title: Rapid cytometric antibiotic susceptibility testing utilizing adaptive multidimensional statistical metrics.
abstract: 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.
authors.affiliation: School of Chemistry & Biochemistry, Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology , Atlanta, Georgia 30305-0400, United States.
authors.forename: Tzu-Hsueh
authors.initials: TH
authors.lastname: Huang
authors.forename: Xinghai
authors.initials: X
authors.lastname: Ning
authors.forename: Xiaojian
authors.initials: X
authors.lastname: Wang
authors.forename: Niren
authors.initials: N
authors.lastname: Murthy
authors.forename: Yih-Ling
authors.initials: YL
authors.lastname: Tzeng
authors.forename: Robert M
authors.initials: RM
authors.lastname: Dickson
mesh_terms: D000900:Anti-Bacterial Agents / Q000494:pharmacology*; D004926:Escherichia coli / Q000187:drug effects*; D004927:Escherichia coli Infections / Q000188:drug therapy / Q000382:microbiology; D005434:Flow Cytometry / Q000191:economics / Q000379:methods*; D006801:Humans; D008826:Microbial Sensitivity Tests / Q000191:economics / Q000379:methods*; D011552:Pseudomonas Infections / Q000188:drug therapy / Q000382:microbiology; D011550:Pseudomonas aeruginosa / Q000187:drug effects*; D013997:Time Factors
chemical_list: D000900:Anti-Bacterial Agents