Human sweat metabolomics for lung cancer screening

Multiomic Approaches for Cancer Biomarker Discovery in Liquid Biopsies: Advances and Challenges

Cancer is a complex and heterogeneous disease that poses a significant threat to global health. Early diagnosis and treatment are critical for improving patient outcomes, and the use of liquid biopsies has emerged as a promising approach for cancer detection and monitoring. Traditionally, cancer diagnosis has relied on invasive tissue biopsies, the collection of which can prove challenging for patients and the results of which may not always provide accurate results due to tumor heterogeneity. Liquid biopsies have gained increasing attention as they provide a non-invasive and accessible source of cancer biomarkers, which can be used to diagnose cancer, monitor treatment response, and detect relapse. The integration of -omics technologies, such as proteomics, genomics, and metabolomics, has further enhanced the capabilities of liquid biopsies by introducing precision oncology and enabling the tailoring of treatment for individual patients based on their unique tumor biology. In this review, we will discuss the challenges and advances in the field of cancer liquid biopsies and the integration of -omics technologies for different types of liquid biopsies, including blood, tear, urine, sweat, saliva, and cerebrospinal fluid.

Skin-Interfaced Wearable Sweat Sensors for Precision Medicine

Wearable sensors hold great potential in empowering personalized health monitoring, predictive analytics, and timely intervention toward personalized healthcare. Advances in flexible electronics, materials science, and electrochemistry have spurred the development of wearable sweat sensors that enable the continuous and noninvasive screening of analytes indicative of health status. Existing major challenges in wearable sensors include: improving the sweat extraction and sweat sensing capabilities, improving the form factor of the wearable device for minimal discomfort and reliable measurements when worn, and understanding the clinical value of sweat analytes toward biomarker discovery. This review provides a comprehensive review of wearable sweat sensors and outlines state-of-the-art technologies and research that strive to bridge these gaps. The physiology of sweat, materials, biosensing mechanisms and advances, and approaches for sweat induction and sampling are introduced. Additionally, design considerations for the system-level development of wearable sweat sensing devices, spanning from strategies for prolonged sweat extraction to efficient powering of wearables, are discussed. Furthermore, the applications, data analytics, commercialization efforts, challenges, and prospects of wearable sweat sensors for precision medicine are discussed.

No skin off your back: the sampling and extraction of sebum for metabolomics

INTRODUCTION

Sebum-based metabolomics (a subset of "sebomics") is a developing field that involves the sampling, identification, and quantification of metabolites found in human sebum. Sebum is a lipid-rich oily substance secreted by the sebaceous glands onto the skin surface for skin homeostasis, lubrication, thermoregulation, and environmental protection. Interest in sebomics has grown over the last decade due to its potential for rapid analysis following non-invasive sampling for a range of clinical and environmental applications.

OBJECTIVES

To provide an overview of various sebum sampling techniques with their associated challenges. To evaluate applications of sebum for clinical research, drug monitoring, and human biomonitoring. To provide a commentary of the opportunities of using sebum as a diagnostic biofluid in the future.

METHODS

Bibliometric analyses of selected keywords regarding skin surface analysis using the Scopus search engine from 1960 to 2022 was performed on 12th January 2023. The published literature was compartmentalised based on what the work contributed to in the following areas: the understanding about sebum, its composition, the analytical technologies used, or the purpose of use of sebum. The findings were summarised in this review.

RESULTS

Historically, about 15 methods of sampling have been used for sebum collection. The sample preparation approaches vary depending on the analytes of interest and are summarised. The use of sebum is not limited to just skin diseases or drug monitoring but also demonstrated for other systemic disease. Most of the work carried out for untargeted analysis of metabolites associated with sebum has been in the recent two decades.

CONCLUSION

Sebum has a huge potential beyond skin research and understanding how one's physiological state affects or reflects on the skin metabolome via the sebaceous glands itself or by interactions with sebaceous secretion, will open doors for simpler biomonitoring. Sebum acts as a sink to environmental metabolites and has applications awaiting to be explored, such as biosecurity, cross-border migration, localised exposure to harmful substances, and high-throughput population screening. These applications will be possible with rapid advances in volatile headspace and lipidomics method development as well as the ability of the metabolomics community to annotate unknown species better. A key issue with skin surface analysis that remains unsolved is attributing the source of the metabolites found on the skin surface before meaningful biological interpretation.

Non-invasive screening of breast cancer from fingertip smears-a proof of concept study

Breast cancer is a global health issue affecting 2.3 million women per year, causing death in over 600,000. Mammography (and biopsy) is the gold standard for screening and diagnosis. Whilst effective, this test exposes individuals to radiation, has limitations to its sensitivity and specificity and may cause moderate to severe discomfort. Some women may also find this test culturally unacceptable. This proof-of-concept study, combining bottom-up proteomics with Matrix Assisted Laser Desorption Ionisation Mass Spectrometry (MALDI MS) detection, explores the potential for a non-invasive technique for the early detection of breast cancer from fingertip smears. A cohort of 15 women with either benign breast disease (n = 5), early breast cancer (n = 5) or metastatic breast cancer (n = 5) were recruited from a single UK breast unit. Fingertips smears were taken from each patient and from each of the ten digits, either at the time of diagnosis or, for metastatic patients, during active treatment. A number of statistical analyses and machine learning approaches were investigated and applied to the resulting mass spectral dataset. The highest performing predictive method, a 3-class Multilayer Perceptron neural network, yielded an accuracy score of 97.8% when categorising unseen MALDI MS spectra as either the benign, early or metastatic cancer classes. These findings support the need for further research into the use of sweat deposits (in the form of fingertip smears or fingerprints) for non-invasive screening of breast cancer.

The use of lipids from textiles as soft-tissue biomarkers of human decomposition

The ability to determine the post-mortem interval (PMI) in complex death investigations involving human remains, is a vital task faced by law enforcement. Establishing the PMI in a case can significantly aid in the reconstruction of forensically relevant events surrounding that death. However, due to the complexities surrounding the decomposition of human remains, the determination of the PMI still remains a challenge in some cases. Thus, the identification of biomarkers of human decomposition are an emerging, and essential, area of research. Previous studies have also demonstrated great success in the use of textiles as a host to indirectly capture decomposition by-products. This study reports the successful adaptation and optimisation of an analytical chemical workflow for the targeted analysis of lipids from textiles associated with decomposing human remains using gas-chromatography (GC) coupled with tandem mass spectrometry (MS/MS). This study discusses novel information regarding the complex challenges of matrix effects observed with decomposition samples. In addition, the first lipid profiles obtained from textiles associated with two decomposing human donors from the Australian Facility for Taphonomic Experimental Research (AFTER) using GC-MS/MS are presented.

