Lipidomic Method Research Articles

Exploring the freshness biomarker and volatiles formation in stored pork by means of lipidomics and volatilomics

Raw pork is prone to oxidation and rancidity as it contains a high level of unsaturated lipid molecules. Reliable biomarkers to benchmark pork freshness and their formation have not been systematically investigated. The results indicated that the peroxide values, TVB-N and rancid volatiles dramatically increased in pork during the storage period (4 °C, 0–9 d). Concentrations of most volatile compounds with carbonyl groups were increased markedly in pork during storing, including hexanal, acetic acid, and hexadecanoic acid methyl ester. Lipidomics, volatilomics and chemometrics methods were used to discriminate the freshness of pork, among which acetic acid and PC O-20:3 emerged as the most reliable freshness biomarkers. Phospholipids and neutral lipids, including phosphatidylcholines (PC), triglycerides (TG), and phosphatidylethanolamine (PE), played a crucial role in the formation of rancid volatiles and the decreased freshness. This work will provide technical supports for the efficient storage and preservation of raw meat.

Enhanced Lipidomics Analysis of Breast Cancer Cells Using Three-phase Liquid Extraction and Ultra High-performance Liquid Chromatography Coupled With Quadrupole Time-of-Flight Tandem Mass Spectrometry.

Lipid extraction of complex biological samples is essential for high-quality data in liquid chromatography-mass spectrometry (LC-MS)-based lipidomics. This study introduces a three-phase liquid extraction (3PLE)-ultra-high-performance LC coupled with quadrupole time-of-flight tandem MS method. This method was successfully applied to lipidomics analysis of breast cancer cells, including highly metastatic MDA-MB-231 and slightly metastatic MCF7 cells. The 3PLE method employed an n-hexane/methyl tert-butyl ether/acetonitrile/water solvent system that formed one aqueous and two organic phases. Neutral and polar lipids were enriched in the upper and middle organic phases, respectively, and combined for detection, thereby reducing analysis time. Compared with the Bligh and Dyer method, 3PLE achieved higher sensitivity and detected more features, with over a 50% increase in the relative abundance of nearly 50% of the differential lipids. In total, 21 differential lipids were identified in the MDA-MB-231 group and 22 in the MCF7 group compared to normal breast epithelial cells (MCF10A). Pathway analysis suggested that lipid changes in breast cancer cells were associated with glycerophospholipid metabolism, arachidonic acid metabolism, sphingolipid metabolism, and linoleic acid metabolism. The study presents a highly efficient lipidomics method, providing a scientific foundation for understanding breast cancer pathogenesis and aiding in diagnosis.

Hyssopus cuspidatus extract inhibited OVA-sensitized allergic asthma through PI3K/JNK/P38 signaling pathway and lipid homeostasis regulation

ObjectiveTo investigate the effect and mechanism of Hyssopus cuspidatus Boriss. extract (HCE) in ovalbumin (OVA)-induced allergic asthma. MethodsComponents identification of HCE was conducted using ultra performance liquid chromatography-quadrupole-time of flight-mass spectrometry. Mice were sensitized with OVA to establish asthmatic model, and dexamethasone was used as positive control. Respiratory reactivity, white cells counting in bronchoalveolar lavage fluid and peripheral blood, cytokine level measurement in serum and lung tissue, and histologic examination were performed to evaluate the therapeutic effect of HCE on asthma. Network pharmacology approach was used for mechanism prediction. Western blotting and untargeted lipidomics method were applied for mechanism validation. ResultsFifty-two compounds were identified in HCE, predominantly terpenoids and flavonoids. HCE markedly reduced airway resistance, the eosinophil infiltration in lung tissues, and the levels of immunoglobulin E, interleukin-4, interleukin-5, and interleukin-13. Network pharmacology analysis suggested phosphatidylinositol 3-kinases (PI3K), c-Jun N-terminal kinase (JNK), and p38 Mitogen-activated protein kinase (P38) may be key proteins of HCE in the treatment of allergic asthma. Western blot results indicated that the levels of phosphorylated PI3K, JNK, and P38 were downregulated in HCE-treated group. Moreover, HCE significantly upregulated the levels of ceramide and sphingomyelin and downregulated the level of phosphatidylcholine ConclusionHCE inhibited allergic asthma via PI3K/JNK/P38 signaling pathway and lipid homeostasis regulation.

