Improving Lipid Identification and Quantification: Chromatogram Deconvolution for LC-MS/MS Workflows

Clicks: 20
ID: 329097
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #523 of 983 articles by views in BMC Bioinformatics

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 983 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Abstract Motivation Lipidomics relies on mass spectrometry-based workflows to identify and quantify complex lipid species. Due to the modular architecture of lipids, including headgroups, backbones, and fatty acyl chains, distinct precursor ions often produce isobaric or identical fragment ions. This problem is amplified in data-independent acquisition (DIA), where wide isolation windows (e.g., 25 Da) allow co-eluting precursors with different m/z values to generate highly chimeric MS/MS spectra. Consequently, fragments originating from multiple precursors, including isobars, isomers, and lipid-class-specific ions, are merged into a single MS/MS spectrum. Current lipid identification strategies often process such chimeric spectra in an uncontrolled manner, assigning them to one or more candidate lipids, thereby increasing false-positive identifications. Results Here, we introduce an algorithm that deconvolutes chimeric MS/MS spectra and chromatograms by exploiting their temporal correlation with associated precursor chromatographic profiles, independent of elution peak shape. Using simulated and real experimental lipidomics data, we demonstrate that this approach substantially improves lipid fragment assignment, reduces false-positive identifications, and enables more reliable fragment-level quantification, leading to more robust downstream statistical analyses and biological interpretations. Availability and Implementation Source code of the software library: GitLab (Apache 2.0 License): https://gitlab.com/computational-multiomics/mixture-model-deconvolution; Data: Zenodo (Apache 2.0 License): https://doi.org/10.5281/zenodo.21218594. Supplementary information Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W7213552950 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Felix Niedermaier, Denise Wolrab, Robert Ahrends, Dominik Kopczynski
Are you one of these authors? Claim your profile
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag695
URL
https://doi.org/10.1093/bioinformatics/btag695
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.