Impact of collection conditions on the metabolite content of human urine samples as analyzed by liquid chromatography coupled to mass spectrometry and nuclear magnetic resonance spectroscopy

Nitrosamine formation driven by electrochemical chlorination of urine-containing source waters: Effects of operational conditions

We investigated the formation of nitrosamines from urine during electrochemical chlorination (EC) using dimensionally stable anodes. Short-term electrolysis (< 1 h) of urine at 25 mA cm-2 generated seven nitrosamines (0.1-7.4 µg L-1), where N-nitrosodimethylamine, N-nitrosomethylethylamine, and N-nitrosodiethylamine were predominant with concentrations ranging from 1.2 to 7.4 µg L-1. Mechanistic studies showed that the formation kinetics of nitrosamines was influenced by urine aging and composition, with fresh urine generating the highest levels (0.9-5.8 µg L-1) compared with aged, centrifuged, or filtered urine (0.2-4.1 µg L-1). Concurrently, studies on urine pretreatment through filtration and centrifugation underscored the significance of nitrogenous metabolites (such as protein-like products and urinary amino acids) and particle-associated humic fractions in nitrosamine formation during EC of urine. This finding was confirmed through chromatographic and spectroscopic studies utilizing LCOCD, Raman spectra, and 3DEEM fluorescence spectra. Parametric studies demonstrated that the ultimate [nitrosamines] increased at a pH range of 4.5-6.2, and with increasing [bromide], [ammonium], and current density. Conversely, sulfate and carbonate ions inhibited nitrosamine formation. Moreover, the implications of EC in urine-containing source waters were evaluated. The results indicate that regardless of the urine source (individual volunteers, septic tank, swimming pool, untreated municipal wastewater), high levels of nitrosamines (0.1-17.6 µg L-1) were generated, surpassing the potable reuse guideline of 10 ng L-1. Overall, this study provides insights to elucidate the mechanisms underlying nitrosamine formation and optimize the operating conditions. Such insights facilitate suppressing the generation of nitrosamine byproducts during electrochemical treatment of urine-containing wastewater.

Development and validation of a versatile non-invasive urinary steroidomics method for wildlife biomonitoring

Wildlife conservation is often challenged by a lack of knowledge about the reproduction biology and adaptability of endangered species. Although monitoring steroids and related molecules can increase this knowledge, the applicability of current techniques (e.g. immunoassays) is hampered by species-specific steroid metabolism and the requisite to avoid invasive sampling. This study presents a validated steroidomics method for the (un)targeted screening of a wide range of sex and stress steroids and related molecules in urine using ultra-high performance liquid chromatography coupled to high-resolution mass spectrometry (UHPLC-HRMS). In total, 50 steroids (conjugated and non-conjugated androgens, estrogens, progestogens and glucocorticoids) and 6 prostaglandins could be uniquely detected. A total of 45 out of 56 compounds demonstrated a detection limit below 0.01 ng μL-1. Excellent linearity (R2 > 0.99), precision (CV < 20 %), and recovery (80-120 %) were observed for 46, 41, and 39 compounds, respectively. Untargeted screening of pooled giant panda and human samples yielded 9691 and 8366 features with CV < 30 %, from which 84.1 % and 83.0 %, respectively, also demonstrated excellent linearity (R2 > 0.90). The biological validity of the method was investigated on male and female giant panda urine (n = 20), as well as pooled human samples (n = 10). A total of 24 different steroids were detected with clear qualitative and quantitative differences between human and giant panda samples. Furthermore, expected differences were revealed between female giant panda samples from different reproductive phases. In contrast to traditional biomonitoring techniques, the developed steroidomics method was able to screen a wide range of compounds and provide information on the putative identities of metabolites potentially important for reproductive monitoring in giant pandas. These results illustrate the advancements steroidomics brings to the field of wildlife biomonitoring in the pursuit to better understand the biology of endangered species.

Patient-Centric Quantitative Microsampling for Accurate Determination of Urine Albumin to Creatinine Ratio (UACR) in a Clinical Setting

BACKGROUND

Developing and implementing new patient-centric strategies for drug trials lowers the barrier to participation for some patients by reducing the need to travel to research sites. In early chronic kidney disease (CKD) trials, albuminuria is the key measure for determining treatment effect prior to pivotal kidney outcome trials.

