Ion mobility mass spectrometry (IM-MS) expands the analyte coverage of existing multi-omic workflows by providing an additional separation dimension as well as a parameter for characterization and identification of molecules – the collision cross section (CCS). This work presents a large, Unified CCS compendium of >3800 experimentally acquired CCS values obtained from traceable molecular standards and measured with drift tube ion mobility-mass spectrometers. An interactive visualization of this compendium along with data analytic tools have been made openly accessible. Represented in the compendium are 14 structurally-based chemical super classes, consisting of a total of 80 classes and 157 subclasses. Using this large data set, regression fitting and predictive statistics have been performed to describe mass-CCS correlations specific to each chemical ontology. These structural trends provide a rapid and effective filtering method in the traditional untargeted workflow for identification of unknown biochemical species. The utility of the approach is illustrated by an application to metabolites in human serum, quantified trends of which were used to assess the probability of an unknown compound belonging to a given class. CCS-based filtering narrowed the chemical search space by 60% while increasing the confidence in the remaining isomeric identifications from a single class, thus demonstrating the value of integrating predictive analyses into untargeted experiments to assist in identification workflows. The predictive abilities of this compendium will improve in specificity and expand to more chemical classes as additional data from the IM-MS community is contributed. Instructions for data submission to the compendium and criteria for inclusion are provided.

中文翻译：

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用于注释和预测多组学化合物身份的碰撞横截面概要†

离子淌度质谱 (IM-MS) 通过提供额外的分离维度以及用于分子表征和识别的参数——碰撞截面 (CCS)，扩大了现有多组学工作流程的分析物覆盖范围。这项工作提出了一个大型、统一的 CCS 纲要，其中包含超过 3800 个通过实验获得的 CCS 值，这些 CCS 值是从可追踪的分子标准中获得的，并使用漂移管离子淌度质谱仪进行测量。该纲要的交互式可视化以及数据分析工具已可供公开访问。该纲要包含 14 个基于结构的化学超类，总共由 80 个类和 157 个小类组成。使用这个大数据集，进行回归拟合和预测统计来描述特定于每个化学本体的质量-CCS 相关性。这些结构趋势为传统的非目标工作流程中未知生化物种的鉴定提供了快速有效的过滤方法。该方法的实用性通过对人血清中代谢物的应用来说明，其量化趋势用于评估未知化合物属于给定类别的概率。基于 CCS 的过滤将化学搜索空间缩小了 60%，同时提高了单一类别中剩余异构体鉴定的置信度，从而证明了将预测分析集成到非目标实验中以协助鉴定工作流程的价值。随着 IM-MS 社区提供更多数据，该纲要的预测能力将提高特异性，并扩展到更多化学类别。提供了向纲要提交数据的说明和纳入标准。