Liquid chromatography-tandem mass spectrometry-based plasma metabonomics delineate the effect of metabolites' stability on reliability of potential biomarkers

Investigation into temporal changes in the human bloodstain lipidome

Bloodstains are crucial pieces of physical evidences found at violent crime scenes, providing valuable information for reconstructing forensic cases. However, there is limited data on how bloodstain lipidomes change over time after deposition. Hence, we deployed a high-throughput high-performance liquid chromatography-mass spectrometry (HPLC-MS) approach to construct lipidomic atlases of bloodstains, whole blood, plasma, and blood cells from 15 healthy adults. A time-course analysis was also performed on bloodstains deposited for up to 6 months at room temperature (~ 25°C). The molecular levels of 60 out of 400 detected lipid species differed dramatically between bloodstain and whole blood samples, with major disturbances observed in membrane glycerophospholipids. More than half of these lipids were prevalent in the cellular and plasmic fractions; approximately 27% and 10% of the identified lipids were uniquely derived from blood cells and plasma, respectively. Furthermore, a subset of 65 temporally dynamic lipid species arose across the 6-month room-temperature deposition period, with decreased triacylglycerols (TAGs) and increased lysophosphatidylcholines (LPCs) as representatives, accounting for approximately 8% of the total investigated lipids. The instability of lipids increased linearly with time, with the most variability observed in the first 10 days. This study sheds light on the impact of air-drying bloodstains on blood components at room temperature and provides a list of potential bloodstain lipid markers for determining the age of bloodstains.

Targeted Metabolomic Biomarkers for Stroke Subtyping

BACKGROUND AND PURPOSE

Ischemic stroke is a heterogeneous disease with various etiologies. The current subtyping process is complicated, time-consuming, and costly. Metabolite-based biomarkers have the potential to improve classification and deliver optimal treatments. We here aimed to identify novel, targeted metabolomics-based biomarkers to discriminate between large-artery atherosclerosis (LAA) and cardioembolic (CE) stroke.

METHODS

We acquired serum samples and clinical data from a hospital-based acute stroke registry (ischemic stroke within 3 days from symptom onset). We included 346 participants (169 LAA, 147 CE, and 30 healthy older adults) and divided them into training and test sets. Targeted metabolomic analysis was performed using quantitative and quality-controlled liquid chromatography with tandem mass spectrometry. A multivariate regression model using metabolomic signatures was created that could independently distinguish between LAA and CE strokes.

RESULTS

The training set (n = 193) identified metabolomic signatures that were different in patients with LAA and CE strokes. Six metabolomic biomarkers, i.e., lysine, serine, threonine, kynurenine, putrescine, and lysophosphatidylcholine acyl C16:0, could discriminate between LAA and CE stroke after adjusting for sex, age, body mass index, stroke severity, and comorbidities. The enhanced diagnostic power of key metabolite combinations for discriminating between LAA and CE stroke was validated using the test set (n = 123).

CONCLUSIONS

We observed significant differences in metabolite profiles in LAA and CE strokes. Targeted metabolomics may provide enhanced diagnostic yield for stroke subtypes. The pathophysiological pathways of the identified metabolites should be explored in future studies.

Nutritional lipidomics for the characterization of lipids in food

Lipids represent one out of three major macronutrient classes in the human diet. It is estimated to account for about 15-20% of the total dietary intake. Triacylglycerides comprise the majority of them, estimated 90-95%. Other lipid classes include free fatty acids, phospholipids, cholesterol, and plant sterols as minor components. Various methods are used for the characterization of nutritional lipids, however, lipidomics approaches become increasingly attractive for this purpose due to their wide coverage, comprehensiveness and holistic view on composition. In this chapter, analytical methodologies and workflows utilized for lipidomics profiling of food samples are outlined with focus on mass spectrometry-based assays. The chapter describes common lipid extraction protocols, the distinct instrumental mass-spectrometry based analytical platforms for data acquisition, chromatographic and ion-mobility spectrometry methods for lipid separation, briefly mentions alternative methods such as gas chromatography for fatty acid profiling and mass spectrometry imaging. Critical issues of important steps of lipidomics workflows such as structural annotation and identification, quantification and quality assurance are discussed as well. Applications reported over the period of the last 5years are summarized covering the discovery of new lipids in foodstuff, differential profiling approaches for comparing samples from different origin, species, varieties, cultivars and breeds, and for food processing quality control. Lipidomics as a powerful tool for personalized nutrition and nutritional intervention studies is briefly discussed as well. It is expected that this field is significantly growing in the near future and this chapter gives a short insight into the power of nutritional lipidomics approaches.

An integrative exploration of loquat leaf total sesquiterpene glycosides in treating insulin-resistant mice by serum and urine untargeted metabolomics analysis

Loquat leaf is approved to be beneficial in the treatment of diabetes. Total sesquiterpene glycosides (TSG), a major chemical component cluster, has potential ability to improve insulin-resistant diabetes syndrome. Its therapeutic mechanism using metabolomics in vivo is worth to be investigated. This study aimed to reveal the underlying therapeutic mechanism of TSG on insulin-resistant mice by untargeted metabolomics, and to explore the lipid metabolism differences in vivo. High-fat diet was used to induce insulin-resistant mice model. Biochemical indicators were applied to evaluate the model validity and related treatment effect. Ultra-performance liquid chromatography quadrupole-time-of-flight mass spectrometry was utilized to accomplish serum and urine untargeted metabolomics. Oral administration of TSG had a therapeutic effect on high-fat diet induced insulin-resistant mice. Four hundred forty-two metabolites in serum and 1732 metabolites in urine were annotated. Principal component analysis screened 324 differential metabolic signatures in serum sample and 1408 in urine sample. The pathway mainly involved purine metabolism and biosynthesis of unsaturated fatty acids. Lipidomic analysis of urine and serum confirmed that most lipid metabolites were fatty acyls, sterol lipids and polyketides.

Impact of preanalytical freezing delay time on the stability of metabolites in oral squamous cell carcinoma tissue samples

INTRODUCTION

Metabolite stability is critical for tissue metabolomics. However, changes in metabolites in tissues over time from the operating room to the laboratory remain underexplored.

OBJECTIVES

In this study, we evaluated the effect of postoperative freezing delay time on the stability of metabolites in normal and oral squamous cell carcinoma (OSCC) tissues.

METHODS

Tumor and paired normal tissues from five OSCC patients were collected after surgical resection, and samples was sequentially quenched in liquid nitrogen at 30, 40, 50, 60, 70, 80, 90 and 120 min (80 samples). Untargeted metabolic analysis by liquid chromatography-mass spectrometry/mass spectrometry in positive and negative ion modes was used to identify metabolic changes associated with delayed freezing time. The trends of metabolite changes at 30-120 and 30-60 min of delayed freezing were analyzed.

RESULTS

190 metabolites in 36 chemical classes were detected. After delayed freezing for 120 min, approximately 20% of the metabolites changed significantly in normal and tumor tissues, and differences in the metabolites were found in normal and tumor tissues. After a delay of 60 min, 29 metabolites had changed significantly in normal tissues, and 84 metabolites had changed significantly in tumor tissues. In addition, we constructed three tissue freezing schemes based on the observed variation trends in the metabolites.

