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Ion mobility spectrometry (IMS) is an analytical technique where ions are separated in the gas phase based on their mobility through a buffer gas in the presence of an electric field. An ion passing through an IMS device has a characteristic collisional cross section (CCS) value that depends on the buffer gas used. IMS can be coupled with mass spectrometry (MS), which characterizes an ion based on a mass-to-charge ratio (m/z), to increase analytical specificity and provide further physicochemical information. In particular, IMS-MS is of ever-increasing interest for the analysis of lipids, which can be problematic to accurately identify and quantify in bodily fluids by liquid chromatography (LC) with MS alone due to the presence of isomers, isobars, and structurally similar analogs. IMS provides an additional layer of separation when combined with front-end LC approaches, thereby, enhancing peak capacity and analytical specificity. CCS (and also ion mobility drift time) can be plotted against m/z ion intensity and/or LC retention time in order to generate in-depth molecular profiles of a sample. Utilization of IMS-MS for routine clinical laboratory testing remains relatively unexplored, but areas do exist for potential implementation. A brief update is provided here on lipid analysis using IMS-MS with a perspective on some applications in the clinical laboratory.

Large epidemiological studies often require sample transportation and storage, presenting unique considerations when applying advanced lipidomics techniques. The goal of this study was to acquire lipidomics data on plasma and serum samples stored at potential preanalytical conditions (e.g., thawing, extracting, evaporating), systematically monitoring lipid species for a period of one month. Split aliquots of 10 plasma samples and 10 serum samples from healthy individuals were kept in three temperature-related environments: refrigerator, laboratory benchtop, or heated incubator. Samples were analyzed at six different time points over 28 days using a Bligh & Dyer lipid extraction protocol followed by direct infusion into a lipidomics platform using differential mobility with tandem mass spectrometry. The observed concentration changes over time were evaluated relative to method and inter-individual biological variability. In addition, to evaluate the effect of lipase enzyme levels on concentration changes during storage, we compared corresponding fasting and post-prandial plasma samples collected from 5 individuals. Based on our data, a series of low abundance free fatty acid (FFA), diacylglycerol (DAG), and cholesteryl ester (CE) species were identified as potential analytical markers for degradation. These FFA and DAG species are typically produced by endogenous lipases from numerous triacylglycerols (TAGs), and certain high abundance phosphatidylcholines (PCs). The low concentration CEs, which appeared to increase several fold, were likely mass-isobars from oxidation of other high concentration CEs. Although the concentration changes of the high abundant TAG, PC, and CE precursors remained within method variability, the concentration trends of FFA, DAG, and oxidized CE products should be systematically monitored over time to inform analysts about possible pre-analytical biases due to degradation in the study sample sets.

Highlights • Fabry is an X-linked lysosomal storage disease with deficiency in α-galactosidase. • This deficiency results in the accumulation of glycosphinogolipids. • Diagnosis is often made by analysis of globotriaosylceramide in fluids and tissues. • Fabry is often misdiagnosed in female patients due to residual enzyme activity. • Lipidomics by LC-HRMS enables identification of new biomarkers in Fabry., Introduction Lipidomics analysis or lipid profiling is a system-based analysis of all lipids in a sample to provide a comprehensive understanding of lipids within a biological system. In the last few years, lipidomics has made it possible to better understand the metabolic processes associated with several rare disorders and proved to be a powerful tool for their clinical investigation. Fabry disease is a rare X-linked lysosomal storage disorder (LSD) caused by a deficiency in α-galactosidase A (α-GAL A). This deficiency results in the progressive accumulation of glycosphingolipids, mostly globotriaosylceramide (Gb3), globotriaosylsphingosine (lyso-Gb3), as well as galabiosylceramide (Ga2) and their isoforms/analogs in the vascular endothelium, nerves, cardiomyocytes, renal glomerular podocytes, and biological fluids. Objectives The primary objective of this study was to evaluate lipidomic signatures in renal biopsies to help understand variations in Fabry disease markers that could be used in future diagnostic tests. Methods Lipidomic analysis was performed by ultra-high pressure liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) on kidney biopsies that were left over after clinical pathology analysis to diagnose Fabry disease. Results We employed UHPLC-HRMS lipidomics analysis on the renal biopsy of a patient suspicious for Fabry disease. Our result confirmed α-GAL A enzyme activity declined in this patient since a Ga2-related lipid biomarker was substantially higher in the patient's renal tissue biopsy compared with two controls. This suggests this patient has a type of LSD that could be non-classical Fabry disease. Conclusion This study shows that lipidomics analysis is a valuable tool for rare disorder diagnosis, which can be conducted on leftover tissue samples without disrupting normal patient care.

