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BactoSpin: Novel Technology for Rapid Bacteria Detection and Antibiotic Susceptibility Testing.

ABSTRACT: Inappropriate use of antibiotics is one of the leading causes of the increasing numbers of resistant bacteria strains, resulting in 700,000 deaths worldwide each year. Reducing unnecessary use of antibiotics and choosing the most effective antibiotics instead of broad-spectrum drugs will slow the arms race between germs and humans. Urinary tract infections (UTIs) are among the most common bacterial infections. Currently, accurate diagnosis of UTI requires approximately 48 h from the time of urine sample collection until antibiotic susceptibility test (AST) results. This work presents a rapid bacterial detection device that integrates a centrifuge, microscope, and incubator. Two disposable microfluidic chips were developed. The first chip was designed for bacteria concentration, detection, and medium exchange. A second multi-channel chip was developed for AST. This chip contains superhydrophobic and hydrophilic coatings to ensure liquid separation between the channels without the need for valves. The designed chips supported the detection of E. coli at a concentration as low as 5 × 10³ cells/mL within 5 min and AST in under 2 h. AST was also successfully performed with Klebsiella pneumonia isolated from a human urine sample. In addition, machine-learning-based image recognition was shown to reduce the required time for AST and to provide results within 1 h for E. coli cells. Thus, the BactoSpin device can serve as an efficient and rapid platform for UTI diagnostics and AST.

SUBMITTER: Shumeiko V

PROVIDER: S-EPMC8434515 | biostudies-literature |

REPOSITORIES: biostudies-literature

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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.

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BactoSpin: Novel Technology for Rapid Bacteria Detection and Antibiotic Susceptibility Testing.

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