CONCLUSION

Delayed freezing of tissue samples has a certain impact on the stability of metabolites. For metabolites with significant changes, we suggest that the freezing time of tissues be reasonably selected according to the freezing schemes and the actual clinical situation.

Preanalytical Pitfalls in Untargeted Plasma Nuclear Magnetic Resonance Metabolomics of Endocrine Hypertension

Despite considerable morbidity and mortality, numerous cases of endocrine hypertension (EHT) forms, including primary aldosteronism (PA), pheochromocytoma and functional paraganglioma (PPGL), and Cushing's syndrome (CS), remain undetected. We aimed to establish signatures for the different forms of EHT, investigate potentially confounding effects and establish unbiased disease biomarkers. Plasma samples were obtained from 13 biobanks across seven countries and analyzed using untargeted NMR metabolomics. We compared unstratified samples of 106 PHT patients to 231 EHT patients, including 104 PA, 94 PPGL and 33 CS patients. Spectra were subjected to a multivariate statistical comparison of PHT to EHT forms and the associated signatures were obtained. Three approaches were applied to investigate and correct confounding effects. Though we found signatures that could separate PHT from EHT forms, there were also key similarities with the signatures of sample center of origin and sample age. The study design restricted the applicability of the corrections employed. With the samples that were available, no biomarkers for PHT vs. EHT could be identified. The complexity of the confounding effects, evidenced by their robustness to correction approaches, highlighted the need for a consensus on how to deal with variabilities probably attributed to preanalytical factors in retrospective, multicenter metabolomics studies.

Intrapersonal Stability of Plasma Metabolomic Profiles over 10 Years among Women

In epidemiological studies, samples are often collected long before disease onset or outcome assessment. Understanding the long-term stability of biomarkers measured in these samples is crucial. We estimated within-person stability over 10 years of metabolites and metabolite features (n = 5938) in the Nurses’ Health Study (NHS): the primary dataset included 1880 women with 1184 repeated samples donated 10 years apart while the secondary dataset included 1456 women with 488 repeated samples donated 10 years apart. We quantified plasma metabolomics using two liquid chromatography mass spectrometry platforms (lipids and polar metabolites) at the Broad Institute (Cambridge, MA, USA). Intra-class correlations (ICC) were used to estimate long-term (10 years) within-person stability of metabolites and were calculated as the proportion of the total variability (within-person + between-person) attributable to between-person variability. Within-person variability was estimated among participants who donated two blood samples approximately 10 years apart while between-person variability was estimated among all participants. In the primary dataset, the median ICC was 0.43 (1st quartile (Q1): 0.36; 3rd quartile (Q3): 0.50) among known metabolites and 0.41 (Q1: 0.34; Q3: 0.48) among unknown metabolite features. The three most stable metabolites were N6,N6-dimethyllysine (ICC = 0.82), dimethylguanidino valerate (ICC = 0.72), and N-acetylornithine (ICC = 0.72). The three least stable metabolites were palmitoylethanolamide (ICC = 0.05), ectoine (ICC = 0.09), and trimethylamine-N-oxide (ICC = 0.16). Results in the secondary dataset were similar (Spearman correlation = 0.87) to corresponding results in the primary dataset. Within-person stability over 10 years is reasonable for lipid, lipid-related, and polar metabolites, and varies by metabolite class. Additional studies are required to estimate within-person stability over 10 years of other metabolites groups.

Cross platform solutions to improve the zebrafish polar metabolome coverage using LC-QTOF MS: Optimization of separation mechanisms, solvent additives, and resuspension solvents

Untargeted metabolomics has been widely used for studies with zebrafish embryos. Until now, the number of analytical approaches to determine metabolites in zebrafish is limited, and there is a lack of consensus on the best platforms for comprehensive metabolomics analysis of zebrafish embryos. In addition, the capacity of these methods to detect metabolites is unsatisfactory and the confidence level for identifying compounds is relatively low. To improve the metabolome coverage, we mainly focused on the optimization of separation mechanisms, mobile phase additives, and resuspension solvents based on liquid chromatography (LC) coupling to high-resolution mass spectrometry (HRMS) techniques. Moreover, the procedures for optimizing methods were assessed when taking metabolite profiles in both positive and negative ionization modes into account. Four LC columns were studied: C18, T3, PFP, and HILIC. In positive ionization mode, it was strongly recommended to employ the HILIC approach operated at the neutral condition, which led to the presence of more than 4700 features and the annotation of 151 metabolites, mainly zwitterionic and basic compounds, in comparison to reverse phase (RP)-based methods with less than 1000 features. In negative ionization mode, the PFP column operated at 0.02% acetic acid showed the best performance in terms of metabolite coverage: 3100 metabolic features were detected and 218 metabolites were annotated in zebrafish embryos. Metabolite profiles mainly contained acidic and zwitterionic compounds. HILIC-based platforms were complementary to RP columns when analyzing highly polar metabolites. Additionally, it was preferable to reconstitute zebrafish extracts in 100% water for analysis of metabolites on RP columns, with a 20-30% increase in the number of identified metabolites compared to a 50% water in methanol solution. However, water/methanol (1:9, v/v), as resuspension solution, was advantageous over water/methanol (1:1, v/v) for HILIC analysis showing an 8-15% increase in detected metabolites. In total 336 polar metabolites were annotated by the combination of the optimized HILIC (positive) and PFP (negative) approaches. The largest metabolome coverage of polar metabolites in zebrafish embryos was obtained when three approaches were combined (negative PFP and HILIC, and HILIC positive) resulting in more than 420 annotated compounds.

From bedside to bench-practical considerations to avoid pre-analytical pitfalls and assess sample quality for high-resolution metabolomics and lipidomics analyses of body fluids

The stability of lipids and other metabolites in human body fluids ranges from very stable over several days to very unstable within minutes after sample collection. Since the high-resolution analytics of metabolomics and lipidomics approaches comprise all these compounds, the handling of body fluid samples, and thus the pre-analytical phase, is of utmost importance to obtain valid profiling data. This phase consists of two parts, sample collection in the hospital (“bedside”) and sample processing in the laboratory (“bench”). For sample quality, the apparently simple steps in the hospital are much more critical than the “bench” side handling, where (bio)analytical chemists focus on highly standardized processing for high-resolution analysis under well-controlled conditions. This review discusses the most critical pre-analytical steps for sample quality from patient preparation; collection of body fluids (blood, urine, cerebrospinal fluid) to sample handling, transport, and storage in freezers; and subsequent thawing using current literature, as well as own investigations and practical experiences in the hospital. Furthermore, it provides guidance for (bio)analytical chemists to detect and prevent potential pre-analytical pitfalls at the “bedside,” and how to assess the quality of already collected body fluid samples. A knowledge base is provided allowing one to decide whether or not the sample quality is acceptable for its intended use in distinct profiling approaches and to select the most suitable samples for high-resolution metabolomics and lipidomics investigations.

