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Liquid chromatography and ultra-performance liquid chromatography-mass spectrometry fingerprinting of human urine. Sample stability under different handling and storage conditions for metabonomics studies
Abstract
Typically following collection biological samples are kept in a freezer for periods ranging from a few days to several months before analysis. Experience has shown that in LC-MS-based metabonomics research the best analytical practice is to store samples as these are collected, complete the sample set and analyse it in a single run. However, this approach is prudent only if the samples stored in the refrigerator or in the freezer are stable. Another important issue is the stability of the samples following the freeze-thaw process. To investigate these matters urine samples were collected from 6 male volunteers and analysed by LC-MS and ultra-performance liquid chromatography (UPLC)-MS [in both positive and negative electrospray ionization (ESI)] on the day of collection or at intervals of up to 6 months storage at -20 degrees C and -80 degrees C. Other sets of these samples underwent a series of up to nine freeze-thaw cycles. The stability of samples kept at 4 degrees C in an autosampler for up to 6 days was also assessed, with clear differences appearing after 48h. Data was analysed using multivariate statistical analysis (principal component analysis). The results show that sample storage at both -20 and -80 degrees C appeared to ensure sample stability. Similarly up to nine freeze thaw cycles were without any apparent effect on the profile.
... The time count began with the addition of extraction solvent to the ground floret samples until the time of their injection. The reproducibility of the data acquired daily from ground floret samples stored at −80 • C was demonstrated by overlaying total ion current (TIC) plots for all runs [26,34]. Samples for day 1 (red), day 2 (blue), and day 3 (green) correspond to each day in Figure 1. ...
... The time count began with the addition of extraction solvent to the ground floret samples until the time of their injection. The reproducibility of the data acquired daily from ground floret samples stored at −80 °C was demonstrated by overlaying total ion current (TIC) plots for all runs [26,34]. Samples for day 1 (red), day 2 (blue), and day 3 (green) correspond to each day in Figure 1. ...
... As part of their study on human biofluids, Gika, Theodoridis, and Wilson [34] veri- These compounds are represented in red for each quadrant of the volcano plot. If the comparison is X vs. Y, the compounds that are higher in X will be visualized in the right quadrant. ...
A typical metabolomic analysis consists of a multi-step procedure. Variation can be introduced in any analysis segment if proper care in quality assurance is not taken, thus compromising the final results. Sample stability is one of those factors. Although sophisticated studies addressing sample decay over time have been performed in the medical field, they are emerging in plant metabolomics. Here, we focus on the stability of wheat floret extracts on queue inside an auto-injector held at 25 °C. The objective was to locate an analytical time window from extraction to injection with no significant difference occurring in the sample. Total ion current chromatograms, principal component analysis, and volcano plots were used to measure changes in the samples. Results indicate a maximum work window time of 7:45 h for Steele-ND wheat methanolic extractions in an auto-sampler at 25 °C. Comparisons showed a significant gradual increase in the number and intensity of compounds observed that may be caused by the degradation of other molecules in the sample extract. The approach can be applied as preliminary work in a metabolite profiling study, helping to set the appropriate workload to produce confident results.
... Thus, numerous studies have been conducted during the last years to evaluate the stability of urine and blood-derived biofluids under different storage conditions. For urine, it has been reported that freezing at −20 • C for up to 6 months [66,86,87], and storage in the fridge (4 • C) or using cooling packs (10 • C) for 24-72 h [63,81,86] allows preservation of the urinary metabolic profile. Nonetheless, small urinary alterations can been detected during the first days of storage, even at −80 • C, so that it is recommended to freeze urine samples for a minimum period of one week prior to conducting metabolomics analysis with the aim of ensuring a consistent evolution of urinary components in all samples across the study cohort [60]. ...
... Thus, numerous studies have been conducted during the last years to evaluate the stability of urine and blood-derived biofluids under different storage conditions. For urine, it has been reported that freezing at −20 • C for up to 6 months [66,86,87], and storage in the fridge (4 • C) or using cooling packs (10 • C) for 24-72 h [63,81,86] allows preservation of the urinary metabolic profile. Nonetheless, small urinary alterations can been detected during the first days of storage, even at −80 • C, so that it is recommended to freeze urine samples for a minimum period of one week prior to conducting metabolomics analysis with the aim of ensuring a consistent evolution of urinary components in all samples across the study cohort [60]. ...
... On the other hand, urine seems to be more stable upon repetitive thawing compared with blood samples. Gika et al. showed that LC/MS-based urinary metabolomic profiles were not affected by the thawing of samples for up to nine freeze-thaw cycles [86], whereas in another study significant alterations were only observed after thawing urine samples twice a week over four consecutive weeks [60]. However, the effect of thawing is much more pronounced when considering urinary volatiles, which may be impacted after two freeze-thaw cycles [88]. ...
Metabolomics can be significantly influenced by a range of pre-analytical factors, such as sample collection, pre-processing, aliquoting, transport, storage and thawing. This therefore shows the crucial need for standardizing the pre-analytical phase with the aim of minimizing the inter-sample variability driven by these technical issues, as well as for maintaining the metabolic integrity of biological samples to ensure that metabolomic profiles are a direct expression of the in vivo biochemical status. This review article provides an updated literature revision of the most important factors related to sample handling and pre-processing that may affect metabolomics results, particularly focusing on the most commonly investigated biofluids in metabolomics, namely blood plasma/serum and urine. Finally, we also provide some general recommendations and best practices aimed to standardize and accurately report all these pre-analytical aspects in metabolomics research.
... Barton et al. and Dunn et al. both found that a 24 h delay at 4 °C did not make any significant effect on the metabolomic profiles, respectively on NMR platform and MS-based platform (Barton et al. 2008;Dunn et al. 2008). Other studies suggested that 48 h at 4 °C did not significantly alter the urinary metabolome (Gika et al. 2008(Gika et al. , 2007. As such, it is recommended that only 48 h worth of samples should be stored at any one time in an auto-sampler. ...
... Appearance of acetate and a decrease of the intensity of citrate resonance were found in samples stored at 4 °C after one week presumably due to microbial contamination (Lauridsen et al. 2007). For long-term storage, − 20 °C or − 80 °C has been demonstrated to have no effect on the urinary metabolome after storage for 6 months in a LC-MS analysis (Gika et al. 2008). Similarly, Lauridsen et al. found that storage for up to 26 weeks at − 80 °C didn't alter metabolomics profile tested by NMR (Lauridsen et al. 2007). ...
... These results do not mean that all the components of the sample are stable under these various conditions, and it is quite possible that individual metabolites do decompose with time, but that in doing so they do not grossly impact the principal component analysis (PCA) result (Fernández-Peralbo and Luque de Castro 2012). When a research project moves from non-targeted to targeted analysis of specific metabolites it would clearly be prudent to perform more rigorous studies on the stability of the analyses (Saude and Sykes 2007;Gika et al. 2008). ...
Background
Metabolomics provides measurement of numerous metabolites in human samples, which can be a useful tool in clinical research. Blood and urine are regarded as preferred subjects of study because of their minimally invasive collection and simple preprocessing methods. Adhering to standard operating procedures is an essential factor in ensuring excellent sample quality and reliable results.Aim of reviewIn this review, we summarize the studies about the impacts of various preprocessing factors on metabolomics studies involving clinical blood and urine samples in order to provide guidance for sample collection and preprocessing.Key scientific concepts of reviewClinical information is important for sample grouping and data analysis which deserves attention before sample collection. Plasma and serum as well as urine samples are appropriate for metabolomics analysis. Collection tubes, hemolysis, delay at room temperature, and freeze–thaw cycles may affect metabolic profiles of blood samples. Collection time, time between sampling and examination, contamination, normalization strategies, and storage conditions may alter analysis results of urine samples. Taking these collection and preprocessing factors into account, this review provides suggestions of standard sample preprocessing.
... For short-term storage, Gika et al. have shown that the storage of urine samples at 4 • C for up to 48 h maintained the metabolic integrity of the samples [44]. However, it is important to minimize sample storage at 4 • C as it has been shown that samples stored for more than 9 months will present an altered metabolome when compared to samples stored at −20 • C [45]. ...
... However, it is important to minimize sample storage at 4 • C as it has been shown that samples stored for more than 9 months will present an altered metabolome when compared to samples stored at −20 • C [45]. For long-term storage, metabolic profiles of urine samples stored at either −20 or −80 • C for 6 months did not show any significant differences [44]. This study, however, did not confirm whether or not the stored samples were identical to the original samples. ...
... Data regarding the number of freeze-thaw cycles acceptable are variable [42,44,46]. Unfractionated serum samples can be stored frozen for later quantitative lipid analysis as minor effects occur on quantitative lipid composition for most of the biologically relevant lipid species in humans, even with one to three freeze-thaw cycles. ...
Metabolomics has found numerous applications in the study of liver metabolism in health and disease. Metabolomics studies can be conducted in a variety of biological matrices ranging from easily accessible biofluids such as urine, blood or feces, to organs, tissues or even cells. Sample collection and storage are critical steps for which standard operating procedures must be followed. Inappropriate sample collection or storage can indeed result in high variability, interferences with instrumentation or degradation of metabolites. In this review, we will first highlight important general factors that should be considered when planning sample collection in the study design of metabolomic studies, such as nutritional status and circadian rhythm. Then, we will discuss in more detail the specific procedures that have been described for optimal pre-analytical handling of the most commonly used matrices (urine, blood, feces, tissues and cells).
... For immobile subjects it can be collected with a catheter, and for babies urine can be collected with absorbent pads in nappies (Chetwynd et al., 2017). After collection, samples should be frozen at −80°C as soon as possible, as prior work has shown that urine modifications can occur in a short period of time (Gika et al., 2008), especially at room temperature. At −20°C, samples can remain stable for a relatively long time, but −80°C remains the best choice (Gika et al., 2008). ...
