LipidCCS: Prediction of Collision Cross-Section Values for Lipids with High Precision To Support Ion Mobility–Mass Spectrometry-Based Lipidomics

Abstract

The use of collision cross-section (CCS) values derived from ion mobility-mass spectrometry (IM-MS) has been proven to facilitate lipid identifications. Its utility is restricted by the limited availability of CCS values. Recently, the machine-learning algorithm-based prediction (e.g., MetCCS) is reported to generate CCS values in a large-scale. However, the prediction precision is not sufficient to differentiate lipids due to their high structural similarities and subtle differences on CCS values. To address this challenge, we developed a new approach, namely, LipidCCS, to precisely predict lipid CCS values. In LipidCCS, a set of molecular descriptors were optimized using bioinformatic approaches to comprehensively describe the subtle structure differences for lipids. The use of optimized molecular descriptors together with a large set of standard CCS values for lipids (458 in total) to build the prediction model significantly improved the precision. The prediction precision of LipidCCS was externally validated with median relative errors (MRE) of ∼1% using independent data sets across different instruments (Agilent DTIM-MS and Waters TWIM-MS) and laboratories. We also demonstrated that the improved precision in the predicted LipidCCS database (15 646 lipids and 63 434 CCS values in total) could effectively reduce false-positive identifications of lipids. Common users can freely access our LipidCCS web server for the following: (1) the prediction of lipid CCS values directly from SMILES structure; (2) database search; and (3) lipid match and identification. We believe LipidCCS will be a valuable tool to support IM-MS-based lipidomics. The web server is freely available on the Internet ( http://www.metabolomics-shanghai.org/LipidCCS/ ).

Use of ion mobility mass spectrometry to enhance cumulative analytical specificity and separation to profile 6‐C/8‐C‐glycosylflavone critical isomer pairs and known–unknowns in medicinal plants

“…Additionally Zhou et al . have illustrated theoretical/machine learning ion mobility CCS data base development, which has enabled the large‐scale prediction of CCS values for metabolites (MetCCS) and lipids (LipidCCS) . Additionally the speed at which theoretical calculation can be determined increases, theoretical calculations (HPCCS) can be determined using high performance computing techniques .…”

Detection and Structural Characterization of Ether Glycerophosphoethanolamine from Cortical Lysosomes Following Traumatic Brain Injury Using UPLC‐HDMSE

“…The CCS values for the identified ether PE lipids are reported in Table . The CCS values were compared to in silico calculations using the on‐line tool LipidCCS Predictor . The calculated CCS values were within 2% of the predicted CCS values generated by LipidCCS predictor.…”

Trapped ion mobility spectrometry (TIMS) and parallel accumulation - serial fragmentation (PASEF) enable in-depth lipidomics from minimal sample amounts

“…The investigation of the correlation of lipids mass and ion mobility has been a long term interest in ion mobility spectrometry-based lipidomics 26,27 . TIMS and PASEF provide a very efficient way to extend the scope of such studies to complex biological samples.…”

“…To further explore this data space, we Lipid CCS values were predicted in MetaboScape based on a support vector machine learning approach by Zhou et al 27 .…”

“…This is particular interesting for lipidomics, as it provides an opportunity to separate otherwise unresolved isomers [22][23][24][25] . Furthermore, the chemical structure of lipids is closely linked to the CCS, which allows predictions by machine learning and could facilitate lipid identification [26][27][28][29] .…”