Analysis of volatile human urinary metabolome by solid-phase microextraction in combination with gas chromatography–mass spectrometry for biomarker discovery: Application in a pilot study to discriminate patients with renal cell carcinoma

SPME arrow-based extraction for enhanced targeted and untargeted urinary volatilomics

BACKGROUND

Volatile organic compounds (VOCs) present in human urine are promising biomarkers for various health conditions and environmental exposures. However, their reliable detection is challenging due to the complexity of urinary matrices and the low concentrations of VOCs. Moreover, untargeted approaches present considerable challenges in terms of data interpretation, increasing the complexity of method development. Here we address these challenges by developing a new method that combines solid-phase microextraction (SPME) Arrow with gas chromatography-high resolution mass spectrometry (GC-HRMS), using a design of experiments (DOE) approach for targeted and untargeted compounds. This methodology, specifically tailored for SPME Arrow, represents a significant advancement in untargeted urinary analysis.

RESULTS

The method was developed based on targeted and untargeted outcomes, were ranking results focus on the highest response area of 11 spiked target VOCs representative of urinary volatilomics, and on identifying the maximum untargeted number of VOCs. The method was developed focusing on the highest response area of 11 spiked target VOCs representative of urinary volatilomics and identifying the maximum number of VOCs. A univariate method determined the optimal coating type, urine volume, and salt addition. Subsequently, a central composite design (CCD) DOE was used to determine ideal temperature, extraction, and incubation times. The best method obtained has an extraction time of 60 min at a temperature of 53 °C, with an SPME Arrow CAR/PDMS using 2 mL of urine, with 0.25 % w/v of NaCl and a pH of 2. Compared to conventional SPME fibers, the SPME Arrow showed improved extraction efficiency, detecting more VOCs. Finally, the enhanced method was successfully applied to urine samples from children exposed and non-exposed to tobacco smoke, identifying specific VOCs, like p-cymene and p-isopropenyl toluene related to tobacco exposure.

SIGNIFICANCE

By integrating both targeted and untargeted approaches, the developed method comprehensively captures the complexity of urinary metabolomics. This dual strategy ensures the precise identification of known compounds and the discovery of novel biomarkers, thereby providing a more complete metabolic profile. Such an approach is crucial for advancing in non-invasive diagnostics and environmental health studies, as it offers deeper insights into the intricate relationships between metabolic processes and various health conditions.

Detection and Validation of Organic Metabolites in Urine for Clear Cell Renal Cell Carcinoma Diagnosis

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) comprises the majority, approximately 70-80%, of renal cancer cases and often remains asymptomatic until incidentally detected during unrelated abdominal imaging or at advanced stages. Currently, standardized screening tests for renal cancer are lacking, which presents challenges in disease management and improving patient outcomes. This study aimed to identify ccRCC-specific volatile organic compounds (VOCs) in the urine of ccRCC-positive patients and develop a urinary VOC-based diagnostic model.

METHODS

This study involved 233 pretreatment ccRCC patients and 43 healthy individuals. VOC analysis utilized stir-bar sorptive extraction coupled with thermal desorption gas chromatography/mass spectrometry (SBSE-TD-GC/MS). A ccRCC diagnostic model was established via logistic regression, trained on 163 ccRCC cases versus 31 controls, and validated with 70 ccRCC cases versus 12 controls, resulting in a ccRCC diagnostic model involving 24 VOC markers.

RESULTS

The findings demonstrated promising diagnostic efficacy, with an Area Under the Curve (AUC) of 0.94, 86% sensitivity, and 92% specificity.

CONCLUSIONS

This study highlights the feasibility of using urine as a reliable biospecimen for identifying VOC biomarkers in ccRCC. While further validation in larger cohorts is necessary, this study's capability to differentiate between ccRCC and control groups, despite sample size limitations, holds significant promise.

Unraveling the potential of breath and sweat VOC capture devices for human disease detection: a systematic-like review of canine olfaction and GC-MS analysis

The development of disease screening methods using biomedical detection dogs relies on the collection and analysis of body odors, particularly volatile organic compounds (VOCs) present in body fluids. To capture and analyze odors produced by the human body, numerous protocols and materials are used in forensics or medical studies. This paper provides an overview of sampling devices used to collect VOCs from sweat and exhaled air, for medical diagnostic purposes using canine olfaction and/or Gas Chromatography-Mass spectrometry (GC-MS). Canine olfaction and GC-MS are regarded as complementary tools, holding immense promise for detecting cancers and infectious diseases. However, existing literature lacks guidelines for selecting materials suitable for both canine olfaction and GC-MS. Hence, this review aims to address this gap and pave the way for efficient body odor sampling materials. The first section of the paper describes the materials utilized in training sniffing dogs, while the second section delves into the details of sampling devices and extraction techniques employed for exhaled air and sweat analysis using GC-MS. Finally, the paper proposes the development of an ideal sampling device tailored for detection purposes in the field of odorology. By bridging the knowledge gap, this study seeks to advance disease detection methodologies, harnessing the unique abilities of both dogs and GC-MS analysis in biomedical research.

Development of a headspace-solid phase microextraction gas chromatography-high resolution mass spectrometry method for analyzing volatile organic compounds in urine: Application in breast cancer biomarker discovery

BACKGROUND AND AIM

Breast cancer (BC) is the leading cause of cancer-related death in females. The development of non-invasive methods for the early diagnosis of BC still remains challenge. Here, we aimed to discover the urinary volatile organic compounds (VOCs) pattern of BC patients and identify potential VOC biomarkers for BC diagnosis.

METHODS

Urine samples were analyzed by headspace-solid phase microextraction (HS-SPME) combined with gas chromatography-high resolution mass spectrometry (GC-HRMS). To assure reliable analysis, the factors influencing HS-SPME extraction efficiency were comprehensively investigated and optimized by combing the Plackett-Burman design (PBD) with the central composite design (CCD). The established HS-SPME/GC-HRMS method was validated and applied to analyze urine samples from BC patients (n = 80) and healthy controls (n = 88).

