Background: One of the current problems in the field of metabolomics is the difficulty in integrating data collected using different equipment at different facilities, because many metabolomic methods have been developed independently and are unique to each laboratory. Methods: In this study, we examined whether different analytical methods among 12 different laboratories provided comparable relative quantification data for certain metabolites. Identical samples extracted from two cell lines (HT-29 and AsPc-1) were distributed to each facility, and hydrophilic and hydrophobic metabolite analyses were performed using the daily routine protocols of each laboratory. Results: The results indicate that there was no difference in the relative quantitative data (HT-29/AsPc-1) for about half of the measured metabolites among the laboratories and assay methods. Data review also revealed that errors in relative quantification were derived from issues such as erroneous peak identification, insufficient peak separation, a difference in detection sensitivity, derivatization reactions, and extraction solvent interference. Conclusion: The results indicated that relative quantification data obtained at different facilities and at different times would be integrated and compared by using a reference materials shared for data normalization.

The isobaric tagging method widely used in proteomic and lipidomic fields, with the multiple reaction monitoring (MRM) approach using a triple quadrupole mass spectrometer, was applied to identify biomarker candidates from plasma samples for Alzheimer’s disease (AD). We focused on the following phospholipids that have amino groups as the functional group: phosphatidylethanolamine (PE), Lyso-PE, phosphatidylserine, and Lyso-phosphatidylserine. We also investigated fatty acids that have a carboxy group. A sixplex tandem mass tag (TMT) was used for the isobaric tagging method in this study. The TMT reaction had high reproducibility in human plasma. A total of 196 human plasma samples from three AD cohorts were used for the study, and compared to pooled plasma quality control (QC) samples. The described method required only 40 MRM measurements, including the pooled QC samples, for a full comparison of the data. We found that the content of free fatty acids increased in AD samples in all the three cohorts, alkenyl PEs (ePEs) decreased over a one-year interval in AD patients, and ePEs weakly correlated with amyloid peptide (a-beta) 1–42 in cerebrospinal fluid. In conclusion, total free fatty acids in plasma are a risk factor for AD, and ePEs monitor candidates for AD. Therefore, TMT-lipidomics is a powerful approach for the determination of plasma biomarkers because of the high sample throughput.

In mass spectrometry-based metabolomics, the differences in the analytical results from different laboratories/machines are an issue to be considered because various types of machines are used in each laboratory. Moreover, the analytical methods are unique to each laboratory. It is important to understand the reality of inter-laboratory differences in metabolomics. Therefore, we have evaluated whether the differences in analytical methods, with the exception sample pretreatment and including metabolite extraction, are involved in the inter-laboratory differences or not. In this study, nine facilities are evaluated for inter-laboratory comparisons of metabolomic analysis. Identical dried samples prepared from human and mouse plasma are distributed to each laboratory, and the metabolites are measured without the pretreatment that is unique to each laboratory. In these measurements, hydrophilic and hydrophobic metabolites are analyzed using 11 and 7 analytical methods, respectively. The metabolomic data acquired at each laboratory are integrated, and the differences in the metabolomic data from the laboratories are evaluated. No substantial difference in the relative quantitative data (human/mouse) for a little less than 50% of the detected metabolites is observed, and the hydrophilic metabolites have fewer differences between the laboratories compared with hydrophobic metabolites. From evaluating selected quantitatively guaranteed metabolites, the proportion of metabolites without the inter-laboratory differences is observed to be slightly high. It is difficult to resolve the inter-laboratory differences in metabolomics because all laboratories cannot prepare the same analytical environments. However, the results from this study indicate that the inter-laboratory differences in metabolomic data are due to measurement and data analysis rather than sample preparation, which will facilitate the understanding of the problems in metabolomics studies involving multiple laboratories.

In targeted metabolomic analysis using liquid chromatography–multiple reaction monitoring–mass spectrometry (LC-MRM-MS), hundreds of MRMs are performed in a single run, yielding a large dataset containing thousands of chromatographic peaks. Automation tools for processing large MRM datasets have been reported, but a visual review of chromatograms is still critical, as real samples with biological matrices often cause complex chromatographic patterns owing to non-specific, insufficiently separated, isomeric, and isotopic components. Herein, we report the development of new software, TRACES, a lightweight chromatogram browser for MRM-based targeted LC-MS analysis. TRACES provides rapid access to all MRM chromatograms in a dataset, allowing users to start ad hoc data browsing without preparations such as loading compound libraries. As a special function of the software, we implemented a chromatogram-level deisotoping function that facilitates the identification of regions potentially affected by isotopic signals. Using MRM libraries containing precursor and product formulae, the algorithm reveals all possible isotopic interferences in the dataset and generates deisotoped chromatograms. To validate the deisotoping function in real applications, we analyzed mouse tissue phospholipids in which isotopic interference by molecules with different fatty-acyl unsaturation levels is known. TRACES successfully removed isotopic signals within the MRM chromatograms, helping users avoid inappropriate regions for integration.

Proximity Extension Assay (PEA) and mass spectrometry (MS) methodologies were utilized for the proteomic and lipidomic characterization of plasma specimens from patients who developed Alzheimer's disease. Proteomics was performed by both PEA and Liquid Chromatography (LC)/MS in this study, but all the more because LC/MS generally tends to be biased towards proteins with high expression and high variability, generating hypotheses proved challenging. Consequently, attempt was made to interpret the results from the PEA data. There were 150 significantly variable proteins and 68 lipids among 1000 proteins and 400 lipids. Pathway analysis was performed for both total and variable proteins measured to reduce bias, and it appeared that vascular fragility was related to AD. Furthermore, a multitude of lipid-associated proteins exhibited statistical changes. In certain instances, the function of individual proteins affected the factors associated with them, whereas others demonstrated trends contrary to anticipated outcomes. These trends seem indicative of diverse feedback mechanisms that provide homeostatic equilibrium. The degree of unsaturation of fatty acids, correlated with cardiovascular risk, warrants specific attention. Certain bile acids exhibited the potential to cause vascular endothelial damage. Contemplating these discoveries in tandem with previously documented phenomena, subtle shifts in homeostatic functions seem to be linked to the fragility of vascular endothelial cells. This is evidenced by the slow and chronic evolution of Alzheimer's disease from preclinical stages to its manifestation.