Wearable and flexible electrochemical sensors for sweat analysis: a review

Flexible wearable sweat sensors allow continuous, real-time, noninvasive detection of sweat analytes, provide insight into human physiology at the molecular level, and have received significant attention for their promising applications in personalized health monitoring. Electrochemical sensors are the best choice for wearable sweat sensors due to their high performance, low cost, miniaturization, and wide applicability. Recent developments in soft microfluidics, multiplexed biosensing, energy harvesting devices, and materials have advanced the compatibility of wearable electrochemical sweat-sensing platforms. In this review, we summarize the potential of sweat for medical detection and methods for sweat stimulation and collection. This paper provides an overview of the components of wearable sweat sensors and recent developments in materials and power supply technologies and highlights some typical sensing platforms for different types of analytes. Finally, the paper ends with a discussion of the challenges and a view of the prospective development of this exciting field.

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

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

Avatar-like body imaging of dermal exposure to melamine in factory workers analyzed by ambient mass spectrometry

Ambient mass spectrometry thermal desorption-electrospray ionization/mass spectrometry (TD-ESI/MS) can rapidly identify chemicals without pretreatment of biological samples. This study used a rapid semi-quantitative TD-ESI/MS screening technique for the probe skin sampling of melamine workers occupationally exposed to different ambient melamine concentrations to create avatar-like body images, which were then used to study temporal and dynamic changes in nephrotoxic melamine exposure. We enrolled four voluntary melamine workers from one factory, each from one of four worksites. Melamine exposure was highest in manufacturing and molding, followed by grinding and polishing, packing, and administration, the lowest. Skin samples were collected Friday (end-of-shift) and Monday (pre-shift). Early morning one-spot urine samples were also collected right after skin sampling. 2198 probe skin samples were collected and subjected to semi-quantitative TD-ESI/MS analyses of melamine chemical within 40 h. After normalization, converted body image scores revealed exposure to be highest in the manufacturing worker on Friday and lowest in the administrative worker on Monday. The absolute differences (Friday minus Monday) of normalized body image scores were all significantly positive in each individual worker and across all four workers (permutation test, all p-values < 0.002). The slope estimates of the linear regression line between body image scores and urinary melamine levels were 0.81 (p-value = 0.008). We concluded that this fast and non-invasive technique can potentially be used to study temporal and dynamic changes in exposure to occupational hazards. A future study of developing an automatic and reproducible TD-ESI/MS sampling platform is needed.

An Overview of the Latest Metabolomics Studies on Atopic Eczema with New Directions for Study

Atopic eczema (AE) is an inflammatory skin disorder affecting approximately 20% of children worldwide and early onset can lead to asthma and allergies. Currently, the mechanisms of the disease are not fully understood. Metabolomics, the analysis of small molecules in the skin produced by the host and microbes, opens a window to observe the mechanisms of the disease which then may lead to new drug targets for AE treatment. Here, we review the latest advances in AE metabolomics, highlighting both the lipid and non-lipid molecules, along with reviewing the metabolites currently known to reside in the skin.

Current Knowledge in Skin Metabolomics: Updates from Literature Review

Metabolomic profiling is an emerging field consisting of the measurement of metabolites in a biological system. Since metabolites can vary in relation to different stimuli, specific metabolic patterns can be closely related to a pathological process. In the dermatological setting, skin metabolomics can provide useful biomarkers for the diagnosis, prognosis, and therapy of cutaneous disorders. The main goal of the present review is to present a comprehensive overview of the published studies in skin metabolomics. A search for journal articles focused on skin metabolomics was conducted on the MEDLINE, EMBASE, Cochrane, and Scopus electronic databases. Only research articles with electronically available English full text were taken into consideration. Studies specifically focused on cutaneous microbiomes were also excluded from the present search. A total of 97 papers matched all the research criteria and were therefore considered for the present work. Most of the publications were focused on inflammatory dermatoses and immune-mediated cutaneous disorders. Skin oncology also turned out to be a relevant field in metabolomic research. Only a few papers were focused on infectious diseases and rarer genetic disorders. All the major metabolomic alterations published so far in the dermatological setting are described extensively in this review.

diaPASEF Proteomics and Feature Selection for the Description of Sputum Proteome Profiles in a Cohort of Different Subtypes of Lung Cancer Patients and Controls

The high mortality, the presence of an initial asymptomatic stage and the fact that diagnosis in early stages reduces mortality justify the implementation of screening programs in the populations at risk of lung cancer. It is imperative to develop less aggressive methods that can complement existing diagnosis technologies. In this study, we aimed to identify lung cancer protein biomarkers and pathways affected in sputum samples, using the recently developed diaPASEF mass spectrometry (MS) acquisition mode. The sputum proteome of lung cancer cases and controls was analyzed through nano-HPLC-MS using the diaPASEF mode. For functional analysis, the results from differential expression analysis were further analyzed in the STRING platform, and feature selection was performed using sparse partial least squares discriminant analysis (sPLS-DA). Our results showed an activation of inflammation, with an alteration of pathways and processes related to acute-phase, complement, and immune responses. The resulting sPLS-DA model separated between case and control groups with high levels of sensitivity and specificity. In conclusion, we showed how new-generation proteomics can be used to detect potential biomarkers in sputum samples, and ultimately to discriminate patients from controls and even to help to differentiate between different cancer subtypes.