A combined flow injection/reversed-phase chromatography-high-resolution mass spectrometry workflow for accurate absolute lipid quantification with 13C internal standards

We propose a fully automated novel workflow for lipidomics based on flow injection, followed by liquid chromatography-high-resolution mass spectrometry (FI/LC-HRMS). The workflow combined in-depth characterization of the lipidome achieved via reversed-phase LC-HRMS with absolute quantification by using a large number of lipid species-specific and/or retention time (RT)-matched/class-specific calibrants. The lipidome of 13C-labelled yeast (LILY) provided a large panel of cost-effective internal standards (ISTDs) covering triacylglycerols (TG), steryl esters (SE), free fatty acids (FA), diacylglycerols (DG), sterols (ST), ceramides (Cer), hexosyl ceramides (HexCer), phosphatidylglycerols (PG), phosphatidylethanolamines (PE), phosphatidic acids (PA), cardiolipins (CL), phosphatidylinositols (PI), phosphatidylserines (PS), phosphatidylcholines (PC), lysophosphatidylcholines (LPC) and lysophosphatidylethanolamines (LPE). The workflow in combination with the LILY lipid panel enables simultaneous quantification via (1) external multi-point calibration with internal standardization and (2) internal one-point calibration with LILY as a surrogate ISTD, increasing the coverage while keeping the accuracy and throughput high. Extensive measures on quality control allowed us to rank the calibration strategies and to automatically select the calibration strategy of the highest metrological order for the respective lipid species. Overall, the workflow enabled a streamlined analysis, with a limit of detection in the low femtomolar range, and provided validation tools together with absolute concentration values for >350 lipids in human plasma on a species level. Based on the selected standard panel, lipids from 7 classes (LPC, LPE, PC, PE, PI, DG, TG) passed stringent quality filters, which included QC accuracy, a precision and recovery bias of <30% and concentrations within the 99% confidence interval of the international laboratory comparison of SRM 1950, NIST, USA. The quantitative values are independent of common deuterated or non-endogenous ISTDs, thus offering cross-validation of different lipid methods and further standardizing lipidomics.

Stability of amino acids and related amines in human serum under different preprocessing and pre-storage conditions based on iTRAQ®-LC-MS/MS

Amino acid analysis or metabonomics requires large-scale sample collection, which makes sample storage a critical consideration. However, functional amino acids are often neglected in metabolite stability studies because of the difficulty in detecting and accurately quantifying them with most analysis methods. Here, we investigated the stability of amino acids and related amines in human serum following different preprocessing and pre-storage procedures. Serum samples were collected and subjected to three storage conditions; cold storage (4°C), room temperature storage (22°C), and freezing (−80°C). The concentration of amino acids and related amines were quantified using iTRAQ^®-LC-MS/MS with isobaric tagging reagents. Approximately 54.84%, 58.06%, and 48.39% of detectable and target analytes were altered at the 4°C condition, 22°C condition, and when subjected to freeze-thaw cycles, respectively. Some amino acids which are unstable and relatively stable were found. Our study provides detailed amino acid profiles in human serum and suggests pre-treatment measures that could be taken to improve stability.

Summary: We investigated the stability of amino acids in serum samples that underwent prolonged storage at 4°C and 22°C, and repeated freeze-thaw cycles at −80°C using stable isotope iTRAQ labeling and liquid chromatography tandem mass spectrometry.

Advancing Biomarker Development Through Convergent Engagement: Summary Report of the 2nd International Danube Symposium on Biomarker Development, Molecular Imaging and Applied Diagnostics; March 14-16, 2018; Vienna, Austria

Here, we report on the outcome of the 2nd International Danube Symposium on advanced biomarker development that was held in Vienna, Austria, in early 2018. During the meeting, cross-speciality participants assessed critical aspects of non-invasive, quantitative biomarker development in view of the need to expand our understanding of disease mechanisms and the definition of appropriate strategies both for molecular diagnostics and personalised therapies. More specifically, panelists addressed the main topics, including the current status of disease characterisation by means of non-invasive imaging, histopathology and liquid biopsies as well as strategies of gaining new understanding of disease formation, modulation and plasticity to large-scale molecular imaging as well as integrative multi-platform approaches. Highlights of the 2018 meeting included dedicated sessions on non-invasive disease characterisation, development of disease and therapeutic tailored biomarkers, standardisation and quality measures in biospecimens, new therapeutic approaches and socio-economic challenges of biomarker developments. The scientific programme was accompanied by a roundtable discussion on identification and implementation of sustainable strategies to address the educational needs in the rapidly evolving field of molecular diagnostics. The central theme that emanated from the 2nd Donau Symposium was the importance of the conceptualisation and implementation of a convergent approach towards a disease characterisation beyond lesion-counting "lumpology" for a cost-effective and patient-centric diagnosis, therapy planning, guidance and monitoring. This involves a judicious choice of diagnostic means, the adoption of clinical decision support systems and, above all, a new way of communication involving all stakeholders across modalities and specialities. Moreover, complex diseases require a comprehensive diagnosis by converging parameters from different disciplines, which will finally yield to a precise therapeutic guidance and outcome prediction. While it is attractive to focus on technical advances alone, it is important to develop a patient-centric approach, thus asking "What can we do with our expertise to help patients?"

Prognostic value of endogenous and exogenous metabolites in liver transplantation

Liver transplantation has been widely accepted as an effective intervention for end-stage liver diseases and early hepatocellular carcinomas. However, a variety of postoperative complications and adverse reactions have baffled medical staff and patients. Currently, transplantation monitoring relies primarily on nonspecific biochemical tests, whereas diagnosis of multiple complications depends on invasive pathological examination. Therefore, a noninvasive monitoring method with high selectivity and specificity is desperately needed. This review summarized the potential of endogenous small-molecule metabolites as biomarkers for assessing graft function, ischemia-reperfusion injury and liver rejection. Exogenous metabolites, mainly those immunosuppressive agents with high intra- and inter-individual variability, were also discussed for transplantation monitoring.

Test-Retest Reliability and Correlates of Vertebral Bone Marrow Lipid Composition by Lipidomics Among Children With Varying Degrees of Bone Fragility

The reliability of lipidomics, an approach to identify the presence and interactions of lipids, to analyze the bone marrow lipid composition among pediatric populations with bone fragility is unknown. The objective of this study was to assess the test–retest reliability, standard error of measurement (SEM), and the minimal detectable change (MDC) of vertebral bone marrow lipid composition determined by targeted lipidomics among children with varying degrees of bone fragility undergoing routine orthopedic surgery. Children aged 10 to 19 years, with a confirmed diagnosis of adolescent idiopathic scoliosis (n = 13) or neuromuscular scoliosis and cerebral palsy (n = 3), undergoing posterior spinal fusion surgery at our institution were included in this study. Transpedicular vertebral body bone marrow samples were taken from thoracic vertebrae (T11, 12) or lumbar vertebrae (L1 to L4). Lipid composition was assessed via targeted lipidomics and all samples were analyzed in the same batch. Lipid composition measures were examined as the saturated, monounsaturated, and polyunsaturated index and as individual fatty acids. Relative and absolute test–retest reliability was assessed using the intraclass correlation coefficient (ICC), SEM, and MDC. Associations between demographics and index measures were explored. The ICC, SEM, and MDC were 0.81 (95% CI, 0.55–0.93), 1.6%, and 4.3%, respectively, for the saturated index, 0.66 (95% CI, 0.25–0.87), 3.5%, and 9.7%, respectively, for the monounsaturated index, and 0.60 (95% CI, 0.17–0.84), 3.6%, and 9.9%, respectively, for the polyunsaturated index. For the individual fatty acids, the ICC showed a considerable range from 0.04 (22:2n‐6) to 0.97 (18:3n‐3). Age was positively correlated with the saturated index (r² = 0.36; p = 0.014) and negatively correlated with the polyunsaturated index (r² = 0.26; p = 0.043); there was no difference in index measures by sex (p > 0.58). The test–retest reliability was moderate‐to‐good for index measures and poor to excellent for individual fatty acids; this information can be used to power research studies and identify measures for clinical or research monitoring. © 2020 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.