RESULTS

A total number of 134 VOCs belonging to distinct chemical classes were identified by GC-HRMS. BC patients demonstrated unique urinary VOCs pattern. Orthogonal partial least squares-discriminant analysis (OPLS-DA) showed a clear separation between BC patients and healthy controls. Eight potential VOC biomarkers were identified using multivariate and univariate statistical analysis. The predictive ability of candidate VOC biomarkers was further investigated by the random forest (RF) algorithm. The candidate VOC biomarkers yielded 76.3% sensitivity and 85.4% specificity on the training set, and achieved 76.0% sensitivity and 92.3% specificity on the validation set.

CONCLUSIONS

Overall, this work not only established a standardized HS-SPME/GC-HRMS approach for urinary VOCs analysis, but also highlighted the value of urinary VOCs for BC diagnosis. The knowledge gained from this study paves the way for early diagnosis of BC using urine in a non-invasive manner.

Hybrid volatilomics in cancer diagnosis by HS-GC-FID fingerprinting

Assessing volatile organic compounds (VOCs) as cancer signatures is one of the most promising techniques toward developing non-invasive, simple, and affordable diagnosis. Here, we have evaluated the feasibility of employing static headspace extraction (HS) followed by gas chromatography with flame ionization detector (GC-FID) as a screening tool to discriminate between cancer patients (head and neck-HNC,n= 15; and gastrointestinal cancer-GIC,n= 19) and healthy controls (n= 37) on the basis of a non-target (fingerprinting) analysis of oral fluid and urine. We evaluated the discrimination considering a single bodily fluid and adopting the hybrid approach, in which the oral fluid and urinary VOCs profiles were combined through data fusion. We used supervised orthogonal partial least squares discriminant analysis for classification, and we assessed the prediction power of the models by analyzing the values of goodness of prediction (Q²Y), area under the curve (AUC), sensitivity, and specificity. The individual models HNC urine, HNC oral fluid, and GIC oral fluid successfully discriminated between healthy controls and positive samples (Q²Y = 0.560, 0.525, and 0.559; AUC = 0.814, 0.850, and 0.926; sensitivity = 84.8, 70.2, and 78.6%; and specificity = 82.3; 81.5; 87.5%, respectively), whereas GIC urine was not adequate (Q²Y = 0.292, AUC = 0.694, sensitivity = 66.1%, and specificity = 77.0%). Compared to the respective individual models, Q²Y for the hybrid models increased (0.623 for hybrid HNC and 0.562 for hybrid GIC). However, sensitivity was higher for HNC urine and GIC oral fluid than for hybrid HNC (75.6%) and hybrid GIC (69.8%), respectively. These results suggested that HS-GC-FID fingerprinting is suitable and holds great potential for cancer screening. Additionally, the hybrid approach tends to increase the predictive power if the individual models present suitable quality parameter values. Otherwise, it is more advantageous to use a single body fluid for analysis.

Identification and semi-quantification of metabolites of new plasticizers in urine collected from flemish adults and children

A suspect screening workflow combined with a semi-quantification method was applied for the investigation of metabolites of the plasticizers di-propylene glycol dibenzoate (DiPGDB) and tri-n-butyl trimellitate (TBTM) in human urine collected from adults and children during winter (W) and summer (S) seasons. Liquid chromatography - quadrupole time of flight mass spectrometry (LC-QTOF-MS) was applied for the analyses. Two direct and one indirect metabolites of DiPGDB were identified: 3-(3-hydroxypropoxy) propyl benzoate (DiPGDB-M194), 3,4,5-trihydroxy-6-[3-(3-hydroxypropoxy) propoxy] oxane-2-carboxylic acid (DiPGDB-M310), hippuric acid (DiPGDB-M179) and one metabolite of TBTM: bis(butoxycarbonyl) benzoyloxy]-3,4,5-trihydroxyoxane-2-carboxylic acid (TBTM-M498). The identified metabolites were reported with levels of confidence (LoC) 2 and 3 and their concentrations were assessed using a semi-quantification approach. The respective concentration ranges for W and S samples were 0.20-42 ng/mL and 0.07-29 ng/mL for DiPGDB-M194, 2.5-1420 ng/mL and 5.0-2320 ng/mL for DiPGDB-M310, 230-10840 ng/mL and 320-8420 ng/mL for DiPGDB-M179, and 0.40-30 ng/mL and 0.65-30 ng/mL for TBTM-M498. The detection frequency order in urine samples was DiPGDB-M310 = DiPGDB-M179 (100%) >TBTM-M498 (44%) > DiPGDB-M194 (28%) for W and DiPGDB-M179 (99%)> DiPGDB-M310 (98%) > TBTM-M498 (57%) > DiPGDB-M194 (30%) for S. The identified metabolites DiPGDB-M310, DiPGDB-M194 and TBTM-M498 are potential biomarkers for the evaluation of human exposure to DiPGDB and TBTM. DiPGDB-M179 cannot be used for the same purpose due to its formation from compounds with multi-source origin. The application of the semi-quantification method could be useful for further studies where analytical standards are not available.

Molecular signature of renal cell carcinoma by means of a multiplatform metabolomics analysis

Renal cell carcinoma (RCC) is a disease with no specific diagnostic method or treatment. Thus, the evaluation of novel diagnostic tools or treatment possibilities is essential. In this study, a multiplatform untargeted metabolomics analysis of urine was applied to search for a metabolic pattern specific for RCC, which could enable comprehensive assessment of its biochemical background. Thirty patients with diagnosed RCC and 29 healthy volunteers were involved in the first stage of the study. Initially, the utility of the application of the selected approach was checked for RCC with no differentiation for cancer subtypes. In the second stage, this approach was used to study clear cell renal cell carcinoma (ccRCC) in 38 ccRCC patients and 38 healthy volunteers. Three complementary analytical platforms were used: reversed-phase liquid chromatography coupled with time-of-flight mass spectrometry (RP-HPLC-TOF/MS), capillary electrophoresis coupled with time-of-flight mass spectrometry (CE-TOF/MS), and gas chromatography triple quadrupole mass spectrometry (GC-QqQ/MS). As a result of urine sample analyses, two panels of metabolites specific for RCC and ccRCC were selected. Disruptions in amino acid, lipid, purine, and pyrimidine metabolism, the TCA cycle and energetic processes were observed. The most interesting differences were observed for modified nucleosides. This is the first time that the levels of these compounds were found to be changed in RCC and ccRCC patients, providing a framework for further studies. Moreover, the application of the CE-MS technique enabled the determination of statistically significant changes in symmetric dimethylarginine (SDMA) in RCC.