Wearable Sensor for Continuous Sweat Biomarker Monitoring

In recent years, wearable sensors have enabled the unique mode of real-time and noninvasive monitoring to develop rapidly in medical care, sports, and other fields. Sweat contains a wide range of biomarkers such as metabolites, electrolytes, and various hormones. Combined with wearable technology, sweat can reflect human fatigue, disease, mental stress, dehydration, and so on. This paper comprehensively describes the analysis of sweat components such as glucose, lactic acid, electrolytes, pH, cortisol, vitamins, ethanol, and drugs by wearable sensing technology, and the application of sweat wearable devices in glasses, patches, fabrics, tattoos, and paper. The development trend of sweat wearable devices is prospected. It is believed that if the sweat collection, air permeability, biocompatibility, sensing array construction, continuous monitoring, self-healing technology, power consumption, real-time data transmission, specific recognition, and other problems of the wearable sweat sensor are solved, we can provide the wearer with important information about their health level in the true sense.

Evaluation of Sweat-Sampling Procedures for Human Stress-Biomarker Detection

Sweat is a potential biological fluid for the non-invasive analytical assessment of diverse molecules, including biomarkers. Notwithstanding, the sampling methodology is critical, and it must be assessed prior to using sweat for clinical diagnosis. In the current work, the analytical methodology was further developed taking into account the sampling step, in view of the identification and level variations of sweat components that have potential to be stress biomarkers using separation by liquid chromatography and detection by tandem mass spectrometry, in order to attain a screening profile of 26 molecules in just one stage. As such, the molecule identification was used as a test for the evaluation of the sampling procedures, including the location on the body, using patches for long-term sampling and vials for direct sampling, through a qualitative approach. From this evaluation it was possible to conclude that the sampling may be performed on the chest or back skin. Additionally, possible interference was evaluated. The long-term sampling with patches can be used under both rest and exercise conditions with variation of the detected molecule’s levels. The direct sampling, using vials, has the advantage of not having interferences but the disadvantage of only being effective after exercise in order to have enough sample for sweat analysis.

Comprehensive Review on Wearable Sweat-Glucose Sensors for Continuous Glucose Monitoring

The incidence of diabetes is increasing at an alarming rate, and regular glucose monitoring is critical in order to manage diabetes. Currently, glucose in the body is measured by an invasive method of blood sugar testing. Blood glucose (BG) monitoring devices measure the amount of sugar in a small sample of blood, usually drawn from pricking the fingertip, and placed on a disposable test strip. Therefore, there is a need for non-invasive continuous glucose monitoring, which is possible using a sweat sensor-based approach. As sweat sensors have garnered much interest in recent years, this study attempts to summarize recent developments in non-invasive continuous glucose monitoring using sweat sensors based on different approaches with an emphasis on the devices that can potentially be integrated into a wearable platform. Numerous research entities have been developing wearable sensors for continuous blood glucose monitoring, however, there are no commercially viable, non-invasive glucose monitors on the market at the moment. This review article provides the state-of-the-art in sweat glucose monitoring, particularly keeping in sight the prospect of its commercialization. The challenges relating to sweat collection, sweat sample degradation, person to person sweat amount variation, various detection methods, and their glucose detection sensitivity, and also the commercial viability are thoroughly covered.

Urinary Volatomic Expression Pattern: Paving the Way for Identification of Potential Candidate Biosignatures for Lung Cancer

The urinary volatomic profiling of Indian cohorts composed of 28 lung cancer (LC) patients and 27 healthy subjects (control group, CTRL) was established using headspace solid phase microextraction technique combined with gas chromatography mass spectrometry methodology as a powerful approach to identify urinary volatile organic metabolites (uVOMs) to discriminate among LC patients from CTRL. Overall, 147 VOMs of several chemistries were identified in the intervention groups-including naphthalene derivatives, phenols, and organosulphurs-augmented in the LC group. In contrast, benzene and terpenic derivatives were found to be more prevalent in the CTRL group. The volatomic data obtained were processed using advanced statistical analysis, namely partial least square discriminative analysis (PLS-DA), support vector machine (SVM), random forest (RF), and multilayer perceptron (MLP) methods. This resulted in the identification of nine uVOMs with a higher potential to discriminate LC patients from CTRL subjects. These were furan, o-cymene, furfural, linalool oxide, viridiflorene, 2-bromo-phenol, tricyclazole, 4-methyl-phenol, and 1-(4-hydroxy-3,5-di-tert-butylphenyl)-2-methyl-3-morpholinopropan-1-one. The metabolic pathway analysis of the data obtained identified several altered biochemical pathways in LC mainly affecting glycolysis/gluconeogenesis, pyruvate metabolism, and fatty acid biosynthesis. Moreover, acetate and octanoic, decanoic, and dodecanoic fatty acids were identified as the key metabolites responsible for such deregulation. Furthermore, studies involving larger cohorts of LC patients would allow us to consolidate the data obtained and challenge the potential of the uVOMs as candidate biomarkers for LC.

Wearable Sweat Loss Measuring Devices: From the Role of Sweat Loss to Advanced Mechanisms and Designs

Wearable sweat sensors have received significant research interest and have become popular as sweat contains considerable health information about physiological and psychological states. However, measured biomarker concentrations vary with sweat rates, which has a significant effect on the accuracy and reliability of sweat biosensors. Wearable sweat loss measuring devices (SLMDs) have recently been proposed to overcome the limitations of biomarker tracking and reduce inter- and intraindividual variability. In addition, they offer substantial potential for monitoring human body homeostasis, because sweat loss plays an indispensable role in thermoregulation and skin hydration. Previous studies have not carried out a comprehensive and systematic review of the principles, importance, and development of wearable SLMDs. This paper reviews wearable SLMDs with a new health perspective from the role of sweat loss to advanced mechanisms and designs. Two types of sweat and their measurement significance for practical applications are highlighted. Then, a comprehensive review of advances in different wearable SLMDs based on hygrometers, absorbent materials, and microfluidics is presented by describing their respective device architectures, present situations, and future directions. Finally, concluding remarks on opportunities for future application fields and challenges for future sweat sensing are presented.

OUP accepted manuscript

Host carbohydrates, or glycans, have been implicated in the pathogenesis of many bacterial infections. Group A Streptococcus (GAS) is a Gram-positive bacterium that readily colonises the skin and oropharynx, and is a significant cause of mortality in humans. While the glycointeractions orchestrated by many other pathogens are increasingly well-described, the understanding of the role of human glycans in GAS disease remains incomplete. Although basic investigation into the mechanisms of GAS disease is ongoing, several glycointeractions have been identified and are examined herein. The majority of research in this context has focussed on bacterial adherence, however, glycointeractions have also been implicated in carbohydrate metabolism; evasion of host immunity; biofilm adaptations; and toxin-mediated haemolysis. The involvement of human glycans in these diverse avenues of pathogenesis highlights the clinical value of understanding glycointeractions in combatting GAS disease.

