Differences in metabolite profiles caused by pre-analytical blood processing procedures

Plasma and serum metabolic analysis of healthy adults shows characteristic profiles by subjects' sex and age

INTRODUCTION

Pre-analytical factors like sex, age, and blood processing methods introduce variability and bias, compromising data integrity, and thus deserve close attention.

OBJECTIVES

This study aimed to explore the influence of participant characteristics (age and sex) and blood processing methods on the metabolic profile.

METHOD

A Thermo UPLC-TSQ-Quantiva-QQQ Mass Spectrometer was used to analyze 175 metabolites across 9 classes in 208 paired serum and lithium heparin plasma samples from 51 females and 53 males.

RESULTS

Comparing paired serum and plasma samples from the same cohort, out of the 13 metabolites that showed significant changes, 4 compounds related to amino acids and derivatives had lower levels in plasma, and 5 other compounds had higher levels in plasma. Sex-based analysis revealed 12 significantly different metabolites, among which most amino acids and derivatives and nitrogen-containing compounds were higher in males, and other compounds were elevated in females. Interestingly, the volcano plot also confirms the similar patterns of amino acids and derivatives higher in males. The age-based analysis suggested that metabolites may undergo substantial alterations during the 25-35-year age range, indicating a potential metabolic turning point associated with the age group. Moreover, a more distinct difference between the 25-35 and above 35 age groups compared to the below 25 and 25-35 age groups was observed, with the most significant compound decreased in the above 35 age groups.

CONCLUSION

These findings may contribute to the development of comprehensive metabolomics analyses with confounding factor-based adjustment and enhance the reliability and interpretability of future large-scale investigations.

User-friendly relative quantification procedure for gas chromatography/mass spectrometry-based plasma metabolome analysis

RATIONALE

Recently, metabolome analysis has been applied to a variety of research fields, but differences between batches or facilities can cause discrepancies in the results of such analyses. To resolve these issues using comprehensive metabolome analysis, in which it is difficult to perform quantitative analyses of all detected metabolites, internal standard compounds are used to obtain relative metabolite levels. This study investigated gas chromatography/mass spectrometry-based plasma metabolome analysis methods that are superior to relative quantification using internal standard compounds.

METHODS

In experiment I, four analyses were performed under different analytical conditions at one facility, and then the data from the four analyses were compared. In experiment II, the same samples were analyzed at three facilities, and then the data from the three facilities were compared.

RESULTS

Regarding the relative values obtained through comparisons with the internal standard compound, differences in the analytical results were observed among the four analytical conditions in experiment I and among the three facilities in experiment II, and the differences observed among the three facilities (experiment II) were larger. When correction was performed using plasma as a quality control, which is the procedure suggested in this study, these differences were markedly ameliorated.

CONCLUSION

The suggested procedure involves the analysis of a plasma standard as a quality control for each batch and the calculation of relative target plasma to quality-control plasma values for each metabolite. This is an easy and low-cost method and could be readily employed by researchers during comprehensive plasma metabolome analysis.

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.

Optimized protocol for quantification of extracellular nicotinamide adenine dinucleotide: evaluating clinical parameters and pre-analytical factors for translational research

