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 lineardistance. A 99% confidence level was established by statistical bootstrappingfor each antibiotic-bacteria pair. For the susceptible E.coli strain, statistically significant increments from this99% confidence level were observed from 1/16x MIC to 1x MIC for allthe antibiotics. The same increments were recorded for P.aeruginosa, which has been reported to cause difficulty inflow-based viability tests. For the multidrug resistant E.coli, significant distances from control samples were observedonly when an effective antibiotic treatment was utilized. Our resultssuggest that a rapid and robust antimicrobial susceptibility test(AST) can be constructed by statistically characterizing the differencesbetween sample and control flow cytometric populations, even in alabel-free scheme with scattered light alone. These distances vs pairedcontrols coupled with rigorous statistical confidence limits offera new path toward investigating initial biological responses, screeningfor drugs, and shortening time to result in antimicrobial sensitivitytesting.
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