Finger sweat analysis enables short interval metabolic biomonitoring in humans

Metabolic biomonitoring in humans is typically based on the sampling of blood, plasma or urine. Although established in the clinical routine, these sampling procedures are often associated with a variety of compliance issues, which are impeding time-course studies. Here, we show that the metabolic profiling of the minute amounts of sweat sampled from fingertips addresses this challenge. Sweat sampling from fingertips is non-invasive, robust and can be accomplished repeatedly by untrained personnel. The sweat matrix represents a rich source for metabolic phenotyping. We confirm the feasibility of short interval sampling of sweat from the fingertips in time-course studies involving the consumption of coffee or the ingestion of a caffeine capsule after a fasting interval, in which we successfully monitor all known caffeine metabolites as well as endogenous metabolic responses. Fluctuations in the rate of sweat production are accounted for by mathematical modelling to reveal individual rates of caffeine uptake, metabolism and clearance. To conclude, metabotyping using sweat from fingertips combined with mathematical network modelling shows promise for broad applications in precision medicine by enabling the assessment of dynamic metabolic patterns, which may overcome the limitations of purely compositional biomarkers.

Beyond liquid biopsy: Toward non-invasive assays for distanced cancer diagnostics in pandemics

Liquid biopsy technologies have seen a significant improvement in the last decade, offering the possibility of reliable analysis and diagnosis from several biological fluids. The use of these technologies can overcome the limits of standard clinical methods, related to invasiveness and poor patient compliance. Along with this there are now mature examples of lab-on-chips (LOC) which are available and could be an emerging and breakthrough technology for the present and near-future clinical demands that provide sample treatment, reagent addition and analysis in a sample-in/answer-out approach. The possibility of combining non-invasive liquid biopsy and LOC technologies could greatly assist in the current need for minimizing exposure and transmission risks. The recent and ongoing pandemic outbreak of SARS-CoV-2, indeed, has heavily influenced all aspects of life worldwide. Ordinary tasks have been forced to switch from “in presence” to “distanced”, limiting the possibilities for a large number of activities in all fields of life outside of the home. Unfortunately, one of the settings in which physical distancing has assumed noteworthy consequences is the screening, diagnosis and follow-up of diseases.

In this review, we analyse biological fluids that are easily collected without the intervention of specialized personnel and the possibility that they may be used -or not-for innovative diagnostic assays. We consider their advantages and limitations, mainly due to stability and storage and their integration into Point-of-Care diagnostics, demonstrating that technologies in some cases are mature enough to meet current clinical needs.

Current and Future Development in Lung Cancer Diagnosis

Lung cancer is the leading cause of cancer-related deaths in North America and other developed countries. One of the reasons lung cancer is at the top of the list is that it is often not diagnosed until the cancer is at an advanced stage. Thus, the earliest diagnosis of lung cancer is crucial, especially in screening high-risk populations, such as smokers, exposure to fumes, oil fields, toxic occupational places, etc. Based on the current knowledge, it looks that there is an urgent need to identify novel biomarkers. The current diagnosis of lung cancer includes different types of imaging complemented with pathological assessment of biopsies, but these techniques can still not detect early lung cancer developments. In this review, we described the advantages and disadvantages of current methods used in diagnosing lung cancer, and we provide an analysis of the potential use of body fluids as carriers of biomarkers as predictors of cancer development and progression.

Sweat metabolome and proteome: Recent trends in analytical advances and potential biological functions

Metabolome and proteome profiling of biofluids, e.g., urine, plasma, has generated vast and ever-increasing amounts of knowledge over the last few decades. Paradoxically, omics analyses of sweat, one of the most readily available human biofluids, have lagged behind. This review capitalizes on the current knowledge and state of the art analytical advances of sweat metabolomics and proteomics. Moreover, current applications of sweat omics such as the discovery of disease biomarkers and monitoring athletic performance are also presented in this review. Another area of emerging knowledge that has been highlighted herein lies in the role of skin host-microbiome interactions in shaping the sweat metabolite-protein profiles. Discussion of future research directions describes the need to have a better grasp of sweat chemicals and to better understand how they function as aided by advances in omics tools. Overall, the role of sweat as an information-rich biofluid that could complement the exploration of the skin metabolome/proteome is emphasized.

The Impact of Nutritional Supplementation on Sweat Metabolomic Content: A Proof-of-Concept Study

Sweat is emerging as a prominent biosource for real-time human performance monitoring applications. Although promising, sources of variability must be identified to truly utilize sweat for biomarker applications. In this proof-of-concept study, a targeted metabolomics method was applied to sweat collected from the forearms of participants in a 12-week exercise program who ingested either low or high nutritional supplementation twice daily. The data establish the use of dried powder mass as a method for metabolomic data normalization from sweat samples. Additionally, the results support the hypothesis that ingestion of regular nutritional supplementation semi-quantitatively impact the sweat metabolome. For example, a receiver operating characteristic (ROC) curve of relative normalized metabolite quantities show an area under the curve of 0.82 suggesting the sweat metabolome can moderately predict if an individual is taking nutritional supplementation. Finally, a significant correlation between physical performance and the sweat metabolome are established. For instance, the data illustrate that by utilizing multiple linear regression modeling approaches, sweat metabolite quantities can predict VO₂ max (p = 0.0346), peak lower body Windage (p = 0.0112), and abdominal circumference (p = 0.0425). The results illustrate the need to account for dietary nutrition in biomarker discovery applications involving sweat as a biosource.

