Mediator lipidomics by liquid chromatography-tandem mass spectrometry

Market Needs and Methodologies Associated with Patient Lipidomic Diagnoses and Analyses

This chapter conducts an in-depth exploration of the impact of musculoskeletal (MSK) disorders and injuries, with a specific emphasis on their consequences within the older population demographic. It underscores the escalating demand for innovative interventions in MSK tissue engineering. The chapter also highlights the fundamental role played by lipid signaling mediators (LSMs) in tissue regeneration, with relevance to bone and muscle recovery. Remarkably, Prostaglandin E2 (PGE2) emerges as a central orchestrator in these regenerative processes. Furthermore, the chapter investigates the complex interplay between bone and muscle tissues, explaining the important influence exerted by LSMs on their growth and differentiation. The targeted modulation of LSM pathways holds substantial promise as a beneficial way for addressing muscle disorders. In addition to these conceptual understandings, the chapter provides a comprehensive overview of methodologies employed in the identification of LSMs, with a specific focus on the Liquid Chromatography-Mass Spectrometry (LC-MS). Furthermore, it introduces a detailed LC MS/MS-based protocol tailored for the detection of PGE2, serving as an invaluable resource for researchers immersed in this dynamic field of study.

Simultaneous profiling and quantification of 25 eicosanoids in human serum by ultrahigh-performance liquid chromatography coupled to tandem mass spectrometry

The eicosanoid metabolic pathway is responsible for mediating the production of various inflammatory factors that are closely related to the development and resolution of inflammation. In biological matrices, the major quantifying obstacles were shown to be the oxidation and low quantities of eicosanoids and their metabolites. This study aimed to develop a reliable, sensitive ultrahigh-performance liquid chromatography coupled to a tandem mass spectrometry (UPLC–MS/MS) method to quantify eicosanoids in human serum. Solid-phase extraction (SPE) was used for sample preparation. The approach employed continuous ionization polarity switching. The target eicosanoids showed good linearity over the investigated concentration range (r2 > 0.99). The recovery rates were over 64.5%, and the matrix effects ranged from 73.0 to 128.0%. The limits of quantification were 0.048 ~ 0.44 ng/mL. For the broad concentration range, the CV % for accuracy and precision were less than ± 20%. We successfully applied this method to rapidly analyse 74 serum samples from severe influenza pneumonia, severe bacterial pneumonia and healthy individuals. Eicosanoid-related metabolite concentrations were quantified within a range similar to those of previously published articles. Compared to healthy individuals, our application found that 20-HETE, 14,15-EET and 11,12-EET were upregulated in severe influenza pneumonia patients, while LTB4 was downregulated. 8-HETE and 5-HETE were upregulated in severe bacterial pneumonia patients, while LTE4 was downregulated. This approach provides a means for monitoring the low quantities of eicosanoids in biological matrices, and our finding that different characteristic metabolite profiles may help discriminate the induction of severe pneumonia patients.

A map of mass spectrometry-based in silico fragmentation prediction and compound identification in metabolomics

Metabolomics, the comprehensive study of the metabolome, and lipidomics-the large-scale study of pathways and networks of cellular lipids-are major driving forces in enabling personalized medicine. Complicated and error-prone data analysis still remains a bottleneck, however, especially for identifying novel metabolites. Comparing experimental mass spectra to curated databases containing reference spectra has been the gold standard for identification of compounds, but constructing such databases is a costly and time-demanding task. Many software applications try to circumvent this process by utilizing cutting-edge advances in computational methods-including quantum chemistry and machine learning-and simulate mass spectra by performing theoretical, so called in silico fragmentations of compounds. Other solutions concentrate directly on experimental spectra and try to identify structural properties by investigating reoccurring patterns and the relationships between them. The considerable progress made in the field allows recent approaches to provide valuable clues to expedite annotation of experimental mass spectra. This review sheds light on individual strengths and weaknesses of these tools, and attempts to evaluate them-especially in view of lipidomics, when considering complex mixtures found in biological samples as well as mass spectrometer inter-instrument variability.

Overview of Lipid Mass Spectrometry and Lipidomics

Mass spectrometry has played a critical role in the identification and quantitation of lipids present in biological extracts. Various strategies have emerged in order to carry out lipidomic studies. These include both shotgun approaches as well as those engaging liquid chromatographic separation of lipid species prior to mass spectrometric analysis. Nonetheless challenges remain at every level of the lipidomic experiment, including extraction of lipids, identification of specific species, and quantitation of the vast array of lipids present in the sample extract. New strategies have emerged to address some of these issues; however, precise quantitation remains a significant challenge. The use of the ratio of the abundance of the molecular ion species to that of an internal standard enables quite accurate assessment of fold changes within complex lipid species without the need for exact quantitation. Challenges continue to remain in terms of availability of reference standard material as well as relevant internal standards.