Quality markers addressing preanalytical variations of blood and plasma processing identified by broad and targeted metabolite profiling

Discovery of Hypoxanthine and Inosine as Robust Biomarkers for Predicting the Preanalytical Quality of Human Plasma and Serum for Metabolomics

In cold human blood, the anomalous dynamics of adenosine triphosphate (ATP) result in the progressive accumulation of adenosine diphosphate (ADP), adenosine monophosphate (AMP), inosine monophosphate (IMP), inosine, and hypoxanthine. While the ATP, ADP, AMP, and IMP are confined to red blood cells (RBCs), inosine and hypoxanthine are excreted into plasma/serum. The plasma/serum levels of inosine and hypoxanthine depend on the temperature of blood and the plasma/serum contact time with the RBCs, and hence they represent robust biomarkers for evaluating the preanalytical quality of plasma/serum. These biomarkers are highly specific since they are generally absent or at very low levels in fresh plasma/serum and are highly sensitive since they are derived from ATP, one of the most abundant metabolites in blood. Further, whether blood was kept at room temperature or on ice could be predicted based on inosine levels. An analysis of >2000 plasma/serum samples processed for metabolomics-centric analyses showed alarmingly high levels of inosine and hypoxanthine. The results highlight the gravity of sample quality challenges with high risk of grossly inaccurate measurements and incorrect study outcomes. The discovery of these robust biomarkers provides new ways to address the longstanding and underappreciated preanalytical sample quality challenges in the blood metabolomics field.

An Overview of Pre-Analytical Factors Impacting Metabolomics Analyses of Blood Samples

Discrepant sample processing remains a significant challenge within blood metabolomics research, introducing non-biological variation into the measured metabolome and biasing downstream results. Inconsistency during the pre-analytical phase can influence experimental processes, producing metabolome measurements that are non-representative of in vivo composition. To minimize variation, there is a need to create and adhere to standardized pre-analytical protocols for blood samples intended for use in metabolomics analyses. This will allow for reliable and reproducible findings within blood metabolomics research. In this review article, we provide an overview of the existing literature pertaining to pre-analytical factors that influence blood metabolite measurements. Pre-analytical factors including blood tube selection, pre- and post-processing time and temperature conditions, centrifugation conditions, freeze-thaw cycles, and long-term storage conditions are specifically discussed, with recommendations provided for best practices at each stage.

An exercise physiologist's guide to metabolomics

The field of exercise physiology has undergone significant technological advancements since the pioneering works of exercise physiologists in the early to mid-20th century. Historically, the ability to detect metabolites in biofluids from exercising participants was limited to single-metabolite analyses. However, the rise of metabolomics, a discipline focused on the comprehensive analysis of metabolites within a biological system, has facilitated a more intricate understanding of metabolic pathways and networks in exercise. This review explores some of the pivotal technological and bioinformatic advancements that have propelled metabolomics to the forefront of exercise physiology research. Metabolomics offers a unique 'fingerprint' of cellular activity, offering a broader spectrum than traditional single-metabolite assays. Techniques, including mass spectrometry and nuclear magnetic resonance spectroscopy, have significantly improved the speed and sensitivity of metabolite analysis. Nonetheless, challenges persist, including study design and data interpretation issues. This review aims to serve as a guide for exercise physiologists to facilitate better research design, data analysis and interpretation within metabolomics. The potential of metabolomics in bridging the gap between genotype and phenotype is emphasised, underscoring the critical importance of careful study design and the selection of appropriate metabolomics techniques. Furthermore, the paper highlights the need to deeply understand the broader scientific context to discern meaningful metabolic changes. The emerging field of fluxomics, which seeks to quantify metabolic reaction rates, is also introduced as a promising avenue for future research.

ALISTER - Application for lipid stability evaluation and research

BACKGROUND AND AIMS

In lipidomic and metabolomic studies, pre-analytical pitfalls enhance the risk of misusing resources such as time and money, as samples that are analyzed may not yield accurate or reliable data due to poor sample handling. Guidance and pre-analytic know-how are necessary for translation of omics technologies into routine clinical testing. The present work aims to enable decision making regarding sample stability in every phase of lipidomics- and metabolomics-centered studies.

MATERIALS AND METHODS

Data of multiple pre-analytic studies were aggregated into a database. Flexible approaches for evaluating these data were implemented in an RShiny-based web-application, tailored towards broad applicability in clinical and bioanalytic research.

RESULTS

Our "Application for lipid stability evaluation & research" - ALISTER facilitates decision making on blood sample stability during lipidomic and metabolomic studies, such as biomarker research, analysis of biobank samples and clinical testing. The interactive tool gives sampling recommendations when planning sample collection or aids in the assessment of sample quality of experiments retrospectively.

CONCLUSION

ALISTER is available for use under https://itmp.shinyapps.io/alister/. The application enables and simplifies data-driven decision making concerning pre-analytic blood sample handling and fits the needs of clinical investigations from multiple perspectives.

QComics: Recommendations and Guidelines for Robust, Easily Implementable and Reportable Quality Control of Metabolomics Data

The implementation of quality control strategies is crucial to ensure the reproducibility, accuracy, and meaningfulness of metabolomics data. However, this pivotal step is often overlooked within the metabolomics workflow and frequently relies on the use of nonstandardized and poorly reported protocols. To address current limitations in this respect, we have developed QComics, a robust, easily implementable and reportable method for monitoring and controlling data quality. The protocol operates in various sequential steps aimed to (i) correct for background noise and carryover, (ii) detect signal drifts and "out-of-control" observations, (iii) deal with missing data, (iv) remove outliers, (v) monitor quality markers to identify samples affected by improper collection, preprocessing, or storage, and (vi) assess overall data quality in terms of precision and accuracy. Notably, this tool considers important issues often neglected along quality control, such as the need of separately handling missing values and truly absent data to avoid losing relevant biological information, as well as the large impact that preanalytical factors may elicit on metabolomics results. Altogether, the guidelines compiled in QComics might contribute to establishing gold standard recommendations and best practices for quality control within the metabolomics community.

Fingerprinting and profiling in metabolomics of biosamples

This review focuses on metabolomics from an NMR point of view. It attempts to cover the broad scope of metabolomics and describes the NMR experiments that are most suitable for each sample type. It is addressed not only to NMR specialists, but to all researchers who wish to approach metabolomics with a clear idea of what they wish to achieve but not necessarily with a deep knowledge of NMR. For this reason, some technical parts may seem a bit naïve to the experts. The review starts by describing standard metabolomics procedures, which imply the use of a dedicated 600 MHz instrument and of four properly standardized 1D experiments. Standardization is a must if one wants to directly compare NMR results obtained in different labs. A brief mention is also made of standardized pre-analytical procedures, which are even more essential. Attention is paid to the distinction between fingerprinting and profiling, and the advantages and disadvantages of fingerprinting are clarified. This aspect is often not fully appreciated. Then profiling, and the associated problems of signal assignment and quantitation, are discussed. We also describe less conventional approaches, such as the use of different magnetic fields, the use of signal enhancement techniques to increase sensitivity, and the potential of field-shuttling NMR. A few examples of biomedical applications are also given, again with the focus on NMR techniques that are most suitable to achieve each particular goal, including a description of the most common heteronuclear experiments. Finally, the growing applications of metabolomics to foodstuffs are described.