Few-Layer MoS2 Photodetector Arrays for Ultrasensitive On-Chip Enzymatic Colorimetric Analysis

Enzymatic colorimetric analysis of metabolites provides signatures of energy conversion and biosynthesis associated with disease onsets and progressions. Miniaturized photodetectors based on emerging two-dimensional transition metal dichalcogenides (TMDCs) promise to advance point-of-care diagnosis employing highly sensitive enzymatic colorimetric detection. Reducing diagnosis costs requires a batched multisample assay. The construction of few-layer TMDC photodetector arrays with consistent performance is imperative to realize optical signal detection for a miniature batched multisample enzymatic colorimetric assay. However, few studies have promoted an optical reader with TMDC photodetector arrays for on-chip operation. Here, we constructed 4 × 4 pixel arrays of miniaturized molybdenum disulfide (MoS₂) photodetectors and integrated them with microfluidic enzyme reaction chambers to create an optoelectronic biosensor chip device. The fabricated device allowed us to achieve arrayed on-chip enzymatic colorimetric detection of d-lactate, a blood biomarker signifying the bacterial translocation from the intestine, with a limit of detection that is 1000-fold smaller than the clinical baseline, a 10 min assay time, high selectivity, and reasonably small variability across the entire arrays. The enzyme (Ez)/MoS₂ optoelectronic biosensor unit consistently detected d-lactate in clinically important biofluids, such as saliva, urine, plasma, and serum of swine and humans with a wide detection range (10^-3-10³ μg/mL). Furthermore, the biosensor enabled us to show that high serum d-lactate levels are associated with the symptoms of systemic infection and inflammation. The lensless, optical waveguide-free device architecture should readily facilitate development of a monolithically integrated hand-held module for timely, cost-effective diagnosis of metabolic disorders in near-patient settings.

Skin Metabolomics

Skin retains numerous low-molecular-weight compounds (metabolites). Some of these compounds fulfill specific physiological roles, while others are by-products of metabolism. The skin surface can be sampled to detect and quantify skin metabolites related to diseases. Miniature probes have been developed to detect selected high-abundance metabolites secreted with sweat. To characterize a broad spectrum of skin metabolites, specimens are collected with one of several available methods, and the processed specimens are analyzed by chromatography, mass spectrometry (MS), or other techniques. Diseases for which skin-related biomarkers have been found include cystic fibrosis (CF), psoriasis, Parkinson's disease (PD), and lung cancer. To increase the clinical significance of skin metabolomics, it is desirable to verify correlations between metabolite levels in skin and other biological tissues/matrices.

Switching from Multiplex to Multimodal Colorimetric Lateral Flow Immunosensor

Multiplex lateral flow immunoassay (LFIA) is largely used for point-of-care testing to detect different pathogens or biomarkers in a single device. The increasing demand for multitargeting diagnostics requires multi-informative single tests. In this study, we demonstrated three strategies to upgrade standard multiplex LFIA to multimodal capacity. As a proof-of-concept, we applied the strategies to the differential diagnosis of Human Immunodeficiency Virus (HIV) infection, a widespread pathogen, for which conventional multiplex LFIA testing is well-established. In the new two-parameter LFIA (x2LFIA), we exploited color encoding, in which the binding of multiple targets occurs in one reactive band and the color of the probe reveals which one is present in the sample. By combining the sequential alignment of several reactive zones along the membrane of the LFIA strip and gold nanoparticles and gold nanostars for the differential visualization, in this demonstration, the x2LFIA can furnish information on HIV serotype and stage of infection in a single device. Three immunosensors were designed. The use of bioreagents as the capturing ligand anchored onto the membrane or as the detection ligand labelled with gold nanomaterials affected the performance of the x2LFIA. Higher detectability was achieved by the format involving the HIV-specific antigens as capturing agent and labelled secondary bioligands (anti-human immunoglobulins M and protein G) as the probes.

Rate normalization for sweat metabolomics biomarker discovery

As the demand for real-time exercise performance feedback increases, excreted sweat has become a biosource of interest for continuous human performance assessment. For sweat to truly fulfill this requirement, analyte concentrations must be normalized to adequately assess day-to-day differences within and among individuals. In this manuscript, data are presented highlighting the use of accurate localized sweat rate as a means for ion and global metabolomic data normalization. The results illustrate large sweat rate variability among individuals over the course of two distinct exercises protocols. Furthermore, the data show sweat rate is not symmetrical at similar locations among right and left forearms of individuals (p = 0.0007). Sweat ion conductivity analysis suggest overall sweat rate normalization reduces variability collectively among ion values and participants with principal component analysis showing 77.8% of variation in the data set attributable to sweat rate normalization. Global metabolomic analysis of sweat illustrated overall rate normalization increases the variability among test subjects with 72.7% of the variation explained by sweat rate normalization. Finally, overall rate normalized metabolomic features of sweat significantly correlated (ρ ≥ 0.7, ρ ≤ -0.7) with measured performance metrics of the individual, establishing the potential for sweat to be used as a biosource for performance monitoring. Collectively, these data illustrate the importance of accurate localized sweat rate determination, for analyte data normalization, in support for the use of sweat in biomarker discovery efforts to predict human performance.

Non-Invasive Biomarkers for Early Detection of Breast Cancer

Breast cancer is the most common cancer in women worldwide. Accurate early diagnosis of breast cancer is critical in the management of the disease. Although mammogram screening has been widely used for breast cancer screening, high false-positive and false-negative rates and radiation from mammography have always been a concern. Over the last 20 years, the emergence of "omics" strategies has resulted in significant advances in the search for non-invasive biomarkers for breast cancer diagnosis at an early stage. Circulating carcinoma antigens, circulating tumor cells, circulating cell-free tumor nucleic acids (DNA or RNA), circulating microRNAs, and circulating extracellular vesicles in the peripheral blood, nipple aspirate fluid, sweat, urine, and tears, as well as volatile organic compounds in the breath, have emerged as potential non-invasive diagnostic biomarkers to supplement current clinical approaches to earlier detection of breast cancer. In this review, we summarize the current progress of research in these areas.