METHODS

To facilitate albuminuria sample collection outside of a clinical research site, we developed 2 quantitative microsampling methods to determine the urinary albumin to creatinine ratio (UACR). Readout was performed by LC-MS/MS.

RESULTS

For the Mitra device the within-batch precision (CV%) was 2.8% to 4.6% and the between-batch precision was 5.3% to 6.1%. Corresponding data for the Capitainer device were 4.0% to 8.6% and 6.7% to 9.0%, respectively. The storage stability at room temperature for 3 weeks was 98% to 103% for both devices. The recovery for the Mitra and Capitainer devices was 104% (SD 7.0%) and 95 (SD 7.4%), respectively. The inter-assay comparison of UACR assessment generated results that were indistinguishable regardless of microsampling technique. The accuracy based on LC-MS/MS vs analysis of neat urine using a clinical chemistry analyzer was assessed in a clinical setting, resulting in 102 ± 8.0% for the Mitra device and 95 ± 10.0% for the Capitainer device.

CONCLUSIONS

Both UACR microsampling measurements exhibit excellent accuracy and precision compared to a clinical chemistry analyzer using neat urine. We applied our patient-centric sampling strategy to subjects with heart failure in a clinical setting. Precise UACR measurements using quantitative microsampling at home would be beneficial in clinical drug development for kidney therapies.

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.

A wide-range UAC sensor for the classification of hyperuricemia in spot samples

Hyperuricemia (HUA) has received wide attention as an independent risk factor for various chronic diseases. HUA is usually asymptomatic, and the related damage can be reduced by effective classification and treatment according to uric acid clearance (UAC). UAC is a calculated ratio based on the uric acid level in blood and urine. This important method is not universally used due to the inconvenience of collecting 24-h urine samples in the clinic, and most sensors are limited by the need for wide ranges and for two testing samples. In this study, a pH-sensitive urate oxidase-modified electrochemical sensor with filter membrane was proposed to calculate UAC by detecting uric acid in blood and urine. The results demonstrated that the sensor had high selectivity for uric acid with a detection limit of 0.25 μM in 5 μL spot sample, the wide linear range was 2.5-7000 μM, and the impact of the sample pH was calibrated. The linear correlation of the measurement results between the UAC sensor and clinical instrument was higher than 0.980 for 87 patients. The change in UAC in spot urine may reflect alteration in body-transport mechanisms. Thus, the UAC sensor may open a new window for the management of HUA and broaden its application in point-of-care testing.

Development of a kit for urine collection on filter paper as an alternative for Pompe disease screening and monitoring by LC-HRMS

Pompe disease (PD) is an inborn error of metabolism caused by α-glucosidase acid enzyme deficiency. It significantly impacts patients' health and life quality and may lead to death in the first few years of life. Among the well-established diagnostic methods, urinary glucose tetrasaccharide (Glc4) screening by high performance-liquid chromatography has been helpful in monitoring Glc4 levels in patients on enzyme replacement therapy, demonstrating therapy efficacy. However, the specimen shipping process from a sample collecting location to a specialized laboratory for monitoring the Glc4 is costly and presents preanalytical challenges. In this work, we developed a filter paper based-urine collection kit to facilitate specimen shipment, and liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) analysis to determine Glc4 and creatinine in dried urine on filter paper. The LC-HRMS was based on a combination of targeted and untargeted screening on the same specimen injection and was successfully developed and validated. Bland-Altman statistics revealed a good relationship between dried and liquid urine samples and Glc4 and creatinine. Glc4 and other metabolites in dried urine showed stability for at least 7 days at 4 and 22 °C, and 3 days at 50 °C. The stability of the analytes and the efficiency of the kit were tested simulating real conditions by sending it by post. After two days in transit without refrigeration, the stability of compounds was maintained, showing the reliability of the urine collection kit and analysis method to determine the PD biomarker Glc4.

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 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.

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.

