Matrix-assisted laser desorption ionization (MALDI) time-of-flight mass spectrometry (TOF MS) is now accepted as a quick, easy-to-use, cost-effective, and accurate technique for the identification of microorganisms. However, the successful identification of microorganisms is dependent upon careful attention to factors such as growth conditions, extraction methods, mass spectral data collection, and data analysis procedures. Currently, most microorganism identification has been limited to the species level, and only a limited number of publications have been successful in achieving strain-level identification. In this work, a “cell-free” approach is introduced where peptide analytes secreted by several Saccharomyces cerevisiae strains during their growth period are analyzed. The analysis of the cell supernatant generates mass spectral patterns that are specific to each strain. The patterns generated in combination with a robust data analysis workflow using the open-source programs MALDIquant and Mass-Up allows for strain-level identification of S. cerevisiae. The cell-free approach using the yeast supernatant to accurately identify yeast strains is presented here as a proof of concept.

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基质辅助激光解吸电离（MALDI）飞行时间质谱（TOF MS）现在被认为是一种快速，易于使用，经济高效且准确的微生物鉴定技术。然而，微生物的成功鉴定取决于对诸如生长条件，提取方法，质谱数据收集和数据分析程序等因素的仔细关注。当前，大多数微生物鉴定仅限于物种水平，并且只有有限的出版物成功地实现了菌株水平鉴定。在这项工作中，引入了“无细胞”方法，其中几种酿酒酵母分泌的肽分析物对其生长期的菌株进行了分析。细胞上清液的分析产生了每个菌株特有的质谱图。使用开源程序MALDIquant和Mass-Up与强大的数据分析工作流程结合生成的模式可用于酿酒酵母的菌株级鉴定。本文提出了使用酵母上清液准确鉴定酵母菌株的无细胞方法，作为概念证明。

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