Isopropanol extraction for cerebrospinal fluid lipidomic profiling analysis

Human Cerebrospinal Fluid Sample Preparation and Annotation for Integrated Lipidomics and Metabolomics Profiling Studies

Cerebrospinal fluid (CSF) is a metabolically diverse biofluid and a key specimen for exploring biochemical changes in neurodegenerative diseases. Detecting lipid species in CSF using mass spectrometry (MS)-based techniques remains challenging because lipids are highly complex in structure, and their concentrations span over a broad dynamic range. This work aimed to develop a robust lipidomics and metabolomics method based on commonly used two-phase extraction systems from human CSF samples. Prioritizing lipid detection, biphasic extraction methods, Folch, Bligh and Dyer (B&D), Matyash, and acidified Folch and B&D (aFolch and aB&D) were compared using 150 μL of human CSF samples for the simultaneous extraction of lipids and metabolites with a wide range of polarity. Multiple chromatographical separation approaches, including reversed-phase liquid chromatography (RPLC), hydrophilic interaction liquid chromatography (HILIC), and gas chromatography (GC), were utilized to characterize human CSF metabolome. The aB&D method was found as the most reproducible technique (RSD < 15%) for lipid extraction. The aB&D and B&D yielded the highest peak intensities for targeted lipid internal standards and displayed superior extracting power for major endogenous lipid classes. A total of 674 unique metabolites with a wide polarity range were annotated in CSF using, combining RPLC-MS/MS lipidomics (n = 219), HILIC-MS/MS (n = 304), and GC-quadrupole time of flight (QTOF) MS (n = 151). Overall, our findings show that the aB&D extraction method provided suitable lipid coverage, reproducibility, and extraction efficiency for global lipidomics profiling of human CSF samples. In combination with RPLC-MS/MS lipidomics, complementary screening approaches enabled a comprehensive metabolite signature that can be employed in an array of clinical studies.

Evaluation of Lipid Extraction Protocols for Untargeted Analysis of Mouse Tissue Lipidome

Lipidomics refers to the full characterization of lipids present within a cell, tissue, organism, or biological system. One of the bottlenecks affecting reliable lipidomic analysis is the extraction of lipids from biological samples. An ideal extraction method should have a maximum lipid recovery and the ability to extract a broad range of lipid classes with acceptable reproducibility. The most common lipid extraction relies on either protein precipitation (monophasic methods) or liquid-liquid partitioning (bi- or triphasic methods). In this study, three monophasic extraction systems, isopropanol (IPA), MeOH/MTBE/CHCl3 (MMC), and EtOAc/EtOH (EE), alongside three biphasic extraction methods, Folch, butanol/MeOH/heptane/EtOAc (BUME), and MeOH/MTBE (MTBE), were evaluated for their performance in characterization of the mouse lipidome of six different tissue types, including pancreas, spleen, liver, brain, small intestine, and plasma. Sixteen lipid classes were investigated in this study using reversed-phase liquid chromatography/mass spectrometry. Results showed that all extraction methods had comparable recoveries for all tested lipid classes except lysophosphatidylcholines, lysophosphatidylethanolamines, acyl carnitines, sphingomyelines, and sphingosines. The recoveries of these classes were significantly lower with the MTBE method, which could be compensated by the addition of stable isotope-labeled internal standards prior to lipid extraction. Moreover, IPA and EE methods showed poor reproducibility in extracting lipids from most tested tissues. In general, Folch is the optimum method in terms of efficacy and reproducibility for extracting mouse pancreas, spleen, brain, and plasma. However, MMC and BUME methods are more favored when extracting mouse liver or intestine.

Acute Stress-Induced Changes in the Lipid Composition of Cow's Milk in Healthy and Pathological Animals

Producers of milk and dairy products have been faced with the challenge of responding to European society's demand for guaranteed animal welfare production. In recent years, measures have been taken to improve animal welfare conditions on farms and evaluation systems have been developed to certify them, such as the Welfare Quality^® protocol. Among the markers used for this purpose, acute phase proteins stand out, with haptoglobin being one of the most relevant. However, the diagnostic power of these tools is limited and more sensitive and specific technologies are required to monitor animal health status. Different factors such as diet, stress, and diseases modify the metabolism of the animals, altering the composition of the milk in terms of oligosaccharides, proteins, and lipids. Thus, in order to study oxidative-stress-associated lipids, a collection of well-characterized milk samples, both by veterinary diagnosis and by content of the acute stress biomarker haptoglobin, was analyzed by mass spectrometry and artificial intelligence. Two lipid species (sphingomyelin and phosphatidylcholine) were identified as potential biomarkers of health status in dairy cows. Both lipids allow for the discrimination of milk from sick animals and also milk from those with stress. Moreover, lipidomics revealed specific lipid profiles depending on the origin of the samples and the degree of freedom of the animals on the farm. These data provide evidence for specific lipid changes in stressed animals and open up the possibility that haptoglobin could also affect lipid metabolism in cow's milk.