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Multiplatform untargeted metabolomics analysis was applied for selection of tentative diagnostic indicators of RCC.

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LC-MS, GC-MS and CE-MS techniques were utilized for analysis of urine samples collected from RCC and ccRCC patients.

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Alterations in amino acid, purine, and pyrimidine metabolism, as well as TCA cycle and energy processes, were observed.

Analysis of VOCs in Urine Samples Directed towards of Bladder Cancer Detection

Bladder cancer is one of most common types of cancer diagnosed in the genitourinary tract. Typical tests are costly and characterized by low sensitivity, which contributes to a growing interest in volatile biomarkers. Head space solid phase microextraction (SPME) was applied for the extraction of volatile organic compounds from urine samples, and gas chromatography time of flight mass spectrometry (GC×GC TOF MS) was used for the separation and detection of urinary volatiles. A cohort of 40 adult patients with bladder cancer and 57 healthy persons was recruited. Different VOC profiles were obtained for urine samples taken from each group. Twelvecompounds were found only in the samples from theBC group.The proposed candidate biomarkers are butyrolactone; 2-methoxyphenol; 3-methoxy-5-methylphenol; 1-(2,6,6-trimethylcyclohexa-1,3-dien-1-yl)-2-buten-1-one; nootkatone and 1-(2,6,6-trimethyl-1-cyclohexenyl)-2-buten-1-one.Since most of the studies published in the field are proving the potential of VOCs detected in urine samples for the screening and discrimination of patients with bladder cancer from healthy, but rarely presenting the identity of proposed biomarkers, our study represents a novel approach.

Evaluation of fresh, frozen, and lyophilized fecal samples by SPME and derivatization methods using GC×GC-TOFMS

INTRODUCTION

Feces is a highly complex matrix containing thousands of metabolites. It also contains live bacteria and enzymes, and does not have a static chemistry. Consequently, proper control of pre-analytical parameters is critical to minimize unwanted variations in the samples. However, no consensus currently exists on how fecal samples should be stored/processed prior to analysis.

OBJECTIVE

The effects of sample handling conditions on fecal metabolite profiles and abundances were examined using comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC×GC-TOFMS).

METHODS

Solid-phase microextraction (SPME) and derivatization via trimethylsilylation (TMS) were employed as complementary techniques to evaluate fresh, frozen, and lyophilized fecal samples with expanded coverage of the fecal metabolome. The total number of detected peaks and the signal intensities were compared among the different handling conditions.

RESULTS

Our analysis revealed that the metabolic profiles of fecal samples depend greatly on sample handling and processing conditions, which had a more pronounced effect on results obtained by SPME than by TMS derivatization. Overall, lyophilization resulted in a greater amount of total and class-specific metabolites, which may be attributed to cell lysis and/or membrane disintegration.

CONCLUSIONS

A comprehensive comparison of the sample handling conditions provides a deeper understanding of the physicochemical changes that occur within the samples during freezing and lyophilization. Based on our results, snap-freezing at -80 °C would be preferred over lyophilization for handling samples in the field of fecal metabolomics as this imparts the least change from the fresh condition.

Optimization of Headspace Solid-Phase Micro-Extraction Conditions (HS-SPME) and Identification of Major Volatile Aroma-Active Compounds in Chinese Chive (Allium tuberosum Rottler)

In order to rapidly and precisely identify the volatile compounds in Chinese chive (Allium tuberosum Rottler), seven key parameters of headspace solid-phase micro-extraction conditions (HS-SPME) from Chinese chive were optimized. A total of 59 volatile compounds were identified by using the optimized method, including 28 ethers, 15 aldehydes, 6 alcohols, 5 ketones, 2 hydrocarbons, 1 ester, and 2 phenols. Ethers are the most abundant, especially dimethyl trisulfide (10,623.30 μg/kg). By calculating the odor activity values (OAVs), 11 volatile compounds were identified as the major aroma-active compounds of Chinese chive. From the analysis of the composition of Chinese chive aroma, the “garlic and onion” odor (OAV = 2361.09) showed an absolute predominance over the other 5 categories of aroma. The results of this study elucidated the main sources of Chinese chive aroma from a chemical point of view and provided the theoretical basis for improving the flavor quality of Chinese chive.

Urinary volatomic profile of traditional tobacco smokers and electronic cigarettes users as a strategy to unveil potential health issues

Many harmful and potentially harmful constituents are present in tobacco products. Cigarette smoke is known to cause different forms of cancer and trigger the development of chronic diseases. In the last decade, electronic cigarettes have emerged as a healthier alternative associated to less harmful effects in comparison to traditional tobacco. However, the lack of standardization of electronic cigarettes products makes it difficult to establish and compare the real effects on health of products from different manufacturers. To better understand the impact of smoking and vaping, the volatomic composition of urine samples from traditional tobacco smokers and electronic cigarette users was established and compared with nonsmokers (control group), using headspace solid-phase microextraction combined with gas chromatography-mass spectrometry. A total of 45 urinary volatile organic metabolites belonging to different chemical families were identified in the urine of the studied groups. Benzene derivatives, terpenes, and aromatics were the chemical families that contributed the most to the urinary profile of smokers. The vapers urinary volatomic pattern was also dominated by terpenes and aromatics, in addition to alcohols. The orthogonal partial least squares-discriminant analysis of the data obtained indicated that the urinary profile of vapers is more closely related to the control group, reinforcing the hypothesis of the lowest harmfulness of electronic cigarettes. Further studies recruiting a higher number of subjects are therefore necessary to consolidate the data obtained.