Nicotinamide adenine dinucleotide (NAD+), a coenzyme for more than 500 enzymes, plays a central role in energy production, metabolism, cellular signaling, and DNA repair. Until recently, NAD+ was primarily considered to be an intracellular molecule (iNAD+), however, its extracellular species (eNAD+) has recently been discovered and has since been associated with a multitude of pathological conditions. Therefore, accurate quantification of eNAD+ in bodily fluids such as plasma is paramount to answer important research questions. In order to create a clinically meaningful and reliable quantitation method, we analyzed the relationship of cell lysis, routine clinical laboratory parameters, blood collection techniques, and pre-analytical processing steps with measured plasma eNAD+ concentrations. Initially, NAD+ levels were assessed both intracellularly and extracellularly. Intriguingly, the concentration of eNAD+ in plasma was found to be approximately 500 times lower than iNAD+ in peripheral blood mononuclear cells (0.253 ± 0.02 μM vs. 131.8 ± 27.4 μM, p = 0.007, respectively). This stark contrast suggests that cellular damage or cell lysis could potentially affect the levels of eNAD+ in plasma. However, systemic lactate dehydrogenase in patient plasma, a marker of cell damage, did not significantly correlate with eNAD+ (n = 33; r = -0.397; p = 0.102). Furthermore, eNAD+ was negatively correlated with increasing c-reactive protein (CRP, n = 33; r = -0.451; p = 0.020), while eNAD+ was positively correlated with increasing hemoglobin (n = 33; r = 0.482; p = 0.005). Next, variations in blood drawing, sample handling and pre-analytical processes were examined. Sample storage durations at 4°C (0-120 min), temperature (0° to 25°C), cannula sizes for blood collection and tourniquet times (0 - 120 s) had no statistically significant effect on eNAD+ (p > 0.05). On the other hand, prolonged centrifugation (> 5 min) and a faster braking mode of the centrifuge rotor (< 4 min) resulted in a significant decrease in eNAD+ levels (p < 0.05). Taken together, CRP and hemoglobin appeared to be mildly correlated with eNAD+ levels whereas cell damage was not correlated significantly to eNAD+ levels. The blood drawing trial did not show any influence on eNAD+, in contrast, the preanalytical steps need to be standardized for accurate eNAD+ measurement. This work paves the way towards robust eNAD+ measurements, for use in future clinical and translational research, and provides an optimized hands-on protocol for reliable eNAD+ quantification in plasma.

Extracellular Vesicle-Enriched miRNA-Biomarkers Show Improved Utility for Detecting Alzheimer's Disease Dementia and Medial Temporal Atrophy

Background

Emerging diagnostic modalities suggest that miRNA profiles within extracellular vesicles (EVs) isolated from peripheral blood specimens may provide a non-invasive diagnostic alternative for dementia and neurodegenerative disorders. Given that EVs confer a protective environment against miRNA enzymatic degradation, the miRNAs enriched in the EV fraction of blood samples could serve as more stable and clinically relevant biomarkers compared to those obtained from serum.

Objective

To compare miRNAs isolated from EVs versus serum in blood taken from Alzheimer's disease (AD) dementia patients and control cohorts.

Methods

We compared 25 AD patients to 34 individuals who exhibited no cognitive impairments (NCI). Subjects were Singapore residents with Chinese heritage. miRNAs purified from serum versus blood-derived EVs were analyzed for associations with AD dementia and medial temporal atrophy detected by magnetic resonance imaging.

Results

Compared to serum-miRNAs, we identified almost twice as many EV-miRNAs associated with AD dementia, and they also correlated more significantly with medial temporal atrophy, a neuroimaging marker of AD-brain pathology. We further developed combination panels of serum-miRNAs and EV-miRNAs with improved performance in identifying AD dementia. Dominant in both panels was miRNA-1290.

Conclusions

This data indicates that miRNA profiling from EVs offers diagnostic superiority. This underscores the role of EVs as vectors harboring prognostic biomarkers for neurodegenerative disorders and suggests their potential in yielding novel biomarkers for AD diagnosis.

Comparative targeted metabolomic profiles of porcine plasma and serum

Metabolomics has been used to characterise many biological matrices and obtain detailed pictures of biological systems based on many metabolites. Plasma and serum are two blood-derived biofluids commonly used to assess and monitor the organismal metabolism and obtain information on the physiological and health conditions of an animal. Plasma is the supernatant that is separated from the cellular components after centrifugation of the blood that is first added with an anticoagulant. Serum is obtained after centrifugation of the blood that has been coagulated. The choice of one or the other biofluid for metabolomic analyses is related to specific analytical needs and technical issues, to problems derived by the collection and preparation steps, in particular when specimens are sampled from animals involved in field studies. Thus far, most of the metabolomic studies that compared plasma and serum have been carried out in humans and very little is known on the pigs. In this study, we used a targeted metabolomic platform that can detect about 180 metabolites of five biochemical classes to compare plasma and serum profiles of samples collected from 24 pigs. To also obtain a cross-species comparative metabolomic analysis, information for human plasma and serum derived from the same platform was retrieved from previous studies. Statistical analyses included univariate and multivariate approaches aimed at identifying stable and/or differentially abundant metabolites between the two porcine biofluids. A total of 154 (∼83%) metabolites passed the initial quality control, indicating a good repeatability of the analytical platform in pigs. Discarded metabolites included aspartate and biogenic amines that were already reported to be unstable in human studies. More than 80% of the metabolites had similar profiles in both porcine biofluids (average correlation was 0.75). Concentrations were usually higher in serum than in plasma, in agreement with what was already reported in humans. The univariate analysis identified 44 metabolites that had statistically different concentrations between porcine plasma and serum, of which 28 metabolites were also confirmed by the multivariate analysis. The obtained picture described similarities and differences between these two biofluids in pigs and the related human-pig comparisons. The obtained information can be useful for the choice of one or the other matrix for the implementation of metabolomic studies in this livestock species. The results can also provide useful hints to valuing the pig as animal model, in particular when metabolite-derived physiological states are relevant.

