Modern biomarker and translational research as well as personalized health care studies rely heavily on powerful omics’ technologies, including metabolomics and lipidomics. However, to translate metabolomics and lipidomics discoveries into a high-throughput clinical setting, standardization is of utmost importance. Here, we compared and benchmarked a quantitative lipidomics platform. The employed Lipidyzer platform is based on lipid class separation by means of differential mobility spectrometry with subsequent multiple reaction monitoring. Quantitation is achieved by the use of 54 deuterated internal standards and an automated informatics approach. We investigated the platform performance across nine laboratories using NIST SRM 1950–Metabolites in Frozen Human Plasma, and three NIST Candidate Reference Materials 8231–Frozen Human Plasma Suite for Metabolomics (high triglyceride, diabetic, and African-American plasma). In addition, we comparatively analyzed 59 plasma samples from individuals with familial hypercholesterolemia from a clinical cohort study. We provide evidence that the more practical methyl-tert-butyl ether extraction outperforms the classic Bligh and Dyer approach and compare our results with two previously published ring trials. In summary, we present standardized lipidomics protocols, allowing for the highly reproducible analysis of several hundred human plasma lipids, and present detailed molecular information for potentially disease relevant and ethnicity-related materials.

It is essential to measure lipid biomarkers with a high reproducibility to prevent biased results. We compared the lipid composition and inter-day reproducibility of lipid measurements in plasma and erythrocytes. Samples from 42 individuals (77% women, mean age 65 years, mean body mass index (BMI) 27 kg/m2), obtained non-fasted at baseline and after 6 weeks, were used for quantification of up to 1000 lipid species across 13 lipid classes with the Lipidyzer platform. Intraclass correlation coefficients (ICCs) were calculated to investigate the variability of lipid concentrations between timepoints. The ICC distribution of lipids in plasma and erythrocytes were compared using Wilcoxon tests. After data processing, the analyses included 630 lipids in plasma and 286 in erythrocytes. From these, 230 lipids overlapped between sample types. In plasma, 78% of lipid measurements were reproduced well to excellently, compared to 37% in erythrocytes. The ICC score distribution in plasma (median ICC 0.69) was significantly better than in erythrocytes (median ICC 0.51) (p-value < 0.001). At the class level, reproducibility in plasma was superior for triacylglycerols and cholesteryl esters while ceramides, diacylglycerols, (lyso)phosphatidylethanolamines, and sphingomyelins showed better reproducibility in erythrocytes. Although in plasma overall reproducibility was superior, differences at individual and class levels may favor the use of erythrocytes.

Characterization of antibody−drug conjugates (ADCs) using mass spectrometry (MS) is important in drug discovery and formulation development and as part of the quality control processes. To facilitate electrospray ionization (ESI) and produce high-quality mass spectra, common components of storage solutions for monoclonal antibodies (mAbs) and ADCs, such as nonvolatile phosphate-buffered saline (PBS), should be replaced before analysis. Centrifugal spin-type kits are extensively used for the desalting or buffer-exchange of mAbs and ADCs samples. The commercially available kits commonly require at least 100 μL of a sample at 1 mg/mL for optimal recovery. However, most ESI-MS based analyses require no more than 25 μg of protein for triplicate injection. In this study, we present a novel method for desalting of ADCs and mAbs building on the SP3 approach with nonfunctionalized carboxylate coated magnetic beads without affinity ligands. The analytes bind to the hydrophilic beads upon the addition of organic solvent, and various solutions of volatile salts or acids can be used in the elution step. The optimized protocol allowed for 88% recovery of ADC at a 25 μL sample volume and 90% recovery at 100 μL. More than 90% of the salts were removed using a process of 20 min. The intra-and interday precision showed little variation with an RSD of 1% and 2%, respectively. The compatibility of this new workflow with low sample volumes is highly valuable since a smaller fraction of the sample is wasted for analysis of the expensive analytes, without compromising recovery.

Metabolite levels in peripheral body fluids can correlate with attack features in migraine patients, which underscores the potential of plasma metabolites as possible disease biomarkers. Migraine headache can be preceded by an aura that is caused by cortical spreading depolarization (CSD), a transient wave of neuroglial depolarization. We previously identified plasma amino acid changes after CSD in familial hemiplegic migraine type 1 (FHM1) mutant mice that exhibit increased neuronal excitability and various migraine-related features. Here, we aimed to uncover lipid metabolic pathways affected by CSD, guided by findings on the involvement of lipids in hemiplegic migraine pathophysiology. Using targeted lipidomic analysis, we studied plasma lipid metabolite levels at different time points after CSD in wild-type and FHM1 mutant mice. Following CSD, the most prominent plasma lipid change concerned a transient increase in PGD2, which lasted longer in mutant mice. In wild-type mice only, levels of anti-inflammatory lipid mediators DPAn-3, EPA, ALA, and DHA were elevated 24 h following CSD compared to Sham-treated animals. Given the role of PGs and neuroinflammation in migraine pathophysiology, our findings underscore the potential of monitoring peripheral changes in lipids to gain insight in central brain mechanisms.