... After collection, samples should be frozen at −80°C as soon as possible, as prior work has shown that urine modifications can occur in a short period of time (Gika et al., 2008), especially at room temperature. At −20°C, samples can remain stable for a relatively long time, but −80°C remains the best choice (Gika et al., 2008). Freeze thaw cycles should be kept to a minimum (Khamis et al., 2017). ...
PurposeData from healthy eyes is needed to interpret optical coherence tomography angiography (OCTA) findings. However, very little normative data is available for wide-field swept-source OCTA (WF SS-OCTA), particularly 12 × 12-mm and disc-centered angiograms. Therefore, we aim to report quantitative metrics in a large sample of control eyes.Methods
In this cross-sectional observational study, 482 eyes of 375 healthy adults were imaged on the 100 kHz Zeiss PLEX® Elite 9000 using protocols centered on the fovea (3 × 3, 6 × 6, and 12 × 12-mm) and optic disc (6 × 6 and 12 × 12-mm) between December 2018 and January 2022. The ARI Network (Zeiss Portal v5.4) was used to calculate vessel density (VD) and vessel skeletonized density (VSD) in the superficial capillary plexus, deep capillary plexus, and whole retina, as well as foveal avascular zone (FAZ) parameters. Mixed-effect multiple linear regression models were used for statistical analysis.ResultsThe subjects’ median age was 55 (38–63) years, and 201 (53.6%) were female. Greater age and worse best-corrected visual acuity (BCVA) were associated with significantly lower VD and VSD (p < 0.05). VD and VSD differed based on race and cataract status, but not sex, on some scan protocols (p < 0.05). FAZ circularity decreased with age, and FAZ dimensions differed based on race and ethnicity in certain scan protocols.Conclusions
We report a large database of parafoveal and peripapillary vascular metrics in several angiogram sizes. In referencing these values, researchers must consider characteristics such as age, race, and BCVA, but will have a valuable point of comparison for OCTA measurements in pathologic settings.
... Variable contains a series of information, e.g., molecular weight and retention time, with every marker compound corresponding to its unique variable, that is to say, the process to pursue marker compounds is actually a process to pursue eligible variables. Multivariate statistical analysis including principal component analysis (PCA) [38][39][40] and orthogonal partial least squares discriminant analysis (OPLS-DA) [41,42] was performed in SIMCA 14.1 software [43] after importing the data matrix. A permutation test with 200 iterations was employed for over-fitting judgement of the OPLS-DA model [43,44]. ...
... As Taguchi [51] pointed out, PCA can make a natural classification for sample groups and eliminate the extreme data without knowing their categories, thus PCA can be used in metabolomics to assess the data quality and to identify outliers [38][39][40]. As indicated in Figure 2, no extreme data and outliers were observed. ...
The metabolomics approach has proved to be promising in achieving non-targeted screening for those unknown and unexpected (U&U) contaminants in foods, but data analysis is often the bottleneck of the approach. In this study, a novel metabolomics analytical method via seeking marker compounds in 50 pharmaceutical and personal care products (PPCPs) as U&U contaminants spiked into lettuce and maize matrices was developed, based on ultrahigh-performance liquid chromatography-tandem mass spectrometer (UHPLC-MS/MS) output results. Three concentration groups (20, 50 and 100 ng mL−1) to simulate the control and experimental groups applied in the traditional metabolomics analysis were designed to discover marker compounds, for which multivariate and univariate analysis were adopted. In multivariate analysis, each concentration group showed obvious separation from other two groups in principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) plots, providing the possibility to discern marker compounds among groups. Parameters including S-plot, permutation test and variable importance in projection (VIP) in OPLS-DA were used for screening and identification of marker compounds, which further underwent pairwise t-test and fold change judgement for univariate analysis. The results indicate that marker compounds on behalf of 50 PPCPs were all discovered in two plant matrices, proving the excellent practicability of the metabolomics approach on non-targeted screening of various U&U PPCPs in plant-derived foods. The limits of detection (LODs) for 50 PPCPs were calculated to be 0.4~2.0 µg kg−1 and 0.3~2.1 µg kg−1 in lettuce and maize matrices, respectively.
... LC-MS is a powerful alternative detection tool for metabolomics with the advantages of high separation efficiency, a simple pretreatment process, and high resolution (Steinmann and Ganzera, 2011). The results showed that the technique was mature and the metabonomics method was a powerful way of potential biomarker discovery (Helen et al., 2008). In this paper, we focused on the changes of PSB metabolites under different culture time and light intensity. ...
... Through the statistical analysis of samples, it was found that the synthesis of metabolites was staged, and the change of growth conditions would affect the normal synthesis and decomposition of metabolites. Based on the results of metabolic pathway enrichment and previous research, it is speculated that the synthesis of photosynthetic bacterial carotenoids uses acetyl-CoA as a precursor substance, which is catalyzed by HMG-CoA reductase to synthesize IPP and participate in the synthesis of carotenoids (Helen et al., 2008). The data acquired in this study will facilitate future functional studies of genes/pathways associated with metabolic reactions during the photosynthetic bacterial carotenoid synthesis pathway. ...
... About the first step, the following rules should always be applied: collection of samples in a sterile container containing micromolar quantities of inorganic bacteriostatic agents such as sodium azide (0.01-0.1%) to avoid metabolic alterations due to bacterial metabolism; quick centrifugation of the samples at high speed to eliminate cellular debris, including active enzymes that can modify the metabolic content; early freezing of the samples at very low temperatures (−40 °C, −80 °C), up to the analysis; thawing the samples on ice to avoid rapid and harmful temperature variations [46]. ...
... About the first step, the following rules should always be applied: collection of samples in a sterile container containing micromolar quantities of inorganic bacteriostatic agents such as sodium azide (0.01-0.1%) to avoid metabolic alterations due to bacterial metabolism; quick centrifugation of the samples at high speed to eliminate cellular debris, including active enzymes that can modify the metabolic content; early freezing of the samples at very low temperatures (−40 • C, −80 • C), up to the analysis; thawing the samples on ice to avoid rapid and harmful temperature variations [46]. ...
Despite advances in supportive and protective therapy for myocardial function, cardiovascular diseases due to antineoplastic therapy—primarily cardiomyopathy associated with contractile dysfunction—remain a major cause of morbidity and mortality. Because of the limitations associated with current therapies, investigators are searching for alternative strategies that can timely recognise cardiovascular damage—thus permitting a quick therapeutic approach—or prevent the development of the disease. Damage to the heart can result from both traditional chemotherapeutic agents, such as anthracyclines, and new targeted therapies, such as tyrosine kinase inhibitors. In recent years, metabolomics has proved to be a practical tool to highlight fundamental changes in the metabolic state in several pathological conditions. In this article, we present the state-of-the-art technology with regard to the metabolic mechanisms underlying cardiotoxicity and cardioprotection.
... Receiver operating characteristic (ROC) curve was utilized to evaluate the diagnostic value of the altered molecules. When the area under curve (AUC) was much larger than 0.75, the biomarker was considered as a candidate diagnostic Due to the close degree of QC samples cluster on the score plots was positive linked to the stability of the analysis (Gika et al., 2008), this result suggested that the system in our study was highly reproducible. What's more, the reproducibility of the system was estimated by relative standard deviation (RSD%) of 5 randomly selected metabolites in the 8 within-run QCs. ...
Introduction
Femur head necrosis (FHN) is a challenging clinical disease with unclear underlying mechanism, which pathologically is associated with disordered metabolism. However, the disordered metabolism in cancellous bone of FHN was never analyzed by gas chromatography-mass spectrometry (GC-MS).
Objectives
To elucidate altered metabolism pathways in FHN and identify putative biomarkers for the detection of FHN.
Methods
We recruited 26 patients with femur head necrosis and 22 patients with femur neck fracture in this study. Cancellous bone tissues from the femoral heads were collected after the surgery and were analyzed by GC-MS based untargeted metabolomics approach. The resulting data were analyzed via uni- and multivariate statistical approaches. The changed metabolites were used for the pathway analysis and potential biomarker identification.
Results
Thirty-seven metabolites distinctly changed in FHN group were identified. Among them, 32 metabolites were upregulated and 5 were downregulated in FHN. The pathway analysis showed that linoleic acid metabolism were the most relevant to FHN pathology. On the basis of metabolites network, L-lysine, L-glutamine and L-serine were deemed as the junctions of the whole metabolites. Finally, 9,12-octadecadienoic acid, inosine, L-proline and octadecanoic acid were considered as the potential biomarkers of FHN.
Conclusion
This study provides a new insight into the pathogenesis of FHN and confirms linoleic acid metabolism as the core.
... The PCA score plots showed that the QCs tightly clustered together in the whole samples. Due to the close degree of QC samples cluster on the score plots was positive linked to the stability of the analysis (Gika et al., 2008). This result suggested that the system was highly reproducible. ...
Introduction
Femur head necrosis (FHN) is a challengeable clinical disease with unclear underlying mechanism and a low rate of early diagnosis.
Objectives
To elucidate altered metabolism pathways in FHN and identify putative biomarkers for the detection of FHN.
Methods
we recruited 26 patients with femur head necrosis and 22 patients with femur neck fracture in this study. Cancellous bone tissues from the femoral heads were collected after the surgery and were analyzed using an untargeted metabolomics approach on the basis of gas chromatography-mass spectrometry (GC-MS).