Introduction Arachidonoyl ethanolamide (AEA) and 2-arachidonoyl glycerol (2-AG) are central lipid mediators of the endocannabinoid system. They are highly relevant due to their involvement in a wide variety of inflammatory, metabolic or malign diseases. Further elucidation of their modes of action and use as biomarkers in an easily accessible matrix, like blood, is restricted by their susceptibility to deviations during blood sampling and physiological co-dependences, which results in high variability of reported concentrations in low ng/mL ranges. Objectives The objective of this review is the identification of critical parameters during the pre-analytical phase and proposal of minimum requirements for reliable determination of endocannabinoids (ECs) in blood samples. Methods Reported physiological processes influencing the EC concentrations were put into context with published pre-analytical research and stability data from bioanalytical method validation. Results The cause for variability in EC concentrations is versatile. In part, they are caused by inter-individual factors like sex, metabolic status and/or diurnal changes. Nevertheless, enzymatic activity in freshly drawn blood samples is the main reason for changing concentrations of AEA and 2-AG, besides additional non-enzymatic isomerization of the latter. Conclusion Blood samples for EC analyses require immediate processing at low temperatures (>0 °C) to maintain sample integrity. Standardization of the respective blood tube or anti-coagulant, sampling time point, applied centrifugal force and complete processing time can further decrease variability caused by sample handling. Nevertheless, extensive characterization of study participants is needed to reduce distortion of clinical data caused by co-variables and facilitate research on the endocannabinoid system.

Plasmalogens (Pls) levels are reported to be altered in several neurological and metabolic diseases. Identification of sn-1 fatty alcohols and sn-2 fatty acids of different Pls species is necessary to determine the roles and mechanisms of action of Pls in different diseases. Previously, full-scan tandem mass spectrometry (MS/MS) was used for this purpose but is not effective for low-abundance Pls species. Recently, multiplexed selected reaction monitoring MS (SRM/MS) was found to be more selective and sensitive than conventional full-scan MS/MS for the identification of low-abundance compounds. In the present study, we developed a liquid chromatography (LC)-targeted multiplexed SRM/MS system for the identification and quantification of different Pls choline (Pls-PC) and Pls ethanolamine (Pls-PE) species. We determined five precursor-product ion transitions to identify sn-1 and sn-2 fragments of each Pls species. Consequently, sn-1 and sn-2 fatty acyl chains of 22 Pls-PC and 55 Pls-PE species were identified in mouse brain samples. Among them, some species had C20:0 and C20:1 fatty alcohols at the sn-1 position. For quantification of Pls species in mouse brain samples, a single SRM transition was employed. Thus, our results suggest that the LC-targeted multiplexed SRM/MS system is very sensitive for the identification and quantification of low-abundance lipids such as Pls, and is thus expected to make a significant contribution to basic and clinical research in this field in the future.

Highlights • Lipids change overtime in normal fascial healing in the early post-surgery period. • Specific lipid species are correlated with the changes of inflammation cells and fibroblasts. • Lipid species in the present study are considered as predictive markers for the formation of incisional hernia., Background Failure of fascial healing in the abdominal wall can result in incisional hernia, which is one of the most common complications after laparotomy. Understanding the molecular healing process of abdominal fascia may provide lipid markers of incisional hernia or therapeutic targets that allow prevention or treatment of incisional hernias. Purpose This study aims to investigate temporal and in situ changes of lipids during the normal healing process of abdominal fascia in the first postoperative week. Methods Open hemicolectomy was performed in a total of 35 Wistar rats. The midline fascia was closed identically for all rats using a single continuous suturing technique. These animals were sacrificed with equal numbers (n = 5) at each of 7-time points (6, 12, 24, 48, 72, 120, and 168 h. The local and temporal changes of lipids were examined with mass spectrometry imaging and correlated to histologically scored changes during healing using hematoxylin and eosin staining. Results Two phosphatidylcholine lipid species (PC O-38:5 and PC 38:4) and one phosphatidylethanolamine lipid (PE O-16:1_20:4) were found to significantly correlate with temporal changes of inflammation. A phosphatidylcholine (PC 32:0) and a monosialodihexosylganglioside (GM3 34:1;2) were found to correlate with fibroblast cell growth. Conclusion Glycerophospholipids and gangliosides are strongly involved in the normal healing process of abdominal fascia and their locally fluctuating concentrations are considered as potential lipid markers and therapeutic targets of fascial healing.

