LipiDex: An Integrated Software Package for High-Confidence Lipid Identification

Paul D Hutchins; Jason D Russell; Joshua J Coon

doi:10.1016/j.cels.2018.03.011

LipiDex: An Integrated Software Package for High-Confidence Lipid Identification

Cell Syst. 2018 May 23;6(5):621-625.e5. doi: 10.1016/j.cels.2018.03.011. Epub 2018 Apr 25.

Authors

Paul D Hutchins¹, Jason D Russell², Joshua J Coon³

Affiliations

¹ Department of Chemistry, University of Wisconsin-Madison, Madison, WI 53706, USA; Genome Center of Wisconsin, University of Wisconsin-Madison, Madison, WI 53706, USA.
² Morgridge Institute for Research, Madison, WI 53715, USA; Genome Center of Wisconsin, University of Wisconsin-Madison, Madison, WI 53706, USA.
³ Department of Chemistry, University of Wisconsin-Madison, Madison, WI 53706, USA; Morgridge Institute for Research, Madison, WI 53715, USA; Genome Center of Wisconsin, University of Wisconsin-Madison, Madison, WI 53706, USA; Department of Biomolecular Chemistry, University of Wisconsin-Madison, Madison, WI 53706, USA. Electronic address: jcoon@chem.wisc.edu.

Abstract

State-of-the-art proteomics software routinely quantifies thousands of peptides per experiment with minimal need for manual validation or processing of data. For the emerging field of discovery lipidomics via liquid chromatography-tandem mass spectrometry (LC-MS/MS), comparably mature informatics tools do not exist. Here, we introduce LipiDex, a freely available software suite that unifies and automates all stages of lipid identification, reducing hands-on processing time from hours to minutes for even the most expansive datasets. LipiDex utilizes flexible in silico fragmentation templates and lipid-optimized MS/MS spectral matching routines to confidently identify and track hundreds of lipid species and unknown compounds from diverse sample matrices. Unique spectral and chromatographic peak purity algorithms accurately quantify co-isolation and co-elution of isobaric lipids, generating identifications that match the structural resolution afforded by the LC-MS/MS experiment. During final data filtering, ionization artifacts are removed to significantly reduce dataset redundancy. LipiDex interfaces with several LC-MS/MS software packages, enabling robust lipid identification to be readily incorporated into pre-existing data workflows.

Keywords: data analysis; lipid fragmentation; lipidomics; mass spectrometry; open-source software; spectral library.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Algorithms
Chromatography, Liquid / methods
Data Analysis
Lipids / analysis*
Peptides / analysis
Proteomics / methods*
Software
Tandem Mass Spectrometry / methods*

Substances

Lipids
Peptides

Grants and funding

P41 GM108538/GM/NIGMS NIH HHS/United States