Analysis of the Mammalian Lipidome by DMS Shotgun Lipidomics.

Shotgun Lipidomics is a robust methodology for the characterization of the lipidome of complex biological samples. This assay is among the most quantitative lipidomics methods and is capable of surveying a wide breadth of lipid subclasses, both neutral and polar. The shortfalls of the technique include limitations in lipid species characterization and computationally demanding data analysis requiring isotopic and isobaric overlap correction. Differential Mobility Spectrometry (DMS) has demonstrated its utility in enabling acyl tail characterization within a Shotgun Lipidomics experiment. Here, we present a workflow for DMS Shotgun Lipidomics that measures 1400 possible lipid species. It utilizes the Shotgun Lipidomics Assistant (SLA) application, an open-source application that supervises the data analysis for an expansive Shotgun Lipidomics experiment.

Application of microdialysis combined with lipidomics to analyze fatty acid metabolic changes in the disease process of rheumatoid arthritis

Rheumatoid arthritis (RA) is a metabolically active disease, with shifts in fatty acid metabolism during disease progression profoundly affecting the systemic inflammatory response. Altered fatty acid biomarker metabolism may be a key target for the treatment of RA. To investigate the changes of fatty acid metabolism in RA, collagen-induced arthritis (CIA) model was established. Microdialysis sampling was utilized to overcome the characteristic of occlusive joint cavity in vivo synovial fluid (SF) sampling. Lipidomic methods were established with the UHPLC-Orbitrap Exploris120 platform, and lipid measurements were performed on serum and SF samples. Then, multivariate statistical analyses were performed to detect changes in lipid metabolites induced by CIA. Consequently, a total of 22 potential biomarkers associated with differential fatty acids were screened and identified in serum, and 13 were identified in SF. Notably, alterations were observed in metabolites such as Hexadecanoic acid, Octadecanoic acid, Arachidonic acid, (+/-)11,12-EpETrE, DHA, DPA, Myristic acid, Suberic acid, and others. This study explored a new mechanism of the RA disease process from the perspective of fatty acid metabolism. It provided a new strategy for experimental research on determining the optimal time for establishing CIA model and screening clinical diagnostic biomarkers.

Plasma lipidomics in early APP/PS1 female mouse model and its relationship with brain: Is it affected by the estrous cycle?

BackgroundAlzheimer's disease (AD) is the most prevalent dementia, showing higher incidence in women. Besides, lipids play an essential role in brain, and they could be dysregulated in neurodegeneration. Specifically, impaired plasma lipid levels could predict early AD diagnosis. This work aims to identify the main plasma lipids altered in early AD female mouse model and evaluate their relationship with brain lipidome. Also, the possible involvement of the estrous cycle in lipid metabolism has been evaluated.MethodsPlasma samples of wild-type (n = 10) and APP/PS1 (n = 10) female mice of 5 months of age were collected, processed, and analysed using a lipidomic mass spectrometry-based method. A statistical analysis involving univariate and multivariate approaches was performed to identify significant lipid differences related to AD between groups. Also, cytology tests were conducted to confirm estrous cycle phases.ResultsThree hundred thirty lipids were detected in plasma, 18 of them showed significant differences between groups; specifically, some triacylglycerols, cholesteryl esters, lysophosphatidylcholines, phosphatidylcholines, and ether-linked phosphatidylcholines, increased in early AD; while other phosphatidylcholines, phosphatidylethanolamines, ceramides, and ether-linked phosphatidylethanolamines decreased in early AD. A multivariate approach was developed from some lipid variables, showing high diagnostic indexes (70% sensitivity, 90% specificity, 80% accuracy). From brain and plasma lipidome, some significant correlations were observed, mainly in the glycerophospholipid family. Also, some differences were found in both plasma and brain lipids, according to the estrous cycle phase.ConclusionsTherefore, lipid alterations can be identified in plasma at early AD stages in mice females, with a relationship with brain lipid metabolism for most of the lipid subfamilies, suggesting some lipids as potential AD biomarkers. In addition, the estrous cycle monitoring could be relevant in female studies.