The Influence of Preanalytical Biospecimen Handling on the Measurement of B Vitamers, Amino Acids, and Other Metabolites in Blood

Introduction: Sample handling can influence biomarker measurement and introduce variability when combining data from multiple studies or study sites. To inform the development of blood collection protocols within a multisite cohort study, we directly quantified concentrations of 54 biomarkers in blood samples subjected to different handling conditions. Materials and Methods: We obtained serum, lithium heparin plasma, and EDTA plasma from 20 adult volunteers. Tubes of chilled whole blood were either centrifuged and processed within 2 hours of collection (the "reference standard") or were stored with cool packs for 24 or 48 hours; centrifuged before and/or after this delay; or collected in tubes with/without gel separators. We used linear mixed models with random intercepts to estimate geometric mean concentrations and relative percent differences across the conditions. Results: Compared to the reference standard tubes, concentrations of many biomarkers changed after processing delays, but changes were often small. In serum, we observed large differences for B vitamers, glutamic acid (37% and 73% increases with 24- and 48-hour delays, respectively), glycine (12% and 23% increases), serine (16% and 27% increases), and acetoacetate (-19% and -26% decreases). Centrifugation timing and separator tube use did not affect concentrations of most biomarkers. Conclusion: Sample handling should be consistent across samples within an analysis. The length of processing delays should be recorded and accounted for when this is not feasible.

Role of untargeted omics biomarkers of exposure and effect for tobacco research

Tobacco research remains a clear priority to improve individual and population health, and has recently become more complex with emerging combustible and noncombustible tobacco products. The use of omics methods in prevention and cessation studies are intended to identify new biomarkers for risk, compared risks related to other products and never use, and compliance for cessation and reinitation. to assess the relative effects of tobacco products to each other. They are important for the prediction of reinitiation of tobacco use and relapse prevention. In the research setting, both technical and clinical validation is required, which presents a number of complexities in the omics methodologies from biospecimen collection and sample preparation to data collection and analysis. When the results identify differences in omics features, networks or pathways, it is unclear if the results are toxic effects, a healthy response to a toxic exposure or neither. The use of surrogate biospecimens (e.g., urine, blood, sputum or nasal) may or may not reflect target organs such as the lung or bladder. This review describes the approaches for the use of omics in tobacco research and provides examples of prior studies, along with the strengths and limitations of the various methods. To date, there is little consistency in results, likely due to small number of studies, limitations in study size, the variability in the analytic platforms and bioinformatic pipelines, differences in biospecimen collection and/or human subject study design. Given the demonstrated value for the use of omics in clinical medicine, it is anticipated that the use in tobacco research will be similarly productive.

Endogenous Plasma Peptides Modulated by Protease in a Time-Dependent Manner as Effective Biomarkers for Preanalytical Quality Control

Non-cryopreservation temperature exposure (NCE) is a vital preanalytical factor for assessing plasma quality. NCE can introduce undesirable errors in clinical diagnosis or when developing biomarkers of diseases. Biomarkers that can effectively indicate the changes in sample quality caused by long-term NCE (0-several days) are limited. Low-molecular-weight (LMW) peptides in the plasma are modulated by endogenous proteases. These protease activities are significantly correlated with NCE temperatures and duration, indicating a potential link of these protease reactions with the preanalytical quality of plasma samples. In this study, two groups of plasma samples were aged at room temperature (RT, 57 samples) and 4 °C (69 samples) for different durations (0, 1, 2, 5, and 10 days), and LMW peptidomics were analyzed through nanopore-assisted matrix-assisted laser desorption ionization time-of-flight mass spectrometry. The analysis revealed 10 peptides that consistently exhibited time-dependent changes, which were used to develop multiple-variable models for predicting the changes in sample quality resulting from extended NCE. These biomarker models exhibited outstanding performance in distinguishing poor-quality samples aged at both RT and 4 °C. To validate the findings, tests on samples from validation sets were conducted by analysts who were blinded to the detailed conditions, which revealed a high specificity (94.3-96.9%) and sensitivity (90.5-99.3%). These results indicate the potential of these peptides as novel biomarkers of quality control.

Anomalous Dynamics of Labile Metabolites in Cold Human Blood Detected Using 1H NMR Spectroscopy

Recent efforts in our laboratory have enabled access to an unprecedented number (∼90) of quantifiable metabolites in human blood by a simple nuclear magnetic resonance (NMR) spectroscopy method, which includes energy coenzymes, redox coenzymes, and antioxidants that are fundamental to cellular functions [ J. Magn. Reson. Open 2022, 12-13, 100082]. The coenzymes and antioxidants, however, are notoriously labile and are extremely sensitive to specimen harvesting, extraction, and measurement conditions. This problem is largely underappreciated and carries the risk of grossly inaccurate measurements and incorrect study outcomes. As a part of addressing this challenge, in this study, human blood specimens were comprehensively and quantitatively investigated using 1H NMR spectroscopy. Freshly drawn human blood specimens were treated or not treated with methanol, ethanol, or a mixture of methanol and chloroform, and stored on ice or on bench, at room temperature for different time periods from 0 to 24 h, prior to storing at -80 °C. Interestingly, the labile metabolite levels were stable in blood treated with an organic solvent. However, their levels in blood in untreated samples increased or decreased by factors of up to 5 or more within 3 h. Further, surprisingly, and contrary to the current knowledge about metabolite stability, the variation of coenzyme levels was more dramatic in blood stored on ice than on bench, at room temperature. In addition, unlike the generally observed phenomenon of oxidation of redox coenzymes, reduction was observed in untreated blood. Such preanalytical dynamics of the labile metabolites potentially arises from the active cellular metabolism. From the metabolomics perspective, the massive variation of the labile metabolite levels even in blood stored on ice is alarming and stresses the critical need to immediately quench the cellular metabolism for reliable analyses. Overall, the results provide compelling evidence that warrants a paradigm shift in the sample collection protocol for blood metabolomics involving labile metabolites.

Ex vivo instability of lipids in whole blood: preanalytical recommendations for clinical lipidomics studies

Reliability, robustness, and interlaboratory comparability of quantitative measurements is critical for clinical lipidomics studies. Lipids' different ex vivo stability in blood bears the risk of misinterpretation of data. Clear recommendations for the process of blood sample collection are required. We studied by UHPLC-high resolution mass spectrometry, as part of the "Preanalytics interest group" of the International Lipidomics Society, the stability of 417 lipid species in EDTA whole blood after exposure to either 4°C, 21°C, or 30°C at six different time points (0.5 h-24 h) to cover common daily routine conditions in clinical settings. In total, >800 samples were analyzed. 325 and 288 robust lipid species resisted 24 h exposure of EDTA whole blood to 21°C or 30°C, respectively. Most significant instabilities were detected for FA, LPE, and LPC. Based on our data, we recommend cooling whole blood at once and permanent. Plasma should be separated within 4 h, unless the focus is solely on robust lipids. Lists are provided to check the ex vivo (in)stability of distinct lipids and potential biomarkers of interest in whole blood. To conclude, our results contribute to the international efforts towards reliable and comparable clinical lipidomics data paving the way to the proper diagnostic application of distinct lipid patterns or lipid profiles in the future.

