Comprehensive Lipidomic Workflow for Multicohort Population Phenotyping Using Stable Isotope Dilution Targeted Liquid Chromatography-Mass Spectrometry

Dysregulated lipid metabolism underpins many chronic diseases including cardiometabolic diseases. Mass spectrometry-based lipidomics is an important tool for understanding mechanisms of lipid dysfunction and is widely applied in epidemiology and clinical studies. With ever-increasing sample numbers, single batch acquisition is often unfeasible, requiring advanced methods that are accurate and robust to batch-to-batch and interday analytical variation. Herein, an optimized comprehensive targeted workflow for plasma and serum lipid quantification is presented, combining stable isotope internal standard dilution, automated sample preparation, and ultrahigh performance liquid chromatography-tandem mass spectrometry with rapid polarity switching to target 1163 lipid species spanning 20 subclasses. The resultant method is robust to common sources of analytical variation including blood collection tubes, hemolysis, freeze-thaw cycles, storage stability, analyte extraction technique, interinstrument variation, and batch-to-batch variation with 820 lipids reporting a relative standard deviation of <30% in 1048 replicate quality control plasma samples acquired across 16 independent batches (total injection count = 6142). However, sample hemolysis of ≥0.4% impacted lipid concentrations, specifically for phosphatidylethanolamines (PEs). Low interinstrument variability across two identical LC-MS systems indicated feasibility for intra/inter-lab parallelization of the assay. In summary, we have optimized a comprehensive lipidomic protocol to support rigorous analysis for large-scale, multibatch applications in precision medicine. The mass spectrometry lipidomics data have been deposited to massIVE: data set identifiers MSV000090952 and 10.25345/C5NP1WQ4S.

[1]  R. Tekade,et al.  Critical strategies to pinpoint carryover problems in liquid chromatography-mass spectrometry: a systematic direction for their origin identification and mitigation , 2022, Microchemical journal (Print).

[2]  A. Dehghan,et al.  Finding Correspondence between Metabolomic Features in Untargeted Liquid Chromatography–Mass Spectrometry Metabolomics Datasets , 2022, Analytical chemistry.

[3]  P. Meikle,et al.  Clinical lipidomics – A community-driven roadmap to translate research into clinical applications , 2022, Journal of mass spectrometry and advances in the clinical lab.

[4]  Qisong Zhang,et al.  Serum untargeted lipidomics by UHPLC-ESI-HRMS aids the biomarker discovery of colorectal adenoma , 2022, BMC cancer.

[5]  Joshua A. Dubland.  Lipid analysis by ion mobility spectrometry combined with mass spectrometry: A brief update with a perspective on applications in the clinical laboratory , 2021, Journal of mass spectrometry and advances in the clinical lab.

[6]  M. Snyder,et al.  A DMS Shotgun Lipidomics Workflow Application to Facilitate High-Throughput, Comprehensive Lipidomics. , 2021, Journal of the American Society for Mass Spectrometry.

[7]  N. Urban,et al.  Quantitative global lipidomics analysis of patients with ovarian cancer versus benign adnexal mass , 2021, Scientific Reports.

[8]  J. Nicholson,et al.  Diagnostic Potential of the Plasma Lipidome in Infectious Disease: Application to Acute SARS-CoV-2 Infection , 2021, Metabolites.

[9]  Yue Huang,et al.  Differences in levels of phosphatidylinositols in healthy and stable Coronary Artery Disease subjects revealed by HILIC-MRM method with SERRF normalization , 2021, PloS one.

[10]  A. Kennedy,et al.  Assessment of the effects of repeated freeze thawing and extended bench top processing of plasma samples using untargeted metabolomics , 2021, Metabolomics.

[11]  Christer S. Ejsing,et al.  Accurate quantification of lipid species affected by isobaric overlap in Fourier-transform mass spectrometry , 2021, Journal of lipid research.

[12]  M. Giera,et al.  Reproducibility of Targeted Lipidome Analyses (Lipidyzer) in Plasma and Erythrocytes over a 6-Week Period , 2020, Metabolites.

