Interpreting the Lipidome: Bioinformatic Approaches to Embrace the Complexity

Kyle, Jennifer E.; Aimo, Lucila; Bridge, Alan J.; Clair, Geremy CD; Fedorova, Maria; Helms, J B.; Molenaar, Martijn R.; Ni, Zhixu; Oresic, Matej; Slenter, Denise (BIGCAT); Willighagen, Egon L.; Webb-Robertson, Bobbie-Jo M.

doi:10.1007/s11306-021-01802-6

Title: Interpreting the Lipidome: Bioinformatic Approaches to Embrace the Complexity

Journal Article · Mon Aug 02 00:00:00 EDT 2021 · Metabolomics

DOI:https://doi.org/10.1007/s11306-021-01802-6· OSTI ID:1811833

Kyle, Jennifer E. ^[1]; Aimo, Lucila ^[2]; Bridge, Alan J. ^[2];

^[1]; Fedorova, Maria ^[3]; Helms, J B. ^[4]; Molenaar, Martijn R. ^[5]; Ni, Zhixu ^[3]; Oresic, Matej ^[6]; Slenter, Denise (BIGCAT) ^[7]; Willighagen, Egon L. ^[7]; Webb-Robertson, Bobbie-Jo M. ^[1]

BATTELLE (PACIFIC NW LAB)
Swiss Institute for Bioinformatics
Universitat Leipzig
Utrecht University
European Molecular Biology Laboratory
Orebro University
Maastricht University

Background Improvements in mass spectrometry (MS) technologies coupled with bioinformatics developments have allowed considerable advancement in the measurement and interpretation of lipidomics data in recent years. Since research areas employing lipidomics are rapidly increasing, there is a great need for bioinformatic tools that capture and utilize the complexity of the data. Currently, the diversity and complexity within the lipidome is often concealed by summing over or averaging individual lipids up to (sub)class-based descriptors, losing valuable information about biological function and interactions with other distinct lipids molecules, proteins and/or metabolites. Aim of review To address this gap in knowledge, novel bioinformatics methods are needed to improve identification, quantification, integration and interpretation of lipidomics data. The purpose of this mini-review is to summarize exemplary methods to explore the complexity of the lipidome. Key scientific concepts of review Here we describe six approaches that capture three core focus areas for lipidomics: (1) lipidome annotation including a resolvable database identifier, (2) interpretation via pathway- and enrichment-based methods, and (3) understanding complex interactions to emphasize specific steps in the analytical process and highlight challenges in analyses associated with the complexity of lipidome data.

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Research Organization:: Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

Sponsoring Organization:: USDOE

DOE Contract Number:: AC05-76RL01830

OSTI ID:: 1811833

Report Number(s):: PNNL-SA-162774

Journal Information:: Metabolomics, Vol. 17, Issue 8

Country of Publication:: United States

Language:: English

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