Lipidomics in Biomarker Research

Leveraging ML for profiling lipidomic alterations in breast cancer tissues: a methodological perspective

In this study, a comprehensive methodology combining machine learning and statistical analysis was employed to investigate alterations in the metabolite profiles, including lipids, of breast cancer tissues and their subtypes. By integrating biological and machine learning feature selection techniques, along with univariate and multivariate analyses, a notable lipid signature was identified in breast cancer tissues. The results revealed elevated levels of saturated and monounsaturated phospholipids in breast cancer tissues, consistent with external validation findings. Additionally, lipidomics analysis in both the original and validation datasets indicated lower levels of most triacylglycerols compared to non-cancerous tissues, suggesting potential alterations in lipid storage and metabolism within cancer cells. Analysis of cancer subtypes revealed that levels of PC 30:0 were relatively reduced in HER2(-) samples that were ER(+) and PR(+) compared to those that were ER(-) and PR(-). Conversely, HER2(+) tumors, which were ER(-) and PR(-), exhibited increased concentrations of PC 30:0. This increase could potentially be linked to the role of Stearoyl-CoA-Desaturase 1 in breast cancer. Comprehensive metabolomic analyses of breast cancer can offer crucial insights into cancer development, aiding in early detection and treatment evaluation of this devastating disease.

High-resolution ion mobility based on traveling wave structures for lossless ion manipulation resolves hidden lipid features

High-resolution ion mobility (resolving power > 200) coupled with mass spectrometry (MS) is a powerful analytical tool for resolving isobars and isomers in complex samples. High-resolution ion mobility is capable of discerning additional structurally distinct features, which are not observed with conventional resolving power ion mobility (IM, resolving power ~ 50) techniques such as traveling wave IM and drift tube ion mobility (DTIM). DTIM in particular is considered to be the "gold standard" IM technique since collision cross section (CCS) values are directly obtained through a first-principles relationship, whereas traveling wave IM techniques require an additional calibration strategy to determine accurate CCS values. In this study, we aim to evaluate the separation capabilities of a traveling wave ion mobility structures for lossless ion manipulation platform integrated with mass spectrometry analysis (SLIM IM-MS) for both lipid isomer standards and complex lipid samples. A cross-platform investigation of seven subclass-specific lipid extracts examined by both DTIM-MS and SLIM IM-MS showed additional features were observed for all lipid extracts when examined under high resolving power IM conditions, with the number of CCS-aligned features that resolve into additional peaks from DTIM-MS to SLIM IM-MS analysis varying between 5 and 50%, depending on the specific lipid sub-class investigated. Lipid CCS values are obtained from SLIM IM (TW(SLIM)CCS) through a two-step calibration procedure to align these measurements to within 2% average bias to reference values obtained via DTIM (DTCCS). A total of 225 lipid features from seven lipid extracts are subsequently identified in the high resolving power IM analysis by a combination of accurate mass-to-charge, CCS, retention time, and linear mobility-mass correlations to curate a high-resolution IM lipid structural atlas. These results emphasize the high isomeric complexity present in lipidomic samples and underscore the need for multiple analytical stages of separation operated at high resolution.

Circulating perturbation of phosphatidylcholine (PC) and phosphatidylethanolamine (PE) is associated to cardiac remodeling and NLRP3 inflammasome in cardiovascular patients with insulin resistance risk

Lipidome perturbation occurring during meta-inflammation is associated to left ventricle (LV) remodeling though the activation of the NLRP3 inflammasome, a key regulator of chronic inflammation in obesity-related disorders. Little is known about phosphatidylcholine (PC) and phosphatidylethanolamine (PE) as DAMP-induced NLRP3 inflammasome. Our study is aimed to evaluate if a systemic reduction of PC/PE molar ratio can affect NLRP3 plasma levels in cardiovascular disease (CVD) patients with insulin resistance (IR) risk. Forty patients from IRCCS Policlinico San Donato were enrolled, and their blood samples were drawn before heart surgery. LV geometry measurements were evaluated by echocardiography and clinical data associated to IR risk were collected. PC and PE were quantified by ESI-MS/MS. Circulating NLRP3 was quantified by an ELISA assay. Our results have shown that CVD patients with IR risk presented systemic lipid impairment of PC and PE species and their ratio in plasma was inversely associated to NLRP3 levels. Interestingly, CVD patients with IR risk presented LV changes directly associated to increased levels of NLRP3 and a decrease in PC/PE ratio in plasma, highlighting the systemic effect of meta-inflammation in cardiac response. In summary, PC and PE can be considered bioactive mediators associated to both the NLRP3 and LV changes in CVD patients with IR risk.