Exploring molecular mechanisms underlying changes in lipid fingerprinting of salmon (Salmo salar) during air frying integrating machine learning-guided REIMS and lipidomics analysis

Lipid oxidation in air-fried seafood poses a risk to human health. However, the effect of a prooxidant environment on lipid oxidation in seafood at different air frying (AF) temperatures remains unknown. An integrated machine learning (ML) - guided REIMS and lipidomics method was applied to explore lipid profiles, lipid oxidation, and lipid metabolic pathways of salmons under different AF temperatures (140, 160, 180, and 200 °C). A significant difference in the lipidomic fingerprinting of air-dried salmon at different temperatures was shown by the main ML methods (neural networks, support vector machines, ensemble learning, and naïve bayes). In total, 773 differential expression metabolites (DEMs) were identified, including glycerophospholipids (GPs), glycerides (GLs), and sphingolipids. A total of 34 DEMs with p values <0.05 and variable importance of projection values >1.0 were analyzed, belonging to linoleic acid metabolism, GL metabolism, and GP metabolism pathways. Correlation network analysis revealed that some characteristic DEMs (phosphatidylcholine, lyso-phosphatidylcholine, triglycerides, fatty acids, and phosphatidylethanolamine) were highly correlated with lipid oxidation. In addition, variations of volatile compounds, color values, texture characteristics, and thiobarbituric acid-reactive substance values were analyzed to corroborate the oxidation characteristics.

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.

Cohort-based strategies as an in-house tool to evaluate and improve phenotyping robustness of LC–MS/MS lipidomics platforms

In recent years, instrumental improvements have enabled the spread of mass spectrometry–based lipidomics platforms in biomedical research. In mass spectrometry, the reliability of generated data varies for each compound, contingent on, among other factors, the availability of labeled internal standards. It is challenging to evaluate the data for lipids without specific labeled internal standards, especially when dozens to hundreds of lipids are measured simultaneously. Thus, evaluation of the performance of these platforms at the individual lipid level in interlaboratory studies is generally not feasible in a time-effective manner. Herein, using a focused subset of sphingolipids, we present an in-house validation methodology for individual lipid reliability assessment, tailored to the statistical analysis to be applied. Moreover, this approach enables the evaluation of various methodological aspects, including discerning coelutions sharing identical selected reaction monitoring transitions, pinpointing optimal labeled internal standards and their concentrations, and evaluating different extraction techniques. While the full validation according to analytical guidelines for all lipids included in a lipidomics method is currently not possible, this process shows areas to focus on for subsequent method development iterations as well as the robustness of data generated across diverse methodologies.Graphical abstract

Lipidomic and Fatty Acid Biomarkers in Whole Blood Can Predict the Dietary Intake of Eicosapentaenoic and Docosahexaenoic Acids in a Danish Population