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The lipid composition of lipoprotein particles is determinative of their respective formation and function. In turn, the combination and correlation of nuclear magnetic resonance (NMR)-based lipoprotein measurements with mass spectrometry (MS)-based lipidomics is an appealing technological combination for a better understanding of lipid metabolism in health and disease. Here, we developed a combined workflow for subsequent NMR- and MS-based analysis on single sample aliquots of human plasma. We evaluated the quantitative agreement of the two platforms for lipid quantification and benchmarked our combined workflow. We investigated the congruence and complementarity between the platforms in order to facilitate a better understanding of patho-physiological lipoprotein and lipid alterations. We evaluated the correlation and agreement between the platforms. Next, we compared lipid class concentrations between healthy controls and rheumatoid arthritis patient samples to investigate the consensus among the platforms on differentiating the two groups. Finally, we performed correlation analysis between all measured lipoprotein particles and lipid species. We found excellent agreement and correlation (r > 0.8) between the platforms and their respective diagnostic performance. Additionally, we generated correlation maps detailing lipoprotein/lipid interactions and describe disease-relevant correlations.

Background:Lipidomics analysis has become a valuable technology for understanding patho-physiological mechanisms and the identification of candidate biomarkers in rheumatic musculoskeletal disorders. Variability in within-subject repeated measurements may lead to bias towards the null when estimating the association between biomarkers and a disease or treatment. Hence, information regarding the stability of the metabolite levels over time is essential.Objectives:We aimed to assess the lipid composition and biological reproducibility of lipid measurements in plasma and erythrocytes.Methods:Plasma and erythrocyte samples from 42 osteoarthritis patients (77% women, mean age 65 years, mean BMI 27 kg/m2), obtained non-fasted at baseline and six weeks, were used for the quantitative measurement of up to 1000 lipid species across 13 lipid classes with the LipidyzerTM platform in nmol/mL. Data was processed based on the relative standard deviation of quality controls, taking batch effects into account. Intraclass correlation coefficients (ICCs) and corresponding 95% confidence intervals (CI) were calculated to investigate the variability of the lipid concentrations between timepoints. The ICC distribution of lipid metabolites in plasma and erythrocytes were compared using two-sided paired Wilcoxon tests.Results:We measured 778 lipids in plasma, compared to 916 lipids in erythrocytes. After data processing, the analyses included 630 lipids in plasma, and 286 in erythrocytes. From these, 243 lipids overlapped between sample types. Major differences were observed between the sample types in the number of lipids per lipid class and the total concentration of the lipids within a class. Triacylglycerols (TAG) and cholesteryl esters (CE) were more abundant in plasma. Conversely, phosphatidylethanolamines (PE), sphingomyelins (SM) and ceramides (CER) were less abundant in plasma compared to erythrocytes (table 1). In plasma 78% of lipid measurements were good to excellently reproduced, with an overall median ICC 0.69. Compared to plasma, a considerably lower amount (35%) of lipids were well reproduced in erythrocytes. Median reproducibility of lipids in erythrocytes was 0.51. Figure 1 shows the ICC score distribution in plasma with erythrocytes, with a significantly better reproducibility in plasma (p-value<0.001). However, while overall reproducibility was better in plasma, this was not observed for all lipid classes. At class-level, reproducibility in plasma was superior for TAGs and CEs, while CERs, DAGs, (L)PEs and SMs showed better reproducibility in erythrocytes.Table 1.Number of individual lipids per class and class concentrations in plasma and erythrocytesPlasmaErythrocytesNumber of lipid speciesClass concentration (nmol/mL)Number of lipid speciesClass concentration (nmol/mL)Triacylglycerols4821579.4 (1064.9-3195.2)1346.5 (5.6-9.4)Diacylglycerols913.3 (8.4-22.2)105.8 (4.7-6.2)Free fatty acids20745.3 (552.0-1202.9)20486.9 (379.2-669.2)Cholesteryl esters244571.6 (4065.1-5521.3)51.2 (0.9-1.7)Phosphatidylcholines314013.7 (3203.1-4661.6)423899.2 (3723.0-4296.6)Phosphatidylethanolamines26156.2 (120.9-180.3)423954.6 (3721.9-4323.3)Lysophosphatidylcholines9385.9 (335.6-442.9)7119.8 (109.7-168.9)Lysophosphatidylethanolamines24.2 (3.5-4.9)48.6 (6.8-9.7)Sphingomyelins121204.6 (1037.0-1351.9)82695.8 (2434.8-2815.6)Ceramides614.1 (11.9-17.4)7163.0 (133.3-186.4)Dihydroceramides21.0 (0.8-1.3)11.8 (1.4-2.1)Hexosylceramides55.1 (4.7-5.9)45.6 (5.0-7.4)Lactosylceramides23.4 (2.7-3.8)223.8 (20.6-33.5)Numbers represent median (interquartile range) unless otherwise specified. Data represents baseline measurements.Conclusion:In plasma biological reproducibility was good for most lipid measurements. Although overall reproducibility was better in plasma compared to erythrocytes, notable differences were observed at individual- and lipid class-level that may favour the use of a particular sample type.Disclosure of Interests:Marieke Loef: None declared, Johannes von Hegedus: None declared, Mohan Ghorasaini: None declared, Féline Kroon: None declared, Martin Giera Shareholder of: Pfizer, Consultant of: Boehringer Ingelheim Pharma, Andreea Ioan-Facsinay: None declared, Margreet Kloppenburg: None declared