Interleukin 10 and Heart Fatty Acid-Binding Protein as Early Outcome Predictors in Patients With Traumatic Brain Injury

Background: Patients with traumatic brain injury (TBI) exhibit a variable and unpredictable outcome. The proteins interleukin 10 (IL-10) and heart fatty acid-binding protein (H-FABP) have shown predictive values for the presence of intracranial lesions. Aim: To evaluate the individual and combined outcome prediction ability of IL-10 and H-FABP, and to compare them to the more studied proteins S100β, glial fibrillary acidic protein (GFAP), and neurofilament light (NF-L), both with and without clinical predictors. Methods: Blood samples from patients with acute TBI (all severities) were collected <24 h post trauma. The outcome was measured >6 months post injury using the Glasgow Outcome Scale Extended (GOSE) score, dichotomizing patients into: (i) those with favorable (GOSE≥5)/unfavorable outcome (GOSE ≤ 4) and complete (GOSE = 8)/incomplete (GOSE ≤ 7) recovery, and (ii) patients with mild TBI (mTBI) and patients with TBIs of all severities. Results: When sensitivity was set at 95-100%, the proteins' individual specificities remained low. H-FABP showed the best specificity (%) and sensitivity (100%) in predicting complete recovery in patients with mTBI. IL-10 had the best specificity (50%) and sensitivity (96%) in identifying patients with favorable outcome in patients with TBIs of all severities. When individual proteins were combined with clinical parameters, a model including H-FABP, NF-L, and ISS yielded a specificity of 56% and a sensitivity of 96% in predicting complete recovery in patients with mTBI. In predicting favorable outcome, a model consisting IL-10, age, and TBI severity reached a specificity of 80% and a sensitivity of 96% in patients with TBIs of all severities. Conclusion: Combining novel TBI biomarkers H-FABP and IL-10 with GFAP, NF-L and S100β and clinical parameters improves outcome prediction models in TBI.

Advances in lung cancer biomarkers: The role of (metal-) metabolites and selenoproteins

Lung cancer (LC) is the second most common cause of death in men after prostate cancer, and the third most recurrent type of tumor in women after breast and colon cancers. Unfortunately, when LC symptoms begin to appear, the disease is already in an advanced stage and the survival rate only reaches 2%. Thus, there is an urgent need for early diagnosis of LC using specific biomarkers, as well as effective therapies and strategies against LC. On the other hand, the influence of metals on more than 50% of proteins is responsible for their catalytic properties or structure, and their presence in molecules is determined in many cases by the genome. Research has shown that redox metal dysregulation could be the basis for the onset and progression of LC disease. Moreover, metals can interact between them through antagonistic, synergistic and competitive mechanisms, and for this reason metals ratios and correlations in LC should be explored. One of the most studied antagonists against the toxic action of metals is selenium, which plays key roles in medicine, especially related to selenoproteins. The study of potential biomarkers able to diagnose the disease in early stage is conditioned by the development of new analytical methodologies. In this sense, omic methodologies like metallomics, proteomics and metabolomics can greatly assist in the discovery of biomarkers for LC early diagnosis.

Simultaneous imaging of latent fingerprint and quantification of nicotine residue by NaYF4:Yb/Tm upconversion nanoparticles

Numerous investigations have been devoted to visualizing latent fingerprints (LFPs) for personal identification in forensic investigation and age estimation. While simultaneous detection of chemicals in fingerprint residues has yet to be explored. Herein, fluorescent NaYF₄:Yb/Tm up-conversion nanoparticles (UCNPs), which can emit blue fluorescence upon 980 nm near-infrared irradiation, have been employed in the imaging of LFPs with high contrast and high sensitivity. The furrows and ridges which provide detailed information of LFP have been displayed clearly by Tm-doped UCNPs. Meanwhile, the quantitation of nicotine residue in LFPs has been achieved based on the blue fluorescence absorbed by I₂-nicotine complex, and the data was analyzed by MATLAB with modified OPTA algorithm. It was proved that the established strategy was specific, sensitive, reliable and practical toward nicotine residue in LFPs with a detection limit of 0.05 μg.

Development of a multiplex and sensitive lateral flow immunoassay for the diagnosis of periprosthetic joint infection

The diagnosis of periprosthetic joint infection (PJI) remains a challenge. However, recent studies showed that synovial fluid biomarkers have demonstrated greater diagnostic accuracy than the currently used PJI diagnostic tests. In many diagnostic tests, combining several biomarkers into panels is critical for improving diagnostic efficiency, enhancing the diagnostic precision for specific diseases, and reducing cost. In this study, we prove that combining alpha-defensin and C-reactive protein (CRP) as biomarkers possesses the potential to provide accurate PJI diagnosis. To further verify the result, we developed a multi-target lateral flow immunoassay strip (msLFIA) with staking pad design to obtain on-site rapid response for clinical diagnosis of PJI. A total of 10 synovial fluid samples were tested using the msLFIA, and the results showed that the combined measurements of synovial fluid alpha-defensin and CRP levels were consistent with those obtained from a commercial enzyme-linked immunosorbent assay kit. In addition, we developed a multi-target lateral flow immunoassay strip (msLFIA) with staking pad design to obtain on-site rapid response for clinical diagnosis of PJI, which the multi-target design is used to increase specificity and the stacking pad design is to enhance detection sensitivity. As a result, the turnaround time of the highly sensitive test can be limited from several hours to 20 min. We expect that the developed msLFIA possesses the potential for routine monitoring of PJI as a convenient, low-cost, rapid and easy to use detection device for PJI.

Metabolomic, transcriptomic and genetic integrative analysis reveals important roles of adenosine diphosphate in haemostasis and platelet activation in non-small-cell lung cancer

Lung cancer is the leading cause of cancer‐related deaths in the world. The most prevalent subtype, accounting for 85% of cases, is non‐small‐cell lung cancer (NSCLC). Lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD) are the most common subtypes. Despite recent advances in treatment, the low 5‐year survival rate of NSCLC patients (approximately 13%) reflects the lack of early diagnostic biomarkers and incomplete understanding of the underlying disease mechanisms. We hypothesized that integration of metabolomic, transcriptomic and genetic profiles of tumours and matched normal tissues could help to identify important factors and potential therapeutic targets that contribute to tumorigenesis. We integrated omics profiles in tumours and matched adjacent normal tissues of patients with LUSC (N = 20) and LUAD (N = 17) using multiple system biology approaches. We confirmed the presence of previously described metabolic pathways in NSCLC, particularly those mediating the Warburg effect. In addition, through our combined omics analyses we found that metabolites and genes that contribute to haemostasis, angiogenesis, platelet activation and cell proliferation were predominant in both subtypes of NSCLC. The important roles of adenosine diphosphate in promoting cancer metastasis through platelet activation and angiogenesis suggest this metabolite could be a potential therapeutic target.

