Investigating the Potential of Drift Tube Ion Mobility for the Analysis of Oxidized Lipids

The Octadecanoids: Synthesis and Bioactivity of 18-Carbon Oxygenated Fatty Acids in Mammals, Bacteria, and Fungi

The octadecanoids are a broad class of lipids consisting of the oxygenated products of 18-carbon fatty acids. Originally referring to production of the phytohormone jasmonic acid, the octadecanoid pathway has been expanded to include products of all 18-carbon fatty acids. Octadecanoids are formed biosynthetically in mammals via cyclooxygenase (COX), lipoxygenase (LOX), and cytochrome P450 (CYP) activity, as well as nonenzymatically by photo- and autoxidation mechanisms. While octadecanoids are well-known mediators in plants, their role in the regulation of mammalian biological processes has been generally neglected. However, there have been significant advancements in recognizing the importance of these compounds in mammals and their involvement in the mediation of inflammation, nociception, and cell proliferation, as well as in immuno- and tissue modulation, coagulation processes, hormone regulation, and skin barrier formation. More recently, the gut microbiome has been shown to be a significant source of octadecanoid biosynthesis, providing additional biosynthetic routes including hydratase activity (e.g., CLA-HY, FA-HY1, FA-HY2). In this review, we summarize the current field of octadecanoids, propose standardized nomenclature, provide details of octadecanoid preparation and measurement, summarize the phase-I metabolic pathway of octadecanoid formation in mammals, bacteria, and fungi, and describe their biological activity in relation to mammalian pathophysiology as well as their potential use as biomarkers of health and disease.

Eliminating the Deadwood: A Machine Learning Model for CCS Knowledge-Based Conformational Focusing for Lipids

Accurate elucidation of gas-phase chemical structures using collision cross section (CCS) values obtained from ion-mobility mass spectrometry benefits from a synergism between experimental and in silico results. We have shown in recent work that for a molecule of modest size with a proscribed conformational space we can successfully capture a conformation(s) that can match experimental CCS values. However, for flexible systems such as fatty acids that have many rotatable bonds and multiple intramolecular London dispersion interactions, it becomes necessary to sample a much greater conformational space. Sampling more conformers, however, accrues significant computational cost downstream in optimization steps involving quantum mechanics. To reduce this computational expense for lipids, we have developed a novel machine learning (ML) model to facilitate conformer filtering according to the estimated gas-phase CCS values. Herein we report that the implementation of our CCS knowledge-based approach for conformational sampling resulted in improved structure prediction agreement with experiment by achieving favorable average CCS prediction errors of ∼2% for lipid systems in both the validation set and the test set. Moreover, most of the gas-phase candidate conformations obtained by using CCS focusing achieved lower energy-minimum geometries than the candidate conformations without focusing. Altogether, the implementation of this ML model into our modeling workflow has proven to be beneficial for both the quality of the results and the turnaround time. Finally, while our approach is limited to lipids, it can be readily extended to other molecules of interest.

Ferroptosis in health and disease

Ferroptosis is a pervasive non-apoptotic form of cell death highly relevant in various degenerative diseases and malignancies. The hallmark of ferroptosis is uncontrolled and overwhelming peroxidation of polyunsaturated fatty acids contained in membrane phospholipids, which eventually leads to rupture of the plasma membrane. Ferroptosis is unique in that it is essentially a spontaneous, uncatalyzed chemical process based on perturbed iron and redox homeostasis contributing to the cell death process, but that it is nonetheless modulated by many metabolic nodes that impinge on the cells' susceptibility to ferroptosis. Among the various nodes affecting ferroptosis sensitivity, several have emerged as promising candidates for pharmacological intervention, rendering ferroptosis-related proteins attractive targets for the treatment of numerous currently incurable diseases. Herein, the current members of a Germany-wide research consortium focusing on ferroptosis research, as well as key external experts in ferroptosis who have made seminal contributions to this rapidly growing and exciting field of research, have gathered to provide a comprehensive, state-of-the-art review on ferroptosis. Specific topics include: basic mechanisms, in vivo relevance, specialized methodologies, chemical and pharmacological tools, and the potential contribution of ferroptosis to disease etiopathology and progression. We hope that this article will not only provide established scientists and newcomers to the field with an overview of the multiple facets of ferroptosis, but also encourage additional efforts to characterize further molecular pathways modulating ferroptosis, with the ultimate goal to develop novel pharmacotherapies to tackle the various diseases associated with - or caused by - ferroptosis.

Spatially and temporally probing distinctive glycerophospholipid alterations in Alzheimer's disease mouse brain via high-resolution ion mobility-enabled sn-position resolved lipidomics

Dysregulated glycerophospholipid (GP) metabolism in the brain is associated with the progression of neurodegenerative diseases including Alzheimer's disease (AD). Routine liquid chromatography-mass spectrometry (LC-MS)-based large-scale lipidomic methods often fail to elucidate subtle yet important structural features such as sn-position, hindering the precise interrogation of GP molecules. Leveraging high-resolution demultiplexing (HRdm) ion mobility spectrometry (IMS), we develop a four-dimensional (4D) lipidomic strategy to resolve GP sn-position isomers. We further construct a comprehensive experimental 4D GP database of 498 GPs identified from the mouse brain and an in-depth extended 4D library of 2500 GPs predicted by machine learning, enabling automated profiling of GPs with detailed acyl chain sn-position assignment. Analyzing three mouse brain regions (hippocampus, cerebellum, and cortex), we successfully identify a total of 592 GPs including 130 pairs of sn-position isomers. Further temporal GPs analysis in the three functional brain regions illustrates their metabolic alterations in AD progression.