Results
The resulting data were analyzed via uni- and multivariate statistical approaches, and we identified 38 metabolites distinctly changed in FHN group. Among them, 33 metabolites were upregulated and 5 were downregulated in FHN. The pathway analysis results showed that linoleic acid metabolism were the most relevant to FHN pathology. On the basis of metabolites network, L-lysine, L-glutamine and L-serine were deemed as the junctions of the whole metabolites. At last, we figured out that 9,12-octadecadienoic acid, inosine, L-proline and octadecanoic acid can be considered as the potential biomarkers of FHN.
Conclusion
Our study provided a new insight into the pathogenesis of FHN and identified 4 biomarkers in FHN. Linoleic acid metabolism could be considered as the core in FHN and 9,12-Octadecadienoic acid could be considered as the diagnostic marker.
... In most cases, urine, blood, and stool samples are collected. For plasma and urine samples, the collection and processing time should not be more than 2 h, and the holding time of samples should be kept at 4 • C. The sample can be preserved at -80 • C for long-term storage biobank environments (Bernini et al., 2011;Gika et al., 2008). ...
... Table S4: Concentrations of plasma biogenic amines: survivors and non-survivors of ARDS patients. References [43][44][45][46][47] are cited in the supplementary materials. ...
Acute respiratory distress syndrome (ARDS) involves dysregulated immune-inflammatory responses, characterized by severe oxidative stress and high mortality. Metabolites modulating the inflammatory and immune responses may play a central role in the pathogenesis of ARDS. Most biogenic amines may induce the production of reactive oxygen species, oxidative stress, mitochondrial dysfunction, and programmed cell death. We conducted a prospective study on metabolic profiling specific to the amino acids and biogenic amines of 69 patients with ARDS. Overall, hospital mortality was 52.2%. Between day 1 and day 7 after ARDS onset, plasma kynurenine levels and the kynurenine/tryptophan ratio were significantly higher among non-survivors than in survivors (all p < 0.05). Urine metabolic profiling revealed a significantly higher prevalence of tryptophan degradation and higher concentrations of metabolites downstream of the kynurenine pathway among non-survivors than among survivors upon ARDS onset. Cox regression models revealed that plasma kynurenine levels and the plasma kynurenine/tryptophan ratio on day 1 were independently associated with hospital mortality. The activation of the kynurenine pathway was associated with mortality in patients with ARDS. Metabolic phenotypes and modulating metabolic perturbations of the kynurenine pathway could perhaps serve as prognostic markers or as a target for therapeutic interventions aimed at reducing oxidative stress and mortality in ARDS.
... Reports regarding metabolome stability studies of other biological samples had previously shown that this temperature enables one to maintain the stability of metabolome. 4,8,21 The integral experimental design is detailed in Figure 1. ...
Human milk (HM) lipidome stability during storage is crucial in lipidomic studies to avoid misinterpretations. Facing the lack of comprehensive work on the HM lipidome stability, we performed a study on a potential alteration in the lipid profiles of HM samples stored under different conditions. An untargeted LC-Q-TOF-MS-based approach was applied to study the influence of storage conditions as well as the interaction of the storage temperature and time on HM lipid profiles. The samples were stored for 4–84 days at temperatures in the range from 4 to −80 °C and also were exposed to up to three freeze–thaw cycles. The results showed that the storage at 4 °C for just 4 days as well as being subjected to three freeze–thaw cycles can lead to a change in the content of lipids. The observed differences in levels of some lipid species in samples stored at −20 °C in comparison to the concentration level of those lipids in samples stored at −80 °C were not statistically significant, and inter-individual variance regardless of sample storage condition was maintained. The storage of HM samples at −20 °C for up to 3 weeks and −80 °C for up to 12 weeks ensures sample lipidome stability.
... The specific strategy for a metabolomics' study requires miscellaneous types of initial information, which is determined by the choice between targeted analysis (demonstrated by qualitative and quantitative results) [11], metabolomics' profiling [12], fingerprinting, or footprinting. Each of the approaches demands different practical procedures depending on the essential purpose of the assay [13]. A prospective view of sample diversity will constitute a promising field considering sufficient laboratory work organization. ...
Recently, the diagnostic methods used by scientists in forensic examinations have enormously expanded. Metabolomics provides an important contribution to analytical method development. The main purpose of this review was to investigate and summarize the most recent applications of metabolomics in forensic science. The primary research method was an extensive review of available international literature in PubMed. The keywords “forensic” and “metabolomics” were used as search criteria for the PubMed database scan. Most authors emphasized the analysis of different biological sample types using chromatography methods. The presented review is a summary of recently published implementations of metabolomics in forensic science and types of biological material used and techniques applied. Possible opportunities for valuable metabolomics’ applications are discussed to emphasize the essential necessities resulting in numerous nontargeted metabolomics’ assays.
... Although urine collection might be an easy alternative, the urine output in the first days after birth is typically low, and infants with perinatal asphyxia often have impaired renal function with oliguria or anuria. Urine for metabolomics analysis must be collected under sterile conditions because bacterial metabolism influences the urine metabolome, and samples must be frozen at −80 • C immediately after collection [20]. Urine is suitable for repeat analysis and assessment of markers of lipid peroxidation up to days or weeks after an insult or intervention, with the associated disadvantage of limited time resolution. ...
There is a need for feasible and non-invasive diagnostics in perinatal asphyxia. Metabolomics is the study of small molecular weight products of cellular metabolism that may, directly and indirectly, reflect the level of oxidative stress. Saliva analysis is a novel approach that has a yet unexplored potential in metabolomics in perinatal asphyxia. The aim of this review was to give an overview of metabolomics studies of oxidative stress in perinatal asphyxia, particularly searching for studies analyzing non-invasively collected biofluids including saliva. We searched the databases PubMed/Medline and included 11 original human and 4 animal studies. In perinatal asphyxia, whole blood, plasma, and urine are the most frequently used biofluids used for metabolomics analyses. Although changes in oxidative stress-related salivary metabolites have been reported in adults, the utility of this approach in perinatal asphyxia has not yet been explored. Human and animal studies indicate that, in addition to antioxidant enzymes, succinate and hypoxanthine, as well acylcarnitines may have discriminatory diagnostic and prognostic properties in perinatal asphyxia. Researchers may utilize the accumulating evidence of discriminatory metabolic patterns in perinatal asphyxia to develop bedside methods to measure oxidative stress metabolites in perinatal asphyxia. Although only supported by indirect evidence, saliva might be a candidate biofluid for such point-of-care diagnostics.
... It is important to take meticulous care in performing sample preparation steps, i.e., sample collection, preservation, and preparation, as they all play a vital role in a metabolomics analysis. 56,57 The most popular analytical platforms for metabolomics are NMR and Furthermore, some metabolites such as fatty acids and triglycerides, possess higher ionization potential, which can be detected using atmospheric pressure chemical ionization (APCI) techniques. 66 79 This technique allows the mapping of metabolites in 2D or 3D space by merging analytical chemistry information with molecular imaging data. ...
Medicinal plant metabolomics has emerged as a goldmine for the natural product chemists. It provides a pool of bioactive phytoconstituents leading to accelerated novel discoveries and the elucidation of a variety of biosynthetic pathways. Further, it also acts as an innovative tool for herbal medicine's scientific validation and quality assurance. This review highlights different strategies and analytical techniques employed in the practice of metabolomics. Further, it also discusses several other applications and advantages of metabolomics in the area of natural product chemistry. Additional examples of integrating metabolomics with multivariate data analysis techniques for some Indian medicinal plants are also reviewed. Recent technical advances in mass spectrometry‐based hyphenated techniques, nuclear magnetic resonance‐based techniques, and comprehensive hyphenated technologies for phytometabolite profiling studies have also been reviewed. Mass Spectral Imaging (MSI) has been presented as a highly promising method for high precision in situ spatiotemporal monitoring of phytometabolites. We conclude by introducing GNPS (Global Natural Products Social Molecular Networking) as an emerging platform to make social networks of related molecules, to explore data and to annotate more metabolites, and expand the networks to novel “predictive” metabolites that can be validated.
... In our study, samples stored for up to one year in −20 °C were applied. The sample storage at −20 °C appeared to ensure sample stability, and freeze-thaw cycles did not affect the sample quality [36]. The urine storage is especially beneficial when the patients live far away from the testing dog. ...
Background:
Breast cancer is a leading cause of cancer death worldwide. Several studies have demonstrated that dogs can sniff and detect cancer in the breath or urine sample of a patient. This study aims to assess whether the urine sample can be used for breast cancer screening by its fingerprints of volatile organic compounds using a single trained sniffer dog. This is a preliminary study for developing the "electronic nose" for cancer screening.
Methods:
A nine-year-old female Labrador Retriever was trained to identify cancer from urine samples of breast cancer patients. Urine samples from patients histologically diagnosed with primary breast cancer, those with non-breast malignant diseases, and healthy volunteers were obtained, and a double-blind test was performed. Total of 40 patients with breast cancer, 142 patients with non-breast malignant diseases, and 18 healthy volunteers were enrolled, and their urine samples were collected.
Results:
In 40 times out of 40 runs of a double-blind test, the trained dog could correctly identify urine samples of breast cancer patients. Sensitivity and specificity of this breast cancer detection method using dog sniffing were both 100%.
Conclusions:
The trained dog in this study could accurately detect breast cancer from urine samples of breast cancer patients. These results indicate the feasibility of a method to detect breast cancer from urine samples using dog sniffing in the diagnosis of breast cancer. Although the methodological standardization is still an issue to be discussed, the current result warrants further study for developing a new breast cancer screening method based on volatile organic compounds in urine samples.