Fabrication of an SPME fiber based on ZnO@GA nanorods coated onto fused silica as a highly efficient absorbent for the analysis of cancer VOCs in water and urine

Analysis of volatile organic compounds (VOCs) secreted in urine, blood, breath, etc. is a new method for monitoring the metabolism and biochemistry of the human body. However, due to the complexity of samples, a pre-concentration step is necessary before the final analysis with gas chromatography-mass spectroscopy (GC-MS). Therefore, miniaturized extraction methods such as solid-phase microextraction (SPME) can be a promising and simple pre-concentration technique. Different strategies have been adopted for the fabrication or modification of SPME fibers. This study presents the preparation and characterization of glass optical fibers coated with ZnO nanorods functionalized with gallic acid (ZnO@GA nanorod) as SPME adsorbent in GC-MS. ZnO@GA nanorods were synthesized separately and then coated onto the fibers. The coated fibers were characterized by using field emission scanning electron microscopy coupled with energy dispersive analysis of X-rays (FESEM/EDAX) and Fourier transform infrared spectroscopy (FTIR) techniques. Possessing a high surface to volume ratio of ZnO nanorods and functional groups of GA, the ZnO@GA nanorod-based SPME fibers exhibited good extraction performance for VOCs comparing with the commercial polydimethylsiloxane (PDMS) coated fibers. Under optimal conditions (NaCl concentration, 30% w/v; extraction time of 25 min; pH, 5-7 and stirring rate of 400 rpm) ZnO@GA nanorods coated fibers achieved low detection limits (0.32-4.8 μg/L), low quantification limits (1.8-16.3 μg/L) and good linearity (5-1000 μg/L) for selected VOCs. The repeatability (n = 3) for a single fiber was within the range of 4.1-7.9% (intra-day) and 5.7-9.6% (inter-day) while the reproducibility (n = 3) of fiber-to-fiber were in the range of 4.7% and 9.9%. This method was successfully used for the determination of six VOCs in water and urine with satisfactory recoveries of 90-112%. ZnO@GA nanorod coated fibers, despite possessing a much thinner coating compared to the commercial fibers, revealed a better overall extraction efficiency towards VOCs. These results indicated that the ZnO@GA provided a promising alternative in sample pretreatment and analysis in GC-MS.

Pre-workout supplements marketed in Brazil: Caffeine quantification and caffeine daily intake assessment

The stimulating and performance-enhancing properties of caffeine are often explored in one the most consumed types of supplements: the pre-workout supplements (PWS). However, despite the popularity of PWS, previous studies have reported incompatibilities between what is described in their labels and their actual caffeine content. This study aimed to develop, to optimize, and to validate a gas-chromatography coupled to nitrogen-phosphorus detector (GC-NPD) method to quantify caffeine in PWS and to analyze commercial PWS marketed in Brazil to estimate the caffeine daily intake. For this purpose, three different extraction procedures were evaluated: agitation in vortex, shaker, and sonication. Sonication yielded the best extraction results. Next, the parameters' temperature and time were optimized by using central composite rotatable design (CCRD) and response surface methodology, which revealed the optimal values of 70°C and 10 min. The method was validated and applied to quantify caffeine in 52 PWS. From the 36 PWS labels that specified the caffeine amount, seven (19%) presented more than 120% of the declared quantity, whereas 15 (42%) contained less than 80% of the labeled caffeine. Additionally, six products presented undeclared caffeine. Considering the label stated doses, five supplements exceeded the safe caffeine daily intake (400 mg). On the basis of these findings, supplement quality control remains an issue that deserves more attention from consumers, manufacturers, and regulatory agencies. Finally, we suggest that PWS consumers be careful of the habit of ingesting caffeine through other sources and avoid ingesting two or more different PWS products in the same day.

Searching for Potential Markers of Glomerulopathy in Urine by HS-SPME-GC×GC TOFMS

Volatile organic compounds (VOCs) exiting in urine are potential biomarkers of chronic kidney diseases. Headspace solid phase microextraction (HS-SPME) was applied for extraction VOCs over the urine samples. Volatile metabolites were separated and identified by means of two-dimensional gas chromatography and time of flight mass spectrometry (GC × GC TOF MS). Patients with glomerular diseases (n = 27) and healthy controls (n = 20) were recruited in the study. Different VOCs profiles were obtained from patients and control. Developed methodology offers the opportunity to examine the metabolic profile associated with glomerulopathy. Four compounds found in elevated amounts in the patients group, i.e., methyl hexadecanoate; 9-hexadecen-1-ol; 6,10-dimethyl-5,9-undecadien-2-one and 2-pentanone were proposed as markers of glomerular diseases.

Coupled Mass-Spectrometry-Based Lipidomics Machine Learning Approach for Early Detection of Clear Cell Renal Cell Carcinoma

A discovery-based lipid profiling study of serum samples from a cohort that included patients with clear cell renal cell carcinoma (ccRCC) stages I, II, III, and IV (n = 112) and controls (n = 52) was performed using ultraperformance liquid chromatography coupled to quadrupole-time-of-flight mass spectrometry and machine learning techniques. Multivariate models based on support vector machines and the LASSO variable selection method yielded two discriminant lipid panels for ccRCC detection and early diagnosis. A 16-lipid panel allowed discriminating ccRCC patients from controls with 95.7% accuracy in a training set under cross-validation and 77.1% accuracy in an independent test set. A second model trained to discriminate early (I and II) from late (III and IV) stage ccRCC yielded a panel of 26 compounds that classified stage I patients from an independent test set with 82.1% accuracy. Thirteen species, including cholic acid, undecylenic acid, lauric acid, LPC(16:0/0:0), and PC(18:2/18:2), identified with level 1 exhibited significantly lower levels in samples from ccRCC patients compared to controls. Moreover, 3α-hydroxy-5α-androstan-17-one 3-sulfate, cis-5-dodecenoic acid, arachidonic acid, cis-13-docosenoic acid, PI(16:0/18:1), PC(16:0/18:2), and PC(O-16:0/20:4) contributed to discriminate early from late ccRCC stage patients. The results are auspicious for early ccRCC diagnosis after validation of the panels in larger and different cohorts.

Development and comprehensive HS-SPME/GC-MS analysis optimization, comparison, and evaluation of different cabbage cultivars (Brassica oleracea L. var. capitata L.) volatile components

Seven parameters of the headspace solid phase micro-extraction (HS-SPME) for extracting volatile compounds from cabbage were optimized comprehensively for the first time. A total of 75 volatiles were identified and quantified in 10 cabbage cultivars, mainly including aldehydes, hydrocarbons, esters, isothiocyanates, alcohols, ethers, nitriles and thiazoles. Dimethyl ether was the most abundant volatile. There were 24 volatiles with the odour activity values (OAVs) greater than 1 making large contributions to the cabbage flavor. Pungent aroma was the strongest odour, followed by green and fruity aromas. In short, the overall OAV of purple cabbages were generally higher than that of green cabbage. The volatile profile of 10 cabbage cultivars could be distinguished on the basis of radar fingerprint chart (RFC), hierarchical cluster analysis (HCA) and principal component analysis (PCA). Therefore, this study not only developed a feasible method to distinguish different cabbage cultivars, but also established a theoretical basis for the genetic improvement of cabbage flavor.

