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

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.