Design and Characterisation of a Randomized Food Intervention That Mimics Exposure to a Typical UK Diet to Provide Urine Samples for Identification and Validation of Metabolite Biomarkers of Food Intake

Poor dietary choices are major risk factors for obesity and non-communicable diseases, which places an increasing burden on healthcare systems worldwide. To monitor the effectiveness of healthy eating guidelines and strategies, there is a need for objective measures of dietary intake in community settings. Metabolites derived from specific foods present in urine samples can provide objective biomarkers of food intake (BFIs). Whilst the majority of biomarker discovery/validation studies have investigated potential biomarkers for single foods only, this study considered the whole diet by using menus that delivered a wide range of foods in meals that emulated conventional UK eating patterns. Fifty-one healthy participants (range 19-77 years; 57% female) followed a uniquely designed, randomized controlled dietary intervention, and provided spot urine samples suitable for discovery of BFIs within a real-world context. Free-living participants prepared and consumed all foods and drinks in their own homes and were asked to follow the protocols for meal consumption and home urine sample collection. This study also assessed the robustness, and impact on data quality, of a minimally invasive urine collection protocol. Overall the study design was well-accepted by participants and concluded successfully without any drop outs. Compliance for urine collection, adherence to menu plans, and observance of recommended meal timings, was shown to be very high. Metabolome analysis using mass spectrometry coupled with data mining demonstrated that the study protocol was well-suited for BFI discovery and validation. Novel, putative biomarkers for an extended range of foods were identified including legumes, curry, strongly-heated products, and artificially sweetened, low calorie beverages. In conclusion, aspects of this study design would help to overcome several current challenges in the development of BFI technology. One specific attribute was the examination of BFI generalizability across related food groups and across different preparations and cooking methods of foods. Furthermore, the collection of urine samples at multiple time points helped to determine which spot sample was optimal for identification and validation of BFIs in free-living individuals. A further valuable design feature centered on the comprehensiveness of the menu design which allowed the testing of biomarker specificity within a biobank of urine samples.

A Review of GC-Based Analysis of Non-Invasive Biomarkers of Colorectal Cancer and Related Pathways

Colorectal cancer (CRC) is the third most commonly diagnosed cancer in the world. In Europe, it is the second most common cause of cancer-related deaths. With the advent of metabolomics approaches, studies regarding the investigation of metabolite profiles related to CRC have been conducted, aiming to serve as a tool for early diagnosis. In order to provide further information about the current status of this field of research, 21 studies were systematically reviewed, regarding their main findings and analytical aspects. A special focus was given to the employment of matrices obtained non-invasively and the use of gas chromatography as the analytical platform. The relationship between the reported volatile and non-volatile biomarkers and CRC-related metabolic alterations was also explored, demonstrating that many of these metabolites are connected with biochemical pathways proven to be involved in carcinogenesis. The most commonly reported CRC indicators were hydrocarbons, aldehydes, amino acids and short-chain fatty acids. These potential biomarkers can be associated with both human and bacterial pathways and the analysis based on such species has the potential to be applied in the clinical practice as a low-cost screening method.

Prognostic value of endogenous and exogenous metabolites in liver transplantation

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

Metabolomic Biomarkers for Detection, Prognosis and Identifying Recurrence in Endometrial Cancer

Metabolic reprogramming is increasingly recognised as one of the defining hallmarks of tumorigenesis. There is compelling evidence to suggest that endometrial cancer develops and progresses in the context of profound metabolic dysfunction. Whilst the incidence of endometrial cancer continues to rise in parallel with the global epidemic of obesity, there are, as yet, no validated biomarkers that can aid risk prediction, early detection, prognostic evaluation or surveillance. Advances in high-throughput technologies have, in recent times, shown promise for biomarker discovery based on genomic, transcriptomic, proteomic and metabolomic platforms. Metabolomics, the large-scale study of metabolites, deals with the downstream products of the other omics technologies and thus best reflects the human phenotype. This review aims to provide a summary and critical synthesis of the existing literature with the ultimate goal of identifying the most promising metabolite biomarkers that can augment current endometrial cancer diagnostic, prognostic and recurrence surveillance strategies. Identified metabolites and their biochemical pathways are discussed in the context of what we know about endometrial carcinogenesis and their potential clinical utility is evaluated. Finally, we underscore the challenges inherent in metabolomic biomarker discovery and validation and provide fresh perspectives and directions for future endometrial cancer biomarker research.

Developing community-based urine sampling methods to deploy biomarker technology for the assessment of dietary exposure

OBJECTIVE

Obtaining objective, dietary exposure information from individuals is challenging because of the complexity of food consumption patterns and the limitations of self-reporting tools (e.g., FFQ and diet diaries). This hinders research efforts to associate intakes of specific foods or eating patterns with population health outcomes.