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.

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.

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.

Untargeted metabolomics of pulmonary tuberculosis patient serum reveals potential prognostic markers of both latent infection and outcome

Currently, there are no particularly effective biomarkers to distinguish between latent tuberculosis infection (LTBI) and active pulmonary tuberculosis (PTB) and evaluate the outcome of TB treatment. In this study, we have characterized the changes in the serum metabolic profiles caused by Mycobacterium tuberculosis (Mtb) infection and standard anti-TB treatment with isoniazid-rifampin-pyrazinamide-ethambutol (HRZE) using GC-MS and LC-MS/MS. Seven metabolites, including 3-oxopalmitic acid, akeboside ste, sulfolithocholic acid, 2-decylfuran (4,8,8-trimethyldecahydro-1,4-methanoazulen-9-yl)methanol, d-(+)-camphor, and 2-methylaminoadenosine, were identified to have significantly higher levels in LTBI and untreated PTB patients (T0) than those in uninfected healthy controls (Un). Among them, akeboside Ste and sulfolithocholic acid were significantly decreased in PTB patients with 2-month HRZE (T2) and cured PTB patients with 2-month HRZE followed by 4-month isoniazid-rifampin (HR) (T6). Receiver operator characteristic curve analysis revealed that the combined diagnostic model showed excellent performance for distinguishing LT from T0 and Un. By analyzing the biochemical and disease-related pathways, we observed that the differential metabolites in the serum of LTBI or TB patients, compared to healthy controls, were mainly involved in glutathione metabolism, ascorbate and aldarate metabolism, and porphyrin and chlorophyll metabolism. The metabolites with significant differences between the T0 group and the T6 group were mainly enriched in niacin and nicotinamide metabolism. Our study provided more detailed experimental data for developing laboratory standards for evaluating LTBI and cured PTB.

Stability of Metabolomic Content during Sample Preparation: Blood and Brain Tissues

Thermal and enzymatic reactions can significantly change the tissue metabolomic content during the sample preparation. In this work, we evaluated the stability of metabolites in human whole blood, serum, and rat brain, as well as in metabolomic extracts from these tissues. We measured the concentrations of 63 metabolites in brain and 52 metabolites in blood. We have shown that metabolites in the extracts from biological tissues are stable within 24 h at 4 °C. Serum and whole blood metabolomes are also rather stable, changes in metabolomic content of the whole blood homogenate become apparent only after 1–2 h of incubation at 4 °C, and become strong after 24 h. The most significant changes correspond to energy metabolites: the concentrations of ATP and ADP decrease fivefold, and the concentrations of NAD, NADH, and NADPH decrease below the detectable level. A statistically significant increase was observed for AMP, IMP, hypoxanthine, and nicotinamide. The brain tissue is much more metabolically active than human blood, and significant metabolomic changes occur already within the first several minutes during the brain harvest and sample homogenization. At a longer timescale (hours), noticeable changes were observed for all classes of compounds, including amino acids, organic acids, alcohols, amines, sugars, nitrogenous bases, nucleotides, and nucleosides.

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.

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.

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.

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.

