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Accurate and rapid antibiotic susceptibility testing using a machine learning-assisted nanomotion technology platform.

ABSTRACT: Antimicrobial resistance (AMR) is a major public health threat, reducing treatment options for infected patients. AMR is promoted by a lack of access to rapid antibiotic susceptibility tests (ASTs). Accelerated ASTs can identify effective antibiotics for treatment in a timely and informed manner. We describe a rapid growth-independent phenotypic AST that uses a nanomotion technology platform to measure bacterial vibrations. Machine learning techniques are applied to analyze a large dataset encompassing 2762 individual nanomotion recordings from 1180 spiked positive blood culture samples covering 364 Escherichia coli and Klebsiella pneumoniae isolates exposed to cephalosporins and fluoroquinolones. The training performances of the different classification models achieve between 90.5 and 100% accuracy. Independent testing of the AST on 223 strains, including in clinical setting, correctly predict susceptibility and resistance with accuracies between 89.5% and 98.9%. The study shows the potential of this nanomotion platform for future bacterial phenotype delineation.

SUBMITTER: Sturm A

PROVIDER: S-EPMC10948838 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature

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Accurate and rapid antibiotic susceptibility testing using a machine learning-assisted nanomotion technology platform.

Sturm Alexander A Jóźwiak Grzegorz G Verge Marta Pla MP Munch Laura L Cathomen Gino G Vocat Anthony A Luraschi-Eggemann Amanda A Orlando Clara C Fromm Katja K Delarze Eric E Świątkowski Michał M Wielgoszewski Grzegorz G Totu Roxana M RM García-Castillo María M Delfino Alexandre A Tagini Florian F Kasas Sandor S Lass-Flörl Cornelia C Gstir Ronald R Cantón Rafael R Greub Gilbert G Cichocka Danuta D

Nature communications 20240318 1

Antimicrobial resistance (AMR) is a major public health threat, reducing treatment options for infected patients. AMR is promoted by a lack of access to rapid antibiotic susceptibility tests (ASTs). Accelerated ASTs can identify effective antibiotics for treatment in a timely and informed manner. We describe a rapid growth-independent phenotypic AST that uses a nanomotion technology platform to measure bacterial vibrations. Machine learning techniques are applied to analyze a large dataset encom ...[more]

PMID: 38499536

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Project description:Flow cytometry holds promise to accelerate antibiotic susceptibility determinations; however, without robust multidimensional statistical analysis, general discrimination criteria have remained elusive. In this study, a new statistical method, probability binning signature quadratic form (PB-sQF), was developed and applied to analyze flow cytometric data of bacterial responses to antibiotic exposure. Both sensitive lab strains (Escherichia coli and Pseudomonas aeruginosa) and a multidrug resistant, clinically isolated strain (E. coli) were incubated with the bacteria-targeted dye, maltohexaose-conjugated IR786, and each of many bactericidal or bacteriostatic antibiotics to identify changes induced around corresponding minimum inhibition concentrations (MIC). The antibiotic-induced damages were monitored by flow cytometry after 1-h incubation through forward scatter, side scatter, and fluorescence channels. The 3-dimensional differences between the flow cytometric data of the no-antibiotic treated bacteria and the antibiotic-treated bacteria were characterized by PB-sQF into a 1-dimensional linear distance. A 99% confidence level was established by statistical bootstrapping for each antibiotic-bacteria pair. For the susceptible E. coli strain, statistically significant increments from this 99% confidence level were observed from 1/16x MIC to 1x MIC for all the antibiotics. The same increments were recorded for P. aeruginosa, which has been reported to cause difficulty in flow-based viability tests. For the multidrug resistant E. coli, significant distances from control samples were observed only when an effective antibiotic treatment was utilized. Our results suggest that a rapid and robust antimicrobial susceptibility test (AST) can be constructed by statistically characterizing the differences between sample and control flow cytometric populations, even in a label-free scheme with scattered light alone. These distances vs paired controls coupled with rigorous statistical confidence limits offer a new path toward investigating initial biological responses, screening for drugs, and shortening time to result in antimicrobial sensitivity testing.

Dataset Information

Accurate and rapid antibiotic susceptibility testing using a machine learning-assisted nanomotion technology platform.

Publications

Accurate and rapid antibiotic susceptibility testing using a machine learning-assisted nanomotion technology platform.

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