An Optimization of Liquid-Liquid Extraction of Urinary Volatile and Semi-Volatile Compounds and Its Application for Gas Chromatography-Mass Spectrometry and Proton Nuclear Magnetic Resonance Spectroscopy

Urinary volatile compounds (VCs) have been recently assessed for disease diagnoses. They belong to very diverse chemical classes, and they are characterized by different volatilities, polarities and concentrations, complicating their analysis via a single analytical procedure. There remains a need for better, lower-cost methods for VC biomarker discovery. Thus, there is a strong need for alternative methods, enabling the detection of a broader range of VCs. Therefore, the main aim of this study was to optimize a simple and reliable liquid-liquid extraction (LLE) procedure for the analysis of VCs in urine using gas chromatography-mass spectrometry (GC-MS), in order to obtain the maximum number of responses. Extraction parameters such as pH, type of solvent and ionic strength were optimized. Moreover, the same extracts were analyzed using Proton Nuclear Magnetic Resonance Spectroscopy (1H-NMR), to evaluate the applicability of a single urine extraction for multiplatform purposes. After the evaluation of experimental conditions, an LLE protocol using 2 mL of urine in the presence of 2 mL of 1 M sulfuric acid and sodium sulphate extracted with dichloromethane was found to be optimal. The optimized method was validated with the external standards and was found to be precise and linear, and allowed for detection of >400 peaks in a single run present in at least 50% of six samples-considerably more than the number of peaks detected by solid-phase microextracton fiber pre-concentration-GC-MS (328 ± 6 vs. 234 ± 4). 1H-NMR spectroscopy of the polar and non-polar extracts extended the range to >40 more (mainly low volatility compounds) metabolites (non-destructively), the majority of which were different from GC-MS. The more peaks detectable, the greater the opportunity of assessing a fingerprint of several compounds to aid biomarker discovery. In summary, we have successfully demonstrated the potential of LLE as a cheap and simple alternative for the analysis of VCs in urine, and for the first time the applicability of a single urine solvent extraction procedure for detecting a wide range of analytes using both GC-MS and 1H-NMR analysis to enhance putative biomarker detection. The proposed method will simplify the transport between laboratories and storage of samples, as compared to intact urine samples.

Sniffing Out Urinary Tract Infection-Diagnosis Based on Volatile Organic Compounds and Smell Profile

Current available methods for the clinical diagnosis of urinary tract infection (UTI) rely on a urine dipstick test or culturing of pathogens. The dipstick test is rapid (available in 1-2 min), but has a low positive predictive value, while culturing is time-consuming and delays diagnosis (24-72 h between sample collection and pathogen identification). Due to this delay, broad-spectrum antibiotics are often prescribed immediately. The over-prescription of antibiotics should be limited, in order to prevent the development of antimicrobial resistance. As a result, there is a growing need for alternative diagnostic tools. This paper reviews applications of chemical-analysis instruments, such as gas chromatography-mass spectrometry (GC-MS), selected ion flow tube mass spectrometry (SIFT-MS), ion mobility spectrometry (IMS), field asymmetric ion mobility spectrometry (FAIMS) and electronic noses (eNoses) used for the diagnosis of UTI. These methods analyse volatile organic compounds (VOCs) that emanate from the headspace of collected urine samples to identify the bacterial pathogen and even determine the causative agent's resistance to different antibiotics. There is great potential for these technologies to gain wide-spread and routine use in clinical settings, since the analysis can be automated, and test results can be available within minutes after sample collection. This could significantly reduce the necessity to prescribe broad-spectrum antibiotics and allow the faster and more effective use of narrow-spectrum antibiotics.

MicroRNAs and Neutrophil Activation Markers Predict Venous Thrombosis in Pancreatic Ductal Adenocarcinoma and Distal Extrahepatic Cholangiocarcinoma

Cancer-associated venous thrombosis (VTE) increases mortality and morbidity. However, limited tools are available to identify high risk patients. Upon activation, neutrophils release their content through different mechanisms, thereby prompting thrombosis. We explored plasma microRNAs (miRNAs) and neutrophil activation markers to predict VTE in pancreatic ductal adenocarcinoma (PDAC) and distal extrahepatic cholangiocarcinoma (DECC). Twenty-six PDAC and 6 DECC patients recruited at cancer diagnosis, were examined for deep vein thrombosis and pulmonary embolisms, and were then followed-up with clinical examinations, blood collections, and biCUS. Ten patients developed VTE and were compared with 22 age- and sex-matched controls. miRNA expression levels were measured at diagnosis and right before VTE, and neutrophil activation markers (cell-free DNA, nucleosomes, calprotectin, and myeloperoxidase) were measured in every sample obtained during follow-up. We obtained a profile of 7 miRNAs able to estimate the risk of future VTE at diagnosis (AUC = 0.95; 95% Confidence Interval (CI) (0.987, 1)) with targets involved in the pancreatic cancer and complement and coagulation cascades pathways. Seven miRNAs were up- or down-regulated before VTE compared with diagnosis. We obtained a predictive model of VTE with calprotectin as predictor (AUC = 0.77; 95% CI (0.57, 0.95)). This is the first study that addresses the ability of plasma miRNAs and neutrophil activation markers to predict VTE in PDAC and DECC.

Identification of a biomarker panel for improvement of prostate cancer diagnosis by volatile metabolic profiling of urine

BACKGROUND

The lack of sensitive and specific biomarkers for the early detection of prostate cancer (PCa) is a major hurdle to improve patient management.

METHODS

A metabolomics approach based on GC-MS was used to investigate the performance of volatile organic compounds (VOCs) in general and, more specifically, volatile carbonyl compounds (VCCs) present in urine as potential markers for PCa detection.