A blood-based metabolomics test to distinguish relapsing-remitting and secondary progressive multiple sclerosis: addressing practical considerations for clinical application

The transition from relapsing–remitting multiple sclerosis (RRMS) to secondary progressive MS (SPMS) represents a huge clinical challenge. We previously demonstrated that serum metabolomics could distinguish RRMS from SPMS with high diagnostic accuracy. As differing sample-handling protocols can affect the blood metabolite profile, it is vital to understand which factors may influence the accuracy of this metabolomics-based test in a clinical setting. Herein, we aim to further validate the high accuracy of this metabolomics test and to determine if this is maintained in a ‘real-life’ clinical environment. Blood from 31 RRMS and 28 SPMS patients was subjected to different sample-handling protocols representing variations encountered in clinics. The effect of freeze–thaw cycles (0 or 1) and time to erythrocyte removal (30, 120, or 240 min) on the accuracy of the test was investigated. For test development, samples from the optimised protocol (30 min standing time, 0 freeze–thaw) were used, resulting in high diagnostic accuracy (mean ± SD, 91.0 ± 3.0%). This test remained able to discriminate RRMS and SPMS samples that had experienced additional freeze–thaw, and increased standing times of 120 and 240 min with accuracies ranging from 85.5 to 88.0%, because the top discriminatory metabolite biomarkers from the optimised protocol remained discriminatory between RRMS and SPMS despite these sample-handling variations. In conclusion, while strict sample-handling is essential for the development of metabolomics-based blood tests, the results confirmed that the RRMS vs. SPMS test is resistant to sample-handling variations and can distinguish these two MS stages in the clinics.

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.

Recommendations and Best Practices for Standardizing the Pre-Analytical Processing of Blood and Urine Samples in Metabolomics

Metabolomics can be significantly influenced by a range of pre-analytical factors, such as sample collection, pre-processing, aliquoting, transport, storage and thawing. This therefore shows the crucial need for standardizing the pre-analytical phase with the aim of minimizing the inter-sample variability driven by these technical issues, as well as for maintaining the metabolic integrity of biological samples to ensure that metabolomic profiles are a direct expression of the in vivo biochemical status. This review article provides an updated literature revision of the most important factors related to sample handling and pre-processing that may affect metabolomics results, particularly focusing on the most commonly investigated biofluids in metabolomics, namely blood plasma/serum and urine. Finally, we also provide some general recommendations and best practices aimed to standardize and accurately report all these pre-analytical aspects in metabolomics research.

Pre-Analytical Factors that Affect Metabolite Stability in Human Urine, Plasma, and Serum: A Review

Metabolomics provides a comprehensive assessment of numerous small molecules in biological samples. As it integrates the effects of exogenous exposures, endogenous metabolism, and genetic variation, metabolomics is well-suited for studies examining metabolic profiles associated with a variety of chronic diseases. In this review, we summarize the studies that have characterized the effects of various pre-analytical factors on both targeted and untargeted metabolomic studies involving human plasma, serum, and urine and were published through 14 January 2019. A standardized protocol was used for extracting data from full-text articles identified by searching PubMed and EMBASE. For plasma and serum samples, metabolomic profiles were affected by fasting status, hemolysis, collection time, processing delays, particularly at room temperature, and repeated freeze/thaw cycles. For urine samples, collection time and fasting, centrifugation conditions, filtration and the use of additives, normalization procedures and multiple freeze/thaw cycles were found to alter metabolomic findings. Consideration of the effects of pre-analytical factors is a particularly important issue for epidemiological studies where samples are often collected in nonclinical settings and various locations and are subjected to time and temperature delays prior being to processed and frozen for storage.

Effects of Long-Term Storage at -80 °C on the Human Plasma Metabolome

High-quality biological samples are required for the favorable outcome of research studies, and valid data sets are crucial for successful biomarker identification. Prolonged storage of biospecimens may have an artificial effect on compound levels. In order to investigate the potential effects of long-term storage on the metabolome, human ethylenediaminetetraacetic acid (EDTA) plasma samples stored for up to 16 years were analyzed by gas and liquid chromatography-tandem mass spectrometry-based metabolomics. Only 2% of 231 tested plasma metabolites were altered in the first seven years of storage. However, upon longer storage periods of up to 16 years and more time differences of few years significantly affected up to 26% of the investigated metabolites when analyzed within subject age groups. Ontology classes that were most affected included complex lipids, fatty acids, energy metabolism molecules, and amino acids. In conclusion, the human plasma metabolome is adequately stable to long-term storage at −80 °C for up to seven years but significant changes occur upon longer storage. However, other biospecimens may display different sensitivities to long-term storage. Therefore, in retrospective studies on EDTA plasma samples, analysis is best performed within the first seven years of storage.

Plasma acylcarnitines and progression of carotid artery atherosclerosis in HIV infection

OBJECTIVE

To evaluate plasma acylcarnitine profiles and their relationships with progression of carotid artery atherosclerosis among individuals with and without HIV infection.

DESIGN

Prospective cohort studies of 499 HIV-positive and 206 HIV-negative individuals from the Women's Interagency HIV Study and the Multicenter AIDS Cohort Study.

METHODS

Twenty-four acylcarnitine species were measured in plasma samples of participants at baseline. Carotid artery plaque was assessed using repeated B-mode carotid artery ultrasound imaging in 2004-2013. We examined the associations of individual and aggregate short-chain (C2-C7), medium-chain (C8-C14) and long-chain acylcarnitines (C16-C26) with incident carotid artery plaque over 7 years.

RESULTS

Among 24 acylcarnitine species, C8-carnitines and C20 : 4-carnitines showed significantly lower levels comparing HIV-positive to HIV-negative individuals (false discovery rate adjusted P < 0.05); and C20-carnitines and C26-carnitines showed significantly higher levels in HIV positive using antiretroviral therapy than those without antiretroviral therapy (false discovery rate adjusted P < 0.05). In the univariate analyses, higher aggregated short-chain and long-chain acylcarnitine scores were associated with increased risk of carotid artery plaque [risk ratios (RRs) = 1.22 (95% confidence interval 1.02-1.45) and 1.20 (1.02-1.41) per SD increment, respectively]. The association for the short-chain acylcarnitine score remained significant [RR = 1.23 (1.05-1.44)] after multivariate adjustment (including traditional cardiovascular disease risk factors). This association was more evident in HIV-positive individuals without persistent viral suppression [RR = 1.37 (1.11-1.69)] compared with those with persistent viral suppression during follow-up [RR = 1.03 (0.76-1.40)] or HIV-negative individuals [RR = 1.02 (0.69-1.52)].

