Cross-Platform Comparison of Untargeted and Targeted Lipidomics Approaches on Aging Mouse Plasma.

Lipidomics - the global assessment of lipids - can be performed using a variety of mass spectrometry (MS)-based approaches. However, choosing the optimal approach in terms of lipid coverage, robustness and throughput can be a challenging task. Here, we compare a novel targeted quantitative lipidomics platform known as the Lipidyzer to a conventional untargeted liquid chromatography (LC)-MS approach. We find that both platforms are efficient in profiling more than 300 lipids across 11 lipid classes in mouse plasma with precision and accuracy below 20% for most lipids. While the untargeted and targeted platforms detect similar numbers of lipids, the former identifies a broader range of lipid classes and can unambiguously identify all three fatty acids in triacylglycerols (TAG). Quantitative measurements from both approaches exhibit a median correlation coefficient (r) of 0.99 using a dilution series of deuterated internal standards and 0.71 using endogenous plasma lipids in the context of aging. Application of both platforms to plasma from aging mouse reveals similar changes in total lipid levels across all major lipid classes and in specific lipid species. Interestingly, TAG is the lipid class that exhibits the most changes with age, suggesting that TAG metabolism is particularly sensitive to the aging process in mice. Collectively, our data show that the Lipidyzer platform provides comprehensive profiling of the most prevalent lipids in plasma in a simple and automated manner

Cross-Platform Comparison of Untargeted and Targeted Lipidomics Approaches on Aging Mouse Plasma.

Lipidomics - the global assessment of lipids - can be performed using a variety of mass spectrometry (MS)-based approaches. However, choosing the optimal approach in terms of lipid coverage, robustness and throughput can be a challenging task. Here, we compare a novel targeted quantitative lipidomics platform known as the Lipidyzer to a conventional untargeted liquid chromatography (LC)-MS approach. We find that both platforms are efficient in profiling more than 300 lipids across 11 lipid classes in mouse plasma with precision and accuracy below 20% for most lipids. While the untargeted and targeted platforms detect similar numbers of lipids, the former identifies a broader range of lipid classes and can unambiguously identify all three fatty acids in triacylglycerols (TAG). Quantitative measurements from both approaches exhibit a median correlation coefficient (r) of 0.99 using a dilution series of deuterated internal standards and 0.71 using endogenous plasma lipids in the context of aging. Application of both platforms to plasma from aging mouse reveals similar changes in total lipid levels across all major lipid classes and in specific lipid species. Interestingly, TAG is the lipid class that exhibits the most changes with age, suggesting that TAG metabolism is particularly sensitive to the aging process in mice. Collectively, our data show that the Lipidyzer platform provides comprehensive profiling of the most prevalent lipids in plasma in a simple and automated manner

Targeted metabolomics identifies perturbations in amino acid metabolism that sub-classify patients with COPD

BACKGROUND: COPD, a leading cause of mortality is currently assessed by spirometry (forced expiratory volume in 1 second, FEV(1)). However FEV(1) does not correlate with patient mortality. ECLIPSE (Evaluation of Chronic obstructive pulmonary disease to Longitudinally Identify Predictive Surrogate Endpoints) aims to identify biomarkers that correlate with clinically relevant COPD subtypes, and to assess how these may predict disease progression. New methods were developed and validated to evaluate small molecules as potential diagnostic tools in patients with COPD, COPD related cachexia and cancer related cachexia. METHODS AND FINDINGS: quantitative LC-MS/MS was developed to measure 34 amino acids and dipeptides for stratification of patient groups. Subsets of the ECLIPSE patients were used to assess biomarkers of lung obstruction; GOLD IV (n = 30) versus control (n = 30); emphysema (n = 38) versus airways disease (n = 21) and cachexia (n = 30) versus normal body mass index (n = 30). Serum from cachexic (n = 7) and non-cachexic (n = 5) pancreatic cancer patients were included as controls. Targeted LC-MS/MS distinguished GOLD IV patients from controls, patients with and without emphysema and patients with and without cachexia. Glutamine, aspartate and arginine were significantly increased (p < 0.05; FDR adjustment alpha < 0.1) in cachexia, emphysema and GOLD IV patients and aminoadipate was decreased. Several amino acid concentrations were significantly altered in patients with COPD but not patients with pancreatic cancer (serine, sarcosine, tryptophan, BCAAs and 3-methylhistdine). Increased gamma-aminobutyrate (GABA, p < 0.01) levels were specific to cachexia in patients with pancreatic cancer. beta-aminoisobutyrate, 1-methylhistidine and asparagine (p < 0.05) were common across the patients with cachexia from both the COPD and pancreatic cancer cohorts. CONCLUSION: these results demonstrate that a metabolomic fingerprint has potential to stratify patients for the treatment of COPD and may provide a means of assessing response to therapy. GOLD IV patients were distinguished from smoker control subjects, patients with emphysema were distinguished from those without emphysema and COPD patients displaying cachexia were distinguished from those not displaying cachexia. General markers of cachexia were discovered reflecting both COPD- and pancreatic cancer-related cachexia (increased glutamine, aspartate, arginine, and asparagine and decreased aminoadipate, beta-aminoisobutyrate and 1-methylhistidine). Metabolomic biomarkers, particularly altered levels of GABA, could be exploited as a way of monitoring treatment efficacy and tumour recurrence for patients with pancreatic cancer experiencing cachexia

