Evidence map›Paper›PMID 42508890›Full record

ArticleAnalytica chimica acta2026

Fast untargeted and targeted high-resolution mass spectrometry workflows for large-scale lipidomics.

Yi Yun Pang, Guo Shou Teo, Wai Kin Tham, Woon Puay Koh, Chew Kiat Heng, Thusitha W T Rupasinghe, Paul R S Baker, Federico Torta, Hyungwon Choi

Abstract read
In one paragraph

Article in Analytica chimica acta, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Yi Yun PangDepartment of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 119228, Singapore; Singapore Lipidomics Incubator, Life Sciences Institute, National University of Singapore, Singapore, 117456, Singapore.
Guo Shou TeoDepartment of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 119228, Singapore; Singapore Lipidomics Incubator, Life Sciences Institute, National University of Singapore, Singapore, 117456, Singapore.
Wai Kin ThamSingapore Lipidomics Incubator, Life Sciences Institute, National University of Singapore, Singapore, 117456, Singapore; Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117596, Singapore.
Woon Puay KohHealthy Longevity Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117596, Singapore.
Chew Kiat HengDepartment of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 119228, Singapore; Cardiovascular-Metabolic Disease Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117599, Singapore.
Thusitha W T RupasingheSCIEX, Australia.
Paul R S BakerSCIEX, USA.
Federico TortaSingapore Lipidomics Incubator, Life Sciences Institute, National University of Singapore, Singapore, 117456, Singapore; Signature Research Programme in Cardiovascular & Metabolic Disorders, Duke-NUS Medical School, Singapore, 169857, Singapore. Electronic address: federico.torta@duke-nus.edu.sg.
Hyungwon ChoiDepartment of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 119228, Singapore; Singapore Lipidomics Incubator, Life Sciences Institute, National University of Singapore, Singapore, 117456, Singapore; Cardiovascular-Metabolic Disease Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117599, Singapore. Electronic address: hyung_won_choi@nus.edu.sg.

Funding

Prospective studies of cancer etiology and prevention in Shanghai and SingaporeR01CA144034 · NCI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI YUAN, JIAN-MIN · 2010 to 2014
$7.0M
Cancer epidemiology cohorts in Shanghai and Singapore UM1CA182876 · NCI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI YUAN, JIAN-MIN · 2014 to 2018
$4.3M
NCI NIH HHS R01 CA144034NCI NIH HHS UM1 CA182876
6 · The paper itself

Abstract

backgroundLipidomes are highly complex and variable, and high-throughput analytical strategies are essential for comprehensive lipidome profiling in large-scale analysis. The use of hydrophilic interaction liquid chromatography (HILIC) coupled with conventional untargeted acquisition strategies remains unpopular due to the challenges in interpreting complex spectral data. To address this gap, we present a detailed lipidomic workflow based on a fast 6.7 min-long HILIC separation method coupled with data-dependent acquisition/sequential window acquisition of all theoretical fragment ions analysis (DDA/SWATH) and improve its applicability with a novel framework for untargeted data processing. In parallel, we also established a targeted high-resolution multiple reaction monitoring (MRMHR) method for comprehensive multi-class lipidomic profiling. To evaluate the applicability of these two approaches in large-scale clinical lipidomics, we used both workflows for the analysis of 240 case-control matched plasma samples from a population-based prospective cohort.

resultsAfter method development and optimization, application of the two complementary workflows to 240 plasma samples generated lipid profiles of over 400 and 500 features for targeted and untargeted analysis, respectively. As expected, the coverage of lipids by the untargeted workflow was notably different from that of the targeted workflow at the lipid species level. In terms of quantitation of lipids detected in both approaches, we observed differences in the quantitation of lipids in individual samples, but statistical comparisons of sample groups remained largely concordant. Furthermore, we demonstrated that the choice of internal standards for normalization can influence downstream statistical outcomes. SIGNIFICANCE: Two fast HILIC-based workflows were demonstrated to be applicable for large-scale clinical lipidomics and represent reliable high-throughput alternatives to existing longer separation methods for both untargeted and targeted analyses using high-resolution mass spectrometry. Furthermore, the influence of targeted and untargeted approaches, together with the assessment of internal standard selection on downstream statistical outcomes, provides important methodological considerations for large-scale lipidomic studies.

Indexed as

LipidomicsLipidsChromatography, LiquidHumansHydrophobic and Hydrophilic InteractionsLiquid Chromatography-Mass SpectrometryMass SpectrometryWorkflowLipidsData-dependent acquisitionData-independent acquisitionHigh-resolution multiple reaction monitoringHuman plasmaHydrophilic interaction liquid chromatography

Identifiers

PMID42508890
PMCPMC13408214

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.