Impact of hemolysis on multi-OMIC pancreatic biomarker discovery to derisk biomarker development in precision medicine studies.

Cancer biomarker discovery is critically dependent on the integrity of biofluid and tissue samples acquired from study participants. Multi-omic profiling of candidate protein, lipid, and metabolite biomarkers is confounded by timing and fasting status of sample collection, participant demographics and treatment exposures of the study population. Contamination by hemoglobin, whether caused by hemolysis during sample preparation or underlying red cell fragility, contributes 0-10 g/L of extraneous protein to plasma, serum, and Buffy coat samples and may interfere with biomarker detection and validation. We analyzed 617 plasma, 701 serum, and 657 buffy coat samples from a 7-year longitudinal multi-omic biomarker discovery program evaluating 400+ participants with or at risk for pancreatic cancer, known as Project Survival. Hemolysis was undetectable in 93.1% of plasma and 95.0% of serum samples, whereas only 37.1% of buffy coat samples were free of contamination by hemoglobin. Regression analysis of multi-omic data demonstrated a statistically significant correlation between hemoglobin concentration and the resulting pattern of analyte detection and concentration. Although hemolysis had the greatest impact on identification and quantitation of the proteome, distinct differentials in metabolomics and lipidomics were also observed and correlated with severity. We conclude that quality control is vital to accurate detection of informative molecular differentials using OMIC technologies and that caution must be exercised to minimize the impact of hemolysis as a factor driving false discovery in large cancer biomarker studies.

Age-Related Changes in Lipidome of Rat Frontal Cortex and Cerebellum Are Partially Reversed by Methionine Restriction Applied in Old Age.

Lipids are closely associated with brain structure and function. However, the potential changes in the lipidome induced by aging remain to be elucidated. In this study, we used chromatographic techniques and a mass spectrometry-based approach to evaluate age-associated changes in the lipidome of the frontal cortex and cerebellum obtained from adult male Wistar rats (8 months), aged male Wistar rats (26 months), and aged male Wistar rats submitted to a methionine restriction diet (MetR)-as an anti-aging intervention-for 8 weeks. The outcomes revealed that only small changes (about 10%) were observed in the lipidome profile in the cerebellum and frontal cortex during aging, and these changes differed, in some cases, between regions. Furthermore, a MetR diet partially reversed the effects of the aging process. Remarkably, the most affected lipid classes were ether-triacylglycerols, diacylglycerols, phosphatidylethanolamine N-methylated, plasmalogens, ceramides, and cholesterol esters. When the fatty acid profile was analyzed, we observed that the frontal cortex is highly preserved during aging and maintained under MetR, whereas in the cerebellum minor changes (increased monounsaturated and decreased polyunsaturated contents) were observed and not reversed by MetR. We conclude that the rat cerebellum and frontal cortex have efficient mechanisms to preserve the lipid profile of their cell membranes throughout their adult lifespan in order to maintain brain structure and function. A part of the small changes that take place during aging can be reversed with a MetR diet applied in old age.

Recommendations for good practice in MS-based lipidomics.

In the last 2 decades, lipidomics has become one of the fastest expanding scientific disciplines in biomedical research. With an increasing number of new research groups to the field, it is even more important to design guidelines for assuring high standards of data quality. The Lipidomics Standards Initiative is a community-based endeavor for the coordination of development of these best practice guidelines in lipidomics and is embedded within the International Lipidomics Society. It is the intention of this review to highlight the most quality-relevant aspects of the lipidomics workflow, including preanalytics, sample preparation, MS, and lipid species identification and quantitation. Furthermore, this review just does not only highlights examples of best practice but also sheds light on strengths, drawbacks, and pitfalls in the lipidomic analysis workflow. While this review is neither designed to be a step-by-step protocol by itself nor dedicated to a specific application of lipidomics, it should nevertheless provide the interested reader with links and original publications to obtain a comprehensive overview concerning the state-of-the-art practices in the field.