BackgroundThe intake of eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) have been associated with health benefits. Blood levels of these fatty acids, measured by gas chromatography (GC), are associated with their dietary intake, but the relationships with lipidomic measurements are not well defined. ObjectivesThis study aimed to determine the lipidomic biomarkers in whole blood that predict intakes of EPA + DHA and examine the relationship between lipidomic and GC-based n–3 polyunsaturated fatty acid (n–3 PUFA) biomarkers. MethodsLipidomic and fatty acid analyses were completed on 120 whole blood samples collected from Danish participants. Dietary intakes were completed using a web-based 7-d food diary. Stepwise multiple linear regression was used to identify the fatty acid and lipidomic variables that predict intakes of EPA + DHA and to determine lipidomic species that predict commonly used fatty acid biomarkers. ResultsStepwise regression selected lipidomic variables with an R2 = 0.52 for predicting EPA + DHA intake compared to R2 = 0.40 for the selected fatty acid GC-based variables. More predictive models were generated when the lipidomic variables were selected for females only (R2 = 0.62, n = 68) and males only (R2 = 0.72, n = 52). Phosphatidylethanolamine plasmalogen species containing EPA or DHA tended to be the most predictive lipidomic variables. Stepwise regression also indicated that selected lipidomic variables can predict commonly used fatty acid GC-based n–3 PUFA biomarkers as the R2 values ranged from 0.84 to 0.91. ConclusionsBoth fatty acid and lipidomic data can be used to predict EPA + DHA intakes, and fatty acid GC-based biomarkers can be emulated by lipidomic species. Lipidomic-based biomarkers appear to be influenced by sex differences, probably in n–3 PUFA and lipoprotein metabolism. These results improve our ability to understand the relationship between novel lipidomic data and GC fatty acid data and will increase our ability to apply lipidomic methods to fatty acid and lipid nutritional research.

Single-Cell Untargeted Lipidomics Using Liquid Chromatography and Data-Dependent Acquisition after Live Cell Selection.

We report the development and validation of an untargeted single-cell lipidomics method based on microflow chromatography coupled to a data-dependent mass spectrometry method for fragmentation-based identification of lipids. Given the absence of single-cell lipid standards, we show how the methodology should be optimized and validated using a dilute cell extract. The methodology is applied to dilute pancreatic cancer and macrophage cell extracts and standards to demonstrate the sensitivity requirements for confident assignment of lipids and classification of the cell type at the single-cell level. The method is then coupled to a system that can provide automated sampling of live, single cells into capillaries under microscope observation. This workflow retains the spatial information and morphology of cells during sampling and highlights the heterogeneity in lipid profiles observed at the single-cell level. The workflow is applied to show changes in single-cell lipid profiles as a response to oxidative stress, coinciding with expanded lipid droplets. This demonstrates that the workflow is sufficiently sensitive to observing changes in lipid profiles in response to a biological stimulus. Understanding how lipids vary in single cells will inform future research into a multitude of biological processes as lipids play important roles in structural, biophysical, energy storage, and signaling functions.

Metabolic characterization of sphere-derived prostate cancer stem cells reveals aberrant urea cycle in stemness maintenance.

Alteration of cell metabolism is one of the essential characteristics of tumor growth. Cancer stem cells (CSCs) are the initiating cells of tumorigenesis, proliferation, recurrence, and other processes, and play an important role in therapeutic resistance and metastasis. Thus, identification of the metabolic profiles in prostate cancer stem cells (PCSCs) is critical to understanding prostate cancer progression. Using untargeted metabolomics and lipidomics methods, we show distinct metabolic differences between prostate cancer cells and PCSCs. Urea cycle is the most significantly altered metabolic pathway in PCSCs, the key metabolites arginine and proline are evidently elevated. Proline promotes cancer stem-like characteristics via the JAK2/STAT3 signaling pathway. Meanwhile, the enzyme pyrroline-5-carboxylate reductase 1 (PYCR1), which catalyzes the conversion of pyrroline-5-carboxylic acid to proline, is highly expressed in PCSCs, and the inhibition of PYCR1 suppresses the stem-like characteristics of prostate cancer cells and tumor growth. In addition, carnitine and free fatty acid levels are significantly increased, indicating reprogramming of fatty acid metabolism in PCSCs. Reduced sphingolipid levels and increased triglyceride levels are also observed. Collectively, our data illustrate the comprehensive landscape of the metabolic reprogramming of PCSCs and provide potential therapeutic strategies for prostate cancer.