DESIGN

Dietary exposure can be assessed by the measurement of food-derived chemicals in urine samples. We aimed to develop methodologies for urine collection that minimised impact on the day-to-day activities of participants but also yielded samples that were data-rich in terms of targeted biomarker measurements.

SETTING

Urine collection methodologies were developed within home settings.

PARTICIPANTS

Different cohorts of free-living volunteers.

RESULTS

Home collection of urine samples using vacuum transfer technology was deemed highly acceptable by volunteers. Statistical analysis of both metabolome and selected dietary exposure biomarkers in spot urine collected and stored using this method showed that they were compositionally similar to urine collected using a standard method with immediate sample freezing. Even without chemical preservatives, samples can be stored under different temperature regimes without any significant impact on the overall urine composition or concentration of forty-six exemplar dietary exposure biomarkers. Importantly, the samples could be posted directly to analytical facilities, without the need for refrigerated transport and involvement of clinical professionals.

CONCLUSIONS

This urine sampling methodology appears to be suitable for routine use and may provide a scalable, cost-effective means to collect urine samples and to assess diet in epidemiological studies.

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.

A review of strategies for untargeted urinary metabolomic analysis using gas chromatography-mass spectrometry

BACKGROUND

Human urine gives evidence of the metabolism in the body and contains different metabolites at various concentrations. A number of analytical techniques including mass spectrometry (MS) and nuclear magnetic resonance (NMR) have been used to obtain metabolites levels in urine samples. However, gas chromatography-mass spectrometry (GC-MS) is one of the most widely used techniques for urinary metabolomics studies due to its higher sensitivity, resolution, reproducibility, reliability, relatively low cost and ease of operation compared to liquid chromatography-mass spectrometry and NMR.

AIM OF REVIEW

This review looks at various aspects of urine preparation prior to analysis by GC-MS including sample storage, urease pretreatment, derivatization, use of internal standard and quality control samples for data correction. In addition, most common types of inlet liners, ionization techniques and columns are discussed and a summary of mass analyzers are also highlighted. Lastly, the role of retention index in metabolite identification and data normalization methods are presented.

KEY SCIENTIFIC CONCEPTS OF REVIEW

The purpose of this review is summarizing methods of sample storage, pretreatment, and GC-MS analysis that are mostly used in urine metabolomics studies. Specific emphasis is given to the critical steps within the GC-MS urine metabolomics that those new to this field need to be aware of and the remaining challenges that require further attention and studies.

Influence of Storage Conditions and Preservatives on Metabolite Fingerprints in Urine

Human urine, which is rich in metabolites, provides valuable approaches for biomarker measurement. Maintaining the stability of metabolites in urine is critical for accurate and reliable research results and subsequent interpretation. In this study, the effect of storage temperature (4, 22, and 40 °C), storage time (24 and 48 h), and use of preservatives (boric acid (BA), thymol) and para-aminobenzoic acid (PABA) on urinary metabolites in the pooled urine samples from 20 participants was systematically investigated using large-scale targeted liquid chromatography tandem mass spectrometry (LC-MS/MS)-based metabolomics. Statistical analysis of 158 reliably detected metabolites showed that metabolites in urine with no preservative remained stable at 4 °C for 24 and 48 h as well as at 22 °C for 24 h, but significant metabolite differences were observed in urine stored at 22 °C for 48 h and at 40 °C. The mere addition of BA caused metabolite changes. Thymol was observed to be effective in maintaining metabolite stability in urine in all the conditions designed, most likely due to the inhibitory effect of thymol on urine microbiota. Our results provide valuable urine preservation guidance during sample storage, which is essential for obtaining reliable, accurate, and reproducible analytical results from urine samples.

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.

Current Trends in Cancer Biomarker Discovery Using Urinary Metabolomics: Achievements and New Challenges

BACKGROUND

The development of effective screening methods for early cancer detection is one of the foremost challenges facing modern cancer research. Urinary metabolomics has recently emerged as a potentially transformative approach to cancer biomarker discovery owing to its noninvasive sampling characteristics and robust analytical feasibility.

OBJECTIVE

To provide an overview of new developments in urinary metabolomics, cover the most promising aspects of hyphenated techniques in untargeted and targeted metabolomics, and to discuss technical and clinical limitations in addition to the emerging challenges in the field of urinary metabolomics and its application to cancer biomarker discovery.

