Related Experiment Video
Updated: Sep 23, 2025
Lipidomics and Transcriptomics in Neurological Diseases
Published on: March 18, 2022
Improving confidence in lipidomic annotations by incorporating empirical ion mobility regression analysis and
Bailey S Rose1, Jody C May1, Jaqueline A Picache1
1Department of Chemistry, Center for Innovative Technology, Vanderbilt-Ingram Cancer Center, Vanderbilt Institute of Chemical Biology, Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, TN 37235, USA.
Motivation:
Mass spectrometry-based untargeted lipidomics aims to globally characterize the lipids and lipid-like molecules in biological systems. Ion mobility increases coverage and confidence by offering an additional dimension of separation and a highly reproducible metric for feature annotation, the collision cross-section (CCS).
Results:
We present a data processing workflow to increase confidence in molecular class annotations based on CCS values. This approach uses class-specific regression models built from a standardized CCS repository (the Unified CCS Compendium) in a parallel scheme that combines a new annotation filtering approach with a machine learning class prediction strategy. In a proof-of-concept study using murine brain lipid extracts, 883 lipids were assigned higher confidence identifications using the filtering approach, which reduced the tentative candidate lists by over 50% on average. An additional 192 unannotated compounds were assigned a predicted chemical class.
Availability And Implementation:
All relevant source code is available at https://github.com/McLeanResearchGroup/CCS-filter.
Supplementary Information:
Supplementary data are available at Bioinformatics online.