CONCLUSION

In two HIV cohorts, plasma levels of most acylcarnitines were not significantly different between HIV-positive and HIV-negative individuals. However, higher levels of aggregated short-chain acylcarnitines were associated with progression of carotid artery atherosclerosis.

Targeted metabolomics of whole blood using volumetric absorptive microsampling

Volumetric absorptive microsampling (VAMS) enables the collection of small and accurate quantities of biological fluids. Therefore, this sampling technique is of great interest for volume-limited samples or serial collection of samples. In this study, we examined the potential of VAMS for targeted mass spectrometry (MS)-based metabolomics. The targeted analysis of 36 major metabolites from only 10 μL of whole blood was optimized. A design of experiments was carried out to maximize the extraction of metabolites. Moreover, critical steps in sample preparation and sample analysis were studied and characterized, such as the addition of internal standards to tips of VAMS devices before sample collection. A reversed-phase UHPLC-MS/MS method was used to analyze organic acids, whereas hydrophilic interaction chromatography (HILIC)-MS/MS was selected for the determination of amino acids. Overall, the optimum extraction solvent was acetonitrile-water in a proportion of 60:40 (v/v), providing good recoveries and resulting in the detection of all target metabolites in whole blood with good repeatability (less than 15% RSD on peak area). Furthermore, the stability of the analytes in dried whole blood, which is of critical importance in metabolomics studies, was investigated. The amino and organic acids were stable for at least 4 days when stored at room temperature. This is in contrast to the instability of these compounds in wet blood, thereby showing the great potential of VAMS in metabolomics studies.

Rapid, Reproducible, Quantifiable NMR Metabolomics: Methanol and Methanol: Chloroform Precipitation for Removal of Macromolecules in Serum and Whole Blood

Background: Though blood is an excellent biofluid for metabolomics, proteins and lipids present in blood can interfere with 1d-¹H NMR spectra and disrupt quantification of metabolites. Here, we present effective macromolecule removal strategies for serum and whole blood (WB) samples. Methods: A variety of macromolecule removal strategies were compared in both WB and serum, along with tests of ultrafiltration alone and in combination with precipitation methods. Results: In healthy human serum, methanol:chloroform:water extraction with ultrafiltration was compared to methanol precipitation with and without ultrafiltration. Methods were tested in healthy pooled human serum, and in serum from patients with sepsis. Effects of long-term storage at −80 °C were tested to explore the impact of macromolecule removal strategy on serum from different conditions. In WB a variety of extraction strategies were tested in two types of WB (from pigs and baboons) to examine the impact of macromolecule removal strategies on different samples. Conclusions: In healthy human serum methanol precipitation of serum with ultrafiltration was superior, but was similar in recovery and variance to methanol:chloroform:water extraction with ultrafiltration in pooled serum from patients with sepsis. In WB, high quality, quantifiable spectra were obtained with the use of a methanol: chloroform precipitation.

Nutrimetabolomics: An Integrative Action for Metabolomic Analyses in Human Nutritional Studies

The life sciences are currently being transformed by an unprecedented wave of developments in molecular analysis, which include important advances in instrumental analysis as well as biocomputing. In light of the central role played by metabolism in nutrition, metabolomics is rapidly being established as a key analytical tool in human nutritional studies. Consequently, an increasing number of nutritionists integrate metabolomics into their study designs. Within this dynamic landscape, the potential of nutritional metabolomics (nutrimetabolomics) to be translated into a science, which can impact on health policies, still needs to be realized. A key element to reach this goal is the ability of the research community to join, to collectively make the best use of the potential offered by nutritional metabolomics. This article, therefore, provides a methodological description of nutritional metabolomics that reflects on the state-of-the-art techniques used in the laboratories of the Food Biomarker Alliance (funded by the European Joint Programming Initiative "A Healthy Diet for a Healthy Life" (JPI HDHL)) as well as points of reflections to harmonize this field. It is not intended to be exhaustive but rather to present a pragmatic guidance on metabolomic methodologies, providing readers with useful "tips and tricks" along the analytical workflow.

Balancing metabolome coverage and reproducibility for untargeted NMR-based metabolic profiling in tissue samples through mixture design methods

Untargeted metabolomics attempts to acquire a comprehensive and reproducible set of small-molecule metabolites in biological systems. However, metabolite extraction method significantly affects the quality of metabolomics data. In the present study, we calculated the number of peaks (NP) and coefficient of variation (CV) to reflect metabolome coverage and reproducibility in untargeted NMR-based metabolic profiling of tissue samples in rats under different methanol/chloroform/water (MCW) extraction conditions. Different MCW extractions expectedly generated diverse characteristics of metabolome. Moreover, the classic MCW method revealed tissue-specific differences in the NP and CV values. To obtain high-quality metabolomics data, therefore, we used mixture design methods to optimize the MCW extraction strategy by maximizing the NP value and minimizing the CV value in each tissue sample. Results show that the optimal formulations of MCW extraction were 2:2:8 (ml/mg tissue) for brain sample, 2:4:6 (ml/mg tissue) for heart sample, 1.3:2:8.7 (ml/mg tissue) for liver sample, 4:2:6 (ml/mg tissue) for kidney sample, 2:3:7 (ml/mg tissue) for muscle sample, and 2:4:6 (ml/mg tissue) for pancreas sample. Therefore, these findings demonstrate that different tissue samples need a specific optimal extraction condition for balancing metabolome coverage and reproducibility in the untargeted metabolomics study. Mixture design method is an effective tool to optimize metabolite extraction strategy for tissue samples. Graphical abstract ᅟ.

MS-based lipidomics of human blood plasma: a community-initiated position paper to develop accepted guidelines

Human blood is a self-regenerating lipid-rich biological fluid that is routinely collected in hospital settings. The inventory of lipid molecules found in blood plasma (plasma lipidome) offers insights into individual metabolism and physiology in health and disease. Disturbances in the plasma lipidome also occur in conditions that are not directly linked to lipid metabolism; therefore, plasma lipidomics based on MS is an emerging tool in an array of clinical diagnostics and disease management. However, challenges exist in the translation of such lipidomic data to clinical applications. These relate to the reproducibility, accuracy, and precision of lipid quantitation, study design, sample handling, and data sharing. This position paper emerged from a workshop that initiated a community-led process to elaborate and define a set of generally accepted guidelines for quantitative MS-based lipidomics of blood plasma or serum, with harmonization of data acquired on different instrumentation platforms across independent laboratories as an ultimate goal. We hope that other fields may benefit from and follow such a precedent.

A systematic review on metabolomics-based diagnostic biomarker discovery and validation in pancreatic cancer

INTRODUCTION

Metabolomics is an emerging approach for early detection of cancer. Along with the development of metabolomics, high-throughput technologies and statistical learning, the integration of multiple biomarkers has significantly improved clinical diagnosis and management for patients.

OBJECTIVES

In this study, we conducted a systematic review to examine recent advancements in the oncometabolomics-based diagnostic biomarker discovery and validation in pancreatic cancer.