METHODS

A systematic review of research conducted in the past five years on the application of urinary metabolomics to cancer biomarker discovery was performed. Given the breadth of this topic, our review focused on the five most widely studied cancers employing urinary metabolomics approaches, including lung, breast, bladder, prostate, and ovarian cancers.

RESULTS

As an extension of conventional metabolomics, urinary metabolomics has benefitted from recent technological developments in nuclear magnetic resonance, mass spectrometry, gas and liquid chromatography, and capillary electrophoresis that have improved urine metabolome coverage and analytical reproducibility. Extensive metabolic profiling in urine has revealed a significant number of altered metabolic pathways and putative biomarkers, including pteridines, modified nucleosides, and acylcarnitines, that have been associated with cancer development and progression.

CONCLUSION

Urinary metabolomics presents a transformative new approach toward cancer biomarker discovery with high translational capacity to early cancer screening.

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

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

Metabolome analysis for investigating host-gut microbiota interactions

Dysbiosis of the gut microbiome is associated with host health conditions. Many diseases have shown to have correlations with imbalanced microbiota, including obesity, inflammatory bowel disease, cancer, and even neurodegeneration disorders. Metabolomics studies targeting small molecule metabolites that impact the host metabolome and their biochemical functions have shown promise for studying host-gut microbiota interactions. Metabolome analysis determines the metabolites being discussed for their biological implications in host-gut microbiota interactions. To facilitate understanding the critical aspects of metabolome analysis, this article reviewed (1) the sample types used in host-gut microbiome studies; (2) mass spectrometry (MS)-based analytical methods and (3) useful tools for MS-based data processing/analysis. In addition to the most frequently used sample type, feces, we also discussed others biosamples, such as urine, plasma/serum, saliva, cerebrospinal fluid, exhaled breaths, and tissues, to better understand gut metabolite systemic effects on the whole organism. Gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), and capillary electrophoresis-mass spectrometry (CE-MS), three powerful tools that can be utilized to study host-gut microbiota interactions, are included with examples of their applications. After obtaining big data from MS-based instruments, noise removal, peak detection, missing value imputation, and data analysis are all important steps for acquiring valid results in host-gut microbiome research. The information provided in this review will help new researchers aiming to join this field by providing a global view of the analytical aspects involved in gut microbiota-related metabolomics studies.

Urine metabolome analysis by gas chromatography-mass spectrometry (GC-MS): Standardization and optimization of protocols for urea removal and short-term sample storage

BACKGROUND

Before derivatization, urine analyzed by gas chromatography-mass spectrometry (GC-MS) requires the complete removal of urea to avoid interferences. We aimed at establishing the most effective sample pretreatment for urea removing; moreover, we explored the impact of two short-term sample storage conditions on urine metabolome.

METHODS

92 aliquots were obtained from a single sample collected from a healthy adult; they were divided into 6 groups. Group 1 consisted of untreated aliquots while groups 2-6 differed from each other for the addition of various defined urease solution volumes combined with either 30 min or 1-hour sonication time. Urine sample storage was tested by comparing 20 fresh aliquots analyzed after collection with 20 aliquots frozen at -80 °C for 72 h.

RESULTS

the most effective protocol consisted of the combination between 200 μL urease solution with 1-h sonication time; urease solution volumes >200 μL increase the risk to underestimate metabolite peaks because of sample dilution. Short-term storage of samples at -80 °C pointed out significant changes in the urine metabolic profile compared with that of fresh samples.

CONCLUSIONS

our study confirms the importance of urea removal for a reliable recognition and quantitation of metabolites; urine short-term storage at -80 °C should be carefully reconsidered.

Preanalytical Processing and Biobanking Procedures of Biological Samples for Metabolomics Research: A White Paper, Community Perspective (for "Precision Medicine and Pharmacometabolomics Task Group"-The Metabolomics Society Initiative)

BACKGROUND

The metabolome of any given biological system contains a diverse range of low molecular weight molecules (metabolites), whose abundances can be affected by the timing and method of sample collection, storage, and handling. Thus, it is necessary to consider the requirements for preanalytical processes and biobanking in metabolomics research. Poor practice can create bias and have deleterious effects on the robustness and reproducibility of acquired data.

CONTENT

This review presents both current practice and latest evidence on preanalytical processes and biobanking of samples intended for metabolomics measurement of common biofluids and tissues. It highlights areas requiring more validation and research and provides some evidence-based guidelines on best practices.

