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Lipidomics aims to characterize lipid alteration in response to internal or external subtle perturbations in complex biological samples. Lipid abnormality is a major risk factor for many diseases. Large-scale lipidomic studies may offer new insights into the pathophysiological mechanisms of diseases, new opportunities in systems biology, functional biology, and personalized medicine. To this end, a highly efficient and stable lipidomic method is highly in demand. We herein present a rapid and relatively high coverage lipidomic profiling approach based on ultra-high performance liquid chromatography–mass spectrometry by comparing the performance of different chromatographic columns, optimizing the elution gradient and selecting an appropriate data acquisition mode of mass spectra. As a result, a total of 481 lipids were detected from 40 μL serum sample within 13 min, covering 20 common lipid (sub)classes. The developed method was well validated with satisfactory analytical characteristics in linearity, repeatability, stability, and lipid coverage. To show the usefulness, the method was employed to investigate serum lipid profiling of 43 subjects with mild diabetic retinopathy and 44 normal controls, and successfully defined the differential lipids related to diabetic retinopathy. We believe that this rapid method will be beneficial for lipidomic analysis of large-scale clinical samples.

Metabolite and lipid profilings usually need two liquid chromatography-mass spectrometry (LC-MS) methods because of a great polarity difference. A pseudotargeted metabolomics method as a novel emerging approach can integrate the advantages of nontargeted and targeted methods. Here, we aim to establish a comprehensive method for metabolome and lipidome by using a parallel column-based two-dimensional LC (PC-2DLC)-MS and pseudotargeted approach. To simultaneously extract as many polar metabolites and nonpolar lipids as possible, we systematically optimized the sample pretreatment process, and isopropanol/methanol (3:1, v/v) and isopropanol/water (7:3, v/v) were selected as the extraction and reconstitution solvents, respectively. The detected triglycerides significantly increased after the sample pretreatment optimization. Then PC-2DLC coupled with Triple TOF MS was applied to analyze a mixed sample from serum, urine, and liver tissue matrixes. The multiple reaction monitoring (MRM) transitions of the metabolome and lipidome were defined according to the "MRM-Ion Pair Finder" software and lipidomics MRM-transition database, respectively. After verification by QTRAP MS in the scheduled MRM mode, 1609 potential metabolites and lipids corresponding to 1294 MRM transitions, and 847 potential metabolites and lipids corresponding to 687 MRM transitions were detected in positive and negative ion modes, respectively. They range at about 30 orders of magnitude in octanol/water partition coefficient. The pseudotargeted 2DLC-MS method was validated to have good analytical characteristics. As a proof of applicability, sera from type 2 diabetic patients were investigated by the established method. The results indicated that the pseudotargeted 2DLC-MS method is reliable and repeatable and can be used in a metabolomics study.

The pituitary gland is a small but important organ located in the base of the brain. Although mostly noncancerous, pituitary adenomas (PAs) can cause serious health problems such as headaches, visual field defects, double vision, and hypopituitarism by invasion of regional structures. Nonfunctioning PAs (NFPAs) approximately account for one-third of PAs manifested by no circulating hormone hypersecretion. Lipid reprogramming has been recognized as a hallmark of tumor cells and proven to play a crucial role in tumorigenesis. However, the lipid molecular pathogenesis of NFPAs has remained obscure to date. To uncover lipid alterations that may contribute to the development of NFPAs and define their molecular characteristics, we investigated tissue lipids of patients with NFPAs including eight null cell adenomas (NCAs) and eight oncocytomas (OCMs) and of five normal pituitary glands as the control (Ctrl) using nontargeted lipidomics based on ultrahigh-performance liquid chromatography-Orbitrap Q-Exactive HF mass spectrometry. The lipidomic results were further validated in another set of subjects consisting of 8 NCAs, 10 OCMs, and 6 Ctrls to define crucial lipids discriminating NFPAs from the normal pituitary tumors. Lipidomic analyses revealed that OCM showed more pronounced changes in lipid compositions than NCA and Ctrl. As expected, mitochondria abundant cardiolipins were remarkably increased in OCM, which was accordant with the biochemical evidence of mitochondria hyperplasia in OCM. Significantly increased levels of phospholipids (PLs), especially arachidonic acid (AA)-enriched PLs, were unique characteristics of lipid profiling in OCM vs Ctrl. Our results indicate that AA-PLs may have diagnostic potential for OCM.

Gliomas are the most aggressive phenotypes of brain tumors and are classified into four grades according to the malignancy degree by the World Health Organization. Metabolic profiling can provide an overview of metabolic reprogramming at a specific stage of tumor initiation and development. Studies about metabolic alterations related to different grades of gliomas are helpful to understand the molecular mechanism for progression of glioma. In the current study, metabolomics and lipidomics analyses based on chromatography-mass spectrometry were performed on different grades of glioma tissues. Differential metabolites between glioma and para-tumor tissues were studied and used as the basis to explore metabolic alterations related to glioma grading. It was found that short-chain acylcarnitines were elevated, whereas lysophosphatidylethanolamines (LPEs) were decreased in high-grade gliomas. Furthermore, the gene expression of short/branched-chain acyl-coenzyme dehydrogenase (ACADSB), which is involved in fatty acid oxidation, was found down-regulated with glioma progression by analyzing related genes and pathways. In addition, LPE metabolism showed a significant difference among different grades of gliomas. These important metabolic pathways related to glioma progression may provide potential clues for further study on the mechanisms and treatment of glioma.