Biomarker Reproducibility Challenge: A Review of Non-Nucleotide Biomarker Discovery Protocols from Body Fluids in Breast Cancer Diagnosis

Breast cancer has now become the most commonly diagnosed cancer, accounting for one in eight cancer diagnoses worldwide. Non-invasive diagnostic biomarkers and associated tests are superlative candidates to complement or improve current approaches for screening, early diagnosis, or prognosis of breast cancer. Biomarkers detected from body fluids such as blood (serum/plasma), urine, saliva, nipple aspiration fluid, and tears can detect breast cancer at its early stages in a minimally invasive way. The advancements in high-throughput molecular profiling (omics) technologies have opened an unprecedented opportunity for unbiased biomarker detection. However, the irreproducibility of biomarkers and discrepancies of reported markers have remained a major roadblock to clinical implementation, demanding the investigation of contributing factors and the development of standardised biomarker discovery pipelines. A typical biomarker discovery workflow includes pre-analytical, analytical, and post-analytical phases, from sample collection to model development. Variations introduced during these steps impact the data quality and the reproducibility of the findings. Here, we present a comprehensive review of methodological variations in biomarker discovery studies in breast cancer, with a focus on non-nucleotide biomarkers (i.e., proteins, lipids, and metabolites), highlighting the pre-analytical to post-analytical variables, which may affect the accurate identification of biomarkers from body fluids.

Small molecule biomarker discovery: Proposed workflow for LC-MS-based clinical research projects

Mass spectrometry focusing on small endogenous molecules has become an integral part of biomarker discovery in the pursuit of an in-depth understanding of the pathophysiology of various diseases, ultimately enabling the application of personalized medicine. While LC-MS methods allow researchers to gather vast amounts of data from hundreds or thousands of samples, the successful execution of a study as part of clinical research also requires knowledge transfer with clinicians, involvement of data scientists, and interactions with various stakeholders. The initial planning phase of a clinical research project involves specifying the scope and design, and engaging relevant experts from different fields. Enrolling subjects and designing trials rely largely on the overall objective of the study and epidemiological considerations, while proper pre-analytical sample handling has immediate implications on the quality of analytical data. Subsequent LC-MS measurements may be conducted in a targeted, semi-targeted, or non-targeted manner, resulting in datasets of varying size and accuracy. Data processing further enhances the quality of data and is a prerequisite for in-silico analysis. Nowadays, the evaluation of such complex datasets relies on a mix of classical statistics and machine learning applications, in combination with other tools, such as pathway analysis and gene set enrichment. Finally, results must be validated before biomarkers can be used as prognostic or diagnostic decision-making tools. Throughout the study, quality control measures should be employed to enhance the reliability of data and increase confidence in the results. The aim of this graphical review is to provide an overview of the steps to be taken when conducting an LC-MS-based clinical research project to search for small molecule biomarkers.

Pre-analytical sample handling standardization for reliable measurement of metabolites and lipids in LC-MS-based clinical research

The emerging disciplines of lipidomics and metabolomics show great potential for the discovery of diagnostic biomarkers, but appropriate pre-analytical sample-handling procedures are critical because several analytes are prone to ex vivo distortions during sample collection. To test how the intermediate storage temperature and storage period of plasma samples from K3EDTA whole-blood collection tubes affect analyte concentrations, we assessed samples from non-fasting healthy volunteers (n = 9) for a broad spectrum of metabolites, including lipids and lipid mediators, using a well-established LC-MS-based platform. We used a fold change-based approach as a relative measure of analyte stability to evaluate 489 analytes, employing a combination of targeted LC-MS/MS and LC-HRMS screening. The concentrations of many analytes were found to be reliable, often justifying less strict sample handling; however, certain analytes were unstable, supporting the need for meticulous processing. We make four data-driven recommendations for sample-handling protocols with varying degrees of stringency, based on the maximum number of analytes and the feasibility of routine clinical implementation. These protocols also enable the simple evaluation of biomarker candidates based on their analyte-specific vulnerability to ex vivo distortions. In summary, pre-analytical sample handling has a major effect on the suitability of certain metabolites as biomarkers, including several lipids and lipid mediators. Our sample-handling recommendations will increase the reliability and quality of samples when such metabolites are necessary for routine clinical diagnosis.

Pre-analytical aspects in metabolomics of human biofluids - sample collection, handling, transport, and storage

Metabolomics is the field of omics research that offers valuable insights into the complex composition of biological samples. It has found wide application in clinical diagnostics, disease investigation, therapy prediction, monitoring of treatment efficiency, drug discovery, or in-depth analysis of sample composition. A suitable study design constitutes the fundamental requirements to ensure robust and reliable results from the study data. The study design process should include a careful selection of conditions for each experimental step, from sample collection to data analysis. The pre-analytical variability that can introduce bias to the subsequent analytical process, decrease the outcome reliability, and confuse the final results of the metabolomics research, should also be considered. Herein, we provide key information regarding the pre-analytical variables affecting the metabolomics studies of biological fluids that are the most desirable type of biological samples. Our work offers a practical review that can serve and guide metabolomics pre-analytical design. It indicates pre-analytical factors, which can introduce artificial data variation and should be identified and understood during experimental design (through literature overview or analytical experiments).

Metabolomics in pediatric lower respiratory tract infections and sepsis: a literature review

Lower respiratory tract infections (LRTIs) are a leading cause of morbidity and mortality in children. The ability of healthcare providers to diagnose and prognose LRTIs in the pediatric population remains a challenge, as children can present with similar clinical features regardless of the underlying pathogen or ultimate severity. Metabolomics, the large-scale analysis of metabolites and metabolic pathways offers new tools and insights that may aid in diagnosing and predicting the outcomes of LRTIs in children. This review highlights the latest literature on the clinical utility of metabolomics in providing care for children with bronchiolitis, pneumonia, COVID-19, and sepsis. IMPACT: This article summarizes current metabolomics approaches to diagnosing and predicting the course of pediatric lower respiratory infections. This article highlights the limitations to current metabolomics research and highlights future directions for the field.

Pre-analytical processing of plasma and serum samples for combined proteome and metabolome analysis

Metabolomic and proteomic analyses of human plasma and serum samples harbor the power to advance our understanding of disease biology. Pre-analytical factors may contribute to variability and bias in the detection of analytes, especially when multiple labs are involved, caused by sample handling, processing time, and differing operating procedures. To better understand the impact of pre-analytical factors that are relevant to implementing a unified proteomic and metabolomic approach in a clinical setting, we assessed the influence of temperature, sitting times, and centrifugation speed on the plasma and serum metabolomes and proteomes from six healthy volunteers. We used targeted metabolic profiling (497 metabolites) and data-independent acquisition (DIA) proteomics (572 proteins) on the same samples generated with well-defined pre-analytical conditions to evaluate criteria for pre-analytical SOPs for plasma and serum samples. Time and temperature showed the strongest influence on the integrity of plasma and serum proteome and metabolome. While rapid handling and low temperatures (4°C) are imperative for metabolic profiling, the analyzed proteomics data set showed variability when exposed to temperatures of 4°C for more than 2 h, highlighting the need for compromises in a combined analysis. We formalized a quality control scoring system to objectively rate sample stability and tested this score using external data sets from other pre-analytical studies. Stringent and harmonized standard operating procedures (SOPs) are required for pre-analytical sample handling when combining proteomics and metabolomics of clinical samples to yield robust and interpretable data on a longitudinal scale and across different clinics. To ensure an adequate level of practicability in a clinical routine for metabolomics and proteomics studies, we suggest keeping blood samples up to 2 h on ice (4°C) prior to snap-freezing as a compromise between stability and operability. Finally, we provide the methodology as an open-source R package allowing the systematic scoring of proteomics and metabolomics data sets to assess the stability of plasma and serum samples.