... However, we included all types of study design, race, geographical area, or population for the systematic review. For meta-analysis extra restriction was considered, the study design had to fulfill the following: (A) minimum N = 20 per group to ensure statistical significance [18]; (B) study groups were matched by age and sex; (C) compounds must be identified and behavior reported (up/down); (D) ethics approval must be reported; and (E) urine storage conditions must be reported, since urine compounds degrade in long-term storage if temperature is higher than −20 • C [19]. ...
Simple Summary
Colorectal cancer is the most frequent neoplasm in Western countries, the second most frequent neoplasm after breast cancer in women and the third most frequent neoplasm after prostate and lung cancer in men. Early diagnosis and disease screening are based on a fecal occult blood test, which, if positive, is complemented by colonoscopy. Currently, efforts are underway to find alternatives to the fecal occult blood test for various reasons. First, there is an ongoing attempt to increase the participation of the population to be screened. Second, there is a need to decrease the number of false positives to reduce the number of unnecessary colonoscopies. A urine test could be more widely accepted than a fecal test, and this is the scenario for which urinary metabolomics and volatilome studies are being developed. Our review provides the first exhaustive evaluation of metabolomics and volatilomics for the determination of colorectal cancer in urine.
Abstract
To increase compliance with colorectal cancer screening programs and to reduce the recommended screening age, cheaper and easy non-invasiveness alternatives to the fecal immunochemical test should be provided. Following the PRISMA procedure of studies that evaluated the metabolome and volatilome signatures of colorectal cancer in human urine samples, an exhaustive search in PubMed, Web of Science, and Scopus found 28 studies that met the required criteria. There were no restrictions on the query for the type of study, leading to not only colorectal cancer samples versus control comparison but also polyps versus control and prospective studies of surgical effects, CRC staging and comparisons of CRC with other cancers. With this systematic review, we identified up to 244 compounds in urine samples (3 shared compounds between the volatilome and metabolome), and 10 of them were relevant in more than three articles. In the meta-analysis, nine studies met the criteria for inclusion, and the results combining the case-control and the pre-/post-surgery groups, eleven compounds were found to be relevant. Four upregulated metabolites were identified, 3-hydroxybutyric acid, L-dopa, L-histidinol, and N1, N12-diacetylspermine and seven downregulated compounds were identified, pyruvic acid, hydroquinone, tartaric acid, and hippuric acid as metabolites and butyraldehyde, ether, and 1,1,6-trimethyl-1,2-dihydronaphthalene as volatiles.
... Urine was collected with the use of a pipette and was immediately placed in a tube and stored at −80 • C until analysis. It has been found that urinary samples remain stable for up to 6 months at this temperature [55], with no research on longer storage periods, to the best of our knowledge. There is research, however, on blood samples that shows stability for up to 30 months [56]. ...
Life expectancy has risen in the past decades, resulting in an increase in the number of aged individuals. Exercise remains one of the most cost-effective treatments against disease and the physical consequences of aging. The purpose of this research was to investigate the effects of aging, long-term and lifelong exercise on the rat urinary metabolome. Thirty-six male Wistar rats were divided into four equal groups: exercise from 3 to 12 months of age (A), lifelong exercise from 3 to 21 months of age (B), no exercise (C), and exercise from 12 to 21 months of age (D). Exercise consisted in swimming for 20 min/day, 5 days/week. Urine samples collection was performed at 3, 12 and 21 months of life and their analysis was conducted by liquid chromatography-mass spectrometry. Multivariate analysis of the metabolite data did not show any discrimination between groups at any of the three aforementioned ages. However, multivariate analysis discriminated the three ages clearly when the groups were treated as one. Univariate analysis showed that training increased the levels of urinary amino acids and possibly protected against sarcopenia, as evidenced by the higher levels of creatine in the exercising groups. Aging was accompanied by decreased levels of urinary amino acids and signs of increased glycolysis. Concluding, both aging and, to a lesser degree, exercise affected the rat urinary metabolome, including metabolites related to energy metabolism, with exercise showing a potential to mitigate the consequences of aging.
... There is a paucity of published evidence to guide best practice in utilisation and storage of urine samples. Previous studies around storage have focused primarily on the liquid phase, utilising, for example, Mass Spectrometry (MS) and nuclear magnetic resonance (NMR) technology [22][23][24]. Only one study investigated the effects of long-term storage of urine samples, in this case using FAIMS (field asymmetric ion mobility spectrometry) analysis. ...
There has been rapidly accelerating interest in the utilization of volatile organic compounds (VOCs) as non-invasive methods for rapid point-of-care medical diagnostics. There is widespread variation in analytical methods and protocols, with little understanding of the effects of sample storage on VOC profiles. This study aimed to determine the effects on VOC profiles of different storage times, at room temperature, prior to freezing, of sealed urine samples from healthy individuals. Analysis using Field Asymmetric Ion Motility Spectrometry (FAIMS) determined the alterations in VOC and total ion count profiles as a result of increasing room temperature storage times. Results indicated that increasing exposure time to room temperature prior to freezing had a threefold effect. Firstly, increased urinary VOC profile variability, with a plateau phase between 12 and 48 hours, before further degradation. Secondly, an increase in total ion count with time exposed to room temperature. Finally, a deterioration in VOCs with each sample run during the analysis process. This provides new insight into the effect of storage of urine samples for VOC analysis using FAIMS technology. Results of this study provide a recommendation for a 12-hour maximum duration at room temperature prior to storage.
... Once sample has been collected, the metabolic activities in the cell must be arrested in order to reliably reflect the metabolomic profile at the time of collection. This is achieved by freezing the sample in liquid nitrogen, which is stored at −80°C, and further extraction of metabolites (Gika, Theodoridis, & Wilson, 2008). Most commonly employed separation techniques for metabolomic studies include high-performance liquid chromatography (HPLC) and capillary electrophoresis as well as gas chromatography (Fiehn, 2008;Juo, Chiu, & Shiao, 2008). ...
Proteomics and metabolomics are emerging as promising tools to investigate the molecular mechanisms associated with male infertility. Proteins and metabolites play a pivotal role in regulating the molecular pathways associated with physiological functions of spermatozoa. Semen analysis, physical examination and laboratory work up cannot identify the etiology of infertility in 30%‐40% of cases, which are classified as idiopathic. Therefore, the application of proteomics and metabolomics in the field of andrology will aid to overcome the limitations of the standard semen analysis. Understanding the molecular pathways associated with male infertility will help in planning ad hoc treatments, contributing to the clinical management of infertile patients. In this review, proteomics and metabolomics studies on spermatozoa and seminal plasma are discussed with a focus on molecular biomarkers associated with male infertility‐related conditions.
... All samples were refrigerated for up to 24 h before processing when they were frozen, stored, and shipped on dry ice to the CDC's National Center for Environmental Health Laboratory where they were stored at −80°C until analysis. OPEs, like many other environmental chemicals, appear to be stable in urine when stored at subfreezing temperatures (Carignan et al., 2017;Gika et al., 2008;Laparre et al., 2017;Rotter et al., 2017). Therefore, OPE concentrations measured for this study are expected to accurately reflect the concentrations at the time the urine was collected. ...
Organophosphate esters (OPEs) are a group of chemicals used as flame retardants and plasticizers that replaced polybrominated diphenyl ethers in consumer products such as furniture and electronics. To characterize exposure to OPEs during fetal development, we measured urinary OPE metabolite concentrations in women twice during pregnancy (16 and 26 weeks' gestation) and at delivery (n = 357). We also previously quantified house dust OPE parent compound concentrations at 20 weeks' gestation (n = 317). Diphenyl phosphate (DPHP) had the highest geometric mean urinary concentrations (1.5-2.3 μg/g creatinine), followed by bis(1,3-dichloro-2-propyl) phosphate (BDCIPP; 0.75-0.99 μg/g creatinine), and bis(2-chloroethyl) phosphate (BCEP; 0.72-0.97 μg/g creatinine), while dibutyl phosphate (DNBP) had the lowest concentrations (0.25-0.28 μg/g creatinine). Urinary OPE metabolites were moderately correlated with each other at 26 weeks (rs: 0.23-0.38, p < 0.001) while the correlations at 16 weeks and delivery were slightly weaker. Intra-class correlations for urinary metabolites measured at three time points were poor (0.16-0.34), indicating high variability within individuals. Dust concentrations of OPE parent compounds were associated with BCEP, BDCIPP, and DPHP concentrations in urine at some but not all time points. In linear mixed models of urinary OPE metabolite concentrations, household size was inversely associated with BCEP concentrations, and being non-white was associated with lower BDCIPP and DPHP concentrations. Urine samples collected in the summer had the highest OPE metabolite concentrations. This study highlights the need to collect multiple urine samples during pregnancy to define exposure patterns and investigate potential periods of susceptibility.
... Metabolomics not only includes the utilization of various analytical methodologies towards identication and quantication of the metabolites pertaining to a biological system, but can monitor the changes at metabolites level in a variety of clinical specimens such as serum, cells, tissue, urine and other biological uids. [8][9][10][11][12][13][14][15][16][17] Earlier metabolomics studies in MM were mostly carried out on in vitro models, such as cell lines, to investigate metabolic alterations induced by the drug resistance. 18,19 Apart from this, identication of potential targets related to apoptosis 20 was also studied in context of MM. ...