[13]  Y. Guitton,et al.  Single-Step Extraction Coupled with Targeted HILIC-MS/MS Approach for Comprehensive Analysis of Human Plasma Lipidome and Polar Metabolome , 2020, Metabolites.

[14]  B. Melichar,et al.  Determination of one year stability of lipid plasma profile and comparison of blood collection tubes using UHPSFC/MS and HILIC-UHPLC/MS. , 2020, Analytica chimica acta.

[15]  B. Bai,et al.  A Reference chart for clinical biochemical tests of hemolyzed serum samples , 2020, Journal of clinical laboratory analysis.

[16]  J. Hui,et al.  High-coverage plasma lipidomics reveals novel sex-specific lipidomic fingerprints of age and BMI: Evidence from two large population cohort studies , 2020, PLoS biology.

[17]  Qin Liu,et al.  Addressing the batch effect issue for LC/MS metabolomics data in data preprocessing , 2020, Scientific Reports.

[18]  John A. Bowden,et al.  A Review of Efforts to Improve Lipid Stability during Sample Preparation and Standardization Efforts to Ensure Accuracy in the Reporting of Lipid Measurements. , 2020, Lipids.

[19]  G. Genta‐Jouve,et al.  Lipid Annotation by Combination of UHPLC-HRMS (MS), Molecular Networking, and Retention Time Prediction: Application to a Lipidomic Study of In Vitro Models of Dry Eye Disease , 2020, Metabolites.

[20]  T. Huan,et al.  Effects of Freeze-Thaw Cycles of Blood Samples on High-Coverage Quantitative Metabolomics. , 2020, Analytical chemistry.

[21]  M. Wabitsch,et al.  Lipidomic Phenotyping Reveals Extensive Lipid Remodeling during Adipogenesis in Human Adipocytes , 2020, Metabolites.

[22]  G. Brenner-Weiss,et al.  Lipid ratios as a marker for red blood cell storage quality and as a possible explanation for donor gender differences in storage quality , 2020, Vox sanguinis.

[23]  Michael J MacCoss,et al.  Skyline for Small Molecules: A Unifying Software Package for Quantitative Metabolomics. , 2020, Journal of proteome research.

[24]  Sven W. Meckelmann,et al.  A comprehensive UHPLC ion mobility QTOF method for profiling and quantification of eicosanoids, other oxylipins and fatty acids. , 2019, Analytical chemistry.

[25]  Zheng-Jiang Zhu,et al.  The emerging role of ion mobility-mass spectrometry in lipidomics to facilitate lipid separation and identification , 2019, TrAC Trends in Analytical Chemistry.

[26]  J. Nicholson,et al.  Systematic Isolation and Structure Elucidation of Urinary Metabolites Optimized for the Analytical-Scale Molecular Profiling Laboratory , 2019, Analytical chemistry.

[27]  L. Lind,et al.  Effects of Long-Term Storage at −80 °C on the Human Plasma Metabolome , 2019, Metabolites.

[28]  Mary C. Boyce,et al.  Introducing Undergraduate Students to Metabolomics Using Liquid Chromatography–High Resolution Mass Spectrometry Analysis of Horse Blood , 2019, Journal of Chemical Education.

[29]  Julia Omar,et al.  Hemolyzed Specimens: Major Challenge for Identifying and Rejecting Specimens in Clinical Laboratories , 2019, Oman medical journal.

[30]  J. Shaw,et al.  High-Throughput Plasma Lipidomics: Detailed Mapping of the Associations with Cardiometabolic Risk Factors. , 2019, Cell chemical biology.

[31]  R. Bowler,et al.  Impact of Blood Collection Tubes and Sample Handling Time on Serum and Plasma Metabolome and Lipidome , 2018, Metabolites.

[32]  Z. Cai,et al.  statTarget: A streamlined tool for signal drift correction and interpretations of quantitative mass spectrometry-based omics data. , 2018, Analytica chimica acta.

[33]  J. Rioux,et al.  Comprehensive and Reproducible Untargeted Lipidomic Workflow Using LC-QTOF Validated for Human Plasma Analysis. , 2018, Journal of proteome research.

