Institute of Physical Chemistry (IPC) and Abbe Center of Photonics, Helmholtzweg 4, 07743, Jena, Germany.
Center of Applied Research, InfectoGnostics Research Campus Jena, Philosophenweg 7, 07743, Jena, Germany.
Anal Bioanal Chem. 2018 Sep;410(23):5839-5847. doi: 10.1007/s00216-018-1196-2. Epub 2018 Jun 30.
Candida-related infections have become a major problem in hospitals. The species identification of yeast is the prerequisite for the initiation of adequate antifungal therapy. In the present study, the connection between inherent UV resonance Raman (RR) spectral profiles of Candida species and taxonomic differences was investigated for the first time. UV RR in combination with statistical modeling was applied to extract taxonomic information from the spectral fingerprints for subsequent differentiation. The identification accuracies of independent batch cultures were determined by applying a leave-one-batch-out cross validation. The quality of differentiation can be divided into three levels. Within a defined taxonomic group comprising the species C. glabrata, C. guilliermondii, and C. haemulonii, the identification accuracy was low. On the next level, the identification results of C. albicans and C. tropicalis were characterized by high sensitivities of 98 and 95% but simultaneously challenged by false-positive predictions due to the misallocation of C. spherica (as C. albicans) and C. viswanathii (as C. tropicalis). The highest level of identification accuracies was reached for the species C. dubliniensis, C. krusei, C. africana, C. novergica, and C. parapsilosis. Reliable identification results were observed with accuracies ranging from 93 up to 100%. The species allocation based on the UV RR spectral profiles could be reproduced by the identification of independent batch cultures. We conclude that the introduced spectroscopic approach is capable of transforming the high-dimensional UV RR data of Candida species into clinically useful decision parameters. Graphical abstract.
念珠菌相关感染已成为医院的一个主要问题。酵母种属鉴定是启动适当抗真菌治疗的前提。本研究首次调查了念珠菌固有紫外共振拉曼（RR）光谱特征与分类差异之间的关系。紫外 RR 结合统计建模被应用于从光谱指纹中提取分类信息，以便随后进行区分。通过应用批处理逐一排除的交叉验证来确定独立批处理培养物的鉴定准确性。区分的质量可以分为三个等级。在包含 C. glabrata、C. guilliermondii 和 C. haemulonii 等物种的定义分类群中，鉴定准确性较低。在下一个级别，C. albicans 和 C. tropicalis 的鉴定结果具有 98%和 95%的高灵敏度，但由于 C. spherica（被分配为 C. albicans）和 C. viswanathii（被分配为 C. tropicalis）的错误分配，导致假阳性预测，从而受到挑战。对 C. dubliniensis、C. krusei、C. africana、C. novergica 和 C. parapsilosis 等物种的鉴定准确率达到了最高水平。观察到的鉴定结果可靠，准确率从 93%到 100%不等。基于紫外 RR 光谱特征的物种分配可以通过鉴定独立的批处理培养物来重现。我们得出结论，引入的光谱方法能够将念珠菌物种的高维紫外 RR 数据转化为临床有用的决策参数。
