Rapid cytometric antibiotic susceptibility testing utilizing adaptive multidimensional statistical metrics.
Rapid cytometric antibiotic susceptibility testing utilizing adaptive multidimensional statistical metrics.
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DOI：
10.1021/ac504241x
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发表时间：
2015-02-03
影响因子：
7.4
通讯作者：
Dickson, Robert M.
中科院分区：
文献类型：
--
作者：
Huang, Tzu-Hsueh;Ning, Xinghai;Wang, Xiaojian;Murthy, Niren;Tzeng, Yih-Ling;Dickson, Robert M.
流式细胞术有望加速抗生素敏感性的测定；然而，如果没有强有力的多维统计分析，一般的鉴别标准仍然难以捉摸。在这项研究中，发展了一种新的统计方法-概率库签名二次形式(PB-SQF)，并应用于分析细菌对抗生素暴露的反应的流式细胞仪数据。将敏感的实验室菌株(大肠杆菌和铜绿假单胞菌)和多药耐药的临床分离菌株(E.coli)与细菌靶向染料麦芽六糖偶联IR786以及多种杀菌或抑菌抗生素孵育，以确定在相应最低抑菌浓度(MIC)附近引起的变化。通过前向散射、侧向散射和荧光通道，在孵育1h后用流式细胞仪监测抗生素诱导的损伤。用PB-SQF将未经抗生素处理的细菌和经抗生素处理的细菌的流式细胞仪数据之间的三维差异表征为一维线性距离。通过对每个抗生素-细菌对进行统计自举，建立了99%的置信度。对于敏感的大肠杆菌菌株，所有抗生素的MIC从1/16倍MIC增加到1倍MIC，从这个99%的可信度水平来看，有统计学意义的增加。铜绿假单胞菌也记录了相同的增量，据报道，这会导致基于流动的生存能力测试的困难。对于多重耐药的大肠杆菌，只有在使用有效的抗生素治疗时，才能观察到与对照样本的显著距离。我们的结果表明，即使在仅有散射光的无标记方案中，也可以通过统计表征样本和对照流式细胞仪群体之间的差异来构建快速而可靠的抗菌素敏感性试验(AST)。这些距离与配对对照结合严格的统计置信度限制，为研究初始生物反应、筛选药物和缩短进行抗菌素敏感性测试的时间提供了一条新的途径。
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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