Independent Validation and Assay Standardization of Improved Metabolic Biomarker Signature to Differentiate Pancreatic Ductal Adenocarcinoma From Chronic Pancreatitis

BACKGROUND & AIMS

Pancreatic ductal adenocarcinoma cancer (PDAC) is a highly lethal malignancy requiring efficient detection when the primary tumor is still resectable. We previously developed the MxPancreasScore comprising 9 analytes and serum carbohydrate antigen 19-9 (CA19-9), achieving an accuracy of 90.6%. The necessity for 5 different analytical platforms and multiple analytical runs, however, hindered clinical applicability. We therefore aimed to develop a simpler single-analytical run, single-platform diagnostic signature.

METHODS

We evaluated 941 patients (PDAC, 356; chronic pancreatitis [CP], 304; nonpancreatic disease, 281) in 3 multicenter independent tests, and identification (ID) and validation cohort 1 (VD1) and 2 (VD2) were evaluated. Targeted quantitative plasma metabolite analysis was performed on a liquid chromatography-tandem mass spectrometry platform. A machine learning-aided algorithm identified an improved (i-Metabolic) and minimalistic metabolic (m-Metabolic) signatures, and compared them for performance.

RESULTS

The i-Metabolic Signature, (12 analytes plus CA19-9) distinguished PDAC from CP with area under the curve (95% confidence interval) of 97.2% (97.1%-97.3%), 93.5% (93.4%-93.7%), and 92.2% (92.1%-92.3%) in the ID, VD1, and VD2 cohorts, respectively. In the VD2 cohort, the m-Metabolic signature (4 analytes plus CA19-9) discriminated PDAC from CP with a sensitivity of 77.3% and specificity of 89.6%, with an overall accuracy of 82.4%. For the subset of 45 patients with PDAC with resectable stages IA-IIB tumors, the sensitivity, specificity, and accuracy were 73.2%, 89.6%, and 82.7%, respectively; for those with detectable CA19-9 >2 U/mL, 81.6%, 88.7%, and 84.5%, respectively; and for those with CA19-9 <37 U/mL, 39.7%, 94.1%, and 76.3%, respectively.

CONCLUSIONS

The single-platform, single-run, m-Metabolic signature of just 4 metabolites used in combination with serum CA19-9 levels is an innovative accurate diagnostic tool for PDAC at the time of clinical presentation, warranting further large-scale evaluation.

Tracking the Stability of Clinically Relevant Blood Plasma Proteins with Delta-S-Cys-Albumin-A Dilute-and-Shoot LC/MS-Based Marker of Specimen Exposure to Thawed Conditions

Biomolecular integrity can be compromised when blood plasma/serum (P/S) specimens are improperly handled. Compromised analytes can subsequently produce erroneous results-without any indication of having done so. We recently introduced an LC/MS-based marker of P/S exposure to thawed conditions called ΔS-Cys-Albumin which, aided by an established rate law, quantitatively tracks exposure of P/S to temperatures greater than their freezing point of -30 °C. The purposes of this study were to (1) evaluate ΔS-Cys-Albumin baseline values in gastrointestinal cancer patients and cancer-free control donors, (2) empirically assess the kinetic profiles of ΔS-Cys-Albumin at 23 °C, 4 °C, and -20 °C, and (3) empirically link ΔS-Cys-Albumin to the stability of clinically relevant proteins. ΔS-Cys-Albumin was measured at ≥ 9 different time points per exposure temperature in serum and K₂EDTA plasma samples from 24 separate donors in aliquots kept separately at 23 °C, 4 °C, and -20 °C. Twenty-one clinically relevant plasma proteins were measured at four time points per temperature via a multiplexed immunoassay on the Luminex platform. Protein stability was assessed by mixed effects models. Coordinated shifts in stability between ΔS-Cys-Albumin and the unstable proteins were documented by repeated measures and Pearson correlations. Plasma ΔS-Cys-Albumin dropped from approximately 20% to under 5% within 96 h at 23 °C, 28 days at 4 °C, and 65 days at -20 °C. On average, 22% of the 21 proteins significantly changed in apparent concentration at each exposure temperature (p < 0.0008 with >10% shift). A linear inverse relationship was found between the percentage of proteins destabilized and ΔS-Cys-Albumin (r = -0.61; p < 0.0001)-regardless of the specific time/temperature of exposure. ΔS-Cys-Albumin tracks cumulative thawed-state exposure. These results now enable ΔS-Cys-Albumin to approximate the percentage of clinically relevant proteins that have been compromised by incidental plasma exposure to thawed-state conditions.

Probabilistic quotient's work and pharmacokinetics' contribution: countering size effect in metabolic time series measurements

Metabolomic time course analyses of biofluids are highly relevant for clinical diagnostics. However, many sampling methods suffer from unknown sample sizes, commonly known as size effects. This prevents absolute quantification of biomarkers. Recently, several mathematical post acquisition normalization methods have been developed to overcome these problems either by exploiting already known pharmacokinetic information or by statistical means. Here we present an improved normalization method, MIX, that combines the advantages of both approaches. It couples two normalization terms, one based on a pharmacokinetic model (PKM) and the other representing a popular statistical approach, probabilistic quotient normalization (PQN), in a single model. To test the performance of MIX, we generated synthetic data closely resembling real finger sweat metabolome measurements. We show that MIX normalization successfully tackles key weaknesses of the individual strategies: it (i) reduces the risk of overfitting with PKM, and (ii), contrary to PQN, it allows to compute sample volumes. Finally, we validate MIX by using real finger sweat as well as blood plasma metabolome data and demonstrate that MIX allows to better and more robustly correct for size effects. In conclusion, the MIX method improves the reliability and robustness of quantitative biomarker detection in finger sweat and other biofluids, paving the way for biomarker discovery and hypothesis generation from metabolomic time course data.

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.

