Collision cross section measurement and prediction methods in omics

Abstract

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.

Predicting the Predicted: A Comparison of Machine Learning-Based Collision Cross-Section Prediction Models for Small Molecules

“…To further compare the ranking performance, we used an adaptation of the rank-biased precision (RBP) algorithm (see Supporting Information), commonly used to assess the ranking precision of ranking algorithms in web search engines. Our adapted RBP version yields a normalized score, from 0 to 1, giving a score of 1 when the ranking is perfect and 0 where the ranking is the worst possible, by weighting the importance of the hit order; i.e., the score drops more significantly if the order changes from [1,2,3,4] to [2,1,3,4] compared to from [1,2,3,4] to [1,2,4,3]. RBP-based ranking scores across prediction methods are shown in Figure 4 b, where statistically significant differences are observed between CCSBase, AllCCS, DeepCCS, and LinECFP compared to CCSP2.0 (Wilcoxon test) and also compared to KerasECFP (p-value < 0.0001, significance not shown in the Figure 4).…”

“…Since the unambiguous annotation of small molecules continues to pose a challenge, IM-MS-generated collision cross-section (CCS), together with tandem MS (MS/MS) and/or retention time (RT) information, can be used as an additional approach to annotate or identify molecules in biological samples. CCS values are relatively robust under the same experimental conditions, providing a complementary, semi-orthogonal measure for small-molecule annotation, addressing some of the drawbacks of chromatographic columns such as poor isomer separation and high temporal and cross platform variance in RTs. , To identify molecules with IM-MS, the experimental CCS value for an observed molecule has to be compared with a reference value. Existing libraries and curated data sets containing reference CCS values continue to grow.…”

“…CCS values are relatively robust under the same experimental conditions, providing a complementary, semi-orthogonal measure for small-molecule annotation, addressing some of the drawbacks of chromatographic columns such as poor isomer separation and high temporal and cross platform variance in RTs. 1 , 2 To identify molecules with IM-MS, the experimental CCS value for an observed molecule has to be compared with a reference value. Existing libraries and curated data sets containing reference CCS values continue to grow.…”

Evaluation of a Reference-Free Collision Cross Section Calibration Strategy for Proteomics Using SLIM-Based High-Resolution Ion Mobility Spectrometry–Mass Spectrometry

“…7 The derivation of CCS from IMS measurements provides structural information on the analytes and greatly increases the confidence of feature annotations, due to the high reproducibility of CCS values across instruments and experimental conditions 8 and the ability to predict CCS values for analytes using theoretical principles or empirical trends via artificial intelligence/machine learning (AI/ML). 9,10 The utility of CCS for supporting the confident identification of analytes is a key advantage of IMS for -omics analyses. However, the effective derivation of CCS from IMS measurements poses a practical impediment to broader adoption.…”

“…The rapid nature of the IMS separation, which typically occurs on a time scale of tens to hundreds of milliseconds (e.g., depending on resolving power), combined with the fact that it is applicable to essentially any species that can be ionized, makes IMS a particularly flexible technique for use with MS, either by direct infusion analysis or in combination with other separations such as liquid chromatography (i.e., LC-IMS-MS) . The derivation of CCS from IMS measurements provides structural information on the analytes and greatly increases the confidence of feature annotations, due to the high reproducibility of CCS values across instruments and experimental conditions and the ability to predict CCS values for analytes using theoretical principles or empirical trends via artificial intelligence/machine learning (AI/ML). , …”