Lipidomics is an important component of most multi-Omics systems biology studies and is largely driven by mass spectrometry (MS). Because lipids are tight regulators of multiple cellular functions, including energy homeostasis, membrane structures and cell signaling, lipidomics can provide a deeper understanding of variations underlying disease states and can become an even more powerful platform when combined with other omics, including genomics or proteomics. However, data analysis, especially in lipid annotation, poses challenges due to the heterogeneity of functional head groups and fatty acyl chains of varying hydrocarbon lengths and degrees of unsaturation. As there are various MS/MS fragmentation sites in lipids that are class-dependent, obtaining MS/MS data that includes as many fragment ions as possible is critical for structural characterization of lipids in lipidomics workflow. Here, we report an improved lipidomics methodology that resulted in increased coverage of lipidome using: 1) An automated data-driven MS/MS acquisition scheme in which inclusion and exclusion lists were automatically generated from the full scan MS of sample injections, followed by creation of updated lists over iterative analyses; and, 2) Incorporation of dual dissociation techniques of higher-energy collision dissociation and collision-induced dissociation for more accurate characterization of phosphatidylcholine species. Inclusion lists were created automatically based on full scan MS signals from samples and through iterative analyses, ions in the inclusion list that were fragmented were automatically moved to the exclusion list in subsequent runs. We confirmed that analytes with low MS response that did not undergo MS/MS events in conventional data-dependent analysis were successfully fragmented using this approach. Overall, this automated data-driven data acquisition approach resulted in a higher coverage of lipidome and the use of dual dissociation techniques provided additional information that was critical in characterizing the side chains of phosphatidylcholine species.

Background: Metabolites, especially lipids, have been shown to be promising therapeutic targets. In conjugation with genes and proteins they can be used to identify phenotypes of disease and support the development of targeted treatments. The majority of clinically collected tissue samples are stored in formalin-fixed and paraffin embedded (FFPE) blocks due to their tissue conservation ability and indefinite storage capacity. For metabolic analysis, however, fresh frozen (FF) samples are currently preferred over FFPE samples due to concerns of metabolic information being lost when preparing the samples. With little or no sample preparation, desorption electrospray ionisation mass spectrometry imaging (DESI-MSI) allows for the study of spatial as well as spectral information. Methods: DESI-MSI analysis was performed on FFPE breast cancer tissue microarray samples from 213 patients collected between the years 1935-2013. Logistic regression (LR) models were built to classify samples based on age and FF samples were used for feature validation. Results: LR models developed on the FFPE samples achieved an average classification accuracy of 96% when predicting their age with a 10-year grouping. Closer examination of the metabolic change over time revealed that the mean signal intensities for the lower mass range (100 - 500 m/z) linearly decrease over time, while the mean intensities for the higher mass range (500 - 900 m/z), remained relatively constant. Conclusions: In our samples, which span over 70 years, sample age has a weak yet quantifiable impact on metabolite content in FFPE samples, while the higher mass range is seemingly unaffected. FFPE samples thus provide an alternative avenue for metabolic analysis of lipids.

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Background The worldwide prevalence of non-alcoholic fatty liver disease (NAFLD) has stimulated work to identify biomarkers and develop effective treatments. Metabolomics is an emerging tool that has been widely applied to discover biomarkers and simultaneously uncover pathological mechanisms. Here, we aim to optimize metabolomic acquisition with the goal of obtaining a systemic metabolic profile to unravel the potential link between dysregulated metabolism and NAFLD. Methods We analyzed serum samples collected from healthy subjects (n = 8) and NAFLD patients (n = 8) via an integrative analytical workflow using two orthogonal separation modes with T3 and amide columns and two ionization polarity modes on a UPLC-ESI-Q/TOF. Data dependent acquisition was employed for data acquisition. Differentially expressed metabolites and lipids were identified by comparing the collected metabolic and lipidomic profiles between the healthy subjects and NAFLD patients. Results The integrative LC-MS/MS analytical workflow employed here features an improved coverage of metabolites and lipids, which leads to the identification of 20 potential biomarkers of NAFLD, including lipids, acylcarnitines, and organic acids. Conclusions This pilot study has identified potential biomarkers for NAFLD and revealed corresponding dysregulated metabolic pathways related to NAFLD's occurrence and progression, establishing a molecular basis for NAFLD diagnosis and therapeutic intervention.