Sweat as a Source of Next-Generation Digital Biomarkers

Sweat has been associated with health and disease ever since it was linked to high body temperature and exercise. It contains a broad range of electrolytes, proteins, and lipids, and therefore hosts a broad panel of potential noninvasive biomarkers. The development of novel smartphone-based biosensors will enable a more sophisticated, patient-driven sweat analysis. This will provide a broad range of novel digital biomarkers. Digital biomarkers are of increasing interest because they deliver various relevant longitudinal health data. To date, investigations on digital biomarkers have focused on creating objective measurements of function. Sweat analysis using smartphone-based biosensors has the potential to provide initial noninvasive metabolic feedback and therefore represents a promising complement and a source for next-generation digital biomarkers. From this viewpoint, we discuss state-of-the-art sweat research, focusing on the clinical implementation of sweat in medicine. Sweat provides biomarkers that represent direct metabolic feedback and is therefore expected to be the next generation of digital biomarkers. With regard to its broad application in various fields of medicine, we see a clear need to evolve the internet-enabled field of sweat expertise: iSudorology.

Wearable Sensors for Biochemical Sweat Analysis

Sweat is a largely unexplored biofluid that contains many important biomarkers ranging from electrolytes and metabolites to proteins, cytokines, antigens, and exogenous drugs. The eccrine and apocrine glands produce and excrete sweat through microscale pores on the epidermal surface, offering a noninvasive means for capturing and probing biomarkers that reflect hydration state, fatigue, nutrition, and physiological changes. Recent advances in skin-interfaced wearable sensors capable of real-time in situ sweat collection and analytics provide capabilities for continuous biochemical monitoring in an ambulatory mode of operation. This review presents a broad overview of sweat-based biochemical sensor technologies with an emphasis on enabling materials, designs, and target analytes of interest. The article concludes with a summary of challenges and opportunities for researchers and clinicians in this swiftly growing field.

Screening for Preterm Birth: Potential for a Metabolomics Biomarker Panel

The aim of this preliminary study was to investigate the potential of maternal serum to provide metabolomic biomarker candidates for the prediction of spontaneous preterm birth (SPTB) in asymptomatic pregnant women at 15 and/or 20 weeks’ gestation. Metabolomics LC-MS datasets from serum samples at 15- and 20-weeks’ gestation from a cohort of approximately 50 cases (GA < 37 weeks) and 55 controls (GA > 41weeks) were analysed for candidate biomarkers predictive of SPTB. Lists of the top ranked candidate biomarkers from both multivariate and univariate analyses were produced. At the 20 weeks’ GA time-point these lists had high concordance with each other (85%). A subset of 4 of these features produce a biomarker panel that predicts SPTB with a partial Area Under the Curve (pAUC) of 12.2, a sensitivity of 87.8%, a specificity of 57.7% and a p-value of 0.0013 upon 10-fold cross validation using PanelomiX software. This biomarker panel contained mostly features from groups already associated in the literature with preterm birth and consisted of 4 features from the biological groups of “Bile Acids”, “Prostaglandins”, “Vitamin D and derivatives” and “Fatty Acids and Conjugates”.

Metabolomics toward personalized medicine

Metabolomics, which is the metabolites profiling in biological matrices, is a key tool for biomarker discovery and personalized medicine and has great potential to elucidate the ultimate product of the genomic processes. Over the last decade, metabolomics studies have identified several relevant biomarkers involved in complex clinical phenotypes using diverse biological systems. Most diseases result in signature metabolic profiles that reflect the sums of external and internal cellular activities. Metabolomics has a major role in clinical practice as it represents >95% of the workload in clinical laboratories worldwide. Many of these metabolites require different analytical platforms, such as Nuclear Magnetic Resonance (NMR), Mass Spectrometry (MS), and Ultra Performance Liquid Chromatography (UPLC), while many clinically relevant metabolites are still not routinely amenable to detection using currently available assays. Combining metabolomics with genomics, transcriptomics, and proteomics studies will result in a significantly improved understanding of the disease mechanisms and the pathophysiology of the target clinical phenotype. This comprehensive approach will represent a major step forward toward providing precision medical care, in which individual is accounted for variability in genes, environment, and personal lifestyle. In this review, we compare and evaluate the metabolomics strategies and studies that focus on the discovery of biomarkers that have "personalized" diagnostic, prognostic, and therapeutic value, validated for monitoring disease progression and responses to various management regimens.

The proteomic and metabolomic characterization of exercise-induced sweat for human performance monitoring: A pilot investigation

Sweat is a biofluid with several attractive attributes. However, investigation into sweat for biomarker discovery applications is still in its infancy. To add support for the use of sweat as a non-invasive media for human performance monitoring, volunteer participants were subjected to a physical exertion model using a treadmill. Following exercise, sweat was collected, aliquotted, and analyzed for metabolite and protein content via high-resolution mass spectrometry. Overall, the proteomic analysis illustrates significant enrichment steps will be required for proteomic biomarker discovery from single sweat samples as protein abundance is low in this medium. Furthermore, the results indicate a potential for protein degradation, or a large number of low molecular weight protein/peptides, in these samples. Metabolomic analysis shows a strong correlation in the overall abundance among sweat metabolites. Finally, hierarchical clustering of participant metabolite abundances show trends emerging, although no significant trends were observed (alpha = 0.8, lambda = 1 standard error via cross validation). However, these data suggest with a greater number of biological replicates, stronger, statistically significant results, can be obtained. Collectively, this study represents the first to simultaneously use both proteomic and metabolomic analysis to investigate sweat. These data highlight several pitfalls of sweat analysis for biomarker discovery applications.

Adipsin, MIP-1b, and IL-8 as CSF Biomarker Panels for ALS Diagnosis

Amyotrophic lateral sclerosis (ALS) is an aggressive neurodegenerative disorder that selectively attacks motor neurons in the brain and spinal cord. Despite important advances in the knowledge of the etiology and progression of the disease, there are still no solid grounds in which a clinician could make an early objective and reliable diagnosis from which patients could benefit. Diagnosis is difficult and basically made by clinical rating scales (ALSRs and El Escorial). The possible finding of biomarkers to aid in the early diagnosis and rate of disease progression could serve for future innovative therapeutic approaches. Recently, it has been suggested that ALS has an important immune component that could represent either the cause or the consequence of the disease. In this report, we analyzed 19 different cytokines and growth factors in the cerebrospinal fluid of 77 ALS patients and 13 controls by decision tree and PanelomiX program. Results showed an increase of Adipsin, MIP-1b, and IL-6, associated with a decrease of IL-8 thresholds, related with ALS patients. This biomarker panel analysis could represent an important aid for diagnosis of ALS alongside the clinical and neurophysiological criteria.