RESULTS

Results showed that PCa patients (n = 40) can be differentiated from cancer-free subjects (n = 42) based on their urinary volatile profile in both VOCs and VCCs models, unveiling significant differences in the levels of several metabolites. The models constructed were further validated using an external validation set (n = 18 PCa and n = 18 controls) to evaluate sensitivity, specificity and accuracy of the urinary volatile profile to discriminate PCa from controls. The VOCs model disclosed 78% sensitivity, 94% specificity and 86% accuracy, whereas the VCCs model achieved the same sensitivity, a specificity of 100% and an accuracy of 89%. Our findings unveil a panel of 6 volatile compounds significantly altered in PCa patients' urine samples that was able to identify PCa, with a sensitivity of 89%, specificity of 83%, and accuracy of 86%.

CONCLUSIONS

It is disclosed a biomarker panel with potential to be used as a non-invasive diagnostic tool for PCa.

Improving diagnosis of genitourinary cancers: Biomarker discovery strategies through mass spectrometry-based metabolomics

The genitourinary oncology field needs integration of results from basic science, epidemiological studies, clinical and translational research to improve the current methods for diagnosis. MS-based metabolomics can be transformative for disease diagnosis and contribute to global health parity. Metabolite panels are promising to translate metabolomic findings into the clinics, changing the current diagnosis paradigm based on single biomarker analysis. This review article describes capabilities of the MS-based oncometabolomics field for improving kidney, prostate, and bladder cancer detection, early diagnosis, risk stratification, and outcome. Published works are critically discussed based on the study design; type and number of samples analyzed; data quality assessment through quality assurance and quality control practices; data analysis workflows; confidence levels reported for identified metabolites; validation attempts; the overlap of discriminant metabolites for the different genitourinary cancers; and the translation capability of findings into clinical settings. Ongoing challenges are discussed, and future directions are delineated.

Implementing a central composite design for the optimization of solid phase microextraction to establish the urinary volatomic expression: a first approach for breast cancer

INTRODUCTION

Breast cancer (BC) is positioned as the second among all cancers remaining at the top of women´s diseases worldwide followed by colorectum, lung, cervix, and thyroid cancers. The main drawback of most the screening/diagnostic methods is their low sensitivity/specificity and in some cases the invasive procedure required to obtain the samples.

OBJECTIVES

On the present investigation, we report a statistical design was to evaluate by central composite design the influence towards the optimization of the most significant variables of solid-phase microextraction (SPME) procedure for the isolation of volatile organic metabolites (VOMs) from urine of BC patients (N = 31) and healthy individuals (CTL; N = 40). The establishment of the urinary volatomic composition, through gas chromatography-mass spectrometry (GC-MS) analysis, can boost the identification of volatile organic metabolites (VOMs) potential BC biomarkers useful to be used together or to complement the current BC diagnostics tools. Better early detection methods are needed to improve the outcomes of patients with BC.

METHODS

Several combinations of experiments were considered with a central composite design (CCD) of response surface methodology (RSM) for the urinary volatomic pattern. Three-level three-factor CCD was employed assessing the most important extraction-influencing variables-fiber coating, NaCl amount, extraction time and temperature. The optimal conditions were achieved using a carboxen/polydimethylsiloxane fiber with 15% (w/v) NaCl during 75 min at 50 °C.

RESULTS

A total of ten VOMs belonging to sulfur compounds, terpenoids and carbonyl compounds presented the highest contribution towards discrimination of BC patients from CTL (variable importance in projection (VIP) > 1, p < 0.05). The discrimination efficiency and accuracy of urinary metabolites was ascertained by receiver operating characteristic (ROC) curve analysis that allowed the identification of some metabolites with highest sensitivity and specificity to discriminate the groups.

CONCLUSIONS

The results obtained with this approach suggest the possibility to identify endogenous metabolites as a platform to discovery potential BC biomarkers and paves a way to explore the related metabolomic pathways in order to improve BC diagnostic tools.

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.

A targeted metabolomic protocol for quantitative analysis of volatile organic compounds in urine of children with celiac disease

Volatile organic compounds (VOCs) in biological samples have gained popularity for disease monitoring and diagnosis. Celiac disease (CD) is one of the many prevalent health conditions which are challenging to diagnose. The aim of this study was to optimize a solid phase microextraction followed by gas chromatography-mass spectrometry, for quantitative analysis of a wide range of VOCs in the urine of patients with CD. Multivariate design of experiment was used to optimize the extraction conditions for the analysis of 15 urinary VOCs. Based on the performed experiments, extraction using 2.98 g of sodium chloride and 21 μL of 6 M hydrochloric acid for 15 min at 30 °C, using a CAR/PDMS fiber in headspace mode was found to be the most effective procedure for the analysis of the selected biomarkers. It was also demonstrated that the proposed method could distinguish between children with CD and healthy children based on the profile of VOCs. It is believed that quantitative analysis of these biomarkers will extend our understanding of CD and could be used for monitoring in patients under treatment.

Exploring the potential of NTME/GC-MS, in the establishment of urinary volatomic profiles. Lung cancer patients as case study

The growing cancer incidence and mortality worldwide claims for the development of novel diagnostic strategies. In this study we aimed to explore the potential of an innovative methodology, based on a needle trap microextraction (NTME), combined with gas chromatography-mass spectrometry (GC-MS), as new approach to isolate and profile urinary volatile organic metabolites (VOMs) from lung cancer (LC) patients and healthy individuals (CTRL). In this context, different experimental parameters with influence of NTME extraction efficiency including, temperature, equilibration time, headspace volume, ionic strength, pH, effects of sample volume and stirring, were investigated and optimized. For the DVB/CarX/Car1000 needle trap device (NTD), the best results were obtained using 40 mL headspace of a 4-mL acidified (pH = 2) urine sample with 20% NaCl and an extraction temperature of 50 °C for 40 min of equilibration time. The stability of the isolated VOMs was investigated up to 72 h after extraction. From the VOMs identified, belonging namely to ketones, sulphur and benzene derivatives, 98 presented a frequency of occurrence above 90%. Data were processed by discriminant analysis, retrieving differentiated clusters for LC and CTRL groups. As far we are aware, this is the first study using NTME/GC-MS to establish urinary volatomic profiles. Preliminary results are very promising, as broad and comprehensive volatile profiles were obtained. Moreover, the extended storage stability of the NTD devices opens new opportunities for sampling other matrices in a wide range of applications.