A Comparison of Serum and Plasma Blood Collection Tubes for the Integration of Epidemiological and Metabolomics Data

Blood is a rich biological sample routinely collected in clinical and epidemiological studies. With advancements in high throughput -omics technology, such as metabolomics, epidemiology can now delve more deeply and comprehensively into biological mechanisms involved in the etiology of diseases. However, the impact of the blood collection tube matrix of samples collected needs to be carefully considered to obtain meaningful biological interpretations and understand how the metabolite signatures are affected by different tube types. In the present study, we investigated whether the metabolic profile of blood collected as serum differed from samples collected as ACD plasma, citrate plasma, EDTA plasma, fluoride plasma, or heparin plasma. We identified and quantified 50 metabolites present in all samples utilizing nuclear magnetic resonance (NMR) spectroscopy. The heparin plasma tubes performed the closest to serum, with only three metabolites showing significant differences, followed by EDTA which significantly differed for five metabolites, and fluoride tubes which differed in eleven of the fifty metabolites. Most of these metabolite differences were due to higher levels of amino acids in serum compared to heparin plasma, EDTA plasma, and fluoride plasma. In contrast, metabolite measurements from ACD and citrate plasma differed significantly for approximately half of the metabolites assessed. These metabolite differences in ACD and citrate plasma were largely due to significant interfering peaks from the anticoagulants themselves. Blood is one of the most banked samples and thus mining and comparing samples between studies requires understanding how the metabolite signature is affected by the different media and different tube types.

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.

Plasma osteopontin as a biomarker of Alzheimer's disease and vascular cognitive impairment

Cerebrovascular disease (CeVD) and neurodegenerative dementia such as Alzheimer’s disease (AD) are frequently associated comorbidities in the elderly, sharing common risk factors and pathophysiological mechanisms including neuroinflammation. Osteopontin (OPN) is an inflammatory marker found upregulated in vascular diseases as well as in AD. However, its involvement in vascular dementia (VaD) and pre-dementia stages, namely cognitive impairment no dementia (CIND), both of which fall under the spectrum of vascular cognitive impairment (VCI), has yet to be examined. Its correlations with inflammatory cytokines in cognitive impairment also await investigation. 80 subjects with no cognitive impairment (NCI), 160 with CIND and 144 with dementia were included in a cross-sectional study on a Singapore-based memory clinic cohort. All subjects underwent comprehensive clinical, neuropsychological and brain neuroimaging assessments, together with clinical diagnoses based on established criteria. Blood samples were collected and OPN as well as inflammatory cytokines interleukin (IL)-6, IL-8 and tumor necrosis factor (TNF) were measured using immunoassays. Multivariate regression analyses showed significant associations between increased OPN and VCI groups, namely CIND with CeVD, AD with CeVD and VaD. Interestingly, higher OPN was also significantly associated with AD even in the absence of CeVD. We further showed that increased OPN significantly associated with neuroimaging markers of CeVD and neurodegeneration, including cortical infarcts, lacunes, white matter hyperintensities and brain atrophy. OPN also correlated with elevated levels of IL-6, IL-8 and TNF. Our findings suggest that OPN may play a role in both VCI and neurodegenerative dementias. Further longitudinal analyses are needed to assess the prognostic utility of OPN in disease prediction and monitoring.

Quantitative and targeted proteomics-based identification and validation of drug efficacy biomarkers

Proteomics refers to the large-scale study of proteins, providing comprehensive and quantitative information on proteins in tissue, blood, and cell samples. In many studies, proteomics utilizes liquid chromatography-mass spectrometry. Proteomics has developed from a qualitative methodology of protein identification to a quantitative methodology for comparing protein expression, and it is currently classified into two distinct methodologies: quantitative and targeted proteomics. Quantitative proteomics comprehensively identifies proteins in samples, providing quantitative information on large-scale comparative profiles of protein expression. Targeted proteomics simultaneously quantifies only target proteins with high sensitivity and specificity. Therefore, in biomarker research, quantitative proteomics is used for the identification of biomarker candidates, and targeted proteomics is used for the validation of biomarkers. Understanding the specific characteristics of each method is important for conducting appropriate proteomics studies. In this review, we introduced the different characteristics and applications of quantitative and targeted proteomics, and then discussed the results of our recent proteomics studies that focused on the identification and validation of biomarkers of drug efficacy. These findings may enable us to predict the outcomes of cancer therapy and drug-drug interactions with antibiotics through changes in the intestinal microbiome.

