Polycystic ovary syndrome in adolescents: Q-TOF LC/MS analysis of human plasma metabolome

Advances in Mass Spectrometry-Based Blood Metabolomics Profiling for Non-Cancer Diseases: A Comprehensive Review

Blood metabolomics profiling using mass spectrometry has emerged as a powerful approach for investigating non-cancer diseases and understanding their underlying metabolic alterations. Blood, as a readily accessible physiological fluid, contains a diverse repertoire of metabolites derived from various physiological systems. Mass spectrometry offers a universal and precise analytical platform for the comprehensive analysis of blood metabolites, encompassing proteins, lipids, peptides, glycans, and immunoglobulins. In this comprehensive review, we present an overview of the research landscape in mass spectrometry-based blood metabolomics profiling. While the field of metabolomics research is primarily focused on cancer, this review specifically highlights studies related to non-cancer diseases, aiming to bring attention to valuable research that often remains overshadowed. Employing natural language processing methods, we processed 507 articles to provide insights into the application of metabolomic studies for specific diseases and physiological systems. The review encompasses a wide range of non-cancer diseases, with emphasis on cardiovascular disease, reproductive disease, diabetes, inflammation, and immunodeficiency states. By analyzing blood samples, researchers gain valuable insights into the metabolic perturbations associated with these diseases, potentially leading to the identification of novel biomarkers and the development of personalized therapeutic approaches. Furthermore, we provide a comprehensive overview of various mass spectrometry approaches utilized in blood metabolomics research, including GC-MS, LC-MS, and others discussing their advantages and limitations. To enhance the scope, we propose including recent review articles supporting the applicability of GC×GC-MS for metabolomics-based studies. This addition will contribute to a more exhaustive understanding of the available analytical techniques. The Integration of mass spectrometry-based blood profiling into clinical practice holds promise for improving disease diagnosis, treatment monitoring, and patient outcomes. By unraveling the complex metabolic alterations associated with non-cancer diseases, researchers and healthcare professionals can pave the way for precision medicine and personalized therapeutic interventions. Continuous advancements in mass spectrometry technology and data analysis methods will further enhance the potential of blood metabolomics profiling in non-cancer diseases, facilitating its translation from the laboratory to routine clinical application.

Optimizing XCMS parameters for GC-MS metabolomics data processing: a case study

BACKGROUND AND AIMS

Optimizing metabolomics data processing parameters is a challenging and fundamental task to obtain reliable results. Automated tools have been developed to assist this optimization for LC-MS data. GC-MS data require substantial modifications in processing parameters, as the chromatographic profiles are more robust, with more symmetrical and Gaussian peaks. This work compared an automated XCMS parameter optimization using the Isotopologue Parameter Optimization (IPO) software with manual optimization of GC-MS metabolomics data. Additionally, the results were compared to online XCMS platform.

METHODS

GC-MS data from control and test groups of intracellular metabolites from Trypanosoma cruzi trypomastigotes were used. Optimizations were performed on the quality control (QC) samples.

RESULTS

The results in terms of the number of molecular features extracted, repeatability, missing values, and the search for significant metabolites showed the importance of optimizing the parameters for peak detection, alignment, and grouping, especially those related to peak width (fwhm, bw) and noise ratio (snthresh).

CONCLUSION

This is the first time that a systematic optimization using IPO has been performed on GC-MS data. The results demonstrate that there is no universal approach for optimization but automated tools are valuable at this stage of the metabolomics workflow. The online XCMS proves to be an interesting processing tool, helping, above all, in the choice of parameters as a starting point for adjustments and optimizations. Although the tools are easy to use, there is still a need for technical knowledge about the analytical methods and instruments used.

Untargeted metabolomic analysis of metabolites related to body dysmorphic disorder (BDD)

Body dysmorphic disorder (BDD) is a disorder associated with depression and eating disorders. It often arises from minor defects in appearance or an individual imagining that he or she is defective. However, the mechanisms causing BDD remain unclear, and its pathogenesis and adjuvant treatment methods still need to be explored. Here, we employed a liquid chromatography-mass spectrometry (LC-MS)-based metabolomics approach to identify key metabolic differences in BDD versus healthy patients. We obtained plasma samples from two independent cohorts (including eight BDD patients and eight healthy control patients). Raw data were analyzed using Compound Discoverer to determine peak alignment, retention time correction, and extraction of peak areas. Metabolite structure identification was also obtained using Compound Discoverer by of accurate mass matching (< 10 ppm) and secondary spectral matching queries of compound databases. Next, multidimensional statistical analyses were performed using the ropls R package. These analyses included: unsupervised principal component analysis, supervised partial Least-Squares Discriminant Analysis, and orthogonal partial Least-Squares Discriminant Analysis. We then identified the most promising metabolic signatures associated with BDD across all metabolomic datasets. Principal component analysis showed changes in small-molecule metabolites in patients, and we also found significant differences in metabolite abundance between the BDD and normal groups. Our findings suggest that the occurrence of BDD may be related to metabolites participating in the following KEGG pathways: ABC transporters, purine metabolism, glycine, serine and threonine metabolism, pyrimidine, pyrimidine metabolism, biosynthesis of 12-, 14-, and 16-membered macrolides, microbial metabolism in diverse environments, biosynthesis of secondary metabolites, and caffeine and insect hormone biosynthesis.