Highly efficient classification and identification of human pathogenic bacteria by MALDI-TOF MS
Highly efficient classification and identification of human pathogenic bacteria by MALDI-TOF MS
复制标题
DOI：
10.1074/mcp.m700339-mcp200
复制
发表时间：
2008-02-01
影响因子：
7
通讯作者：
Chen, Jen-Kun
中科院分区：
文献类型：
--
作者：
Hsieh, Sen-Yung;Tseng, Chiao-Li;Chen, Jen-Kun
病原微生物的准确和快速鉴定在疾病治疗和公共卫生中至关重要。传统的工作流程很耗时，程序也是多方面的。MS可以是一种替代方法，但受限于氨基酸测序的低效率以及光谱指纹的低再现性。系统分析了质谱技术用于快速、准确鉴定细菌的可行性。直接应用于MALDI-TOF MS分析，无需进一步提取蛋白质，峰含量丰富，重现性好。来自57个分离株，包括6个人类致病菌种的MS光谱进行了分析，使用无监督的层次聚类和监督模型的建设，通过遗传算法。系统聚类分析将光谱分为六组，精确对应于六种细菌。即使m/z值的数量减少到6个，在独立制备的一组细菌中也保持了精确的分类。并行地，通过遗传算法分析构建分类模型。包含18个m/z值的模型准确地对独立制备的细菌进行分类，并识别出最初不用于模型构建的那些物种。此外，细菌少于10（4）个细胞和细菌混合物中的不同物种被确定使用分类模型的方法。总之，MALDI-TOF MS结合合适的模型构建的应用为细菌分类和鉴定提供了高度准确的方法。该方法即使在混合植物群中也能鉴定出低丰度的细菌，这表明在不久的将来，即使在培养之前，也可以使用MS技术进行快速准确的细菌鉴定。
Accurate and rapid identification of pathogenic microorganisms is of critical importance in disease treatment and public health. Conventional work flows are time-consuming, and procedures are multifaceted. MS can be an alternative but is limited by low efficiency for amino acid sequencing as well as low reproducibility for spectrum fingerprinting. We systematically analyzed the feasibility of applying MS for rapid and accurate bacterial identification. Directly applying bacterial colonies without further protein extraction to MALDI-TOF MS analysis revealed rich peak contents and high reproducibility. The MS spectra derived from 57 isolates comprising six human pathogenic bacterial species were analyzed using both unsupervised hierarchical clustering and supervised model construction via the Genetic Algorithm. Hierarchical clustering analysis categorized the spectra into six groups precisely corresponding to the six bacterial species. Precise classification was also maintained in an independently prepared set of bacteria even when the numbers of m/z values were reduced to six. In parallel, classification models were constructed via Genetic Algorithm analysis. A model containing 18 m/z values accurately classified independently prepared bacteria and identified those species originally not used for model construction. Moreover bacteria fewer than 10(4) cells and different species in bacterial mixtures were identified using the classification model approach. In conclusion, the application of MALDI-TOF MS in combination with a suitable model construction provides a highly accurate method for bacterial classification and identification. The approach can identify bacteria with low abundance even in mixed flora, suggesting that a rapid and accurate bacterial identification using MS techniques even before culture can be attained in the near future.