Multiple myeloma (MM) is the second most prevalent hematological malignancy characterized by rapid proliferation of plasma cells, which leads to overproduction of antibodies. MM affects around 15% of all hemato-oncology cases across the world. The present study involves identification of metabolomic alterations in the serum of an MM cohort compared to healthy controls using both LC-MRM/MS based targeted and GC-MS based untargeted approaches. Several MM specific serum metabolomic signatures were observed in this study. A total of 54 metabolites were identified as being significantly altered in MM cohort, out of which, 26 metabolites were identified from LC-MRM/MS based targeted analysis, whereas 28 metabolites were identified from the GC-MS based untargeted analysis. Receiver operating characteristic (ROC) curve analysis demonstrated that six metabolites each from both the datasets can be projected as marker metabolites to discriminate MM subjects with higher specificity and sensitivity. Moreover, pathway analysis deciphered that several metabolic pathways were altered in MM including pyrimidine metabolism, purine metabolism, amino acid metabolism, nitrogen metabolism, sulfur metabolism, and the citrate cycle. Comprehensively, this study contributes valuable information regarding MM induced serum metabolite alterations and their pathways, which could offer further insights into this cancer.
... A careful study design, biofluid type and analytical platform selection is necessary to analyze the links between the metabolites and biological alterations. All steps of sample storage and handling must be clearly monitored to prevent confounding factors or biases related to sample degradation [2][3][4][5][6][7][8][9]. Because the analysis of a single sample type is generally not sufficient for the whole organism extrapolation, multiple biofluids or tissues are often required. ...
Chronic kidney disease (CKD) is becoming a major public health issue as prevalence is increasing worldwide. It also represents a major challenge for the identification of new early biomarkers, understanding of biochemical mechanisms, patient monitoring and prognosis. Each metabolite contained in a biofluid or tissue may play a role as a signal or as a driver in the development or progression of the pathology. Therefore, metabolomics is a highly valuable approach in this clinical context. It aims to provide a representative picture of a biological system, making exhaustive metabolite coverage crucial. Two aspects can be considered: analytical and biological coverage. From an analytical point of view, monitoring all metabolites within one run is currently impossible. Multiple analytical techniques providing orthogonal information should be carried out in parallel for coverage improvement. The biological aspect of metabolome coverage can be enhanced by using multiple biofluids or tissues for in-depth biological investigation, as the analysis of a single sample type is generally insufficient for whole organism extrapolation. Hence, recording of signals from multiple sample types and different analytical platforms generates massive and complex datasets so that chemometric tools, including data fusion approaches and multi-block analysis, are key tools for extracting biological information and for discovery of relevant biomarkers. This review presents the recent developments in the field of metabolomic analysis, from sampling and analytical strategies to chemometric tools, dedicated to the generation and handling of multiple complementary metabolomic datasets enabling extended metabolite coverage to improve our biological knowledge of CKD.
... Metabolic status is considered to be affected by diet 57 ; however, we did not make adjustments for diet in any of our analyses. Serum and urine were stored at -20°C; therefore, it is possible that some metabolite profiles were altered, although the stability of urine samples at -20°C has been previously reported 58 . Consequently, the present data might not reflect the precise metabolic status of the participants. ...
Introduction
We aimed to explore novel predictive markers for gestational diabetes mellitus using metabolomic analysis in pregnant Japanese women.
Materials and Methods
We conducted a case‐control study with a cohort of participants enrolled during the first or early second trimester in the Center of Chiba Unit of the Japan Environment and Children's Study. Participants were classified as either gestational diabetes mellitus cases or matched controls based on age, body mass index, and parity. Metabolite levels of their serum and urine obtained randomly before the diagnosis of gestational diabetes mellitus were analyzed using hydrophilic interaction chromatography tandem mass spectrometry. Orthogonal projections to latent structures discriminant analysis was performed to investigate metabolome profiles for the different groups. Metabolites with a variable importance in projection value of >1.5 were identified as potential markers.
Results
In total, 242 participants were enrolled in the study, of which 121 were cases. The R2X, R2Y, and Q2 parameters for the discrimination ability of the resulting models were 0.388, 0.492, and 0.45 for serum and 0.454, 0.674, and 0.483 for urine, respectively. We finally identified 3 metabolites in serum and 20 in urine as potential biomarkers. Glutamine in serum and ethanolamine and 1,3‐diphosphoglycerate in urine showed >0.8 area under the receiver operating characteristic curves.
Conclusions
This study identified serum and urine metabolites that are possible predictive markers of subsequent gestational diabetes mellitus in Japanese women. Further studies are needed to elucidate their efficacy.
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... Earlier studies investigating the impact of freeze-thaw cycles focused on metabolite profiles and assessed the statistical impact using principal component analysis. Gika et al. examined the metabolite profile and reported no major global changes after 9 freeze-thaw cycles (88 ). However, using targeted analysis, Saude and Sykes reported an intermediate amount of change in metabolite concentrations after 8 freeze-thaw cycles (89 ). ...
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.
The field of metabolomics has seen tremendous improvements since its introduction two decades ago, notably with the emergence of highly sensitive and selective analytical instruments and the implementation of sophisticated data analysis procedures. A joint effort among the metabolomics community has fostered the use of standardized protocols, aiming for the generation of high-quality data and increasing confidence in both the identification and quantitation of metabolites in body fluids and tissues. However, an important aspect of the metabolomics workflow that still often remains neglected is the pre-analytical phase, i.e., all the steps occurring between the sample collection at the hospital or study ward and its actual analysis in a laboratory. Indeed, all pre-analytical conditions—from the type of collection tube to the sample handling temperature—can impact the stability of metabolites, potentially leading to inaccurate results. This chapter discusses the pre-analytical considerations relevant to the analysis of blood-based matrices (i.e., whole blood, serum, and plasma) and urine, highlighting the effects of inadequate procedures on the metabolome and lipidome composition. For each step, general practical recommendations are given to help enhance the stability of metabolites through the entire pre-analytical phase and, in turn, the overall quality of the metabolomics data.
Redundancy in animal communication enhances the likelihood that information transmission occurs between the sender and receiver. In olfactory communication, redundancy can occur, for example, when the same message is encoded in both specialised glandular secretions and in metabolic by-products. Although these two modalities often encode similar messages, few studies have compared their deployment and associated behavioural responses within the same species. Here, we used chemical analyses (i.e. gas chromatography-mass spectrometry [GC-MS], bomb calorimetry, enzyme immunoassay [EIA]) and behavioural studies (i.e. scent-presentation experiments) to indirectly compare olfactory communication utilising subcaudal gland secretions (SGSs) with signals encoded in urine from a wild population of European badgers (Meles meles) in Wytham Woods, Oxfordshire, UK. While both SGS and urine encoded biologically relevant information relating to season and reproductive fitness (i.e., sex, age and reproductive condition), SGS encoded additional information regarding individual identity, body condition and social group membership. Furthermore, behavioural observations indicated that age-related variations in SGS chemical composition may affect the perception of SGS energetic content (although the biological relevance of this energetic content requires further research). Overall, our findings may indicate that both SGS and urine appear to be equally important but present varying levels of fitness information.
Aims: Knowledge of optimal storage conditions of drugs is crucial for properly interpreting analytical assessments.
Materials & methods: The current study aimed to investigate the stability of some nonsteroidal anti-inflammatory drugs using a validated method by gas chromatography (GC)–MS. For this propose, long-term, short-term and solution stability were investigated.
Results: The analytes remained stable in the sample, similar to the working solution. The most affected substance over time in both matrix and working solution was phenylbutazone. The freeze–thaw cycle affected flunixin and carprofen, but diclofenac and vedaprofen changed only in the third cycle. In short-term stability, high-temperature conditions changed carprofen.
Conclusion: The present study is a comprehensive assay for nonsteroidal anti-inflammatory drug stability and can be used as a reference for results assessment.
Infrared spectroscopy is a crucial tool to achieve the origin traceability of rice, but it is constrained by data mining. In this study, a novel infrared spectroscopy-based metabolomics analytical method was proposed to discriminate rice products from 14 Chinese cities by seeking 'wave number markers'. Principal component analysis (PCA), cluster analysis and orthogonal partial least squares discriminant analysis (OPLS-DA) were employed to separate all rice groups. The S-plot, permutation test and variable importance in projection (VIP) are used to screen eligible 'markers', which were further verified by a pairwise t-test. There are 55-265 'markers' picked out from 14 rice groups, with their characteristic wave number bands to be 2935.658-3238.482, 3851.846-4000.364, 3329.136-3518.160, 1062.778-1213.225, 1161.147-1386.819, 3348.425-3560.594, 3115.038-3624.245, 2567.254-2872.007, 3334.923-3560.594, 3282.845-3543.235, 3338.780-3518.160, 3197.977-3560.594, 3163.258-3267.414 and 3292.489-3477.655 cm-1, respectively. All but No. 5 rice groups show significantly low absorbance on their 'marker' bands. A mixed rice containing congenial No. 5 and No. 6 rice (80 : 20, m/m) was employed to test the validity of the method, and found that the 'marker' band of the mixed rice is the range of 1170.791-1338.598 cm-1, implying the existence of considerable discrepancy between the mixed rice and other rice. The results indicate that infrared spectroscopy coupled with metabolomics analysis is competent for origin traceability of rice; thus, it provides a novel and workable approach for the accurate and rapid discrimination of rice from different geographical origins, and a distinctive perspective of metabolomics to explore infrared spectroscopy and beyond, especially not confined in the field of origin traceability.
Metabolomics is the field of omics research that offers valuable insights into the complex composition of biological samples. It has found wide application in clinical diagnostics, disease investigation, therapy prediction, monitoring of treatment efficiency, drug discovery, or in-depth analysis of sample composition. A suitable study design constitutes the fundamental requirements to ensure robust and reliable results from the study data. The study design process should include a careful selection of conditions for each experimental step, from sample collection to data analysis. The pre-analytical variability that can introduce bias to the subsequent analytical process, decrease the outcome reliability, and confuse the final results of the metabolomics research, should also be considered. Herein, we provide key information regarding the pre-analytical variables affecting the metabolomics studies of biological fluids that are the most desirable type of biological samples. Our work offers a practical review that can serve and guide metabolomics pre-analytical design. It indicates pre-analytical factors, which can introduce artificial data variation and should be identified and understood during experimental design (through literature overview or analytical experiments).