Lipids are vital biological molecules and play multiple roles in cellular function of mammalian organisms such as cellular membrane anchoring, signal transduction, material trafficking and energy storage. Driven by the biological significance of lipids, lipidomics has become an emerging science in the field of omics. Lipidome in biological systems consists of hundreds of thousands of individual lipid molecules that possess complex structures, multiple categories, and diverse physicochemical properties assembled by different combinations of polar headgroups and hydrophobic fatty acyl chains. Such structural complexity poses a huge challenge for comprehensive lipidome analysis. Thanks to the great innovations in chromatographic separation techniques and the continuous advances in mass spectrometric detection tools, analytical strategies for lipidomics have been highly diversified so that the depth and breadth of lipidomics have been greatly enhanced. This review will present the current state of mass spectrometry-based analytical strategies including untargeted, targeted and pseudotargeted lipidomics. Recent typical applications of lipidomics in biomarker discovery, pathogenic mechanism and therapeutic strategy are summarized, and the challenges facing to the field of lipidomics are also discussed., Graphical abstract Image 1, Highlights • The diversified mass spectrometry (MS)-based untargeted, targeted and pseudotargeted strategies have greatly enhanced lipidomics applications. • Recent advances in MS techniques allow reliable and sensitive detection of hundreds to thousands of lipids. • The integration of multi-omics analytical techniques has a bright prospect in studying the pathogenic mechanism and potentially targetable therapy. • Comprehensive analysis of the lipidome still faces considerable challenges in analytical techniques.

Lipidomics aims to characterize lipid alteration in response to internal or external subtle perturbations in complex biological samples. Lipid abnormality is a major risk factor for many diseases. Large-scale lipidomic studies may offer new insights into the pathophysiological mechanisms of diseases, new opportunities in systems biology, functional biology, and personalized medicine. To this end, a highly efficient and stable lipidomic method is highly in demand. We herein present a rapid and relatively high coverage lipidomic profiling approach based on ultra-high performance liquid chromatography-mass spectrometry by comparing the performance of different chromatographic columns, optimizing the elution gradient and selecting an appropriate data acquisition mode of mass spectra. As a result, a total of 481 lipids were detected from 40 μL serum sample within 13 min, covering 20 common lipid (sub)classes. The developed method was well validated with satisfactory analytical characteristics in linearity, repeatability, stability, and lipid coverage. To show the usefulness, the method was employed to investigate serum lipid profiling of 43 subjects with mild diabetic retinopathy and 44 normal controls, and successfully defined the differential lipids related to diabetic retinopathy. We believe that this rapid method will be beneficial for lipidomic analysis of large-scale clinical samples.

Lipid coverage is crucial in comprehensive lipidomics studies challenged by high diversity in lipid structures and wide dynamic range in lipid levels. Current state-of-the-art lipidomics technologies are mostly based on mass spectrometry (MS), including direct-infusion MS, chromatography-MS, and matrix-assisted laser desorption ionization (MALDI) imaging MS, each with its pros and cons. Due to the need or favorability for measurement of isomers and isobars, chromatography-MS is preferable for lipid profiling. The ultra-high performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS)-based nontargeted lipidomics approach and UHPLC-tandem MS (UHPLC-MS/MS)-based targeted approach are two representative methodological platforms for chromatography-MS. In the present study, we developed a high coverage pseudotargeted lipidomics method combining the advantages of nontargeted and targeted lipidomics approaches. The high coverage of lipids was achieved by integration of the detected lipids derived from nontargeted UHPLC-HRMS lipidomics analysis of multiple matrices (e.g., plasma, cell, and tissue) and the predicted lipids speculated on the basis of the structure and chromatographic retention behavior of the known lipids. A total of 3377 targeted lipid ion pairs with over 7000 lipid molecular structures were defined. The pseudotargeted lipidomics method was well validated with satisfactory analytical characteristics in terms of linearity, precision, reproducibility, and recovery for lipidomics profiling. Importantly, it showed better repeatability and higher coverage of lipids than the nontargeted lipidomics method. The applicability of the developed pseudotargeted lipidomics method was testified in defining differential lipids related to diabetes. We believe that comprehensive lipidomics studies will benefit from the developed high coverage pseudotargeted lipidomics approach.

Diabetic retinopathy (DR) is the main cause of vision loss or blindness in working age adults worldwide. The lack of effective diagnostic biomarkers for DR leads to unsatisfactory curative treatments. To define potential metabolite biomarkers for DR diagnosis, a multiplatform‐based metabolomics study is performed. In this study, a total of 905 subjects with diabetes without DR (NDR) and with DR at different clinical stages are recruited. Multiplatform metabolomics methods are used to characterize the serum metabolic profiles and to screen and validate the DR biomarkers. Based on the criteria p, Multiplatform metabolomics methods are used to comprehensively investigate the association between the serum metabolic profiles and the onset and progression of diabetic retinopathy (DR) in a large Chinese population, and 12‐HETE and 2‐piperidone are identified as a highly effective marker panel for the detection of DR and early‐stage DR.