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Updated: Jun 26, 2025

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通过LC-MS数据的二维交叉相关性识别微生物菌株

Tucker James Collins¹, Cathy Muste¹, Kevin G Owens¹

¹Department of Chemistry, Drexel University, Philadelphia, Pennsylvania 19104, United States.

Journal of the American Society for Mass Spectrometry

|May 14, 2024

概括

使用质谱法识别微生物菌株是具有挑战性的,因为高相似性. 这项研究引入了一种新的2D交叉相关性方法,通过LC-MS数据准确识别微生物菌株.

科学领域:

微生物学微生物学
分析化学分析化学
生物信息学是一种生物信息学.

背景情况:

质谱测量对于微生物鉴定至关重要,但亚种级别的分析受到类似的形状的阻碍.
之前的方法,如PCA,kNN和皮尔森相关性,对应变水平差异化有局限性.
1D交叉相关性已被证明对小分子和的识别是有效的,但不是使用LC-MS进行微生物菌株分析.

研究的目的:

开发和演示一种新的方法用于微生物菌株识别,使用LC-MS数据的2D交叉相关性.
根据它们的质谱数据来解决区分密切相关的微生物菌株的挑战.
适应微生物菌株类型化中的高分辨率LC-MS数据的交叉相关性原则.

主要方法:

在5个不同的菌株中收集了30种酵母分离物的高分辨率LC-MS-Orbitrap数据.
通过结合来自各自分离物的数据,为每个酵母菌株生成参考质谱.
应用了样本光谱和参考光谱之间的二维交叉相关性,并结合了对不对称性的校正因数.

主要成果:

通过计算2D交叉相关性与菌株特定的参考光谱,成功预测了样本菌株.
证明了2D交叉相关性在区分具有高相似性酵母菌株的有效性.
该方法显示了使用LC-MS数据准确识别微生物菌株的潜力.

结论:

对LC-MS数据的二维交叉相关性为微生物菌株识别提供了一个有希望的方法.
这种方法克服了先前技术在区分密切相关的微生物菌株方面的局限性.
开发的技术为高分辨率的微生物菌株类型和分析提供了一个新的工具.

关键词:

这就是LC-MS.轨道上的陷相关性分析的相关性分析.微生物的识别方法蛋白质组学蛋白质组学菌株鉴定菌株鉴定

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