METHODS

PubMed, Scopus, and Web of Science were searched for relevant studies published before September 2017. We examined the study designs, the metabolomics approaches, and the reporting methodological quality following PRISMA statement. RESULTS AND CONCLUSION: The included 25 studies primarily focused on the identification rather than the validation of predictive capacity of potential biomarkers. The sample size ranged from 10 to 8760. External validation of the biomarker panels was observed in nine studies. The diagnostic area under the curve ranged from 0.68 to 1.00 (sensitivity: 0.43-1.00, specificity: 0.73-1.00). The effects of patients' bio-parameters on metabolome alterations in a context-dependent manner have not been thoroughly elucidated. The most reported candidates were glutamic acid and histidine in seven studies, and glutamine and isoleucine in five studies, leading to the predominant enrichment of amino acid-related pathways. Notably, 46 metabolites were estimated in at least two studies. Specific challenges and potential pitfalls to provide better insights into future research directions were thoroughly discussed. Our investigation suggests that metabolomics is a robust approach that will improve the diagnostic assessment of pancreatic cancer. Further studies are warranted to validate their validity in multi-clinical settings.

Prediction and modeling of pre-analytical sampling errors as a strategy to improve plasma NMR metabolomics data

Motivation

Biobanks are important infrastructures for life science research. Optimal sample handling regarding e.g. collection and processing of biological samples is highly complex, with many variables that could alter sample integrity and even more complex when considering multiple study centers or using legacy samples with limited documentation on sample management. Novel means to understand and take into account such variability would enable high-quality research on archived samples.

Results

This study investigated whether pre-analytical sample variability could be predicted and reduced by modeling alterations in the plasma metabolome, measured by NMR, as a function of pre-centrifugation conditions (1–36 h pre-centrifugation delay time at 4 °C and 22 °C) in 16 individuals. Pre-centrifugation temperature and delay times were predicted using random forest modeling and performance was validated on independent samples. Alterations in the metabolome were modeled at each temperature using a cluster-based approach, revealing reproducible effects of delay time on energy metabolism intermediates at both temperatures, but more pronounced at 22 °C. Moreover, pre-centrifugation delay at 4 °C resulted in large, specific variability at 3 h, predominantly of lipids. Pre-analytical sample handling error correction resulted in significant improvement of data quality, particularly at 22 °C. This approach offers the possibility to predict pre-centrifugation delay temperature and time in biobanked samples before use in costly downstream applications. Moreover, the results suggest potential to decrease the impact of undesired, delay-induced variability. However, these findings need to be validated in multiple, large sample sets and with analytical techniques covering a wider range of the metabolome, such as LC-MS.

Availability and implementation

The sampleDrift R package is available at https://gitlab.com/CarlBrunius/sampleDrift.

Supplementary information

Supplementary data are available at Bioinformatics online.

Mass spectrometry-based metabolomics for tuberculosis meningitis

Tuberculosis meningitis (TBM) is a prevalent form of extra-pulmonary tuberculosis that causes substantial morbidity and mortality. Diagnosis of TBM is difficult because of the limited sensitivity of existing laboratory techniques. A metabolomics approach can be used to investigate the sets of metabolites of both bacteria and host, and has been used to clarify the mechanisms underlying disease development, and identify metabolic changes, leadings to improved methods for diagnosis, treatment, and prognostication. Mass spectrometry (MS) is a major analysis platform used in metabolomics, and MS-based metabolomics provides wide metabolite coverage, because of its high sensitivity, and is useful for the investigation of Mycobacterium tuberculosis (Mtb) and related diseases. It has been used to investigate TBM diagnosis; however, the processes involved in the MS-based metabolomics approach are complex and flexible, and often consist of several steps, and small changes in the methods used can have a huge impact on the final results. Here, the process of MS-based metabolomics is summarized and its applications in Mtb and Mtb-related diseases discussed. Moreover, the current status of TBM metabolomics is described.

A review of metabolomics approaches and their application in identifying causal pathways of childhood asthma

Because asthma is a disease that results from host-environment interactions, an approach that allows assessment of the effect of the environment on the host is needed to understand the disease. Metabolomics has appealing potential as an application to study pathways to childhood asthma development. The objective of this review is to provide an overview of metabolomics methods and their application to understanding host-environment pathways in asthma development. We reviewed recent literature on advances in metabolomics and their application to study pathways to childhood asthma development. We highlight the (1) potential of metabolomics in understanding the pathogenesis of disease and the discovery of biomarkers; (2) choice of metabolomics techniques, biospecimen handling, and data analysis; (3) application to studying the role of the environment on asthma development; (4) review of metabolomics applied to the outcome of asthma; (5) recommendations for application of metabolomics-based -omics data integration in understanding disease pathogenesis; and (6) limitations. In conclusion, metabolomics allows use of biospecimens to identify useful biomarkers and pathways involved in disease development and subsequently to inform a greater understanding of disease pathogenesis and endotypes and prediction of the clinical course of childhood asthma phenotypes.

A metabolomic study on early detection of steroid-induced avascular necrosis of the femoral head

The early and accurate diagnosis of steroid-induced avascular necrosis of the femoral head (SANFH) is appealing considering its irreversible progression and serious consequence for the patients. The purpose of this study was to investigate the metabolic change of SANFH for its early detection. Two stages were designed in this study, namely discovery and verification. Except the biochemical index anomaly and the accidental death, 30 adult healthy adult Japanese white rabbits were used for screening out the potential metabolites in discovery experiment and 13 rabbits were used in verification experiment. The femoral heads were assessed with magnetic resonance imaging and transmission electron microscopy. The metabolomic profiling of serum samples were analysis by UHPLC-MS/MS. Metabolomic cluster analysis enable us to differentiate the rabbits without and with injection of the glucocorticoid in 1 week even when there is no obvious abnormal symptom in behaviors or imaging diagnosis. The majority of differential metabolites were identified as phospholipids which were observed significant change after injection of glucocorticoid in 1, 2, 3 weeks. And the results obtained in verification experiment of 6 weeks showed that these differential metabolites exhibited consistent trends in late progression with that in early-stage. At the end of 6 weeks the damage of SANFH could be verified by pathological imaging. Therefore the finding of serum metabolite profile links to the progression of SANFH and provides the potential of early detection of SANFH.

Impact of Prolonged Blood Incubation and Extended Serum Storage at Room Temperature on the Human Serum Metabolome

Metabolomics is a powerful technology with broad applications in life science that, like other -omics approaches, requires high-quality samples to achieve reliable results and ensure reproducibility. Therefore, along with quality assurance, methods to assess sample quality regarding pre-analytical confounders are urgently needed. In this study, we analyzed the response of the human serum metabolome to pre-analytical variations comprising prolonged blood incubation and extended serum storage at room temperature by using gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-tandem mass spectrometry (LC-MS/MS) -based metabolomics. We found that the prolonged incubation of blood results in a statistically significant 20% increase and 4% decrease of 225 tested serum metabolites. Extended serum storage affected 21% of the analyzed metabolites (14% increased, 7% decreased). Amino acids and nucleobases showed the highest percentage of changed metabolites in both confounding conditions, whereas lipids were remarkably stable. Interestingly, the amounts of taurine and O-phosphoethanolamine, which have both been discussed as biomarkers for various diseases, were 1.8- and 2.9-fold increased after 6 h of blood incubation. Since we found that both are more stable in ethylenediaminetetraacetic acid (EDTA) blood, EDTA plasma should be the preferred metabolomics matrix.