Metabolomics in acute myeloid leukemia

Acute myeloid leukemia (AML) is a complex, heterogenous hematological malignancy caused by mutations in myeloid differentiation and proliferation. Response to therapy and long-term outcomes vary widely based on chromosomal and molecular aberrations. Many platforms have been used to characterize and stratify AML. Metabolomics, the global profiling of small molecules in a biological sample, has emerged in the last decade as an important tool for studying the metabolic dependency of cancer cells. Metabolic reprogramming is not only an important manifestation of AML but clinically relevant for diagnosis, risk stratification and targeted drug development. In this review, we discuss notable metabolic studies of the last decade and their application to novel therapies.

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.

The key points in the pre-analytical procedures of blood and urine samples in metabolomics studies

BACKGROUND

Metabolomics provides measurement of numerous metabolites in human samples, which can be a useful tool in clinical research. Blood and urine are regarded as preferred subjects of study because of their minimally invasive collection and simple preprocessing methods. Adhering to standard operating procedures is an essential factor in ensuring excellent sample quality and reliable results.

AIM OF REVIEW

In this review, we summarize the studies about the impacts of various preprocessing factors on metabolomics studies involving clinical blood and urine samples in order to provide guidance for sample collection and preprocessing.

KEY SCIENTIFIC CONCEPTS OF REVIEW

Clinical information is important for sample grouping and data analysis which deserves attention before sample collection. Plasma and serum as well as urine samples are appropriate for metabolomics analysis. Collection tubes, hemolysis, delay at room temperature, and freeze-thaw cycles may affect metabolic profiles of blood samples. Collection time, time between sampling and examination, contamination, normalization strategies, and storage conditions may alter analysis results of urine samples. Taking these collection and preprocessing factors into account, this review provides suggestions of standard sample preprocessing.

From Samples to Insights into Metabolism: Uncovering Biologically Relevant Information in LC-HRMS Metabolomics Data

Untargeted metabolomics (including lipidomics) is a holistic approach to biomarker discovery and mechanistic insights into disease onset and progression, and response to intervention. Each step of the analytical and statistical pipeline is crucial for the generation of high-quality, robust data. Metabolite identification remains the bottleneck in these studies; therefore, confidence in the data produced is paramount in order to maximize the biological output. Here, we outline the key steps of the metabolomics workflow and provide details on important parameters and considerations. Studies should be designed carefully to ensure appropriate statistical power and adequate controls. Subsequent sample handling and preparation should avoid the introduction of bias, which can significantly affect downstream data interpretation. It is not possible to cover the entire metabolome with a single platform; therefore, the analytical platform should reflect the biological sample under investigation and the question(s) under consideration. The large, complex datasets produced need to be pre-processed in order to extract meaningful information. Finally, the most time-consuming steps are metabolite identification, as well as metabolic pathway and network analysis. Here we discuss some widely used tools and the pitfalls of each step of the workflow, with the ultimate aim of guiding the reader towards the most efficient pipeline for their metabolomics studies.

Specimen Collection for Translational Studies in Hidradenitis Suppurativa

Hidradenitis suppurativa (HS) is a chronic inflammatory disorder characterized by painful nodules, sinus tracts, and scars occurring predominantly in intertriginous regions. The prevalence of HS is currently 0.053-4%, with a predominance in African-American women and has been linked to low socioeconomic status. The majority of the reported literature is  retrospective, population based, epidemiologic studies. In this regard, there is a need to establish a repository of biospecimens, which represent appropriate gender and racial demographics amongst HS patients. These efforts will diminish knowledge gaps in understanding the disease pathophysiology. Hence, we sought to outline a step-by-step protocol detailing how we established our HS biobank to facilitate the formation of other HS tissue banks. Equipping researchers with carefully detailed processes for collection of HS specimens would accelerate the accumulation of well-organized human biological material. Over time, the scientific community will have access to a broad range of HS tissue biospecimens, ultimately leading to more rigorous basic and translational research. Moreover, an improved understanding of the pathophysiology is necessary for the discovery of novel therapies for this debilitating disease. We aim to provide high impact translational research methodology for cutaneous biology research and foster multidisciplinary collaboration and advancement of our understanding of cutaneous diseases.