Origin traceability of soybeans using infrared spectroscopy is bound by data mining, which can be solved by metabolomics analysis. In this study, a novel infrared spectroscopy-based metabolomics approach via seeking ‘wave number markers’ was developed to achieve the discrimination of soybeans from ten different cities of China. Multivariate analytical procedures including principal component analysis (PCA), cluster analysis and orthogonal partial least squares discriminant analysis (OPLS-DA) were designed for separation of all soybean groups, which provides a possibility to discern ‘markers’ among groups. S-plot, permutation test and variable importance in projection (VIP) embedded in OPLS-DA model took on the screening of ‘markers’, which were further verified by pairwise t-test in univariate analysis. There are 27 ~ 330 ‘markers’ picked out in ten soybean groups, with the wave number range to be 761.882 ~ 956.693, 2430.308 ~ 2789.068, 974.052 ~ 1068.564, 1504.476 ~ 1554.626, 2796.783 ~ 3431.364, 3890.422 ~ 4000.364, 3805.554 ~ 4000.364, 761.882 ~ 819.747, 457.129 ~ 530.424 and 460.987 ~ 514.994 cm − 1 , during which significantly high absorbance can be observed for No. 2 ~ No. 7 soybeans, but for No. 1 and No. 8 ~ No. 10 soybeans, we can observe significantly low absorbance. The results indicate that infrared spectroscopy coupled with metabolomics analysis is equal to origin traceability of soybeans, thus, it provides a novel and viable approach for the accurate and rapid discrimination of soybeans from different geographical origins.
Urine sample storage after collection at ultra-low-temperature (e.g., -80 °C) is normally required for comparative metabolome analysis of many samples, and therefore, freeze-thaw cycles (FTCs) are unavoidable. However, the reported effects of FTCs on the urine metabolome are controversial. Moreover, there is no report on the study of how urine FTCs affect biomarker discovery. Herein, we present our study of the FTC effects on the urine metabolome and biomarker discovery using a high-coverage quantitative metabolomics platform. Our study involved two centers located in Hangzhou, China, and Edmonton, Canada, to perform metabolome analysis of two separate cohorts of urine samples. The same workflow of sample preparation and dansylation isotope labeling LC-MS was used for in-depth analysis of the amine/phenol submetabolome. The analysis of 320 samples from the Hangzhou cohort consisting of 80 healthy subjects with each urine being subjected to four FTCs resulted in relative quantification of 3682 metabolites with 3307 identified or mass-matched. The analysis of 176 samples from the Edmonton cohort of 44 subjects with four FTCs quantified 3516 metabolites with 3166 identified or mass-matched. Multivariate and univariate analyses indicated that significant variations (fold change ≥ 1.5 with q-value ≤ 0.05) from FTCs were only observed in a very small fraction of the metabolites (<0.3%). Moreover, various metabolites did not show a consistent pattern of concentration changes from one to four FTCs, allowing the use of two separate cohorts of samples to remove these randomly changed metabolites. Three metabolite biomarkers for separating males and females were discovered, and FTC did not influence their discovery.
Data mining of infrared spectroscopy is the bottleneck to identify the geographic origin of coals, which can be solved by metabolomics-based analytical strategy. In this study, a novel method by infrared spectroscopy coupled with metabolomics analysis via seeking ‘wave number markers’ was developed to achieve origin traceability of coals from eight different locations. Multivariate and univariate analysis were adopted to seek ‘markers’. In multivariate analysis, principal component analysis (PCA), cluster analysis and orthogonal partial least squares discriminant analysis (OPLS-DA) worked together to fully separate all coal groups, paving the way to discern ‘markers’ among groups. Other parameters embedded in OPLS-DA including permutation, S-plot and variable importance in projection (VIP) undertook ‘marker’ screening, which was further verified by pairwise t-test in univariate analysis. There are 17 ∼ 58 ‘markers’ identified in eight coal groups, corresponding to the wave number range of 3593.384 ∼ 3703.326, 2167.989 ∼ 2243.213, 989.482 ∼ 1085.923, 1697.358 ∼ 1735.934, 1083.994 ∼ 1114.855, 530.424 ∼ 993.340, 2902.868 ∼ 2980.020 and 1521.835 ∼ 1564.269 cm⁻¹, during which significantly high absorbance can be observed for No. 2, 3, 4 and 8 coal groups, but for No. 1, 5, 6 and 7 coal groups, we can observe significantly low absorbance. The results indicate that infrared spectroscopy coupled with metabolomics analysis is competent to discern coals from different locations, thus, it provides a novel and promising method for the accurate and rapid identification of coal geographic origins.
The metabolomics-based analytical strategy has showed superiority on the non-targeted screening of contaminants, especially for unknown and unexpected (U&U) contaminants in the field of food safety, but data analysis is often the bottleneck of the strategy. In this study, a novel metabolomics-based analytical method via searching for marker compounds was developed on the basis of ultrahigh-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) results to accurately, rapidly and comprehensively achieve the non-targeted screening of 34 pharmaceutical and personal care products (PPCPs) as U&U contaminants spiked in bovine and piscine muscle matrices. Three concentration groups (20, 50 and 100 ng mL-1) were intentionally designed to simulate the control and experimental groups for the discovery of marker compounds, for which multivariate and univariate analyses were adopted. In multivariate analysis, each concentration group was fully separated from the other two groups in PCA and OPLS-DA plots, laying a foundation to distinguish marker compounds among groups. The S-plot, permutation and variable importance in projection (VIP) in OPLS-DA were employed to screen and identify marker compounds, which were further verified by pairwise t-test and fold change judgement in univariate analysis. The results indicate that 34 PPCPs spiked in two muscle matrices were all identified as marker compounds, proving the validity and practicability of this novel metabolomics-based non-targeted screening method, which will exhibit great superiority and broad application prospects, especially in the face of massive PPCPs and various animal matrices in the field of food safety control. In addition, the limits of detection (LODs) for 34 PPCPs were calculated to be 0.2-2.6 μg kg-1 and 0.3-2.1 μg kg-1 in bovine and piscine muscle matrices, respectively.
The selection of solid phase extraction (SPE) columns in the pretreatment process plays a decisive role in the screening and quantification of pharmaceutical and personal care products (PPCPs). As growing PPCPs have frequently been detected in the aquatic environment, it is a burdensome task through one-by-one recovery comparison to judge which column presents relatively ideal pretreatment results for PPCPs. In view of this, we developed a novel metabolomics-based screening method based on ultrahigh-performance liquid chromatography-tandem mass spectrometer (UHPLC-MS/MS) results to accurately, rapidly and comprehensively choose a suitable column from 5 different kinds to handle 64 PPCPs in two water environments (50 μg L-1/pH ≅ 7.0/pure water and 1 μg L-1/pH ≅ 7.0/reservoir water) through seeking 'biomarkers', for which multivariate and univariate analyses were adopted. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) play a crucial role in multivariate analysis, and the pairwise t-test and fold change judgement in univariate analysis. Each column group was fully separated from the other 4 groups in PCA and OPLS-DA plots, laying a foundation to distinguish 'biomarkers' between groups. The S-Plot, permutation and variable importance in projection (VIP) in OPLS-DA were employed to screen and identify 'biomarkers', which were further verified by a pairwise t-test and fold change judgement. Eventually, the 64 PPCPs as 'biomarkers' were divided into 5 groups, which correspond to 5 column groups, consistent with the findings of traditional PPCP recovery comparison, proving the validity of the metabolomics-based screening method. This novel method will exhibit greater superiority in choosing suitable SPE columns to handle a growing and larger number of PPCPs in water environments and beyond.
This chapter provides an overview of gas chromatography–mass spectrometry (GC-MS)-based metabonomics, with special emphasis on the metabolite extraction, sample derivatization, chromatographic conditions, data acquisition, analytical workflow, data preprocessing, chemometric data analysis, model validation, biomarker identification, and pathway mapping. Additionally, applications of GC-MS-based metabonomics on solid (e.g., tissues) and liquid (e.g., urine) biological samples, such as in cancer biology, toxicology, forensics, nutritional studies, diabetes, cardiovascular diseases, neurodegenerative diseases, and metabolic syndromes, are discussed. Lastly, recent advances in GC-MS technologies such as solid-phase microextraction and gas chromatography–tandem mass spectrometry (GC-MS/MS) and their impact on biomedical research are also presented.
Over the last decade, there has been a considerable progress in the discovery and development of novel biomarkers of kidney disease. All of this has been successfully achieved due to the appearance of systems biology that has brought forward the concept of “omics” technologies (i.e., genomics, transcriptomics, proteomics, and metabolomics), which have evolved to become a major component and scientific approach in the analysis of biological systems. This has also allowed a better understanding of the molecular pathogenesis in a variety of renal disorders. Hence, in this chapter, we will summarize the “omics” profiling approaches as well as their role in the discovery of new biomarkers of kidney diseases that might provide guidance for clinical decision making and targeted therapy in individual patients. We will also discuss the integrating role of bioinformatics and the need for standardized procedures for sample preservation, statistical analysis, and reporting of results.
The use of medicinal plants for human ailments precedes the introduction of conventional antibiotics. However, medicinal plants continue to enjoy human patronage because they are cheap, readily available and free from side effects often associated with conventional antibiotics. Reproducibility, on the part of screening plants for antimicrobial activity, has become a challenge both from the pharmacognosy and microbiology points of view. This review addresses the sampling techniques, phytochemical screening and the concept of metabolomics in relation to medicinal plants as well as the applicability and the advantages and disadvantages of some microbiological methodologies used in screening medicinal plants for their antimicrobial activity.
