LiPydomics: A Python Package for Comprehensive Prediction of Lipid Collision Cross Sections and Retention Times and Analysis of Ion Mobility-Mass Spectrometry-Based Lipidomics Data

Abstract

Comprehensive profiling of lipid species in a biological sample, or lipidomics, is a valuable approach to elucidating disease pathogenesis and identifying biomarkers. Currently, a typical lipidomics experiment may track hundreds to thousands of individual lipid species. However, drawing biological conclusions requires multiple steps of data processing to enrich significantly altered features and confident identification of these features. Existing solutions for these data analysis challenges (i.e., multivariate statistics and lipid identification) involve performing various steps using different software applications, which imposes a practical limitation and potentially a negative impact on reproducibility. Hydrophilic interaction liquid chromatography-ion mobility-mass spectrometry (HILIC-IM-MS) has shown advantages in separating lipids through orthogonal dimensions. However, there are still gaps in the coverage of lipid classes in the literature. To enable reproducible and efficient analysis of HILIC-IM-MS lipidomics data, we developed an open-source Python package, LiPydomics, which enables performing statistical and multivariate analyses (“stats” module), generating informative plots (“plotting” module), identifying lipid species at different confidence levels (“identification” module), and carrying out all functions using a user-friendly text-based interface (“interactive” module). To support lipid identification, we assembled a comprehensive experimental database of m/z and CCS of 45 lipid classes with 23 classes containing HILIC retention times. Prediction models for CCS and HILIC retention time for 22 and 23 lipid classes, respectively, were trained using the large experimental data set, which enabled the generation of a large predicted lipid database with 145,388 entries. Finally, we demonstrated the utility of the Python package using Staphylococcus aureus strains that are resistant to various antimicrobials.

Synergy Between Beta-Lactams and Lipo-, Glyco-, and Lipoglycopeptides, Is Independent of the Seesaw Effect in Methicillin-Resistant Staphylococcus aureus

“…Lipid identifications were made based on m/z (within 10 ppm mass accuracy), retention time, and CCS with an in-house version of LipidPioneer, modified to contain the major lipid species observed in S. aureus, including free fatty acids (FFAs), DGDGs, PGs, CLs, and LysylPGs with fatty acyl compositions ranging from 25:0 to 38:0 (total carbons: total degree unsaturation), and LiPydomics (Hines KM. et al, 2017;Ulmer et al, 2017;Ross et al, 2020).…”

Least absolute shrinkage and selection operator-based prediction of collision cross section values for ion mobility mass spectrometric analysis of lipids

“…It is difficult to differentiate lipid isomers using the previously reported prediction methods or theoretical calculation methods. 34,35 In order to test the separating capacity of the LASSO-based prediction method in differentiating lipid isomers, we further compared the LASSO predicted CCS values of 3 pairs of lipid isomers (2 pairs of TG isomers and 1 pair of fatty acid isomers in the positive and negative ion modes, respectively) with their experimental CCS values measured in our laboratory. As shown in Fig.…”

Evolution of Enterococcus faecium to A Combination of Daptomycin and Fosfomycin Reveals Distinct and Diverse Adaptive Strategies

“…The default "All Compounds" method of normalization was used to correct for variation in the total ion current amongst samples. Lipid identifications were made based on m/z (within 10 ppm mass accuracy), retention time, and CCS with an in-house version of LipidPioneer, modified to contain the major lipid species, including free fatty acids (FFAs), DGDGs, PGs, CLs, and LysylPGs with fatty acyl compositions ranging from 25:0 to 38:0 (total carbons:total degree unsaturation), and LiPydomics (44)(45)(46). The quantitation of FFAs was performed based on their ion mobility retained chromatographic peaks using Waters DriftScope 2.5 and TargetLynx (Waters Corp., Milford, MA, USA).…”