Metabolomic profiling of human lung tumor tissues - nucleotide metabolism as a candidate for therapeutic interventions and biomarkers

Although metabolomics has attracted considerable attention in the field of lung cancer (LC) detection and management, only a very limited number of works have applied it to tissues. As such, the aim of this study was the thorough analysis of metabolic profiles of relevant LC tissues, including the most important histological subtypes (adenocarcinoma and squamous cell lung carcinoma). Mass spectrometry‐based metabolomics, along with genetic expression and histological analyses, were performed as part of this study, the widest to date, to identify metabolic alterations in tumors of the most relevant histological subtypes in lung. A total of 136 lung tissue samples were analyzed and 851 metabolites were identified through metabolomic analysis. Our data show the existence of a clear metabolic alteration not only between tumor vs. nonmalignant tissue in each patient, but also inherently intrinsic changes in both AC and SCC. Significant changes were observed in the most relevant biochemical pathways, and nucleotide metabolism showed an important number of metabolites with high predictive capability values. The present study provides a detailed analysis of the metabolomic changes taking place in relevant biochemical pathways of the most important histological subtypes of LC, which can be used as biomarkers and also to identify novel targets.

A fluorometric skin-interfaced microfluidic device and smartphone imaging module for in situ quantitative analysis of sweat chemistry

The rich composition of solutes and metabolites in sweat and its relative ease of collection upon excretion from skin pores make this class of biofluid an attractive candidate for point of care analysis. Wearable technologies that combine electrochemical sensors with conventional or emerging semiconductor device technologies offer valuable capabilities in sweat sensing, but they are limited to assays that support amperometric, potentiometric, and colorimetric analyses. Here, we present a complementary approach that exploits fluorometric sensing modalities integrated into a soft, skin-interfaced microfluidic system which, when paired with a simple smartphone-based imaging module, allows for in situ measurement of important biomarkers in sweat. A network array of microchannels and a collection of microreservoirs pre-filled with fluorescent probes that selectively react with target analytes in sweat (e.g. probes), enable quantitative, rapid analysis. Field studies on human subjects demonstrate the ability to measure the concentrations of chloride, sodium and zinc in sweat, with accuracy that matches that of conventional laboratory techniques. The results highlight the versatility of advanced fluorescent-based imaging modalities in body-worn sweat microfluidics platforms, and they suggest some practical potential for these ideas.

Noninvasive profiling of sweat-derived lipid mediators for cutaneous research

BACKGROUND AND OBJECTIVE

Recent increases in the use of noninvasive matrices for biomedical analysis has led to interest in the evaluation of sweat for both clinical and research applications. However, despite being one of the two main cutaneous secretions, until very recently, only one study actually analyzed sweat in the context of cutaneous disease. This review attempts to make the case for increased use of sweat in cutaneous research, and discusses lipid mediators as potential analytical targets in sweat.

METHODS

Sweat composition and its relationship with the skin and systemic circulation are discussed, as are practical considerations for sweat sampling and analysis. Previous analyses of lipid mediators in skin biopsies are provided to show that lipid mediators can regulate cutaneous processes and disease pathways. Summaries of recent studies involving the analysis of sweat lipid mediators are provided to demonstrate the utility of sweat lipid mediator testing to support future cutaneous research studies.

RESULTS

Sweat has the potential to reflect both local and systemic biochemical changes in response to disease or intervention, and two recent studies of sweat lipid mediators confirm this ability. Additionally, sweat lipid mediators appear to be temporally stable with individual variability comparable to other matrices, suggesting that these analytes could be useful biomarkers.

CONCLUSIONS

Sweat metabolites may be capable of reporting changes in cutaneous biochemical pathways, thereby providing insight into the immunomodulatory biochemistry of the skin. Lipid mediator analysis of sweat appears to be a non invasive approach that could enhance existing cutaneous research and diagnostic methodologies.

Combining H-FABP and GFAP increases the capacity to differentiate between CT-positive and CT-negative patients with mild traumatic brain injury

Mild traumatic brain injury (mTBI) patients may have trauma-induced brain lesions detectable using CT scans. However, most patients will be CT-negative. There is thus a need for an additional tool to detect patients at risk. Single blood biomarkers, such as S100B and GFAP, have been widely studied in mTBI patients, but to date, none seems to perform well enough. In many different diseases, combining several biomarkers into panels has become increasingly interesting for diagnoses and to enhance classification performance. The present study evaluated 13 proteins individually-H-FABP, MMP-1, MMP-3, MMP-9, VCAM, ICAM, SAA, CRP, GSTP, NKDA, PRDX1, DJ-1 and IL-10-for their capacity to differentiate between patients with and without a brain lesion according to CT results. The best performing proteins were then compared and combined with the S100B and GFAP proteins into a CT-scan triage panel. Patients diagnosed with mTBI, with a Glasgow Coma Scale score of 15 and one additional clinical symptom were enrolled at three different European sites. A blood sample was collected at hospital admission, and a CT scan was performed. Patients were divided into two two-centre cohorts and further dichotomised into CT-positive and CT-negative groups for statistical analysis. Single markers and panels were evaluated using Cohort 1. Four proteins-H-FABP, IL-10, S100B and GFAP-showed significantly higher levels in CT-positive patients. The best-performing biomarker was H-FABP, with a specificity of 32% (95% CI 23-40) and sensitivity reaching 100%. The best-performing two-marker panel for Cohort 1, subsequently validated in Cohort 2, was a combination of H-FABP and GFAP, enhancing specificity to 46% (95% CI 36-55). When adding IL-10 to this panel, specificity reached 52% (95% CI 43-61) with 100% sensitivity. These results showed that proteins combined into panels could be used to efficiently classify CT-positive and CT-negative mTBI patients.

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.