Long-Term Stability of Human Plasma Metabolites during Storage at -80 °C

Prolonged storage of biospecimen can lead to artificially altered metabolite concentrations and thus bias data analysis in metabolomics experiments. To elucidate the potential impact of long-term storage on the metabolite profile, a pooled human plasma sample was aliquoted and stored at -80 °C. During a time period of five years, 1012 of the aliquots were measured with the Biocrates AbsoluteIDQ p180 targeted-metabolomics assay at 193 time points. Modeling the concentration courses over time revealed that 55 out of 111 metabolites remained stable. The statistically significantly changed metabolites showed on average an increase or decrease of +13.7% or -14.5%, respectively. In detail, increased concentration levels were observed for amino acids (mean: + 15.4%), the sum of hexoses (+7.9%), butyrylcarnitine (+9.4%), and some phospholipids mostly with chain lengths exceeding 40 carbon atoms (mean: +18.0%). Lipids tended to exhibit decreased concentration levels with the following mean concentration changes: acylcarnitines, -12.1%; lysophosphatidylcholines, -15.1%; diacyl-phosphatidylcholines, -17.0%; acyl-alkyl-phosphatidylcholines, -13.3%; sphingomyelins, -14.8%. We conclude that storage of plasma samples at -80 °C for up to five years can lead to altered concentration levels of amino acids, acylcarnitines, glycerophospholipids, sphingomyelins, and the sum of hexoses. These alterations must be considered when analyzing metabolomics data from long-term epidemiological studies.

The application of lipidomics to biomarker research and pathomechanisms in Alzheimer's disease

PURPOSE OF REVIEW

Alzheimer's disease is the most common cause of dementia. There are still no disease modifying treatments that can cure or slow disease progression. Recently, Alzheimer's disease researchers have attempted to improve early detection and diagnostic criteria for Alzheimer's disease, with the rationale that treatment of disease, or even prevention, may be more successful during the early preclinical stages of Alzheimer's disease when neurodegenerative damage is not as widespread. As the brain has a high lipid content, lipidomics may offer novel insights into the underlying pathogenesis of Alzheimer's disease. This review reports on recent developments in the relatively unexplored field of lipidomics in Alzheimer's disease, including novel biomarkers and pathomechanisms of Alzheimer's disease.

RECENT FINDINGS

Numerous biomarker panels involving phospholipids and sphingolipids have been proposed, indicating perturbed lipid metabolism in early stages of Alzheimer's disease. Future strategies targeting these metabolic changes through dietary supplementation could have therapeutic benefits in at-risk individuals.

SUMMARY

Dysregulated lipid metabolism could reflect pathological changes in synaptic function and neuronal membranes, leading to cognitive decline. However, extensive validation in large independent cohorts is required before lipid biomarkers can be used clinically to assess Alzheimer's disease risk and progression.

Dysregulation of lipids in Alzheimer's disease and their role as potential biomarkers

The brain is highly enriched in lipids, and an intensive study of these lipids may be informative, not only of normal brain function but also of changes with age and in disease. In recent years, the development of highly sensitive mass spectrometry platforms and other high-throughput technologies has enabled the discovery of complex changes in the entire lipidome. This lipidomics approach promises to be a particularly useful tool for identifying diagnostic biomarkers for early detection of age-related neurodegenerative disease, such as Alzheimer's disease (AD), which has till recently been limited to protein- and gene-centric approaches. This review highlights known lipid changes affecting the AD brain and presents an update on the progress of lipid biomarker research in AD. Important considerations for designing large-scale lipidomics experiments are discussed to help standardize findings across different laboratories, as well as challenges associated with moving toward clinical application.

Systematic evaluation of serum and plasma collection on the endogenous metabolome

AIM

In metabolomics research, the use of different blood collection methods may influence endogenous metabolites.

MATERIALS & METHODS

Ultra HPLC coupled with MS/MS was applied together with multivariate statistics to investigate metabolomics differences in serum and plasma samples handled by different anticoagulants. A total of 135 known representative metabolites were assessed for comprehensive evaluation of the effects of anticoagulants.

RESULTS

Exogenous factors, including separation gel ingredients from the serum collection tubes and the anticoagulants, affected mass spectrometer detection. Heparin plasma yielded the best detection of different functional groups and is therefore the optimal blood specimen for metabolomics research, followed by potassium oxalate plasma.

The effects of thawing on the plasma metabolome: evaluating differences between thawed plasma and multi-organ samples

INTRODUCTION

Post-collection handling, storage and transportation can affect the quality of blood samples. Pre-analytical biases can easily be introduced and can jeopardize accurate profiling of the plasma metabolome. Consequently, a mouse study must be carefully planned in order to avoid any kind of bias that can be introduced, in order not to compromise the outcome of the study. The storage and shipment of the samples should be made in such a way that the freeze-thaw cycles are kept to a minimum. In order to keep the latent effects on the stability of the blood metabolome to a minimum it is essential to study the effect that the post-collection and pre-analytical error have on the metabolome.

OBJECTIVES

The aim of this study was to investigate the effects of thawing on the metabolic profiles of different sample types.

METHODS

In the present study, a metabolomics approach was utilized to obtain a thawing profile of plasma samples obtained on three different days of experiment. The plasma samples were collected from the tail on day 1 and 3, while retro-orbital sampling was used on day 5. The samples were analysed using gas chromatography time-of-flight mass spectrometry (GC TOF-MS).

RESULTS

The thawed plasma samples were found to be characterized by higher levels of amino acids, fatty acids, glycerol metabolites and purine and pyrimidine metabolites as a result of protein degradation, cell degradation and increased phospholipase activity. The consensus profile was thereafter compared to the previously published study comparing thawing profiles of tissue samples from gut, kidney, liver, muscle and pancreas.

CONCLUSIONS

The comparison between thawed organ samples and thawed plasma samples indicate that the organ samples are more sensitive to thawing, however thawing still affected all investigated sample types.

Bioanalytical techniques in nontargeted clinical lipidomics

Lipidomic analysis aims at comprehensive characterization of molecular lipids in biological systems. Due to the central role of lipid metabolism in many devastating diseases, lipidomics is being increasingly applied in biomedical research. Over the past years, advances in analytical techniques and bioinformatics enabled increasingly comprehensive and accurate coverage of lipids both in tissues and biofluids, yet many challenges remain. This review highlights recent progress in the domain of analytical lipidomics, with main emphasis on non-targeted methodologies for large scale clinical applications, as well as discusses some of the key challenges and opportunities in this field.

Comparative metabolism of tussilagone in rat and human liver microsomes using ultra-high-performance liquid chromatography coupled with high-resolution LTQ-Orbitrap mass spectrometry

RATIONALE

Tussilagone is a major component in Tussilago farfara that has been widely used as an anti-tussive herbal medicine for the treatment of bronchitis, cough and asthmatic disorders in the clinic. However, its metabolism has been poorly investigated. In order to clarify its in vitro metabolism, a comparative analysis of its metabolic profile in rat liver microsomes (RLMs) and human liver microsomes (HLMs) was carried out. Further, the cytochrome P450 isoforms (CYPs) involved in the metabolism were investigated.