Measuring the Content of Endocannabinoid-Like Compounds in Biological Fluids: A Critical Overview of Sample Preparation Methodologies

Different mass spectrometric techniques have been used over the past decade to quantify endocannabinoids (eCBs) and related lipids. Even with the level of molecular fingerprinting accuracy of an instrument like the most advanced triple quadrupole mass spectrometer, if one is not getting the most optimized sample to the detector in a way that this improved technology can be of use, then advancements can be stymied. Here, our focus is on review and discussion of sample preparation methodologies used to isolate the eCB anandamide and its close congeners N-acyl ethanolamines and structural congeners (i.e., lipo amino acids, lipoamines, N-acyl amides) in biological fluids. Most of our focus will be on the analysis of these lipids in plasma/serum, but we will also discuss how the same techniques can be used for the analysis of saliva and breast milk.

Tri-Reagent Homogenate Is a Suitable Starting Material for UHPLC-MS Lipidomic Analysis

Background: Transcriptomic and lipidomic dual analyses usually initiate with independent extractive procedures. That entails a difficulty in aligning results from both omics platforms, especially in the case of highly heterogeneous tissues, such as the kidney. Methods: Bligh and Dyer lipid extraction was performed using rat kidney homogenates prepared in PBS or commercially available Tri-reagent used for RNA extraction. Samples were analyzed by ultrahigh performance liquid chromatography-mass spectrometry (UHPLC-MS) lipidomic analysis. Results: Comparison of the lipidome obtained from phosphate-buffered saline (PBS) and Tri-reagent homogenates showed qualitative and quantitative validity of the Tri-reagent homogenate with the exception of ether lipids; the acidic nature of the mix seems to promote the hydrolysis of the ether bond, especially in plasmalogens. We tested several conditions in the sample processing, which allowed to optimize the procedure. Conclusions: Aiming to implement a method that allows the extraction of RNA and lipids from the same tissue homogenate not using external tracers, we here report the use of Tri-reagent homogenates as a suitable starting material for UHPLC-MS lipidomic analysis.

Comparison of one-phase and two-phase extraction methods for porcine tissue lipidomics applying a fast and reliable tentative annotation workflow

Lipidomics has great potential for the discovery of biomarkers, elucidation of metabolic processes and identifying dysregulations in complex biological systems. Concerning biofluids like plasma or cerebrospinal fluid, several studies for the comparison of lipid extraction solvents have already been conducted. With respect to tissues, which can differ significantly in terms of dry matter content and composition, only few studies are available. The proper selection of an extraction method that covers the complexity and individuality of different tissues is challenging. The goal of this work was to provide a systematic overview on the potential of different extraction methods for a broad applicability. This study covers six different extraction procedures and four different reconstitution solvents applied to ten different porcine tissues. To get an overview of the individual lipid profiles, a workflow was developed for a fast and reliable tentative lipid annotation. Therefore, several machine learning tools were utilized, like the prediction of collision cross sections to support the tentative lipid identification in case of untargeted lipidomics. In terms of data evaluation, unsupervised (e.g. principal component analysis) and supervised (e.g. partial least square - discriminant analysis) methods were applied to visualize and subsequently interpret all generated information. Furthermore, the influence of the tissue composition on the extraction performance was investigated. It could be shown that the ten porcine tissues can be distinguished based on their lipid profile with the applied workflow and that the methyl-tert-butyl ether (MTBE) based extraction method (two-phase) showed the best overall performance for the 16 examined lipid species. With this method the highest number of features (428 in lung tissue) could be annotated. Upcoming one-phase extractions also showed a high potential concerning total number of extracted lipids. Methanol/MTBE/chloroform (MMC) extracted slightly less lipids (393 in lung and liver) than MTBE but turned out to be the best one-phase extraction method. Additionally, the numbers of extracted lipids obtained by isopropanol/water 90:10 (IPA90) (399 in stomach) and by isopropanol/methanol/chloroform (IMC) (395 in stomach) were similar to those of the modified Folch method (402 in stomach). One-phase extractions can therefore clearly be seen as preferable when a high throughput is needed.