Metabolic Response in Rabbit Urine to Occurrence and Relief of Unilateral Ureteral Obstruction

Ureteral obstruction will lead clinically to hydronephrosis, which may further develop into partial or complete loss of kidney function and even cause permanent histological damage. However, there is little knowledge of metabolic responses during the obstructed process and its recoverability. In this study, a complete unilateral ureteral obstruction (CUUO) model was established in the rabbit, and ¹H NMR-based metabolomic analysis of urine was used to reveal the metabolic perturbations in rabbits caused by CUUO and the metabolic recovery after the CUUO was relieved. Univariate and multivariate statistical analyses were used to identify metabolic characteristics. The gradually decreased levels of 3-hydroxykynurenine, 3-methylhistidine, creatinine, guanidoacetate, meta- and para-hydroxyphenylacetate, and phenylacetylglycine and the gradually increased levels of acetate, alanine, citrate, glycine, lactate, and methionine in urine could be regarded as potential biomarkers for the occurrence and severity of ureteral obstruction. And the reduced levels of 3-methylhistidine, creatinine, guanidoacetate, hippurate, meta-hydroxyphenylacetate, and methylguanidine and the elevated levels of 2-aminoisobutyrate, acetylcholine, citrate, lactate, lysine, valine, and α-ketoglutarate in urine compared with the obstructed level could characterize the metabolic recovery of ureteral obstruction. Our results depicted the disturbed biochemical pathways involved in ureteral obstruction and demonstrated the practicability of recovering renal functions for the patients with severe hydronephrosis in clinical practice by removing causes for obstruction.

Volatile metabolomic signature of bladder cancer cell lines based on gas chromatography-mass spectrometry

INTRODUCTION

Recent studies provide a convincing support that the presence of cancer cells in the body leads to the alteration of volatile organic compounds (VOCs) emanating from biological samples, particularly of those closely related with tumoral tissues. Thus, a great interest emerged for the study of cancer volatilome and subsequent attempts to confirm VOCs as potential diagnostic biomarkers.

OBJECTIVES

The aim of this study was to determine the volatile metabolomic signature of bladder cancer (BC) cell lines and provide an in vitro proof-of-principle that VOCs emanated into the extracellular medium may discriminate BC cells from normal bladder epithelial cells.

METHODS

VOCs in the culture media of three BC cell lines (Scaber, J82, 5637) and one normal bladder cell line (SV-HUC-1) were extracted by headspace-solid phase microextraction and analysed by gas chromatography-mass spectrometry (HS-SPME/GC-MS). Two different pH (pH 2 and 7) were used for VOCs extraction to infer the best pH to be used in in vitro metabolomic studies.

RESULTS

Multivariate analysis revealed a panel of volatile metabolites that discriminated cancerous from normal bladder cells, at both pHs, although a higher number of discriminative VOCs was obtained at neutral pH. Most of the altered metabolites were ketones and alkanes, which were generally increased in BC compared to normal cells, and alcohols, which were significantly decreased in BC cells. Among them, three metabolites, namely 2-pentadecanone, dodecanal and γ-dodecalactone (the latter only tentatively identified), stood out as particularly important metabolites and promising volatile biomarkers for BC detection. Furthermore, our results also showed the potential of VOCs in discriminating BC cell lines according to tumour grade and histological subtype.

CONCLUSIONS

We demonstrate that a GC-MS metabolomics-based approach for analysis of VOCs is a valuable strategy for identifying new and specific biomarkers that may improve BC diagnosis. Future studies should entail the validation of volatile signature found for BC cell lines in biofluids from BC patients.

Discrimination between the human prostate normal and cancer cell exometabolome by GC-MS

Serum prostate-specific antigen (PSA) is currently the most used biomarker in clinical practice for prostate cancer (PCa) detection. However, this biomarker has several drawbacks. In this work, an untargeted gas chromatography-mass spectrometry (GC-MS)-based metabolomic profiling of PCa cells was performed to prove the concept that metabolic alterations might differentiate PCa cell lines from normal prostate cell line. For that, we assessed the differences in volatile organic compounds (VOCs) profile in the extracellular medium (exometabolome) of four PCa cell lines and one normal prostate cell line at two pH values (pH 2 and 7) by GC-MS. Multivariate analysis revealed a panel of volatile metabolites that discriminated cancerous from normal prostate cells. The most altered metabolites included ketones, aldehydes and organic acids. Among these, we highlight pentadecane-2-one and decanoic acid, which were significantly increased in PCa compared to normal cells, and cyclohexanone, 4-methylheptan-2-one, 2-methylpentane-1,3-diol, 4-methylbenzaldehyde, 1-(3,5-dimethylfuran-2-yl)ethanone, methyl benzoate and nonanoic acid, which were significantly decreased in PCa cells. The PCa volatilome was markedly influenced by the VOCs extraction pH, though the discriminant capability was similar. Overall, our data suggest that VOCs monitoring has the potential to be used as a PCa screening methodology.

GC-MS metabolomics-based approach for the identification of a potential VOC-biomarker panel in the urine of renal cell carcinoma patients

The analysis of volatile organic compounds (VOCs) emanating from biological samples appears as one of the most promising approaches in metabolomics for the study of diseases, namely cancer. In fact, it offers advantages, such as non‐invasiveness and robustness for high‐throughput applications. The purpose of this work was to study the urinary volatile metabolic profile of patients with renal cell carcinoma (RCC) (n = 30) and controls (n = 37) with the aim of identifying a potential specific urinary volatile pattern as a non‐invasive strategy to detect RCC. Moreover, the effect of some confounding factors such as age, gender, smoking habits and body mass index was evaluated as well as the ability of urinary VOCs to discriminate RCC subtypes and stages. A headspace solid‐phase microextraction/gas chromatography–mass spectrometry‐based method was performed, followed by multivariate data analysis. A variable selection method was applied to reduce the impact of potential redundant and noisy chromatographic variables, and all models were validated by Monte Carlo cross‐validation and permutation tests. Regarding the effect of RCC on the urine VOCs composition, a panel of 21 VOCs descriptive of RCC was defined, capable of discriminating RCC patients from controls in principal component analysis. Discriminant VOCs were further individually validated in two independent samples sets (nine RCC patients and 12 controls, seven RCC patients with diabetes mellitus type 2) by univariate statistical analysis. Two VOCs were found consistently and significantly altered between RCC and controls (2‐oxopropanal and, according to identification using NIST14, 2,5,8‐trimethyl‐1,2,3,4‐tetrahydronaphthalene‐1‐ol), strongly suggesting enhanced potential as RCC biomarkers. Gender, smoking habits and body mass index showed negligible and age‐only minimal effects on the urinary VOCs, compared to the deviations resultant from the disease. Moreover, in this cohort, the urinary volatilome did not show ability to discriminate RCC stages and histological subtypes. The results validated the value of urinary volatilome for the detection of RCC and advanced with the identification of potential RCC urinary biomarkers.