MobiLipid: A Tool for Enhancing CCS Quality Control of Ion Mobility-Mass Spectrometry Lipidomics by Internal Standardization

Ion mobility-mass spectrometry (IM-MS) offers benefits for lipidomics by obtaining IM-derived collision cross sections (CCS), a conditional property of an ion that can enhance lipid identification. While drift tube (DT) IM-MS retains a direct link to the primary experimental method to derive CCS values, other IM technologies rely solely on external CCS calibration, posing challenges due to dissimilar chemical properties between lipids and calibrants. To address this, we introduce MobiLipid, a novel tool facilitating the CCS quality control of IM-MS lipidomics workflows by internal standardization. MobiLipid utilizes a newly established DTCCSN2 library for uniformly (U)13C-labeled lipids, derived from a U13C-labeled yeast extract, containing 377 DTCCSN2 values. This automated open-source R Markdown tool enables internal monitoring and straightforward compensation for CCSN2 biases. It supports lipid class- and adduct-specific CCS corrections, requiring only three U13C-labeled lipids per lipid class-adduct combination across 10 lipid classes without requiring additional external measurements. The applicability of MobiLipid is demonstrated for trapped IM (TIM)-MS measurements of an unlabeled yeast extract spiked with U13C-labeled lipids. Monitoring the CCSN2 biases of TIMCCSN2 values compared to DTCCSN2 library entries utilizing MobiLipid resulted in mean absolute biases of 0.78% and 0.33% in positive and negative ionization mode, respectively. By applying the CCS correction integrated into the tool for the exemplary data set, the mean absolute CCSN2 biases of 10 lipid classes could be reduced to approximately 0%.

Ironing out the role of ferroptosis in immunity

Ferroptosis is a type of regulated cell death that drives the pathophysiology of many diseases. Oxidative stress is detectable in many types of regulated cell death, but only ferroptosis involves lipid peroxidation and iron dependency. Ferroptosis originates and propagates from several organelles, including the mitochondria, endoplasmic reticulum, Golgi, and lysosomes. Recent data have revealed that immune cells can both induce and undergo ferroptosis. A mechanistic understanding of how ferroptosis regulates immunity is critical to understanding how ferroptosis controls immune responses and how this is dysregulated in disease. Translationally, more work is needed to produce ferroptosis-modulating immunotherapeutics. This review focuses on the role of ferroptosis in immune-related diseases, including infection, autoimmune diseases, and cancer. We discuss how ferroptosis is regulated in immunity, how this regulation contributes to disease pathogenesis, and how targeting ferroptosis may lead to novel therapies.

Aza-Prilezhaev Aziridination-Enabled Multidimensional Analysis of Isomeric Lipids via High-Resolution U-Shaped Mobility Analyzer-Mass Spectrometry

Unsaturated lipids constitute a significant portion of the lipidome, serving as players of multifaceted functions involving cellular signaling, membrane structure, and bioenergetics. While derivatization-assisted liquid chromatography tandem mass spectrometry (LC-MS/MS) remains the gold standard technique in lipidome, it mainly faces challenges in efficiently labeling the carbon-carbon double bond (C═C) and differentiating isomeric lipids in full dimension. This presents a need for new orthogonal methodologies. Herein, a metal- and additive-free aza-Prilezhaev aziridination (APA)-enabled ion mobility mass spectrometric method is developed for probing multiple levels of unsaturated lipid isomerization with high sensitivity. Both unsaturated polar and nonpolar lipids can be efficiently labeled in the form of N-H aziridine without significant side reactions. The signal intensity can be increased by up to 3 orders of magnitude, achieving the nM detection limit. Abundant site-specific fragmentation ions indicate C═C location and sn-position in MS/MS spectra. Better yet, a stable monoaziridination product is dominant, simplifying the spectrum for lipids with multiple double bonds. Coupled with a U-shaped mobility analyzer, identification of geometric isomers and separation of different lipid classes can be achieved. Additionally, a unique pseudo MS3 mode with UMA-QTOF MS boosts the sensitivity for generating diagnostic fragments. Overall, the current method provides a comprehensive solution for deep-profiling lipidomics, which is valuable for lipid marker discovery in disease monitoring and diagnosis.

From MS/MS library implementation to molecular networks: Exploring oxylipin diversity with NEO-MSMS

Oxylipins, small polar molecules derived from the peroxidation of polyunsaturated fatty acids (PUFAs), serve as biomarkers for many diseases and play crucial roles in human physiology and inflammation. Despite their significance, many non-enzymatic oxygenated metabolites of PUFAs (NEO-PUFAs) remain poorly reported, resulting in a lack of public datasets of experimental data and limiting their dereplication in further studies. To overcome this limitation, we constructed a high-resolution tandem mass spectrometry (MS/MS) dataset comprising pure NEO-PUFAs (both commercial and self-synthesized) and in vitro free radical-induced oxidation of diverse PUFAs. By employing molecular networking techniques with this dataset and the existent ones in public repositories, we successfully mapped a wide range of NEO-PUFAs, expanding the strategies for annotating oxylipins, and NEO-PUFAs and offering a novel workflow for profiling these molecules in biological samples.