Raman spectroscopic identification of single bacterial cells under antibiotic influence
Raman spectroscopic identification of single bacterial cells under antibiotic influence
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DOI：
10.1007/s00216-014-7747-2
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发表时间：
2014-05-01
影响因子：
4.3
通讯作者：
Popp, Jurgen
中科院分区：
文献类型：
--
作者：
Munchberg, Ute;Roesch, Petra;Popp, Jurgen
病原菌的鉴定是一项经常需要的任务。目前的鉴定程序通常要么由于必要的培养步骤而耗时，要么在其应用中昂贵且要求苛刻。此外，先前用抗生素治疗患者通常使得难以通过培养进行常规分析。由于拉曼显微光谱允许鉴定单个细菌细胞，因此它可以用于鉴定这种难以培养的细菌。然而，到目前为止，还没有调查是否抗生素治疗的细菌影响的拉曼光谱鉴定。本研究旨在利用拉曼显微光谱技术在单细胞水平上快速鉴定经过抗生素处理的细菌。两株大肠杆菌和两种假单胞菌已经用四种抗生素进行了治疗，所有抗生素都针对细菌的不同部位。利用未经处理的细菌的拉曼光谱，建立了线性判别分析（LDA）模型，成功地识别了独立的未经处理的细菌种类。在用亚抑制浓度的氨苄青霉素、环丙沙星、庆大霉素和磺胺甲恶唑处理细菌后，LDA模型分别实现了85.4%、95.3%、89.9%和97.3%的物种识别准确度。增加抗生素浓度对鉴定性能没有影响。对氨苄青霉素耐药的E.大肠杆菌和铜绿假单胞菌样品也被成功鉴定。训练数据中抗生素应激的一般表示提高了物种鉴定性能，而特定抗生素的表示提高了菌株区分能力。总之，利用拉曼显微光谱在单细胞水平上对不同抗生素进行鉴定是可能的。
The identification of pathogenic bacteria is a frequently required task. Current identification procedures are usually either time-consuming due to necessary cultivation steps or expensive and demanding in their application. Furthermore, previous treatment of a patient with antibiotics often renders routine analysis by culturing difficult. Since Raman microspectroscopy allows for the identification of single bacterial cells, it can be used to identify such difficult to culture bacteria. Yet until now, there have been no investigations whether antibiotic treatment of the bacteria influences the Raman spectroscopic identification. This study aims to rapidly identify bacteria that have been subjected to antibiotic treatment on single cell level with Raman microspectroscopy. Two strains of Escherichia coli and two species of Pseudomonas have been treated with four antibiotics, all targeting different sites of the bacteria. With Raman spectra from untreated bacteria, a linear discriminant analysis (LDA) model is built, which successfully identifies the species of independent untreated bacteria. Upon treatment of the bacteria with subinhibitory concentrations of ampicillin, ciprofloxacin, gentamicin, and sulfamethoxazole, the LDA model achieves species identification accuracies of 85.4, 95.3, 89.9, and 97.3 %, respectively. Increasing the antibiotic concentrations has no effect on the identification performance. An ampicillin-resistant strain of E. coli and a sample of P. aeruginosa are successfully identified as well. General representation of antibiotic stress in the training data improves species identification performance, while representation of a specific antibiotic improves strain distinction capability. In conclusion, the identification of antibiotically treated bacteria is possible with Raman microspectroscopy for diverse antibiotics on single cell level.