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.

Impact of blood storage and sample handling on quality of high dimensional flow cytometric data in multicenter clinical research

Obtaining reliable and reproducible high quality data in multicenter clinical research settings requires design of optimal standard operating procedures. While the need for standardization in sample processing and data analysis is well-recognized, the impact of sample handling in the pre-analytical phase remains underestimated. We evaluated the impact of sample storage time (≈transport time) and temperature, type of anticoagulant, and limited blood volume on reproducibility of flow cytometric studies. EDTA and Na-Heparin samples processed with the EuroFlow bulk lysis protocol, stained and stored at 4 °C showed fairly stable expression of cell surface markers and distribution of the major leukocyte populations for up to 72 h. Additional sample fixation (1% PFA, Fix & Perm) did not have any beneficial effects. Blood samples stored for <24 h at room temperature before processing and staining seemed suitable for reliable immunophenotyping, although losses in absolute cell numbers were observed. The major losses were observed in myeloid cells and monocytes, while lymphocytes seemed less affected. Expression of cell surface markers and population distribution were more stable in Na-Heparin blood than in EDTA blood. However, storage of Na-Heparin samples was associated with faster decrease in leukocyte counts over time. Whole blood fixation strategies (Cyto-Chex, TransFix) improved long-term population distribution, but were detrimental for expression of cellular markers. The main conclusions from this study on healthy donor blood samples were successfully confirmed in EDTA clinical (patient) blood samples with different time delays until processing. Finally, we recognized the need for adjustments in bulk lysis in case of insufficient blood volumes. Despite clear overall conclusions, individual markers and cell populations had different preferred conditions. Therefore, specific guidelines for sample handling should always be adjusted to the clinical application and the main target leukocyte population.

The Effect of Pre-Analytical Conditions on Blood Metabolomics in Epidemiological Studies

Serum and plasma are commonly used in metabolomic-epidemiology studies. Their metabolome is susceptible to differences in pre-analytical conditions and the impact of this is unclear. Participant-matched EDTA-plasma and serum samples were collected from 37 non-fasting volunteers and profiled using a targeted nuclear magnetic resonance (NMR) metabolomics platform (n = 151 traits). Correlations and differences in mean of metabolite concentrations were compared between reference (pre-storage: 4 °C, 1.5 h; post-storage: no buffer addition delay or NMR analysis delay) and four pre-storage blood processing conditions, where samples were incubated at (i) 4 °C, 24 h; (ii) 4 °C, 48 h; (iii) 21 °C, 24 h; and (iv) 21 °C, 48 h, before centrifugation; and two post-storage sample processing conditions in which samples thawed overnight (i) then left for 24 h before addition of sodium buffer followed by immediate NMR analysis; and (ii) addition of sodium buffer, then left for 24 h before NMR profiling. We used multilevel linear regression models and Spearman’s rank correlation coefficients to analyse the data. Most metabolic traits had high rank correlation and minimal differences in mean concentrations between samples subjected to reference and the different conditions tested, that may commonly occur in studies. However, glycolysis metabolites, histidine, acetate and diacylglycerol concentrations may be compromised and this could bias results in association/causal analyses.

Exploring a Novel Screening Method for Patients with Oral Squamous Cell Carcinoma: A plasma Metabolomics Analysis

AIM

This study aimed to explore novel metabolite biomarker candidates for screening oral squamous cell carcinoma (OSCC).

PATIENTS & METHODS

We collected plasma samples from 48 patients with OSCC and 29 with an oral disease and conducted a plasma metabolomics analysis of patients with OSCC using gas chromatography mass spectrometry. Then, we used the cross-validation procedure to ensure the accuracy of biomarker candidates.

RESULTS

We selected four biomarker candidates against OSCC. Their sensitivity was more than 90%, and the AUC was over 0.9 according to the receiver operating characteristic curve analysis.

CONCLUSIONS

The findings of this study suggest four potential metabolites as biomarkers for OSCC screening.