Differential Mobility Spectrometry-Driven Shotgun Lipidomics

The analysis of lipids by mass spectrometry (MS) can provide in-depth characterization for many forms of biological samples. However, such workflows can also be hampered by challenges like low chromatographic resolution for lipid separations and the convolution of mass spectra from isomeric and isobaric species. To address these issues, we describe the use of differential mobility spectrometry (DMS) as a rapid and predictable separation technique within a shotgun lipidomics workflow, with a special focus on phospholipids (PLs). These analytes, ionized by electrospray ionization (ESI), are filtered using DMS prior to MS analysis. The observed separation (measured in terms of DMS compensation voltage) is affected by several factors, including the <i>m</i>/<i>z</i> of the lipid ion, the structure of an individual ion, and the presence of chemical modifiers in the DMS cell. Such DMS separations can simplify the analysis of complex extracts in a robust and reproducible manner, independent of utilized MS instrumentation. The predictable separation achieved with DMS can facilitate correct lipid assignments among many isobaric and isomeric species independent of the resolution settings of the MS analysis. This leads to highly comprehensive and quantitative lipidomic outputs through rapid profiling analyses, such as Q1 and MRM scans. The ultimate benefit of the DMS separation in this unique shotgun lipidomics workflow is its ability to separate many isobaric and isomeric lipids that by standard shotgun lipidomics workflows are difficult to assess precisely, for example, ether and diacyl species and phosphatidylcholine (PC) and sphingomyelin (SM) lipids

Autonomous Metabolomics for Rapid Metabolite Identification in Global Profiling

An autonomous metabolomic workflow combining mass spectrometry analysis with tandem mass spectrometry data acquisition was designed to allow for simultaneous data processing and metabolite characterization. Although previously tandem mass spectrometry data have been generated on the fly, the experiments described herein combine this technology with the bioinformatic resources of XCMS and METLIN. As a result of this unique integration, we can analyze large profiling datasets and simultaneously obtain structural identifications. Validation of the workflow on bacterial samples allowed the profiling on the order of a thousand metabolite features with simultaneous tandem mass spectra data acquisition. The tandem mass spectrometry data acquisition enabled automatic search and matching against the METLIN tandem mass spectrometry database, shortening the current workflow from days to hours. Overall, the autonomous approach to untargeted metabolomics provides an efficient means of metabolomic profiling, and will ultimately allow the more rapid integration of comparative analyses, metabolite identification, and data analysis at a systems biology level

Dissemination and analysis of the quality assurance (QA) and quality control (QC) practices of LC–MS based untargeted metabolomics practitioners

© 2020, Springer Science+Business Media, LLC, part of Springer Nature. Introduction: The metabolomics quality assurance and quality control consortium (mQACC) evolved from the recognized need for a community-wide consensus on improving and systematizing quality assurance (QA) and quality control (QC) practices for untargeted metabolomics. Objectives: In this work, we sought to identify and share the common and divergent QA and QC practices amongst mQACC members and collaborators who use liquid chromatography-mass spectrometry (LC–MS) in untargeted metabolomics. Methods: All authors voluntarily participated in this collaborative research project by providing the details of and insights into the QA and QC practices used in their laboratories. This sharing was enabled via a six-page questionnaire composed of over 120 questions and comment fields which was developed as part of this work and has proved the basis for ongoing mQACC outreach. Results: For QA, many laboratories reported documenting maintenance, calibration and tuning (82%); having established data storage and archival processes (71%); depositing data in public repositories (55%); having standard operating procedures (SOPs) in place for all laboratory processes (68%) and training staff on laboratory processes (55%). For QC, universal practices included using system suitability procedures (100%) and using a robust system of identification (Metabolomics Standards Initiative level 1 identification standards) for at least some of the detected compounds. Most laboratories used QC samples ( \u3e 86%); used internal standards (91%); used a designated analytical acquisition template with randomized experimental samples (91%); and manually reviewed peak integration following data acquisition (86%). A minority of laboratories included technical replicates of experimental samples in their workflows (36%). Conclusions: Although the 23 contributors were researchers with diverse and international backgrounds from academia, industry and government, they are not necessarily representative of the worldwide pool of practitioners due to the recruitment method for participants and its voluntary nature. However, both questionnaire and the findings presented here have already informed and led other data gathering efforts by mQACC at conferences and other outreach activities and will continue to evolve in order to guide discussions for recommendations of best practices within the community and to establish internationally agreed upon reporting standards. We very much welcome further feedback from readers of this article

Reference materials for MS-based untargeted metabolomics and lipidomics: a review by the metabolomics quality assurance and quality control consortium (mQACC).

IntroductionThe metabolomics quality assurance and quality control consortium (mQACC) is enabling the identification, development, prioritization, and promotion of suitable reference materials (RMs) to be used in quality assurance (QA) and quality control (QC) for untargeted metabolomics research.ObjectivesThis review aims to highlight current RMs, and methodologies used within untargeted metabolomics and lipidomics communities to ensure standardization of results obtained from data analysis, interpretation and cross-study, and cross-laboratory comparisons. The essence of the aims is also applicable to other 'omics areas that generate high dimensional data.ResultsThe potential for game-changing biochemical discoveries through mass spectrometry-based (MS) untargeted metabolomics and lipidomics are predicated on the evolution of more confident qualitative (and eventually quantitative) results from research laboratories. RMs are thus critical QC tools to be able to assure standardization, comparability, repeatability and reproducibility for untargeted data analysis, interpretation, to compare data within and across studies and across multiple laboratories. Standard operating procedures (SOPs) that promote, describe and exemplify the use of RMs will also improve QC for the metabolomics and lipidomics communities.ConclusionsThe application of RMs described in this review may significantly improve data quality to support metabolomics and lipidomics research. The continued development and deployment of new RMs, together with interlaboratory studies and educational outreach and training, will further promote sound QA practices in the community

Cross-Laboratory Standardization of Preclinical Lipidomics Using Differential Mobility Spectrometry and Multiple Reaction Monitoring

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