Timmins E M, Howell S A, Alsberg B K, Noble W C, Goodacre R
Institute of Biological Sciences, University of Wales, Ceredigion, United Kingdom.
J Clin Microbiol. 1998 Feb;36(2):367-74. doi: 10.1128/JCM.36.2.367-374.1998.
Two rapid spectroscopic approaches for whole-organism fingerprinting of pyrolysis-mass spectrometry (PyMS) and Fourier transform-infrared spectroscopy (FT-IR) were used to analyze a group of 29 clinical and reference Candida isolates. These strains had been identified by conventional means as belonging to one of the three species Candida albicans, C. dubliniensis (previously reported as atypical C. albicans), and C. stellatoidea (which is also closely related to C. albicans). To observe the relationships of the 29 isolates as judged by PyMS and FT-IR, the spectral data were clustered by discriminant analysis. On visual inspection of the cluster analyses from both methods, three distinct clusters, which were discrete for each of the Candida species, could be seen. Moreover, these phenetic classifications were found to be very similar to those obtained by genotypic studies which examined the HinfI restriction enzyme digestion patterns of genomic DNA and by use of the 27A C. albicans-specific probe. Both spectroscopic techniques are rapid (typically, 2 min for PyMS and 10 s for FT-IR) and were shown to be capable of successfully discriminating between closely related isolates of C. albicans, C. dubliniensis, and C. stellatoidea. We believe that these whole-organism fingerprinting methods could provide opportunities for automation in clinical microbial laboratories, improving turnaround times and the use of resources.
采用热解质谱（PyMS）和傅里叶变换红外光谱（FT-IR）这两种用于全生物体指纹识别的快速光谱方法，对一组29株临床和参考念珠菌分离株进行分析。这些菌株已通过传统方法鉴定为属于白色念珠菌、都柏林念珠菌（以前报告为非典型白色念珠菌）和星状念珠菌（也与白色念珠菌密切相关）这三种念珠菌之一。为了观察通过PyMS和FT-IR判断的29株分离株之间的关系，通过判别分析对光谱数据进行聚类。在目视检查两种方法的聚类分析时，可以看到三个不同的聚类，每种念珠菌都有各自独立的聚类。此外，发现这些表型分类与通过基因型研究获得的分类非常相似，基因型研究检测了基因组DNA的HinfI限制性内切酶消化模式，并使用了白色念珠菌特异性探针27A。这两种光谱技术都很快速（通常，PyMS为2分钟，FT-IR为10秒），并且能够成功区分白色念珠菌、都柏林念珠菌和星状念珠菌的密切相关分离株。我们认为，这些全生物体指纹识别方法可为临床微生物实验室的自动化提供机会，缩短周转时间并提高资源利用效率。