Plasma Oxylipins and Their Precursors Are Strongly Associated with COVID-19 Severity and with Immune Response Markers

COVID-19 is characterised by a dysregulated immune response, that involves signalling lipids acting as mediators of the inflammatory process along the innate and adaptive phases. To promote understanding of the disease biochemistry and provide targets for intervention, we applied a range of LC-MS platforms to analyse over 100 plasma samples from patients with varying COVID-19 severity and with detailed clinical information on inflammatory responses (>30 immune markers). The second publication in a series reports the results of quantitative LC-MS/MS profiling of 63 small lipids including oxylipins, free fatty acids, and endocannabinoids. Compared to samples taken from ward patients, intensive care unit (ICU) patients had 2−4-fold lower levels of arachidonic acid (AA) and its cyclooxygenase-derived prostanoids, as well as lipoxygenase derivatives, exhibiting negative correlations with inflammation markers. The same derivatives showed 2−5-fold increases in recovering ward patients, in paired comparison to early hospitalisation. In contrast, ICU patients showed elevated levels of oxylipins derived from poly-unsaturated fatty acids (PUFA) by non-enzymatic peroxidation or activity of soluble epoxide hydrolase (sEH), and these oxylipins positively correlated with markers of macrophage activation. The deficiency in AA enzymatic products and the lack of elevated intermediates of pro-resolving mediating lipids may result from the preference of alternative metabolic conversions rather than diminished stores of PUFA precursors. Supporting this, ICU patients showed 2-to-11-fold higher levels of linoleic acid (LA) and the corresponding fatty acyl glycerols of AA and LA, all strongly correlated with multiple markers of excessive immune response. Our results suggest that the altered oxylipin metabolism disrupts the expected shift from innate immune response to resolution of inflammation.

Metabolomics in pulmonary medicine - extracting the most from your data

The metabolome enables unprecedented insight into biochemistry, providing an integrated signature of the genome, transcriptome, proteome and exposome. Measurement requires rigorous protocols combined with specialised data analysis to achieve its promise.https://bit.ly/3yPiYkQ

Introduction of BD Vacutainer® Barricor™ tubes in clinical biobanking and application of amino acid and cytokine quality indicators to Barricor plasma

OBJECTIVES

The use of BD Vacutainer® Barricor™ tubes (BAR) can reduce turnaround time (TAT) and improve separation of plasma from cellular components using a specific mechanical separator. Concentrations of amino acids (AAs) and cytokines, known to be labile during pre-analytical time delays, were compared in heparin (BAR, BD Heparin standard tube [PST]), EDTA and serum gel tubes (SER) to validate previously identified quality indicators (QIs) in BAR.

METHODS

Samples of healthy individuals (n=10) were collected in heparin, EDTA and SER tubes and exposed to varying pre- and post-centrifugation delays at room temperature (RT). Cytokines (interleukin [IL]-8, IL-16 and sCD40L) were analyzed by enzyme-linked immunosorbent assay (ELISA) and AAs were characterized by liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS).

RESULTS

All QIs, AAs/AA ratio and cytokines increased during prolonged blood storage in heparin plasma (PST, BAR) and SER tubes. Comparison of 53 h/1 h pre-centrifugation delay resulted in an increase in taurine (Tau) and glutamic acid (Glu) concentrations by more than three times, soluble CD40L increased by 13.6, 9.2 and 4.3 fold in PST, BAR-CTRL and BAR-FAST, and IL-8 increased even more by 112.8 (PST), 266.1 (BAR-CTRL), 268.1 (BAR-FAST) and 70.0 (SER) fold, respectively. Overall, compared to prolonged blood storage, effects of post-centrifugation delays were less pronounced in all tested materials.

CONCLUSIONS

BAR tubes are compatible with the use of several established QIs and can therefore be used in clinical biobanking to reduce pre-analytical TAT without compromising QIs and thus pre-analytical sample quality analysis.

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.

Serum or Plasma (and Which Plasma), That Is the Question

Blood derivatives are the biofluids of choice for metabolomic clinical studies since blood can be collected with low invasiveness and is rich in biological information. However, the choice of the blood collection tubes has an undeniable impact on the plasma and serum metabolic content. Here, we compared the metabolomic and lipoprotein profiles of blood samples collected at the same time and place from six healthy volunteers but using different collection tubes (each enrolled volunteer provided multiple blood samples at a distance of a few weeks/months): citrate plasma, EDTA plasma, and serum tubes. All samples were analyzed via nuclear magnetic resonance spectroscopy. Several metabolites showed statistically significant alterations among the three blood matrices, and also metabolites’ correlations were shown to be affected. The effects of blood collection tubes on the lipoproteins’ profiles are relevant too, but less marked. Overcoming the issue associated with different blood collection tubes is pivotal to scale metabolomics and lipoprotein analysis at the level of epidemiological studies based on samples from multicenter cohorts. We propose a statistical solution, based on regression, that is shown to be efficient in reducing the alterations induced by the different collection tubes for both the metabolomic and lipoprotein profiles.

Impact of the pre-examination phase on multicenter metabolomic studies

The development of metabolomics in clinical applications has been limited by the lack of validation in large multicenter studies. Large population cohorts and their biobanks are a valuable resource for acquiring insights into molecular disease mechanisms. Nevertheless, most of their collections are not tailored for metabolomics and have been created without specific attention to the pre-analytical requirements for high-quality metabolome assessment. Thus, comparing samples obtained by different pre-analytical procedures remains a major challenge. Here, ¹H NMR-based analyses are used to demonstrate how human serum and plasma samples collected with different operating procedures within several large European cohort studies from the Biobanking and Biomolecular Resources Infrastructure - Large Prospective Cohorts (BBMRI-LPC) consortium can be easily revealed by supervised multivariate statistical analyses at the initial stages of the process, to avoid biases in the downstream analysis. The inter-biobank differences are discussed in terms of deviations from the validated CEN/TS 16945:2016 / ISO 23118:2021 norms. It clearly emerges that biobanks must adhere to the evidence-based guidelines in order to support wider-scale application of metabolomics in biomedicine, and that NMR spectroscopy is informative in comparing the quality of different sample sources in multi cohort/center studies.

Purpose, Partnership, and Possibilities: The Implementation of the Dog Aging Project Biobank

Background:

Biobanks have been supporting longitudinal prospective and retrospective studies by providing standardized services for the acquisition, transport, processing, storage, and distribution of high-quality biological material and associated data. Here, we describe how the Dog Aging Project (DAP), a large-scale longitudinal study of the domestic dog ( Canis familiaris) with translational applications for humans, developed a biobank of canine biospecimens and associated data.

Design and methods:

This was accomplished by working with the Cornell Veterinary Biobank, the first biobank in the world to receive accreditation to ISO 20387:2018—General Requirements for Biobanking. The biobank research team was involved in the early collection stages of the DAP, contributing to the development of appropriate workflows and processing fit-for-purpose biospecimens. In support of a dynamic strategy for real-time adjustment of processes, a pilot phase was implemented to develop, test, and optimize the biospecimen workflows, followed by an early phase of collection, processing, and banking of specimens from DAP participants.

Results:

During the pilot and early phases of collection, the DAP Biobank stored 164 aliquots of whole blood, 273 aliquots of peripheral blood mononuclear cells, 130 aliquots of plasma, and 70 aliquots of serum, and extracted high molecular weight genomic DNA suitable for whole-genome sequencing from 109 whole blood specimens. These specimens, along with their associated preanalytical data, have been made available for distribution to researchers.

Conclusion:

We discuss the challenges and opportunities encountered during the implementation of the DAP Biobank, along with novel strategies for promoting biobanking sustainability such as partnering with a DAP quality assurance manager and a DAP marketing and communication specialist and developing a pilot grant structure to fund small innovative research projects.