Untargeted lipidomics uncovers lipid signatures that distinguish severe from moderate forms of acutely decompensated cirrhosis.

BACKGROUND & AIMS: Acute decompensation (AD) of cirrhosis is a heterogeneous clinical entity associated with moderate mortality. In some patients, this condition develops quickly into the more deadly acute-on-chronic liver failure (ACLF), in which other organs such as the kidneys or brain fail. The aim of this study was to characterize the blood lipidome in a large series of patients with cirrhosis and identify specific signatures associated with AD and ACLF development. METHODS: Serum untargeted lipidomics was performed in 561 patients with AD (518 without and 43 with ACLF) (discovery cohort) and in 265 patients with AD (128 without and 137 with ACLF) in whom serum samples were available to perform repeated measurements during the 28-day follow-up (validation cohort). Analyses were also performed in 78 patients with AD included in a therapeutic albumin trial (43 patients with compensated cirrhosis and 29 healthy individuals). RESULTS: The circulating lipid landscape associated with cirrhosis was characterized by a generalized suppression, which was more manifest during AD and in non-surviving patients. By computing discriminating accuracy and the variable importance projection score for each of the 223 annotated lipids, we identified a sphingomyelin fingerprint specific for AD of cirrhosis and a distinct cholesteryl ester and lysophosphatidylcholine fingerprint for ACLF. Liver dysfunction and infections were the principal net contributors to these fingerprints, which were dynamic and interchangeable between patients with AD whose condition worsened to ACLF and those who improved. Notably, blood lysophosphatidylcholine levels increased in these patients after albumin therapy. CONCLUSIONS: Our findings provide insights into the lipid landscape associated with decompensation of cirrhosis and ACLF progression and identify unique non-invasive diagnostic biomarkers of advanced cirrhosis. LAY SUMMARY: Analysis of lipids in blood from patients with advanced cirrhosis reveals a general suppression of their levels in the circulation of these patients. A specific group of lipids known as sphingomyelins are useful to distinguish between patients with compensated and decompensated cirrhosis. Another group of lipids designated cholesteryl esters further distinguishes patients with decompensated cirrhosis who are at risk of developing organ failures.

A Review of Efforts to Improve Lipid Stability during Sample Preparation and Standardization Efforts to Ensure Accuracy in the Reporting of Lipid Measurements.

Lipidomics is a rapidly growing field, fueled by developments in analytical instrumentation and bioinformatics. To date, most researchers and industries have employed their own lipidomics workflows without a consensus on best practices. Without a community-wide consensus on best practices for the prevention of lipid degradation and transformations through sample collection and analysis, it is difficult to assess the quality of lipidomics data and hence trust results. Clinical studies often rely on samples being stored for weeks or months until they are analyzed, but inappropriate sampling techniques, storage temperatures, and analytical protocols can result in the degradation of complex lipids and the generation of oxidized or hydrolyzed metabolite artifacts. While best practices for lipid stability are sample dependent, it is generally recommended that strategies during sample preparation capable of quenching enzymatic activity and preventing oxidation should be considered. In addition, after sample preparation, lipid extracts should be stored in organic solvents with antioxidants at -20 °C or lower in an airtight container without exposure to light or oxygen. This will reduce or eliminate sublimation, and chemically and physically induced molecular transformations such as oxidation, enzymatic transformation, and photon/heat-induced degradation. This review explores the available literature on lipid stability, with a particular focus on human health and/or clinical lipidomic applications. Specifically, this includes a description of known mechanisms of lipid degradation, strategies, and considerations for lipid storage, as well as current efforts for standardization and quality insurance of protocols.

Nontargeted lipidomics of novel human plasma reference materials: hypertriglyceridemic, diabetic, and African-American.