A UHPLC-Mass Spectrometry View of Human Melanocytic Cells Uncovers Potential Lipid Biomarkers of Melanoma

Melanoma is the deadliest form of skin cancer due to its ability to colonize distant sites and initiate metastasis. Although these processes largely depend on the lipid-based cell membrane scaffold, our understanding of the melanoma lipid phenotype lags behind most other aspects of this tumor cell. Here, we examined a panel of normal human epidermal and nevus melanocytes and primary and metastatic melanoma cell lines to determine whether distinctive cell-intrinsic lipidomes can discern non-neoplastic from neoplastic melanocytes and define their metastatic potential. Lipidome profiles were obtained by UHPLC-ESI mass-spectrometry, and differences in the signatures were analyzed by multivariate statistical analyses. Significant and highly specific changes in more than 30 lipid species were annotated in the initiation of melanoma, whereas less numerous changes were associated with melanoma progression and the non-malignant transformation of nevus melanocytes. Notably, the "malignancy lipid signature" features marked drops in pivotal membrane lipids, like sphingomyelins, and aberrant elevation of ether-type lipids and phosphatidylglycerol and phosphatidylinositol variants, suggesting a previously undefined remodeling of sphingolipid and glycerophospholipid metabolism. Besides broadening the molecular definition of this neoplasm, the different lipid profiles identified may help improve the clinical diagnosis/prognosis and facilitate therapeutic interventions for cutaneous melanoma.

Exploring the Lipidome: Current Lipid Extraction Techniques for Mass Spectrometry Analysis

In recent years, high-throughput lipid profiling has contributed to understand the biological, physiological and pathological roles of lipids in living organisms. Across all kingdoms of life, important cell and systemic processes are mediated by lipids including compartmentalization, signaling and energy homeostasis. Despite important advances in liquid chromatography and mass spectrometry, sample extraction procedures remain a bottleneck in lipidomic studies, since the wide structural diversity of lipids imposes a constrain in the type and amount of lipids extracted. Differences in extraction yield across lipid classes can induce a bias on down-stream analysis and outcomes. This review aims to summarize current lipid extraction techniques used for untargeted and targeted studies based on mass spectrometry. Considerations, applications, and limitations of these techniques are discussed when used to extract lipids in complex biological matrices, such as tissues, biofluids, foods, and microorganisms.

Lipid metabolism of leukocytes in the unstimulated and activated states

Lipidomics has emerged as a powerful technique to study cellular lipid metabolism. As the lipidome contains numerous isomeric and isobaric species resulting in a significant overlap between different lipid classes, cutting-edge analytical technology is necessary for a comprehensive analysis of lipid metabolism. Just recently, differential mobility spectrometry (DMS) has evolved as such a technology, helping to overcome several analytical challenges. We here set out to apply DMS and the Lipidyzer™ platform to obtain a comprehensive overview of leukocyte-related lipid metabolism in the resting and activated states. First, we tested the linearity and repeatability of the platform by using HL60 cells. We obtained good linearities for most of the thirteen analyzed lipid classes (correlation coefficient > 0.95), and good repeatability (%CV < 15). By comparing the lipidome of neutrophils (PMNs), monocytes (CD14+), and lymphocytes (CD4+), we shed light on leukocyte-specific lipid patterns as well as lipidomic changes occurring through differential stimulation. For example, at the resting state, PMNs proved to contain higher amounts of triacylglycerides compared to CD4+ and CD14+ cells. On the other hand, CD4+ and CD14+ cells contained higher levels of phospholipids and ceramides. Upon stimulation, diacylglycerides, hexosylceramides, phosphatidylcholines, phosphoethanolamines, and lysophosphoethanolamines were upregulated in CD4+ cells and PMNs, whereas CD14+ cells did not show significant changes. By exploring the fatty acid content of the significantly upregulated lipid classes, we mainly found increased concentrations of very long and polyunsaturated fatty acids. Our results indicate the usefulness of the Lipidyzer™ platform for studying cellular lipid metabolism. Its application allowed us to explore the lipidome of leukocytes.

Lipidomic data uncover extensive heterogeneity in phosphatidylcholine structural variants in HepG2 cells

The data contain information related to the research article entitled “Profiling of promoter occupancy by the SND1 transcriptional coactivator identifies downstream glycerolipid metabolic genes involved in TNFα response in human hepatoma cells” (DOI: 10.1093/nar/gkv858). In the article alluded to, we reported that tumor necrosis factor alpha (TNFα) increases notably the cellular content of the major glycerolipid phosphatidylcholine (PC). Here, accompanying lipidomic data determine the PC structural variants that have been identified in human hepatoma HepG2 cells and those whose relative abundance is modified by TNFα. We used ultrahigh performance liquid chromatography (UHPLC) coupled to electrospray ionization (ESI) tandem mass spectrometry (MS/MS)-based lipidomic profiling to analyze lipid extracts of control and TNFα-treated HepG2 cells. The identity of PC individual species was elucidated using the values of the retention time and molecular weight in addition to the fragmentation patterns. MS data were then processed and analyzed for the characterization of statistically significant differences in detected structural variants. We have annotated the dataset of PC species that characterize HepG2 cells' phenotype, both under normal and pro-inflammatory conditions.