Department of Chemistry and Biochemistry, Auburn University, Auburn, Alabama, USA.
J Mass Spectrom. 2023 Sep;58(9):e4973. doi: 10.1002/jms.4973. Epub 2023 Aug 24.
Omics studies such as metabolomics, lipidomics, and proteomics have become important for understanding the mechanisms in living organisms. However, the compounds detected are structurally different and contain isomers, with each structure or isomer leading to a different result in terms of the role they play in the cell or tissue in the organism. Therefore, it is important to detect, characterize, and elucidate the structures of these compounds. Liquid chromatography and mass spectrometry have been utilized for decades in the structure elucidation of key compounds. While prediction models of parameters (such as retention time and fragmentation pattern) have also been developed for these separation techniques, they have some limitations. Moreover, ion mobility has become one of the most promising techniques to give a fingerprint to these compounds by determining their collision cross section (CCS) values, which reflect their shape and size. Obtaining accurate CCS enables its use as a filter for potential analyte structures. These CCS values can be measured experimentally using calibrant-independent and calibrant-dependent approaches. Identification of compounds based on experimental CCS values in untargeted analysis typically requires CCS references from standards, which are currently limited and, if available, would require a large amount of time for experimental measurements. Therefore, researchers use theoretical tools to predict CCS values for untargeted and targeted analysis. In this review, an overview of the different methods for the experimental and theoretical estimation of CCS values is given where theoretical prediction tools include computational and machine modeling type approaches. Moreover, the limitations of the current experimental and theoretical approaches and their potential mitigation methods were discussed.
组学研究，如代谢组学、脂质组学和蛋白质组学，对于理解生物体内的机制变得非常重要。然而，检测到的化合物在结构上是不同的，并且包含异构体，每种结构或异构体在它们在生物体中的细胞或组织中所起的作用方面都会导致不同的结果。因此，检测、表征和阐明这些化合物的结构非常重要。几十年来，液相色谱和质谱一直用于关键化合物的结构解析。虽然这些分离技术也已经开发出了用于预测参数（如保留时间和碎裂模式）的预测模型，但它们存在一些局限性。此外，离子淌度已经成为通过确定其碰撞截面（CCS）值来为这些化合物提供指纹的最有前途的技术之一，CCS 值反映了它们的形状和大小。获得准确的 CCS 使其可以用作潜在分析物结构的筛选器。这些 CCS 值可以使用无校准物和校准物依赖的方法从实验中进行测量。基于实验 CCS 值对无靶向分析中的化合物进行鉴定通常需要来自标准品的 CCS 参考值，目前这些参考值有限，如果有，需要花费大量时间进行实验测量。因此，研究人员使用理论工具来预测无靶向和靶向分析中的 CCS 值。在这篇综述中，介绍了实验和理论估算 CCS 值的不同方法概述，其中理论预测工具包括计算和机器建模类型的方法。此外，还讨论了当前实验和理论方法的局限性及其潜在的缓解方法。