Urine and Serum Metabolomics Analyses May Distinguish between Stages of Renal Cell Carcinoma

Renal cell carcinoma (RCC) is a heterogeneous disease that is usually asymptomatic until late in the disease. There is an urgent need for RCC specific biomarkers that may be exploited clinically for diagnostic and prognostic purposes. Preoperative fasting urine and serum samples were collected from patients with clinical renal masses and assessed with ¹H NMR and GCMS (gas chromatography-mass spectrometry) based metabolomics and multivariate statistical analysis. Alterations in levels of glycolytic and tricarboxylic acid (TCA) cycle intermediates were detected in RCC relative to benign masses. Orthogonal Partial Least Square Discriminant Analysis plots discriminated between benign vs. pT1 (R2 = 0.46, Q2 = 0.28; AUC = 0.83), benign vs. pT3 (R2 = 0.58, Q2 = 0.37; AUC = 0.87) for ¹H NMR-analyzed serum and between benign vs. pT1 (R2 = 0.50, Q2 = 0.37; AUC = 0.83), benign vs. pT3 (R2 = 0.72, Q2 = 0.68, AUC = 0.98) for urine samples. Separation was observed between benign vs. pT3 (R2 = 0.63, Q2 = 0.48; AUC = 0.93), pT1 vs. pT3 (R2 = 0.70, Q2 = 0.54) for GCMS-analyzed serum and between benign vs. pT3 (R2Y = 0.87; Q2 = 0.70; AUC = 0.98) for urine samples. This pilot study suggests that urine and serum metabolomics may be useful in differentiating benign renal tumors from RCC and for staging RCC.

Biomarker Discovery in Human Prostate Cancer: an Update in Metabolomics Studies

Prostate cancer (PCa) is the most frequently diagnosed cancer and the second leading cause of cancer death among men in Western countries. Current screening techniques are based on the measurement of serum prostate specific antigen (PSA) levels and digital rectal examination. A decisive diagnosis of PCa is based on prostate biopsies; however, this approach can lead to false-positive and false-negative results. Therefore, it is important to discover new biomarkers for the diagnosis of PCa, preferably noninvasive ones. Metabolomics is an approach that allows the analysis of the entire metabolic profile of a biological system. As neoplastic cells have a unique metabolic phenotype related to cancer development and progression, the identification of dysfunctional metabolic pathways using metabolomics can be used to discover cancer biomarkers and therapeutic targets. In this study, we review several metabolomics studies performed in prostatic fluid, blood plasma/serum, urine, tissues and immortalized cultured cell lines with the objective of discovering alterations in the metabolic phenotype of PCa and thus discovering new biomarkers for the diagnosis of PCa. Encouraging results using metabolomics have been reported for PCa, with sarcosine being one of the most promising biomarkers identified to date. However, the use of sarcosine as a PCa biomarker in the clinic remains a controversial issue within the scientific community. Beyond sarcosine, other metabolites are considered to be biomarkers for PCa, but they still need clinical validation. Despite the lack of metabolomics biomarkers reaching clinical practice, metabolomics proved to be a powerful tool in the discovery of new biomarkers for PCa detection.

Optimizing Mass Spectrometry Analyses: A Tailored Review on the Utility of Design of Experiments

Mass spectrometry (MS) has emerged as a tool that can analyze nearly all classes of molecules, with its scope rapidly expanding in the areas of post-translational modifications, MS instrumentation, and many others. Yet integration of novel analyte preparatory and purification methods with existing or novel mass spectrometers can introduce new challenges for MS sensitivity. The mechanisms that govern detection by MS are particularly complex and interdependent, including ionization efficiency, ion suppression, and transmission. Performance of both off-line and MS methods can be optimized separately or, when appropriate, simultaneously through statistical designs, broadly referred to as "design of experiments" (DOE). The following review provides a tutorial-like guide into the selection of DOE for MS experiments, the practices for modeling and optimization of response variables, and the available software tools that support DOE implementation in any laboratory. This review comes 3 years after the latest DOE review (Hibbert DB, 2012), which provided a comprehensive overview on the types of designs available and their statistical construction. Since that time, new classes of DOE, such as the definitive screening design, have emerged and new calls have been made for mass spectrometrists to adopt the practice. Rather than exhaustively cover all possible designs, we have highlighted the three most practical DOE classes available to mass spectrometrists. This review further differentiates itself by providing expert recommendations for experimental setup and defining DOE entirely in the context of three case-studies that highlight the utility of different designs to achieve different goals. A step-by-step tutorial is also provided.

Metabolomics for laboratory diagnostics

Metabolomics is an emerging approach in a systems biology field. Due to continuous development in advanced analytical techniques and in bioinformatics, metabolomics has been extensively applied as a novel, holistic diagnostic tool in clinical and biomedical studies. Metabolome's measurement, as a chemical reflection of a current phenotype of a particular biological system, is nowadays frequently implemented to understand pathophysiological processes involved in disease progression as well as to search for new diagnostic or prognostic biomarkers of various organism's disorders. In this review, we discussed the research strategies and analytical platforms commonly applied in the metabolomics studies. The applications of the metabolomics in laboratory diagnostics in the last 5 years were also reviewed according to the type of biological sample used in the metabolome's analysis. We also discussed some limitations and further improvements which should be considered taking in mind potential applications of metabolomic research and practice.