Metabolomics for personalized medicine: the input of analytical chemistry from biomarker discovery to point-of-care tests

Metabolomics refers to the large-scale detection, quantification, and analysis of small molecules (metabolites) in biological media. Although metabolomics, alone or combined with other omics data, has already demonstrated its relevance for patient stratification in the frame of research projects and clinical studies, much remains to be done to move this approach to the clinical practice. This is especially true in the perspective of being applied to personalized/precision medicine, which aims at stratifying patients according to their risk of developing diseases, and tailoring medical treatments of patients according to individual characteristics in order to improve their efficacy and limit their toxicity. In this review article, we discuss the main challenges linked to analytical chemistry that need to be addressed to foster the implementation of metabolomics in the clinics and the use of the data produced by this approach in personalized medicine. First of all, there are already well-known issues related to untargeted metabolomics workflows at the levels of data production (lack of standardization), metabolite identification (small proportion of annotated features and identified metabolites), and data processing (from automatic detection of features to multi-omic data integration) that hamper the inter-operability and reusability of metabolomics data. Furthermore, the outputs of metabolomics workflows are complex molecular signatures of few tens of metabolites, often with small abundance variations, and obtained with expensive laboratory equipment. It is thus necessary to simplify these molecular signatures so that they can be produced and used in the field. This last point, which is still poorly addressed by the metabolomics community, may be crucial in a near future with the increased availability of molecular signatures of medical relevance and the increased societal demand for participatory medicine.

Variability of human fasted venous plasma metabolomic profiles with tourniquet induced hemostasis

Venous plasma metabolomics is a potent and highly sensitive tool for identifying and measuring metabolites of interest in human health and disease. Accurate and reproducible insights from such metabolomic studies require extreme care in removing preanalytical confounders; one of these is the duration of tourniquet application when drawing the venous blood sample. Using an untargeted plasma metabolomics approach, we evaluated the effect of varying durations of tourniquet application on the variability in plasma metabolite concentrations in five healthy female subjects. Tourniquet application introduced appreciable variation in the metabolite abundances: 73% of the identified metabolites had higher temporal variation compared to interindividual variation [Intra-Class Correlation (ICC) > 0.50]. As such, we recommend tourniquet application for minimal duration and to wait for 5 min with the needle in situ after removing the tourniquet, to reduce hemostasis-induced variability and false flags in interpretation.

Factors that influence the quality of metabolomics data in in vitro cell toxicity studies: a systematic survey

REACH (Registration, Evaluation, Authorization and Restriction of Chemicals) is a global strategy and regulation policy of the EU that aims to improve the protection of human health and the environment through the better and earlier identification of the intrinsic properties of chemical substances. It entered into force on 1st June 2007 (EC 1907/2006). REACH and EU policies plead for the use of robust high-throughput "omic" techniques for the in vitro investigation of the toxicity of chemicals that can provide an estimation of their hazards as well as information regarding the underlying mechanisms of toxicity. In agreement with the 3R's principles, cultured cells are nowadays widely used for this purpose, where metabolomics can provide a real-time picture of the metabolic effects caused by exposure of cells to xenobiotics, enabling the estimations about their toxicological hazards. High quality and robust metabolomics data sets are essential for precise and accurate hazard predictions. Currently, the acquisition of consistent and representative metabolomic data is hampered by experimental drawbacks that hinder reproducibility and difficult robust hazard interpretation. Using the differentiated human liver HepG2 cells as model system, and incubating with hepatotoxic (acetaminophen and valproic acid) and non-hepatotoxic compounds (citric acid), we evaluated in-depth the impact of several key experimental factors (namely, cell passage, processing day and storage time, and compound treatment) and instrumental factors (batch effect) on the outcome of an UPLC-MS metabolomic analysis data set. Results showed that processing day and storage time had a significant impact on the retrieved cell's metabolome, while the effect of cell passage was minor. Meta-analysis of results from pathway analysis showed that batch effect corrections and quality control (QC) measures are critical to enable consistent and meaningful estimations of the effects caused by compounds on cells. The quantitative analysis of the changes in metabolic pathways upon bioactive compound treatment remained consistent despite the concurrent causes of metabolomic data variation. Thus, upon appropriate data retrieval and correction and by an innovative metabolic pathway analysis, the metabolic alteration predictions remained conclusive despite the acknowledged sources of variability.

Identification and validation of a multivariable prediction model based on blood plasma and serum metabolomics for the distinction of chronic pancreatitis subjects from non-pancreas disease control subjects

OBJECTIVE

Chronic pancreatitis (CP) is a fibroinflammatory syndrome leading to organ dysfunction, chronic pain, an increased risk for pancreatic cancer and considerable morbidity. Due to a lack of specific biomarkers, diagnosis is based on symptoms and specific but insensitive imaging features, preventing an early diagnosis and appropriate management.

DESIGN

We conducted a type 3 study for multivariable prediction for individual prognosis according to the TRIPOD guidelines. A signature to distinguish CP from controls (n=160) was identified using gas chromatography-mass spectrometry and liquid chromatography-tandem mass spectrometry on ethylenediaminetetraacetic acid (EDTA)-plasma and validated in independent cohorts.

RESULTS

A Naive Bayes algorithm identified eight metabolites of six ontology classes. After algorithm training and computation of optimal cut-offs, classification according to the metabolic signature detected CP with an area under the curve (AUC) of 0.85 ((95% CI 0.79 to 0.91). External validation in two independent cohorts (total n=502) resulted in similar accuracy for detection of CP compared with non-pancreatic controls in EDTA-plasma (AUC 0.85 (95% CI 0.81 to 0.89)) and serum (AUC 0.87 (95% CI 0.81 to 0.95)).

CONCLUSIONS

This is the first study that identifies and independently validates a metabolomic signature in plasma and serum for the diagnosis of CP in large, prospective cohorts. The results could provide the basis for the development of the first routine laboratory test for CP.

Prediction and evaluation of the effect of pre-centrifugation sample management on the measurable untargeted LC-MS plasma metabolome

Optimal handling is the most important means to ensure adequate sample quality. We aimed to investigate whether pre-centrifugation delay time and temperature could be accurately predicted and to what extent variability induced by pre-centrifugation management can be adjusted for. We used untargeted liquid chromatography-mass spectrometry (LC-MS) metabolomics to predict and evaluate the influence of pre-centrifugation temperature and delayed time on plasma samples. Pre-centrifugation temperature (4, 25 and 37 °C; classification rate 87%) and time (5-210 min; Q² = 0.82) were accurately predicted using Random Forest (RF). Metabolites uniquely reflecting temperature and temperature-time interactions were discovered using a combination of RF and generalized linear models. Time-related metabolite profiles suggested a perturbed stability of the metabolome at all temperatures in the investigated time period (5-210 min), and the variation at 4 °C was observed in particular before 90 min. Fourteen and eight metabolites were selected and validated for accurate prediction of pre-centrifugation temperature (classification rate 94%) and delay time (Q² = 0.90), respectively. In summary, the metabolite profile was rapidly affected by pre-centrifugation delay at all temperatures and thus the pre-centrifugation delay should be as short as possible for metabolomics analysis. The metabolite panels provided accurate predictions of pre-centrifugation delay time and temperature in healthy individuals in a separate validation sample. Such predictions could potentially be useful for assessing legacy samples where relevant metadata is lacking. However, validation in larger populations and different phenotypes, including disease states, is needed.

