a identificação de alterações nas características morfológicas microbianas em resposta a vários tratamentos e condições de cultura (PETITTE et al, 2019).…”