The unavailability of appropriate quality assurance/quality control materials in many lipidomics applications poses a significant challenge for lipidomics research. It is recommended that samples with certified values and/or consensus estimates, such as NIST SRM 1950-Metabolites in Frozen Human Plasma, be implemented in routine analyses to enable community-wide comparisons of lipidomics results and analytical workflows. Herein, we applied a nontargeted lipidomics method for the analysis of a new human plasma reference material suite developed by NIST (hypertriglyceridemic, diabetic, and African-American plasma pools), in addition to SRM 1950. We identified specific lipidomics fingerprints associated with each sample type, including lauric acid-containing lipids and elevated triacylglycerol levels in hypertriglyceridemic plasma, palmitoleic acid-containing lipids in diabetic plasma, and oxidized fatty acid-containing phospholipids in African-American plasma. This work highlights the importance of developing and profiling application-specific reference materials, while establishing reference data that may be used for system suitability and/or quality control metrics.Graphical abstract.

Evaluation of Cell Harvesting Techniques to Optimize Lipidomic Analysis from Human Meibomian Gland Epithelial Cells in Culture.

The lipidomic analysis of immortalized human meibomian gland epithelial cells (HMGECs) has been proposed as a preclinical model to study meibomian gland dysfunction. An in vitro study was conducted to evaluate neutral lipid recovery following three harvesting techniques and to identify candidate lipid biomarkers of HMGECs. HMGECs were cultured in serum-containing media for two days to promote lipid production. Cells were either harvested by 0.25% trypsin-ethylenediaminetetraacetic acid (EDTA), harvested by 10 mM EDTA, or simultaneously harvested and extracted by 2:1 chloroform-methanol (CM). After extraction by a modified Folch technique, the nonpolar phase was processed and infused into a TripleTOF 5600 mass spectrometer (Sciex, Framingham, MA, USA) with electrospray ionization. MS and MS/MSall spectra were acquired. Nonpolar cholesteryl esters (CEs) were consistently detected in all samples, while wax esters were not. Only small differences in two out of twenty CEs were detected between harvesting methods. CM yielded less CE18:1 than the other methods but greater CE20:4 than the trypsin-EDTA method (p < 0.05 for all). Similar to human meibum, very long-chain CEs with carbon number (nc) ≥ 24 were detected in all samples and may serve as HMGEC lipid biomarkers. Further work is needed to address the absence of wax esters. Overall, the three harvesting methods are reasonably equivalent, though CM promotes much better efficiency and is recommended for higher throughput.

Clinical lipidomics in understanding of lung cancer: Opportunity and challenge.

Disordered lipid metabolisms have been evidenced in lung cancer as well as its subtypes. Lipidomics with in-depth mining is considered as a critical member of the multiple omics family and a lipid-specific tool to understand disease-associated lipid metabolism and disease-specific dysfunctions of lipid species, discover biomarkers and targets for monitoring therapeutic strategies, and provide insights into lipid profiling and pathophysiological mechanisms in lung cancer. The present review describes the characters and patterns of lipidomic profiles in patients with different lung cancer subtypes, important values of comprehensive lipidomic profiles in understanding of lung cancer heterogeneity, urgent needs of standardized methodologies, potential mechanisms by lipid-associated enzymes and proteins, and the importance of integration between clinical phenomes and lipidomic profiles. The characteristics of lipidomic profiles in different lung cancer subtypes are extremely varied among study designs, objects, methods, and analyses. Preliminary data from recent studies demonstrate the specificity of lipidomic profiles specific for lung cancer stage, severity, subtype, and response to drugs. The heterogeneity of lipidomic profiles and lipid metabolism may be part of systems heterogeneity in lung cancer and be responsible for the development of drug resistance, although there are needs for direct evidence to show the existence of intra- or inter-lung cancer heterogeneity of lipidomic profiles. With an increasing understanding of expression profiles of genes and proteins, lipidomic profiles should be associated with activities of enzymes and proteins involved in the processes of lipid metabolism, which can be profiled with genomics and proteomics, and to provide the opportunity for the integration of lipidomic profiles with gene and protein expression profiles. The concept of clinical trans-omics should be emphasized to integrate data of lipidomics with clinical phenomics to identify disease-specific and phenome-specific biomarkers and targets, although there are still a large number of challenges to be overcome in the integration between clinical phenomes and lipidomic profiles.