SUMMARY

Although many researchers and biobanking personnel are familiar with the necessity of standardizing sample collection procedures at the axiomatic level (e.g., fasting status, time of day, "time to freezer," sample volume), other less obvious factors can also negatively affect the validity of a study, such as vial size, material and batch, centrifuge speeds, storage temperature, time and conditions, and even environmental changes in the collection room. Any biobank or research study should establish and follow a well-defined and validated protocol for the collection of samples for metabolomics research. This protocol should be fully documented in any resulting study and should involve all stakeholders in its design. The use of samples that have been collected using standardized and validated protocols is a prerequisite to enable robust biological interpretation unhindered by unnecessary preanalytical factors that may complicate data analysis and interpretation.

Quality assurance procedures for mass spectrometry untargeted metabolomics. a review

Untargeted metabolomics, as a global approach, has already proven its great potential and capabilities for the investigation of health and disease, as well as the wide applicability for other research areas. Although great progress has been made on the feasibility of metabolomics experiments, there are still some challenges that should be faced and that includes all sources of fluctuations and bias affecting every step involved in multiplatform untargeted metabolomics studies. The identification and reduction of the main sources of unwanted variation regarding the pre-analytical, analytical and post-analytical phase of metabolomics experiments is essential to ensure high data quality. Nowadays, there is still a lack of information regarding harmonized guidelines for quality assurance as those available for targeted analysis. In this review, sources of variations to be considered and minimized along with methodologies and strategies for monitoring and improvement the quality of the results are discussed. The given information is based on evidences from different groups among our own experiences and recommendations for each stage of the metabolomics workflow. The comprehensive overview with tools presented here might serve other researchers interested in monitoring, controlling and improving the reliability of their findings by implementation of good experimental quality practices in the untargeted metabolomics study.

Preterm neonatal urinary renal developmental and acute kidney injury metabolomic profiling: an exploratory study

BACKGROUND

Acute kidney injury (AKI) staging has been developed in the adult and pediatric populations, but these do not yet exist for the neonatal population. Metabolomics was utilized to uncover biomarkers of normal and AKI-associated renal function in preterm infants. The study comprised 20 preterm infants with an AKI diagnosis who were matched by gestational age and gender to 20 infants without an AKI diagnosis.

METHODS

Urine samples from pre-term newborn infants collected on day 2 of life were analyzed using broad-spectrum nuclear magnetic resonance (NMR) metabolomics. Multivariate analysis methods were used to identify metabolite profiles that differentiated AKI and no AKI, and to identify a metabolomics profile correlating with gestational age in infants with and without AKI.

RESULTS

There was a clear distinction between the AKI and no-AKI profiles. Two previously identified biomarkers of AKI, hippurate and homovanillate, differentiated AKI from no-AKI profiles. Pathway analysis revealed similarities to cholinergic neurons, prenatal nicotine exposure on pancreatic β cells, and amitraz-induced inhibition of insulin secretion. Additionally, a pH difference was noted. Both pH and the metabolites were found to be associated with AKI; however, only the metabotype was a significant predictor of AKI. Pathways for the no-AKI group that correlated uniquely with gestational age included aminoacyl-t-RNA biosynthesis, whereas pathways in the AKI group yielded potential metabolite changes in pyruvate metabolism.

CONCLUSIONS

Metabolomics was able to differentiate the urinary profiles of neonates with and without an AKI diagnosis and metabolic developmental profiles correlated with gestational age. Further studies in larger cohorts are needed to validate these results.

Stability of targeted metabolite profiles of urine samples under different storage conditions

INTRODUCTION

Few studies have investigated the influence of storage conditions on urine samples and none of them used targeted mass spectrometry (MS).

OBJECTIVES

We investigated the stability of metabolite profiles in urine samples under different storage conditions using targeted metabolomics.

METHODS

Pooled, fasting urine samples were collected and stored at -80 °C (biobank standard), -20 °C (freezer), 4 °C (fridge), ~9 °C (cool pack), and ~20 °C (room temperature) for 0, 2, 8 and 24 h. Metabolite concentrations were quantified with MS using the AbsoluteIDQ™ p150 assay. We used the Welch-Satterthwaite-test to compare the concentrations of each metabolite. Mixed effects linear regression was used to assess the influence of the interaction of storage time and temperature.