Developing a model to predict the early risk of hypertriglyceridemia based on inhibiting lipoprotein lipase (LPL): a translational study

Hypertriglyceridemia (HTG) is an independent risk factor for atherosclerotic cardiovascular disease (ASCVD). One of the multiple origins of HTG alteration is impaired lipoprotein lipase (LPL) activity, which is an emerging target for HTG treatment. We hypothesised that early, even mild, alterations in LPL activity might result in an identifiable metabolomic signature. The aim of the present study was to assess whether a metabolic signature of altered LPL activity in a preclinical model can be identified in humans. A preclinical LPL-dependent model of HTG was developed using a single intraperitoneal injection of poloxamer 407 (P407) in male Wistar rats. A rat metabolomics signature was identified, which led to a predictive model developed using machine learning techniques. The predictive model was applied to 140 humans classified according to clinical guidelines as (1) normal, less than 1.7 mmol/L; (2) risk of HTG, above 1.7 mmol/L. Injection of P407 in rats induced HTG by effectively inhibiting plasma LPL activity. Significantly responsive metabolites (i.e. specific triacylglycerols, diacylglycerols, phosphatidylcholines, cholesterol esters and lysophospholipids) were used to generate a predictive model. Healthy human volunteers with the impaired predictive LPL signature had statistically higher levels of TG, TC, LDL and APOB than those without the impaired LPL signature. The application of predictive metabolomic models based on mechanistic preclinical research may be considered as a strategy to stratify subjects with HTG of different origins. This approach may be of interest for precision medicine and nutritional approaches.

A Five-Year Update on Matrix Compounds for MALDI-MS Analysis of Lipids

Matrix-assisted laser desorption and ionization (MALDI) is a widely used soft-ionization technique of modern mass spectrometry (MS). MALDI enables the analysis of nearly all chemical compounds—including polar and apolar (phospho)lipids—with a minimum extent of fragmentation. MALDI has some particular advantages (such as the possibility to acquire spatially-resolved spectra) and is competitive with the simultaneously developed ESI (electrospray ionization) MS. Although there are still some methodological aspects that need to be elucidated in more detail, it is obvious that the careful selection of an appropriate matrix plays the most important role in (lipid) analysis. Some lipid classes can be detected exclusively if the optimum matrix is used, and the matrix determines the sensitivity by which a particular lipid is detected within a mixture. Since the matrix is, thus, crucial for optimum results, we provide here an update on the progress in the field since our original review in this journal in 2018. Thus, only the development during the last five years is considered, and lipids are sorted according to increasing complexity, starting with free fatty acids and ending with cardiolipins and phosphoinositides.

Four-dimensional trapped ion mobility spectrometry lipidomics for high throughput clinical profiling of human blood samples

Lipidomics encompassing automated lipid extraction, a four-dimensional (4D) feature selection strategy for confident lipid annotation as well as reproducible and cross-validated quantification can expedite clinical profiling. Here, we determine 4D descriptors (mass to charge, retention time, collision cross section, and fragmentation spectra) of 200 lipid standards and 493 lipids from reference plasma via trapped ion mobility mass spectrometry to enable the implementation of stringent criteria for lipid annotation. We use 4D lipidomics to confidently annotate 370 lipids in reference plasma samples and 364 lipids in serum samples, and reproducibly quantify 359 lipids using level-3 internal standards. We show the utility of our 4D lipidomics workflow for high-throughput applications by reliable profiling of intra-individual lipidome phenotypes in plasma, serum, whole blood, venous and finger-prick dried blood spots.

The plasma phospholipidome of the bottlenose dolphin (Tursiops truncatus) is modulated by both sex and developmental stage

Lipidomics represent a valid complementary tool to the biochemical analysis of plasma in humans. However, in cetaceans, these tools have been unexplored. Here, we evaluated how the plasma lipid composition of Tursiops truncatus is modulated by developmental stage and sex, aiming at a potential use of lipidomics in integrated strategies to monitor cetacean health. We characterized the fatty acid profile and detected a total of 26 fatty acids in T. truncatus plasma. The most abundant fatty acids were palmitic acid (C16:0), stearic acid (C18:0) and oleic acid (C18:1n-9). Interestingly, there are consistent differences between the fatty acid profile of mature female and mature male specimens. Phospholipidome analysis identified 320 different lipid species belonging to phosphatidylcholine (PC, 105 lipid species), lysophosphatidylcholine (42), phosphatidylethanolamine (PE, 67), lysophosphatidylethanolamine (18), phosphatidylglycerol (14), lysophosphatidylglycerol (8), phosphatidylinositol (14), lysophosphatidylinositol (2), phosphatidylserine (3), sphingomyelin (45) and ceramides (2) classes. The statistical analysis of the phospholipidome showed that its composition allows discriminating mature animals between sexes and mature males from immature males. Notably, discrimination between sexes is mainly determined by the contents of PE plasmalogens and lysophospholipids (LPC and LPE), while the differences between mature and immature male animals were mainly determined by the levels of PC lipids. This is the first time that a correlation between developmental stage and sex and the lipid composition of the plasma has been established in cetaceans. Being able to discern between age and sex-related changes is an encouraging step towards using these tools to also detect differences related to disease/dysfunction processes.