Objective
Obtaining objective, dietary exposure information from individuals is challenging because of the complexity of food consumption patterns and the limitations of self-reporting tools (e.g., FFQ and diet diaries). This hinders research efforts to associate intakes of specific foods or eating patterns with population health outcomes.
Design
Dietary exposure can be assessed by the measurement of food-derived chemicals in urine samples. We aimed to develop methodologies for urine collection that minimised impact on the day-to-day activities of participants but also yielded samples that were data-rich in terms of targeted biomarker measurements.
Setting
Urine collection methodologies were developed within home settings.
Participants
Different cohorts of free-living volunteers.
Results
Home collection of urine samples using vacuum transfer technology was deemed highly acceptable by volunteers. Statistical analysis of both metabolome and selected dietary exposure biomarkers in spot urine collected and stored using this method showed that they were compositionally similar to urine collected using a standard method with immediate sample freezing. Even without chemical preservatives, samples can be stored under different temperature regimes without any significant impact on the overall urine composition or concentration of forty-six exemplar dietary exposure biomarkers. Importantly, the samples could be posted directly to analytical facilities, without the need for refrigerated transport and involvement of clinical professionals.
Conclusions
This urine sampling methodology appears to be suitable for routine use and may provide a scalable, cost-effective means to collect urine samples and to assess diet in epidemiological studies.
Male infertility is a major concern among men in the reproductive age group. Conventional semen analysis provides fundamental information about sperm parameters but not sperm function. Analysis of the semen using advanced omic tools such as proteomics and metabolomic elucidates molecular pathways that are defective in infertile men. In this chapter, we have discussed about the use of proteomic and metabolomic platforms for profiling of the proteome and metabolome of the spermatozoa and seminal plasma. Furthermore, we have highlighted the findings of major proteomics and metabolomics studies in the infertile male. We have also placed emphasis on the proteins and metabolites present in the human semen and their potential use as biomarkers in the diagnosis and therapeutics of male infertility.
Metabolomics has been increasingly applied to study renal and related cardiometabolic diseases, including diabetes and cardiovascular diseases. These studies span cross-sectional studies correlating metabolites with specific phenotypes, longitudinal studies to identify metabolite predictors of future disease, and physiologic/interventional studies to probe underlying causal relationships. This chapter provides a description of how metabolomic profiling is being used in these contexts, with an emphasis on study design considerations as a practical guide for investigators who are new to this area. Research in kidney diseases is underlined to illustrate key principles. The chapter concludes by discussing the future potential of metabolomics in the study of renal and cardiometabolic diseases.
High-throughput mass spectrometry (MS) metabolomics profiling of highly complex samples allows the comprehensive detection of hundreds to thousands of metabolites under a given condition and point in time and produces information-rich data sets on known and unknown metabolites. One of the main challenges is the identification and annotation of metabolites from these complex data sets since the number of authentic standards available for specialized metabolites is far lower than an account for the number of mass spectral features. Previously, we reported two novel tools, MetNet and MetCirc, for putative annotation and structural prediction on unknown metabolites using known metabolites as baits. MetNet employs differences between m/z values of MS1 features, which correspond to metabolic transformations, and statistical associations, while MetCirc uses MS/MS features as input and calculates similarity scores of aligned spectra between features to guide the annotation of metabolites. Here, we showcase the use of MetNet and MetCirc to putatively annotate metabolites and provide detailed instructions as to how those can be used. While our case studies are from plants, the tools find equal utility in studies on bacterial, fungal, or mammalian xenobiotic samples.
Metabolomics is the qualitative and quantitative assessment of the metabolites (small molecules < 1.5 kDa) in body fluids. The metabolites are the downstream of the genetic transcription and translation processes and also downstream of the interactions with environmental exposures; thus, they are thought to closely relate to the phenotype, especially for multifactorial diseases. In the last decade, metabolomics has been increasingly used to identify biomarkers in disease, and it is currently recognized as a very powerful tool with great potential for clinical translation. The metabolome and the associated pathways also help improve our understanding of the pathophysiology and mechanisms of disease.
While there has been increasing interest and research in metabolomics of the eye, the application of metabolomics to retinal diseases has been limited, even though these are leading causes of blindness. In this manuscript, we perform a comprehensive summary of the tools and knowledge required to perform a metabolomics study, and we highlight essential statistical methods for rigorous study design and data analysis. We review available protocols, summarize the best approaches, and address the current unmet need for information on collection and processing of tissues and biofluids that can be used for metabolomics of retinal diseases. Additionally, we critically analyze recent work in this field, both in animal models and in human clinical disease, including diabetic retinopathy and age-related macular degeneration. Finally, we identify opportunities for future research applying metabolomics to improve our current assessment and understanding of mechanisms of vitreoretinal diseases, and to hence improve patient assessment and care.
The first steps of the workflow in metabolomics include sampling, sample pre processing and sample preparation. In this chapter, the sample types and sample preparation techniques utilized in metabolomics are presented. The sample preparation methods include homogenization, cell disruption, different extraction methods, such as liquid-liquid extraction, solid-liquid extraction, sample clean up and fractionation, and derivatization. Guidelines for the selection of the appropriate sample preparation method based on both the final analysis method and the type of sample and analytes are given.
Hypothesis-driven research has led to many scientific advances, but hypotheses cannot be tested in isolation: rather, they require a framework of aggregated scientific knowledge to allow questions to be posed meaningfully. This framework is largely still lacking in microbiome studies, and the only way to create it is by discovery-driven, tool-driven, and standards-driven research projects. Here we illustrate these issues using several such non-hypothesis-driven projects from our own laboratories, including spatial mapping, the American Gut Project, the Earth Microbiome Project (which is an umbrella project integrating many smaller hypothesis-driven projects), and the knowledgebase-driven tools GNPS and Qiita. We argue that an investment of community resources in infrastructure tasks, and in the controls and standards that underpin them, will greatly enhance the investment in hypothesis-driven research programs.
Metabolomic studies attempt to identify and profile unique metabolic differences among test populations, which may be correlated
with a specific biological stress or pathophysiology. Due to the ease of collection and the metabolite-rich nature of urine,
it is frequently used as a bio-fluid for human and animal metabolic studies. High-resolution 1H-NMR is an analytical tool used to qualitatively and quantitatively identify metabolites in urine. Urine samples were collected
from healthy male and female subjects and prepared: raw, following centrifugation, filtration, or the addition of the bacteriostatic
preservative sodium azide and analyzed by NMR. In addition, these samples were stored at room temperature (22°C), in a refrigerator
(4°C), or in a deep-freeze (−80°C). Samples were analyzed by NMR every week for a month and changes in concentrations of
55 easily identifiable metabolites were followed. The degree of change in metabolite concentrations following storage over
a 4-week period were influenced by the different methods of sample preparation and storage. Significant changes in urine metabolites
are likely due to bacterial contamination of the urine. Our study demonstrates that bacterial contamination of urine in normal
individuals significantly alters the metabolic profile of urine over time and proper preparation and storage procedures must
be followed to reduce these changes. By identifying appropriate methods of urine preparation and storage investigators will
preserve the fidelity of the urine samples in order to better reflect the original metabolic state.
Current emphasis on efficient screening of novel therapeutic agents in toxicological studies has resulted in the evaluation of novel analytical technologies, including genomic (transcriptomic) and proteomic approaches. We have shown that high-resolution 1H NMR spectroscopy of biofluids and tissues coupled with appropriate chemometric analysis can also provide complementary data for use in in vivo toxicological screening of drugs. Metabonomics concerns the quantitative analysis of the dynamic multiparametric metabolic response of living systems to pathophysiological stimuli or genetic modification [Nicholson, J. K., Lindon, J. C., and Holmes, E. (1999) Xenobiotica 11, 1181-1189]. In this study, we have used 1H NMR spectroscopy to characterize the time-related changes in the urinary metabolite profiles of laboratory rats treated with 13 model toxins and drugs which predominantly target liver or kidney. These 1H NMR spectra were data-reduced and subsequently analyzed using a probabilistic neural network (PNN) approach. The methods encompassed a database of 1310 samples, of which 583 comprised a training set for the neural network, with the remaining 727 (independent cases) employed as a test set for validation. Using these techniques, the 13 classes of toxicity, together with the variations associated with strain, were distinguishable to >90%. Analysis of the 1H NMR spectral data by multilayer perceptron networks and principal components analysis gave a similar but less accurate classification than PNN analysis. This study has highlighted the value of probabilistic neural networks in developing accurate NMR-based metabonomic models for the prediction of xenobiotic-induced toxicity in experimental animals and indicates possible future uses in accelerated drug discovery programs. Furthermore, the sensitivity of this tool to strain differences may prove to be useful in investigating the genetic variation of metabolic responses and for assessing the validity of specific animal models.
The development and use of HPLC-MS for the study of metabonomics is reviewed. To date the technique has been applied to the analysis of urine samples obtained from studies in rodents in investigations of physiological variation (e.g., factors such as strain, gender, diurnal variation, etc.) and toxicity. Examples are provided of the use of conventional HPLC, capillary methods and the recently introduced high-resolution systems based on a combination of high pressure and small particle size ("UPLC"). Comparison is also made of the use of 1H NMR spectroscopy and HPLC-MS for the analysis of biofluid samples and the advantages and limitations of the two approaches are assessed. Likely future developments are considered.