The sphingolipids change in exosomes from cancer patients and association between exosome release and sphingolipids level based on a pseudotargeted lipidomics method

The lipid based ESCRT-independent mechanism, which contributes to MVB formation, is one of the crucial procedures in exosome biogenesis. n-SMase is a key lipid metabolism enzyme in this mechanism and can induce the hydrolysis of sphingomyelins (SMs) to ceramides (Cers), thereby promoting the formation of ILVs inside MVBs. Therefore, the regulation of n-SMase can realize the alteration in exosome release. According to the fact that cancer-associated cells have a tendency to release more exosomes than healthy cells, lipid extracts in exosomes from healthy volunteers, HCC and ICC patients were analyzed by a novel pseudotargeted lipidomics method focused on sphingolipids (SLs) to explore whether cancer-related features regulate the release of exosomes through the above pathway. Multivariate analysis based on the SLs expression could distinguish three groups well indicated that the SLs expression among the three groups were different. In cancer groups, two species of critical Cers were up-regulated, denoted as Cer (d18:1_16:0) and Cer (d18:1_18:0), while 55 kinds of SLs were down-regulated, including 40 species of SMs, such as SM (d18:1_16:0), SM (d18:1_18:1) and SM (d18:1_24:0). Meanwhile, several species of SM/Cer exhibited significant down-regulation. This substantial enhancement of the SMs hydrolysis to Cers process during exosome biogenesis suggested that cancer-related features may potentially promote an increase in exosome release through ESCRT-independent mechanism. Moreover, differential SLs have a capability of becoming potential biomarkers for disease diagnosis and classification with an AUC value of 0.9884 or 0.9806 for the comparison between healthy group and HCC or ICC groups, respectively. In addition, an association analysis conducted on the cell lines showed that changes in the SM/Cer contents in cells and their exosomes were negatively correlated with the levels of released exosomes, implied the regulation of exosome release levels can be achieved by modulating n-SMase and subsequent SL expression.

Brain areas lipidomics in female transgenic mouse model of Alzheimer's disease

Lipids are the major component of the brain with important structural and functional properties. Lipid disruption could play a relevant role in Alzheimer’s disease (AD). Some brain lipidomic studies showed significant differences compared to controls, but few studies have focused on different brain areas related to AD. Furthermore, AD is more prevalent in females, but there is a lack of studies focusing on this sex. This work aims to perform a lipidomic study in selected brain areas (cerebellum, amygdala, hippocampus, entire cortex) from wild-type (WT, n = 10) and APPswe/PS1dE9 transgenic (TG, n = 10) female mice of 5 months of age, as a model of early AD, to identify alterations in lipid composition. A lipidomic mass spectrometry-based method was optimized and applied to brain tissue. As result, some lipids showed statistically significant differences between mice groups in cerebellum (n = 68), amygdala (n = 49), hippocampus (n = 48), and the cortex (n = 22). In addition, some lipids (n = 15) from the glycerolipid, phospholipid, and sphingolipid families were statistically significant in several brain areas simultaneously between WT and TG. A selection of lipid variables was made to develop a multivariate approach to assess their discriminant potential, showing high diagnostic indexes, especially in cerebellum and amygdala (sensitivity 70–100%, sensibility 80–100%).

Fast and broad-coverage lipidomics enabled by ion mobility-mass spectrometry.

Aberrant lipid metabolism has been widely recognized as a hallmark of various diseases. However, the comprehensive analysis of distinct lipids is challenging due to the complexity of lipid molecular structures, wide concentration ranges, and numerous isobaric and isomeric lipids. Usually, liquid chromatography-mass spectrometry (LC-MS)-based lipidomics requires a long time for chromatographic separation to achieve optimal separation and selectivity. Ion mobility (IM) adds a new separation dimension to LC-MS, significantly enhancing the coverage, sensitivity, and resolving power. We took advantage of the rapid separation provided by ion mobility and optimized a fast and broad-coverage lipidomics method using the LC-IM-MS technology. The method required only 8 minutes for separation and detected over 1000 lipid molecules in a single analysis of common biological samples. The high reproducibility and accurate quantification of this high-throughput lipidomics method were systematically characterized. We then applied the method to comprehensively measure dysregulated lipid metabolism in patients with colorectal cancer (CRC). Our results revealed 115 significantly changed lipid species between preoperative and postoperative plasma of patients with CRC and also disclosed associated differences in lipid classes such as phosphatidylcholines (PC), sphingomyelins (SM), and triglycerides (TG) regarding carbon number and double bond. Together, a fast and broad-coverage lipidomics method was developed using ion mobility-mass spectrometry. This method is feasible for large-scale clinical lipidomic studies, as it effectively balances the requirements of high-throughput and broad-coverage in clinical studies.