Defining Acute Coronary Syndrome through Metabolomics

As an emerging platform technology, metabolomics offers new insights into the pathomechanisms associated with complex disease conditions, including cardiovascular diseases. It also facilitates assessing the risk of developing the disease before its clinical manifestation. For this reason, metabolomics is of growing interest for understanding the pathogenesis of acute coronary syndromes (ACS), finding new biomarkers of ACS, and its associated risk management. Metabolomics-based studies in ACS have already demonstrated immense potential for biomarker discovery and mechanistic insights by identifying metabolomic signatures (e.g., branched-chain amino acids, acylcarnitines, lysophosphatidylcholines) associated with disease progression. Herein, we discuss the various metabolomics approaches and the challenges involved in metabolic profiling, focusing on ACS. Special attention has been paid to the clinical studies of metabolomics and lipidomics in ACS, with an emphasis on ischemia/reperfusion injury.

Metabolite Ratios as Quality Indicators for Pre-Analytical Variation in Serum and EDTA Plasma

In clinical diagnostics and research, blood samples are one of the most frequently used materials. Nevertheless, exploring the chemical composition of human plasma and serum is challenging due to the highly dynamic influence of pre-analytical variation. A prominent example is the variability in pre-centrifugation delay (time-to-centrifugation; TTC). Quality indicators (QI) reflecting sample TTC are of utmost importance in assessing sample history and resulting sample quality, which is essential for accurate diagnostics and conclusive, reproducible research. In the present study, we subjected human blood to varying TTCs at room temperature prior to processing for plasma or serum preparation. Potential sample QIs were identified by Ultra high pressure liquid chromatography tandem mass spectrometry (UHPLC-MS/MS) based metabolite profiling in samples from healthy volunteers (n = 10). Selected QIs were validated by a targeted MS/MS approach in two independent sets of samples from patients (n = 40 and n = 70). In serum, the hypoxanthine/guanosine (HG) and hypoxanthine/inosine (HI) ratios demonstrated high diagnostic performance (Sensitivity/Specificity > 80%) for the discrimination of samples with a TTC > 1 h. We identified several eicosanoids, such as 12-HETE, 15-(S)-HETE, 8-(S)-HETE, 12-oxo-HETE, (±)13-HODE and 12-(S)-HEPE as QIs for a pre-centrifugation delay > 2 h. 12-HETE, 12-oxo-HETE, 8-(S)-HETE, and 12-(S)-HEPE, and the HI- and HG-ratios could be validated in patient samples.

Proteomics for Low Cell Numbers: How to Optimize the Sample Preparation Workflow for Mass Spectrometry Analysis

Nowadays, massive genomics and transcriptomics data can be generated at the single-cell level. However, proteomics in this setting is still a big challenge. Despite the great improvements in sensitivity and performance of mass spectrometry instruments and the better knowledge on sample preparation processing, it is widely acknowledged that multistep proteomics workflows may lead to substantial sample loss, especially when working with paucicellular samples. Still, in clinical fields, frequently limited sample amounts are available for downstream analysis, thereby hampering comprehensive characterization at protein level. To aim at better protein and peptide recoveries, we compare existing and novel approaches in the multistep sample preparation protocols for mass spectrometry studies, from sample collection, cell lysis, protein quantification, and electrophoresis/staining to protein digestion, peptide recovery, and LC-MS/MS instruments. From this critical evaluation, we conclude that the recent innovations and technologies, together with high quality management of samples, make proteomics on paucicellular samples possible, which will have immediate impact for the proteomics community.

Longitudinal Analysis of Multiple Neurotransmitter Metabolites in Cerebrospinal Fluid in Early Parkinson's Disease

BACKGROUND

Cerebrospinal fluid (CSF) levels of monoamine metabolites may represent biomarkers of Parkinson's disease (PD).

OBJECTIVE

The aim of this study was quantification of multiple metabolites in CSF from PD versus healthy control subjects (HCs), including longitudinal analysis.

METHODS

Absolute levels of multiple monoamine metabolites in CSF were quantified by liquid chromatography coupled with tandem mass spectrometry from 161 individuals with early PD and 115 HCs from the Parkinson's Progression Marker Initiative and de novo PD (DeNoPA) studies.

RESULTS

Baseline levels of homovanillic acid (HVA) and 3,4-dihydroxyphenylacetic acid (DOPAC) were lower in individuals with PD compared with HCs. HVA levels correlated with Movement Disorder Society Unified Parkinson's Disease Rating Scale total scores (P < 0.01). Both HVA/dopamine and DOPAC/dopamine levels correlated with caudate nucleus and raw DOPAC with putamen dopamine transporter single-photon emission computed tomography uptake ratios (P < 0.01). No metabolite changed over 2 years in drug-naive individuals, but some changed on starting levodopa treatment.

CONCLUSIONS

HVA and DOPAC CSF levels mirrored nigrostriatal pathway damage, confirming the central role of dopaminergic degeneration in early PD. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Influence of pregnancy and non-fasting conditions on the plasma metabolome in a rat prenatal toxicity study

The current parameters for determining maternal toxicity (e.g. clinical signs, food consumption, body weight development) lack specificity and may underestimate the extent of effects of test compounds on the dams. Previous reports have highlighted the use of plasma metabolomics for an improved and mechanism-based identification of maternal toxicity. To establish metabolite profiles of healthy pregnancies and evaluate the influence of food consumption as a confounding factor, metabolite profiling of rat plasma was performed by gas- and liquid-chromatography-tandem mass spectrometry techniques. Metabolite changes in response to pregnancy, food consumption prior to blood sampling (non-fasting) as well as the interaction of both conditions were studied. In dams, both conditions, non-fasting and pregnancy, had a marked influence on the plasma metabolome and resulted in distinct individual patterns of changed metabolites. Non-fasting was characterized by increased plasma concentrations of amino acids and diet related compounds and lower levels of ketone bodies. The metabolic profile of pregnant rats was characterized by lower amino acids and glucose levels and higher concentrations of plasma fatty acids, triglycerides and hormones, capturing the normal biochemical changes undergone during pregnancy. The establishment of metabolic profiles of pregnant non-fasted rats serves as a baseline to create metabolic fingerprints for prenatal and maternal toxicity studies.

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.

