复杂样品中小分子的无标准鉴别方法进展
Advancing Standards-Free Methods for the Identification of Small Molecules in Complex Samples
摘要：
目前代谢组学分析中明确识别的金标准是基于比较两个或多个正交特性，这些正交特性来自真实的纯参比物质（标准品）的分析，与在同一实验室中采用相同分析方法获得的实验数据。这对复杂样品中小分子的全面化学鉴定是一个重大限制，因为该过程耗时且成本高，并且大多数分子尚未通过标准品表示，导致需要无标准品鉴定。为了满足这一需求，我们正在推进化学性质计算并开发多属性评分和匹配算法，以通过利用和创建计算机化学库引擎（ISiCLE）和多属性匹配引擎（MAME）来利用来自多个分析平台的数据。在这里，我们描述了我们的合成化学混合物的盲法分析结果，作为美国环境保护署（EPA）非靶向分析合作试验（ENTACT）的一部分。盲法假阴性率（FNR）、假发现率（FDR）和准确率分别为57%、77%和91%。对于高置信度鉴定，FDR为35%。在揭盲样品组成后，我们通过优化用于增加置信度的评分参数来改进我们的方法。最终FNR、FDR和准确性分别为67%、53%和96%。对于高置信度鉴定，FDR为10%。这项研究表明，无标准的小分子鉴定和多属性匹配方法可以显着减少对标准的依赖。
英文摘要：
The current gold standard for unambiguous identification in metabolomics analysis is based on comparing two or more orthogonal properties from the analysis of authentic, pure reference materials (standards) to experimental data acquired in the same laboratory with the same analytical methods. This represents a significant limitation for comprehensive chemical identification of small molecules in complex samples since this process is time-consuming and costly, and the majority of molecules are not yet represented by standards, leading to a need for standards-free identification. To address this need, we are advancing chemical property calculations and developing multi-attribute scoring and matching algorithms to utilize data from multiple analytical platforms through the utilization and creation of the in silico Chemical Library Engine (ISiCLE) and the Multi-Attribute Matching Engine (MAME). Here, we describe our results in a blinded analysis of synthetic chemical mixtures as part of the U.S. Environmental Protection Agency's (EPA) Non-Targeted Analysis Collaborative Trial (ENTACT). The blinded false negative rate (FNR), false discovery rate (FDR), and accuracy were 57%, 77%, and 91%, respectively. For high confidence identifications, the FDR was 35%. After unblinding of the sample compositions, we improved our approach by optimizing the scoring parameters used to increase confidence. The final FNR, FDR, and accuracy were 67%, 53%, and 96%, respectively. For high confidence identifications, the FDR was 10%. This study demonstrates that standards-free small molecule identification and multi-attribute matching methods can significantly reduce reliance on standards.
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