BackgroundRheumatoid arthritis (RA) is an autoimmune disease characterized by chronic and systemic synovial inflammation, cartilage erosion and subsequent joint destruction. Despite the existence of many targeted therapies, RA remains a disease with high morbidity, requiring monitoring with prognostic biomarkers. RA presents with abnormal lipoprotein levels, a phenomenon known as the “lipid paradox”, which requires comprehensive characterization with -omics techniques [1].ObjectivesThe purpose of this study is to describe the distinct lipoprotein and lipidomic profile in active and remission RA populations, in an effort to globally map the effect of inflammation and disease activity on the metabolome and obtain biomarkers with predictive capacity.MethodsPlasma samples of 161 RA patients were collected under fasting conditions. Patients were classified based on their DAS28 as High (>5.1), Moderate (3.2-5.1), Low (2.6-3.2) and Remission (<2.6). Lipoproteins and their subclasses were detected and quantified using 1H NMR spectroscopy with the B.I.LISA method (Bruker), while lipid spieces detection was achieved with mass spectrometry (DMS-MS) using the Lipidyzer method (Sciex). Glycoproteins signals were acquired from 1H NMR led spectra. High Disease Activity (n=21) and Remission (n=33) were analysed pairwise with Multivariate models (OPLS-DA) and univariate t-test (boxplots). The correlation coefficient was calculated for the entire lipoprotein and lipid profiles with DAS28 for each experimental group.ResultsPatients with high disease activity showed relatively increased levels of VLDL (VLPN), IDL (IDPN), total Triglyceride (TPTG), Free fatty acids (FFA(18:1), FFA(16:0)) and Phosphatidylcholines (PC(16:0/20:4), PC(18:0/20:4)) compared to those in remission. RA patients in remission were characterized by high levels of LDL (LDPN, L4PN), HDL Apolipoprotein A1 (HDA1, H4A1), Cholesterol Esters (CE(18:2), CE(18:1)) and Phosphatidylcholines (PC(16:0/18:2), PC(18:0/18:2)). The entire lipoprotein and lipid profiles were correlated with DAS28 in Remission (R2=0.24; R2=0.40) and High disease activity (R2=0.26; R2=0.47) groups, indicating that the lipid profile is more predictive of the disease activity in both groups. Univariate analysis highlighted relatively higher concentration of large density HDL-4 subclasses (HDL-4 -Cholesterol (p=0.0003), ApoA1 (p=0.0003), ApoA2 (p=0.0018) and Phospholipids (p=0.0024)), Sphingomyelin (SM(24:0); p=0.0012), Hexosylceramide (HCER(22:0); p=0.0057), and Cholesterol Esters (CE(18:0); p=0.0025, CE(18:2); p=0.0121), but lower Glycoprotein A and B (p=0.0003; 0.0007) levels in Remission.ConclusionIn summary, comprehensive lipoprotein and lipid analysis identifies markers that characterize RA in exacerbation and remission. Large density HDL-4 Apolipoprotein A1 and Cholesterol Esters are indicative of Remission, while VLDL, Triglycerides and Free fatty acids describe High disease activity in RA. The above markers will be evaluated in comparison with those already existing in clinical laboratory application.Reference[1] Ferreira, H. B., Melo, T., Paiva, A., & Domingues, M. D. R. (2021). Insights in the Role of Lipids, Oxidative Stress and Inflammation in Rheumatoid Arthritis Unveiled by New Trends in Lipidomic Investigations.Antioxidants (Basel, Switzerland),10(1), 45.https://doi.org/10.3390/antiox10010045Figure 1.Multivariate supervised (OPLS-DA) models comparing the lipoprotein and lipid profiles of RA patients in Remission (blue) and with High Disease activity (red).Acknowledgements:NIL.Disclosure of InterestsNone Declared.