Blood Plasma Quality Control by Plasma Glutathione Status

Timely centrifugation of blood for plasma preparation is a key step to ensure high plasma quality for analytics. Delays during preparation can significantly influence readouts of key clinical parameters. However, in a routine clinical environment, a strictly controlled timeline is often not feasible. The next best approach is to control for sample preparation delays by a marker that provides a readout of the time-dependent degradation of the sample. In this study, we explored the usefulness of glutathione status as potential marker of plasma preparation delay. As the concentration of glutathione in erythrocytes is at least two orders of magnitude higher than in plasma, even the slightest leakage of glutathione from the cells can be readily observed. Over the 3 h observation period employed in this study, we observed a linear increase of plasma concentrations of both reduced (GSH) and oxidized glutathione (GSSG). Artificial oxidation of GSH is prevented by rapid alkylation with N-ethylmaleimide directly in the blood sampling vessel as recently published. The observed relative leakage of GSH was significantly higher than that of GSSG. A direct comparison with plasma lactate dehydrogenase activity, a widely employed hemolysis marker, clearly demonstrated the superiority of our approach for quality control. Moreover, we show that the addition of the thiol alkylating reagent NEM directly to the blood tubes does not influence downstream analysis of other clinical parameters. In conclusion, we report that GSH gives an excellent readout of the duration of plasma preparation and the associated pre-analytical errors.

Performance of a cardiac lipid panel compared to four prognostic scores in chronic heart failure

The cardiac lipid panel (CLP) is a novel panel of metabolomic biomarkers that has previously shown to improve the diagnostic and prognostic value for CHF patients. Several prognostic scores have been developed for cardiovascular disease risk, but their use is limited to specific populations and precision is still inadequate. We compared a risk score using the CLP plus NT-proBNP to four commonly used risk scores: The Seattle Heart Failure Model (SHFM), Framingham risk score (FRS), Barcelona bio-HF (BCN Bio-HF) and Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC) score. We included 280 elderly CHF patients from the Cardiac Insufficiency Bisoprolol Study in Elderly trial. Cox Regression and hierarchical cluster analysis was performed. Integrated area under the curves (IAUC) was used as criterium for comparison. The mean (SD) follow-up period was 81 (33) months, and 95 (34%) subjects met the primary endpoint. The IAUC for FRS was 0.53, SHFM 0.61, BCN Bio-HF 0.72, MAGGIC 0.68, and CLP 0.78. Subjects were partitioned into three risk clusters: low, moderate, high with the CLP score showing the best ability to group patients into their respective risk cluster. A risk score composed of a novel panel of metabolite biomarkers plus NT-proBNP outperformed other common prognostic scores in predicting 10-year cardiovascular death in elderly ambulatory CHF patients. This approach could improve the clinical risk assessment of CHF patients.

The holding temperature of blood during a delay to processing can affect serum and plasma protein measurements

Accurate blood-borne biomarkers are sought for diagnosis, prognosis and treatment stratification. Consistent handling of blood is essential for meaningful data interpretation, however, delays during processing are occasionally unavoidable. We investigated the effects of immediately placing blood samples on ice versus room temperature for 1 h (reference protocol), and holding samples on ice versus room temperature during a 3 h delay to processing. Using Luminex multi-plex assays to assess cytokines (n = 29) and diabetes-associated proteins (n = 15) in healthy subjects, we observed that placing blood samples immediately on ice decreased the serum levels of several cytokines, including PAI-1, MIP1-β, IL-9, RANTES and IL-8. During a delay to processing, some analytes, e.g. leptin and insulin, showed little change in serum or plasma values. However, for approximately half of the analytes studied, a delay, regardless of the holding temperature, altered the measured levels compared to the reference protocol. Effects differed between serum and plasma and for some analytes the direction of change in level varied across individuals. The optimal holding temperature for samples during a delay was analyte-specific. In conclusion, deviations from protocol can lead to significant changes in blood analyte levels. Where possible, protocols for blood handling should be pre-determined in an analyte-specific manner.

Analytical challenges in human exposome analysis with focus on environmental analysis combined with metabolomics

Environmental factors, such as chemical exposures, are likely to play a crucial role in the development of several human chronic diseases. However, how the specific exposures contribute to the onset and progress of various diseases is still poorly understood. In part, this is because comprehensive characterization of the chemical exposome is a highly challenging task, both due to its complex dynamic nature as well as due to the analytical challenges. Herein, the analytical challenges in the field of exposome research are reviewed, with specific emphasis on the sampling, sample preparation, and analysis, as well as challenges in the compound identification. The primary focus is on the human chemical exposome, that is, exposures to mixtures of environmental chemicals and its impact on human metabolome. In order to highlight the recent progress in the exposome research in relation to human health and disease, selected examples of human exposome studies are presented.

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.

Preanalytical Sample Handling Conditions and Their Effects on the Human Serum Metabolome in Epidemiologic Studies

Many epidemiologic studies use metabolomics for discovery-based research. The degree to which sample handling may influence findings, however, is poorly understood. In 2016, serum samples from 13 volunteers from the US Department of Agriculture's Beltsville Human Nutrition Research Center were subjected to different clotting (30 minutes/120 minutes) and refrigeration (0 minutes/24 hours) conditions, as well as different numbers (0/1/4) and temperatures (ice/refrigerator/room temperature) of thaws. The median absolute percent difference (APD) between metabolite levels and correlations between levels across conditions were estimated for 628 metabolites. The potential for handling artifacts to induce false-positive associations was estimated using variable hypothetical scenarios in which 1%-100% of case samples had different handling than control samples. All handling conditions influenced metabolite levels. Across metabolites, the median APD when extending clotting time was 9.08%. When increasing the number of thaws from 0 to 4, the median APD was 10.05% for ice and 5.54% for room temperature. Metabolite levels were correlated highly across conditions (all r's ≥ 0.84), indicating that relative ranks were preserved. However, if handling varied even modestly by case status, our hypotheticals showed that results can be biased and can result in false-positive findings. Sample handling affects levels of metabolites, and special care should be taken to minimize effects. Shorter room-temperature thaws should be preferred over longer ice thaws, and handling should be meticulously matched by case status.

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?"

Sphingolipidomics in Translational Sepsis Research-Biomedical Considerations and Perspectives

Scientific Background: Sphingolipids are a highly diverse group of lipids with respect to physicochemical properties controlling either structure, distribution, or function, all of them regulating cellular response in health and disease. Mass spectrometry, on the other hand, is an analytical technique characterizing ionized molecules or fragments thereof by mass-to-charge ratios, which has been prosperingly developed for rapid and reliable qualitative and quantitative identification of lipid species. Parallel to best performance of in-depth chromatographical separation of lipid classes, preconditions of precise quantitation of unique molecular species by preprocessing of biological samples have to be fulfilled. As a consequence, “lipid profiles” across model systems and human individuals, esp. complex (clinical) samples, have become eminent over the last couple of years due to sensitivity, specificity, and discriminatory capability. Therefore, it is significance to consider the entire experimental strategy from sample collection and preparation, data acquisition, analysis, and interpretation.

Areas Covered: In this review, we outline considerations with clinical (i.e., human) samples with special emphasis on sample handling, specific physicochemical properties, target measurements, and resulting profiling of sphingolipids in biomedicine and translational research to maximize sensitivity and specificity as well as to provide robust and reproducible results. A brief commentary is also provided regarding new insights of “clinical sphingolipidomics” in translational sepsis research.

Expert Opinion: The role of mass spectrometry of sphingolipids and related species (“sphingolipidomics”) to investigate cellular and compartment-specific response to stress, e.g., in generalized infection and sepsis, is on the rise and the ability to integrate multiple datasets from diverse classes of biomolecules by mass spectrometry measurements and metabolomics will be crucial to fostering our understanding of human health as well as response to disease and treatment.