METHODS

In this work, the biotransformation of tussilagone in RLMs and HLMs was compared using ultra-high-performance liquid chromatography coupled with high-resolution LTQ-Orbitrap mass spectrometry (UHPLC/HRMS) and the CYPs involved in the metabolism were further investigated by recombinant human CYP enzymes.

RESULTS

Totally, nine metabolites of tussilagone were identified in RLMs and HLMs based on the proposed MS/MS fragmentation pathways of tussilagone and the accurate MS/MS spectra. Among them, one metabolite (M9) was detected in both RLMs and HLMs while the other eight metabolites were only detected in HLMs. Three hydroxylation metabolites (M6, M7 and M8) were detected in the assay with individual recombinant P450s incubation. M6 was detected in all CYPs except CYP2A6 while M7 and M8 were only observed in CYP3A4.

CONCLUSIONS

The HR-ESI-MS/MS fragmentation behavior of tussilagone and its metabolic profile in RLMs and HLMs were investigated for the first time. The results demonstrated that the biotransformation of tussilagone involved hydrolysis of ester bonds at C-14 and hydroxylation in the side chains at C-12, C-5' or C-6'. Among the CYPs, CYP3A4 played an important role in the hydroxylation reaction of tussilagone in vitro. Furthermore, the results indicated a species-related difference in the metabolism of tussilagone between RLMs and HLMs. This work provided basic information for the metabolism of tussilagone in RLMs and HLMs, which would help to better understand the pharmacological activities of tussilagone.

Phenylmethanesulfonyl fluoride pretreatment stabilizes plasma lipidome in lipidomic and metabolomic analysis

Though it is standard practice to test the stability of analytes in the matrix for routine bioanalytical method, stability evaluation is always impractical and skipped in untargeted lipidomic and metabolomic analysis because analytes in these studies are enormous, diverse and sometimes unknown. Lipidome represents a major class of plasma metabolome and shows great potential to be diagnostic and prognostic biomarkers. However, lipidome also faces stability problems because plasma contains kinds of lipid degradation enzyme. Here, using liquid chromatography time of flight mass spectrometry based lipidomic methodology, plasma levels of various lipids including triglyceride (TG), diglyceride (DG), free fatty acid (FFA), phosphatidylethanolamine (PE) phosphatidylcholine (PC), lyso-phosphatidylcholine (LPC), lyso-phosphatidylethanolamine (LPE), and sphingomyelin (SM) were dynamically determined within 4 h at ambient temperature. In mouse and rat plasma, the levels of most TG, DG, PC and PE species significantly decreased with respect to time, whereas those of LPC, LPE and FFA significantly increased with respect to time. However, such changes did not occur in human plasma, thus indicating hepatic lipase and esterase might involve in the species-specified degradation of lipid classes in plasma. Phenylmethanesulfonyl fluoride (PMSF) pretreatment prevented such lipidome instability in mouse plasma. The results suggested the instability of plasma lipidome should be highly concerned, and the enhancement of ex vivo stability of plasma lipidome could enable more reliable clinical translation of lipidomic data for biomarker discovery.

Metabolomics profiling of pre-and post-anesthesia plasma samples of colorectal patients obtained via Ficoll separation

1H NMR spectroscopy was used to investigate the metabolic consequences of general anesthesia in the plasma of two groups of patients with diagnosis for non-metastatic colorectal cancer and metastatic colorectal cancer with liver-metastasis, respectively. Patients were treated with etomidate or propofol, two frequently used sedation agents. Plasma samples were obtained via Ficoll separation. Here, we demonstrated that this procedure introduces a number of limitations for NMR-based metabolomics studies, due to the appearance of spurious signals. Nevertheless, the comparison of the 1H NMR metabolomic profiles of patients treated with etomidate or propofol at equipotent dose ranges was still feasible and proved that both agents significantly decrease the plasma levels of several NMR-detectable metabolites. Consequently, samples collected during anesthesia are not suitable for metabolic profiling studies aimed at patient stratification, because interpersonal variability are reduced by the overall depression of metabolites levels. On the other hand, this study showed that plasma metabolomics could represent a valuable tool to monitor the effect of different sedation agents and/or the individual metabolic response to anesthesia, providing hints for an appropriate tuning of personalized sedation procedures. In our reference groups, the metabolomic signatures were slightly different in patients anesthetized with etomidate versus propofol. The importance of standardized collection procedures and availability of exhaustive metadata of the experimental design for the accurate evaluation of the significance of the observed changes in metabolites levels are critically discussed.

Metabolomics investigation of cutaneous T cell lymphoma based on UHPLC-QTOF/MS

OBJECTIVES

The identification of cutaneous T cell lymphoma (CTCL) biomarkers may serve as a predictor of disease progression and treatment response. The aim of this study was to map potential biomarkers in CTCL plasma.

DESIGN AND METHODS

Plasma metabolic perturbations between CTCL cases and healthy individuals were investigated using metabolomics and ultrahigh performance liquid chromatography–quadrupole time-of-flight mass spectrometry (UHPLC-QTOF/MS).

RESULTS

Principal component analysis (PCA) of the spectra showed clear metabolic changes between the two groups. Thirty six potential biomarkers associated with CTCL were found.

CONCLUSIONS

Based on PCA, several biomarkers were determined and further identified by LC/MS/MS analysis. All of these could be potential early markers of CTCL. In addition, we established that heparin as anticoagulant has better pre-treatment results than EDTA with the UHPLC-QTOF/MS approach.

Pre-analytical sample quality: metabolite ratios as an intrinsic marker for prolonged room temperature exposure of serum samples

Advances in the "omics" field bring about the need for a high number of good quality samples. Many omics studies take advantage of biobanked samples to meet this need. Most of the laboratory errors occur in the pre-analytical phase. Therefore evidence-based standard operating procedures for the pre-analytical phase as well as markers to distinguish between 'good' and 'bad' quality samples taking into account the desired downstream analysis are urgently needed. We studied concentration changes of metabolites in serum samples due to pre-storage handling conditions as well as due to repeated freeze-thaw cycles. We collected fasting serum samples and subjected aliquots to up to four freeze-thaw cycles and to pre-storage handling delays of 12, 24 and 36 hours at room temperature (RT) and on wet and dry ice. For each treated aliquot, we quantified 127 metabolites through a targeted metabolomics approach. We found a clear signature of degradation in samples kept at RT. Storage on wet ice led to less pronounced concentration changes. 24 metabolites showed significant concentration changes at RT. In 22 of these, changes were already visible after only 12 hours of storage delay. Especially pronounced were increases in lysophosphatidylcholines and decreases in phosphatidylcholines. We showed that the ratio between the concentrations of these molecule classes could serve as a measure to distinguish between 'good' and 'bad' quality samples in our study. In contrast, we found quite stable metabolite concentrations during up to four freeze-thaw cycles. We concluded that pre-analytical RT handling of serum samples should be strictly avoided and serum samples should always be handled on wet ice or in cooling devices after centrifugation. Moreover, serum samples should be frozen at or below -80°C as soon as possible after centrifugation.