Shared reference materials harmonize lipidomics across MS-based detection platforms and laboratories.

Quantitative MS of human plasma lipids is a promising technology for translation into clinical applications. Current MS-based lipidomic methods rely on either direct infusion (DI) or chromatographic lipid separation methods (including reversed phase and hydrophilic interaction LC). However, the use of lipid markers in laboratory medicine is limited by the lack of reference values, largely because of considerable differences in the concentrations measured by different laboratories worldwide. These inconsistencies can be explained by the use of different sample preparation protocols, method-specific calibration procedures, and other experimental and data-reporting parameters, even when using identical starting materials. Here, we systematically investigated the roles of some of these variables in multiple approaches to lipid analysis of plasma samples from healthy adults by considering: 1) different sample introduction methods (separation vs. DI methods); 2) different MS instruments; and 3) between-laboratory differences in comparable analytical platforms. Each of these experimental variables resulted in different quantitative results, even with the inclusion of isotope-labeled internal standards for individual lipid classes. We demonstrated that appropriate normalization to commonly available reference samples (i.e., "shared references") can largely correct for these systematic method-specific quantitative biases. Thus, to harmonize data in the field of lipidomics, in-house long-term references should be complemented by a commonly available shared reference sample, such as NIST SRM 1950, in the case of human plasma.

Guidelines for Selection of Internal Standard-Based Normalization Strategies in Untargeted Lipidomic Profiling by LC-HR-MS/MS.

Due to variation in instrument response caused by various sources of errors throughout an analytical assay, data normalization plays an indispensable role in untargeted LC-MS profiling, yet limited accepted guidelines on this topic exist. In this work, a systematic comparison of several normalization techniques, mainly focusing on internal standard-based approaches, has been performed to derive some general recommendations. For generation of untargeted lipidomic data, a comprehensive ultra-high performance liquid chromatography (UHPLC)-electrospray ionization (ESI)-quadrupole time of flight (QTOF)-MS/MS method was utilized. To monitor instrument stability and evaluate normalization performance, quality control (QC) samples, prepared from aliquots of all experimental samples, were embedded in the sequence. Stable isotope labeled standards, representing differing lipid classes, were spiked to each sample as internal standards for postacquisition normalization. Various metrics were used to compare distinct normalization strategies, with reduction of variation in QC samples being the critical requirement for acceptance of successful normalization. The comparison of intragroup coefficients of variation (CVs), median absolute deviations (MADs), and variance enables simple selection of the best performance of normalization with improved and coherent results. Furthermore, the importance for normalization in critical data sets, showing only minor effects between groups with high variation and outliers, is pointed out. Apart from normalization, also, influences of used raw data types are demonstrated. In addition, effects of various factors throughout the processing workflow were investigated and optimized. Eventually, implementation of quality control samples, even if not required for normalization, provided a useful basis for assessing data quality. Due to lack of consensus for selecting optimum normalization, suggestions for validating data integrity are given.

Systematic Error Removal Using Random Forest for Normalizing Large-Scale Untargeted Lipidomics Data.

Large-scale untargeted lipidomics experiments involve the measurement of hundreds to thousands of samples. Such data sets are usually acquired on one instrument over days or weeks of analysis time. Such extensive data acquisition processes introduce a variety of systematic errors, including batch differences, longitudinal drifts, or even instrument-to-instrument variation. Technical data variance can obscure the true biological signal and hinder biological discoveries. To combat this issue, we present a novel normalization approach based on using quality control pool samples (QC). This method is called systematic error removal using random forest (SERRF) for eliminating the unwanted systematic variations in large sample sets. We compared SERRF with 15 other commonly used normalization methods using six lipidomics data sets from three large cohort studies (832, 1162, and 2696 samples). SERRF reduced the average technical errors for these data sets to 5% relative standard deviation. We conclude that SERRF outperforms other existing methods and can significantly reduce the unwanted systematic variation, revealing biological variance of interest.