Adapting lipidomic sample processing methods for boars housed in commercial settings.

Multiple reaction monitoring (MRM) profiling is a sensitive method of lipid screening that has the capability to distinguish between different fertility phenotypes in gilts. However, MRM profiling has not yet been utilized to evaluate fertility phenotypes in boars. Markers indicative of fertility status in boars would be valuable as inclusion of subfertile boars in breeding programs results in a loss of efficiency and negative economic consequences. In addition, semen samples for lipidomic analysis are transported in liquid nitrogen or on dry ice to suspend metabolic activity within the sperm cells, however, these cryopreservation techniques are not commonly available at commercial boar studs. Therefore, the objective of this study was to develop a method of sample processing for MRM profiling that suspends metabolic activity within semen without freezing the sample. Five, sexually mature boars of similar genetics enrolled in a commercial breeding program were collected for the study. Following collection, ejaculates were aliquoted into methanol to suspend metabolic activity and shipped to Purdue University overnight for lipid extraction. Lipids were extracted using the Bligh and Dyer method and MRM profiling was used for lipid screening. A total of 329 ion transitions (MRMs) related to lipids were detected with most lipids being characterized as plasma membrane lipids (74%) which were comprised of phosphatidylcholines (40%), ceramides (16%), phosphatidylethanolamines (11%), and phosphatidylserines (7%). acylcarnitines (AC) represented approximately 8% of the ejaculate lipidome. Hierarchical cluster and principal component analysis revealed that boars have a distinct ejaculate lipidome profile based on major plasma membrane lipid classes. In addition, we observed that one boar was unique in his abundance of AC which are related to progressive motility and sperm cell metabolism. These results indicate that this method of sample processing for MRM profiling is suitable to be used to evaluate the lipidome of ejaculates from commercial boars and has the potential for broader applications across different livestock species in commercial environments.

CAN ACUTE MUSCLE DAMAGE LEAD TO OSTEOARTHRITIS?

Abstract Skeletal muscles are crucial for maintaining musculoskeletal homeostasis, as they provide mechanical support, joint stability, and biochemical functions via myokine secretion. We investigated whether acute muscle damage or weakness could contribute to the development of osteoarthritis (OA). Using a chemical damage model, we induced muscle damage by injecting 1.2% barium chloride into the tibialis anterior muscle, resulting in an immune response characterized by a significant increase in the concentration of Prostaglandin E2 (PGE2) and other lipid mediators derived from the EPA and DHA signaling pathways, as quantified using our innovative lipidomics methods. The muscle damage was confirmed and visualized by muscle histomorphometry. The damage resulted in muscle weakness, detected by a significant decrease in grip strength at 4-days post-injury, but without changes in mice activity measured with force-plate actimeter. After 1-month post-injury, grip strength and muscle histology returned to normal, but histological changes were observed in the articulating cartilage, indicating surface erosion and cartilage matrix loss (OARSI grade 4). We used Raman Spectroscopy and micro-CT to confirm the joint alterations observed in the changes in subchondral bone and increased bone volume fraction in the cortical shell of the subchondral bone, respectively, features characteristic of OA in rodents and humans. These findings suggest that muscle weakness or imbalance due to injury can contribute to joint degeneration. This study highlights the significance of muscles in preventing OA and provides a novel approach for exploring the relationship between muscle damage and OA development. This new model could prove valuable for aging research.

High-Throughput Microbore LC-MS Lipidomics to Investigate APOE Phenotypes.