RESULTS

The concentrations of 63 investigated metabolites were stable at -20 and 4 °C for up to 24 h when compared to samples immediately stored at -80 °C. When stored at ~9 °C for 24 h, few amino acids (Arg, Val and Leu/Ile) significantly decreased by 40% in concentration (P < 7.9E-04); for an additional three metabolites (Ser, Met, Hexose H1) when stored at ~20 °C reduced up to 60% in concentrations. The concentrations of four more metabolites (Glu, Phe, Pro, and Thr) were found to be significantly influenced when considering the interaction between exposure time and temperature.

CONCLUSION

Our findings indicate that 78% of quantified metabolites were stable for all examined storage conditions. Particularly, some amino acid concentrations were sensitive to changes after prolonged storage at room temperature. Shipping or storing urine samples on cool packs or at room temperature for more than 8 h and multiple numbers of freeze and thaw cycles should be avoided.

Impact of storage conditions on the urinary metabolomics fingerprint

Urine stability during storage is essential in metabolomics to avoid misleading conclusions or erroneous interpretations. Facing the lack of comprehensive studies on urine metabolome stability, the present work performed a follow-up of potential modifications in urinary chemical profile using LC-HRMS on the basis of two parameters: the storage temperature (+4 °C, -20 °C, -80 °C and freeze-dried stored at -80 °C) and the storage duration (5-144 days). Both HILIC and RP chromatographies have been implemented in order to globally monitor the urinary metabolome. Using an original data processing associated to univariate and multivariate data analysis, our study confirms that chemical profiles of urine samples stored at +4 °C are very rapidly modified, as observed for instance for compounds such as:N-acetyl Glycine, Adenosine, 4-Amino benzoic acid, N-Amino diglycine, creatine, glucuronic acid, 3-hydroxy-benzoic acid, pyridoxal, l-pyroglutamic acid, shikimic acid, succinic acid, thymidine, trigonelline and valeryl-carnitine, while it also demonstrates that urine samples stored at -20 °C exhibit a global stability over a long period with no major modifications compared to -80 °C condition. This study is the first to investigate long term stability of urine samples and report potential modifications in the urinary metabolome, using both targeted approach monitoring individually a large number (n > 200) of urinary metabolites and an untargeted strategy enabling assessing for global impact of storage conditions.

Analytical pitfalls and challenges in clinical metabolomics

Metabolomics-based strategies have become an integral part of modern clinical research, allowing for a better understanding of pathophysiological conditions and disease mechanisms, as well as providing innovative tools for more adequate diagnostic and prognosis approaches. Metabolomics is considered an essential tool in precision medicine, which aims for personalized prevention and tailor-made treatments. Nevertheless, multiple pitfalls may be encountered in clinical metabolomics during the entire workflow, hampering the quality of the data and, thus, the biological interpretation. This review describes the challenges underlying metabolomics-based experiments, discussing step by step the potential pitfalls of the analytical process, including study design, sample collection, storage, as well as preparation, chromatographic and electrophoretic separation, detection and data analysis. Moreover, it offers practical solutions and strategies to tackle these challenges, ensuring the generation of high-quality data.

Analysis of the Human Adult Urinary Metabolome Variations with Age, Body Mass Index, and Gender by Implementing a Comprehensive Workflow for Univariate and OPLS Statistical Analyses

Urine metabolomics is widely used for biomarker research in the fields of medicine and toxicology. As a consequence, characterization of the variations of the urine metabolome under basal conditions becomes critical in order to avoid confounding effects in cohort studies. Such physiological information is however very scarce in the literature and in metabolomics databases so far. Here we studied the influence of age, body mass index (BMI), and gender on metabolite concentrations in a large cohort of 183 adults by using liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS). We implemented a comprehensive statistical workflow for univariate hypothesis testing and modeling by orthogonal partial least-squares (OPLS), which we made available to the metabolomics community within the online Workflow4Metabolomics.org resource. We found 108 urine metabolites displaying concentration variations with either age, BMI, or gender, by integrating the results from univariate p-values and multivariate variable importance in projection (VIP). Several metabolite clusters were further evidenced by correlation analysis, and they allowed stratification of the cohort. In conclusion, our study highlights the impact of gender and age on the urinary metabolome, and thus it indicates that these factors should be taken into account for the design of metabolomics studies.