[34]  Fong-Fu Hsu,et al.  Mass spectrometry-based shotgun lipidomics – a critical review from the technical point of view , 2018, Analytical and Bioanalytical Chemistry.

[35]  Valerio Chiurchiù,et al.  Bioactive Lipids and Chronic Inflammation: Managing the Fire Within , 2018, Front. Immunol..

[36]  Simone Wahl,et al.  Long-Term Stability of Human Plasma Metabolites during Storage at -80 °C. , 2018, Journal of proteome research.

[37]  C. Hillery,et al.  Phosphatidylethanolamine is progressively exposed in RBCs during storage , 2017, Transfusion medicine.

[38]  J. Shaw,et al.  Lipidomic risk score independently and cost-effectively predicts risk of future type 2 diabetes: results from diverse cohorts , 2016, Lipids in Health and Disease.

[39]  Joost J. B. Keurentjes,et al.  Improved batch correction in untargeted MS-based metabolomics , 2016, Metabolomics.

[40]  R. Laaksonen,et al.  Development and validation of a high-throughput LC–MS/MS assay for routine measurement of molecular ceramides , 2016, Analytical and Bioanalytical Chemistry.

[41]  M. A. Surma,et al.  An automated shotgun lipidomics platform for high throughput, comprehensive, and quantitative analysis of blood plasma intact lipids , 2015, European journal of lipid science and technology : EJLST.

[42]  G B Boylan,et al.  The effect of haemolysis on the metabolomic profile of umbilical cord blood. , 2015, Clinical biochemistry.

[43]  M. Fedorova,et al.  Identification of carbonylated lipids from different phospholipid classes by shotgun and LC-MS lipidomics , 2015, Analytical and Bioanalytical Chemistry.

[44]  Elaine Holmes,et al.  Objective set of criteria for optimization of sample preparation procedures for ultra-high throughput untargeted blood plasma lipid profiling by ultra performance liquid chromatography-mass spectrometry. , 2014, Analytical chemistry.

[45]  Kosuke Saito,et al.  Plasma and Serum Lipidomics of Healthy White Adults Shows Characteristic Profiles by Subjects’ Gender and Age , 2014, PloS one.

[46]  H. Soininen,et al.  Evidence of altered phosphatidylcholine metabolism in Alzheimer's disease , 2014, Neurobiology of Aging.

[47]  S. Tannenbaum,et al.  Serum Metabolome and Lipidome Changes in Adult Patients with Primary Dengue Infection , 2013, PLoS neglected tropical diseases.

[48]  T. Hankemeier,et al.  The influence of citrate, EDTA, and heparin anticoagulants to human plasma LC–MS lipidomic profiling , 2013, Metabolomics.

[49]  Charles Pound,et al.  Identification of Plasma Lipid Biomarkers for Prostate Cancer by Lipidomics and Bioinformatics , 2012, PloS one.

[50]  Coral Barbas,et al.  In-vial dual extraction for direct LC-MS analysis of plasma for comprehensive and highly reproducible metabolic fingerprinting. , 2012, Analytical chemistry.

[51]  Vasant R. Marur,et al.  Serum lipidomics profiling using LC-MS and high-energy collisional dissociation fragmentation: focus on triglyceride detection and characterization. , 2011, Analytical chemistry.

[52]  Ari Tolonen,et al.  Comparison of triple quadrupole, hybrid linear ion trap triple quadrupole, time-of-flight and LTQ-Orbitrap mass spectrometers in drug discovery phase metabolite screening and identification in vitro--amitriptyline and verapamil as model compounds. , 2010, Rapid communications in mass spectrometry : RCM.

[53]  Nicola C Hughes,et al.  Assessing the matrix effects of hemolyzed samples in bioanalysis. , 2009, Bioanalysis.

[54]  Xianlin Han,et al.  Alterations in myocardial cardiolipin content and composition occur at the very earliest stages of diabetes: a shotgun lipidomics study. , 2007, Biochemistry.

[55]  F. Boas,et al.  Phosphatidylserine exposure and red cell viability in red cell aging and in hemolytic anemia. , 1998, Proceedings of the National Academy of Sciences of the United States of America.