Microflow liquid chromatography interfaced with mass spectrometry (μLC-MS/MS) is increasingly applied for high-throughput profiling of biological samples and has been proven to have an acceptable trade-off between sensitivity and reproducibility. However, lipidomics applications are scarce. We optimized a μLC-MS/MS system utilizing a 1 mm inner diameter × 100 mm column coupled to a triple quadrupole mass spectrometer to establish a sensitive, high-throughput, and robust single-shot lipidomics workflow. Compared to conventional lipidomics methods, we achieve a ∼4-fold increase in response, facilitating quantification of 351 lipid species from a single iPSC-derived cerebral organoid during a 15 min LC-MS analysis. Consecutively, we injected 303 samples over ∼75 h to prove the robustness and reproducibility of the microflow separation. As a proof of concept, μLC-MS/MS analysis of Alzheimer's disease patient-derived iPSC cerebral organoid reveals differential lipid metabolism depending on APOE phenotype (E3/3 vs E4/4). Microflow separation proves to be an environmentally friendly and cost-effective method as it reduces the consumption of harmful solvents. Also, the data demonstrate robust, in-depth, high-throughput performance to enable routine clinical or biomedical applications.

Abstract 12762: Plasma Lipidomic Signature of Peripheral Artery Disease in Young and Middle-Aged American Indian Individuals: The Strong Heart Family Study

Introduction: Dyslipidemia is an independent risk factor for peripheral artery disease (PAD). Ankle-brachial index (ABI) is a sensitive and cost-effective tool for screening and predicting PAD. The association between individual lipid species and the progression in subclinical PAD, assessed by change in ABI, remains to be determined, especially in understudied populations at high risk. Hypothesis: To identify individual molecular lipid species associated with PAD progression in American Indians, independent of traditional risk factors. Methods: Using an untargeted lipidomic method by liquid chromatography-mass spectrometry, we repeatedly measured 1,542 individual lipid species in 2,830 fasting plasma samples at two time points (2001-2003 and 2006-2009, mean 5 years apart) from 1,415 unique American Indians in the Strong Heart Family Study. Participants were free of prevalent PAD at both time points. ABI was measured in the right arm and bilateral (both left and right posterior tibial artery) using a handheld Doppler and was analyzed as a continuous variable. We first identified baseline lipid species associated with change in ABI by generalized estimating equation models, adjusting for baseline age, sex, BMI, smoking, eGFR, diabetes, hypertension, lipid-lowering medication and baseline ABI. We then examined the longitudinal association between change in lipid species and change in ABI, adjusting for same covariates plus baseline lipids. Results: Participants (63% female) were aged between 18-65 (mean 38) at baseline and 22-72 (mean 43) at follow-up. Higher baseline levels of 36 lipid species were positively (ORs: 1.01 - 3.60), whereas 16 lipid species were inversely (ORs: 0.26 - 0.98), associated with change in ABI. Longitudinal changes in 77 lipid species, largely glycerolipids, glycerophospholipids, and sphingolipids, were significantly associated with change in ABI during 5-year follow-up. Conclusions: Baseline plasma lipid species and their longitudinal changes over time are significantly associated with PAD progression beyond known risk factors. These findings shed light on the role of dyslipidemia in PAD among young and middle-aged American Indians who suffer high rates of obesity, diabetes and other cardiometabolic disorders.

Development and application of a pseudotargeted lipidomics method for alkylglycerol analysis

Alkylglycerols (1-O-alkyl-sn-glycerols) are microscale but critical lipids in foods. Conventional lipidomics analysis often loses sight of alkylglycerol analysis. In this study, we developed a high coverage pseudotargeted lipidomics method for analyzing alkylglycerols. The developed method integrated the advantages of GC–MS and LC–MS to profile alkylglycerol-type ether lipids comprehensively, with the help of a data processing Dart package termed FFIMA (Feature Fragments Information Matching Algorithm). The developed method exhibited competitive superiority to conventional lipidomics, such as wider coverage and higher accuracy. The validated method was assessed by three aquatic products and three milks. A total of 25 alkylglycerols, 107 diacylglycerol ethers, 21 monoacylglycerol ethers, 28 alkylglycerol-type ether phospholipids, and 35 plasmalogens were identified in the six foods. The results demonstrated that this method offers a comprehensive analysis of a wide spectrum of alkylglycerols.