The combination of a new 1.7 mum reversed-phase packing material, and a chromatographic system, operating at ca. 12,000 psi, (so-called ultra performance liquid chromatography, UPLC) has enabled dramatic increases in chromatographic performance to be obtained for complex mixture separation. This increase in performance is manifested in improved peak resolution, together with increased speed and sensitivity. Here, we show that UPLC offers significant advantages over conventional reversed-phase HPLC amounting to a more than doubling of peak capacity, an almost 10-fold increase in speed and a 3- to 5-fold increase in sensitivity compared to that generated with a conventional 3.5 microm stationary phase. The first functional genomic application of UPLC-MS technology is illustrated here with respect to multivariate metabolic profiling of urines from males and females of two groups of phenotypically normal mouse strains (C57BL19J and Alpk:ApfCD) and a "nude mouse" strain. We have also compared this technology to conventional HPLC-MS under similar analytical conditions and show improved phenotypic classification capability of UPLC-MS analysis together with increased ability to probe differential pathway activities between strains as a result of improved analytical sensitivity and resolution.
In biology, as well as in other branches of science and technology, these is a steady trend towards the use of more variables (properties) to characterize observations (e.g. samples, experiments, t ...
Although a wide range of risk factors for coronary heart disease have been identified from population studies, these measures, singly or in combination, are insufficiently powerful to provide a reliable, noninvasive diagnosis of the presence of coronary heart disease. Here we show that pattern-recognition techniques applied to proton nuclear magnetic resonance (1H-NMR) spectra of human serum can correctly diagnose not only the presence, but also the severity, of coronary heart disease. Application of supervised partial least squares-discriminant analysis to orthogonal signal-corrected data sets allows >90% of subjects with stenosis of all three major coronary vessels to be distinguished from subjects with angiographically normal coronary arteries, with a specificity of >90%. Our studies show for the first time a technique capable of providing an accurate, noninvasive and rapid diagnosis of coronary heart disease that can be used clinically, either in population screening or to allow effective targeting of treatments such as statins.
As the human genome sequencing projects near completion, there is an active search for technologies that can provide insights into the genetic basis for physiological variation and interpreting gene expression in terms of phenotype at the whole organism level in order to understand the pathophysiology of disease. We present a novel metabonomic approach to the investigation of genetic influences on metabolic balance and metabolite excretion patterns in two phenotypically normal mouse models (C57BL10J and Alpk:ApfCD). Chemometric techniques were applied to optimise recovery of biochemical information from complex (1)H NMR urine spectra and to determine metabolic biomarker differences between the two strains. Differences were observed in tricarboxylic acid cycle intermediates and methylamine pathway activity. We suggest here a new 'metabotype' concept, which will be of value in relating quantitative physiological and biochemical data to both phenotypic and genetic variation in animals and man.
The metabolic profiles of three wild mammals that vary in their trophic strategies, the herbivorous bank vole (Clethrionomys glareolus), the granivorous wood mouse (Apodemus sylvaticus), and the insectivorous white-toothed shrew (Crocidura suaveolens), were compared with that of a widely used strain of laboratory rat (Sprague Dawley). In conjunction with NMR spectroscopic investigations into the urine and blood plasma composition for these mammals, high resolution magic angle spinning (HRMAS) 1H-nuclear magnetic resonance (NMR) spectroscopy was applied to investigate the composition of intact kidney samples. Adaptation to natural diet affects both renal metabolism and urinary profiles, and while these techniques have been used to study the metabolism of the laboratory rat little is known about wild small mammals. The species were readily separated by their urinary profiles using either crude metabolite ratios or statistical pattern recognition. Bank vole urine contained higher concentrations of aromatic amino acids compared with the other small mammals, while the laboratory rats produced relatively more hippurate. HRMAS 1H-NMR demonstrated striking differences in both lipid concentration and composition between the wild mammals and Sprague Dawley rats. Bank voles contained high concentrations of the aromatic amino acids phenylalanine, tyrosine and tryptophan in all tissue and biofluids studied. This study demonstrates the analytical power of combined NMR techniques for the study of inter-species metabolism and further demonstrates that metabolic data acquired on laboratory animals cannot be extended to wild species.
An exploratory statistical analysis has been undertaken of 640 (1)H NMR spectra of rat urine, obtained from predose and control animals during the course of eight separate toxicology studies. The aim was to determine the degree and type of variation between (1)H NMR spectra from such control animals and to investigate the variations in the spectral descriptors based on averaged peak intensities. The results showed that many of the spectral descriptors had skew and/or multimodal distributions, and that it was possible to distinguish between samples of urine collected at different times of day with a success rate of (89%) and to classify 90% of the predose spectra into their correct study group using principal component and linear discriminant analyses. The results show that successful classification can be achieved of NMR spectra of control rat urine, which exhibit more subtle changes than those previously reported when treated and control animals were compared. The results presented here suggest that it will be possible to identify very subtle toxicological changes if care is taken to standardize the experimental conditions used during toxicity screens.
We report here the first combined use of NMR-PR (pattern recognition) analysis and directly coupled HPLC--NMR analysis to identify metabolic subpopulations in normal laboratory animals and their discriminating endogenous urinary biomarkers. Urine samples obtained from control Sprague-Dawley rats (n = 68) were analyzed using (1)H NMR spectroscopy and principal components (PC) analysis to investigate physiological variability. Two distinct subpopulations of animals were classified based on metabolite excretion profiles. Analysis of the PC loadings established the spectral regions that were responsible for classification of the subpopulations and was used to direct the identification of biomarkers using a directly coupled HPLC--NMR analysis. One population had low urinary hippurate levels together with an increased concentration of 3-(3-hydroxyphenyl)propionic acid (3-HPPA)and 3-hydroxycinnamic acid (3-HCA). The other subpopulation excreted high levels of hippurate. Thus, we report the bimodal occurrence of hippuric acid and chlorogenic acid metabolites in a genetically homogeneous population of rats maintained under identical conditions, which may have significance in relation to the understanding of the consequences of biochemical variation in animals used for drug toxicity testing.
Metabonomic methods utilizing (1)H NMR spectroscopy and pattern recognition analysis (NMR-PR) have been applied to investigate biochemical variation in a control population of female rats over time in relation to diurnal and estrus cycle fluctuations. Urine samples were collected twice daily (6 AM-6 PM and 6 PM-6 AM) from female rats (n = 10) for a period of 10 days. (1)H NMR spectroscopic analysis and PR were performed on each sample. Subtle differences in the endogenous metabolite excretion profiles of urine samples at the various stages of the estrus cycle were observed. The main inherent metabolic clustering in the principal components analysis (PCA) maps was related to interrat variation and was observed in the first two principal components (PCs), accounting for 66% of the variance in these data. Separation of urinary data according to time of sampling (day and night) was achieved in the lower PCs. Some of the differences in the urinary profiles of day and night samples causing this separation were attributed to the increase in metabolic activity of the rat during the night. Individual rat data were also mapped as a function of time, using PCA, to produce a metabolic trajectory, which in a number of cases facilitated separation of one or more stages of the estrus cycle. Several of the fluctuations observed between urine samples collected during the different stages of the estrus cycle may be related to hormone levels. Although variation in metabolite profiles relating to both diurnal and hormonal variation could be detected these perturbations were minor compared with the effects observed due to interrat variation. This is the first time that a hormonal cycle has been described for individuals based on NMR spectroscopic and multivariate analysis of metabolic data and shows the value of metabonomic methods in the investigation of physiological variation and rhythms.
Metabolic phenotyping, or metabotyping, is increasingly being used as a probe in functional genomics studies. However, such profiling is subject to intrinsic physiological variation found in all animal populations. Using a nuclear magnetic resonance-based metabonomic approach, we show that diurnal variations in metabolism can obscure the interpretation of strain-related metabolic differences in two phenotypically normal mouse strains (C57BL10J and Alpk:ApfCD). To overcome this problem, diurnal-related metabolic variation was removed from these spectral data by application of orthogonal signal correction (OSC), a data filtering method. Interpretation of the removed orthogonal variation indicated that diurnal-related variation had been removed and that the AM samples contained higher levels of creatine, hippurate, trimethylamine, succinate, citrate and 2-oxo-glutarate and lower levels of taurine, trimethylamine-N-oxide, spermine and 3-hydroxy-iso-valerate relative to the PM samples. We propose OSC will have great potential removing confounding variation obscuring subtle changes in metabolism in functional genomic studies and will be of benefit to optimising interpretation of proteomic and genomic datasets.
Although a wide range of risk factors for coronary heart disease have been identified from population studies, these measures, singly or in combination, are insufficiently powerful to provide a reliable, noninvasive diagnosis of the presence of coronary heart disease. Here we show that pattern-recognition techniques applied to proton nuclear magnetic resonance (1H-NMR) spectra of human serum can correctly diagnose not only the presence, but also the severity, of coronary heart disease. Application of supervised partial least squares-discriminant analysis to orthogonal signal-corrected data sets allows >90% of subjects with stenosis of all three major coronary vessels to be distinguished from subjects with angiographically normal coronary arteries, with a specificity of >90%. Our studies show for the first time a technique capable of providing an accurate, noninvasive and rapid diagnosis of coronary heart disease that can be used clinically, either in population screening or to allow effective targeting of treatments such as statins.
The role that metabonomics has in the evaluation of xenobiotic toxicity studies is presented here together with a brief summary of published studies. To provide a comprehensive assessment of this approach, the Consortium for Metabonomic Toxicology (COMET) has been formed between six pharmaceutical companies and Imperial College of Science, Technology and Medicine (IC), London, UK. The objective of this group is to define methodologies and to apply metabonomic data generated using (1)H NMR spectroscopy of urine and blood serum for preclinical toxicological screening of candidate drugs. This is being achieved by generating databases of results for a wide range of model toxins which serve as the raw material for computer-based expert systems for toxicity prediction. The project progress on the generation of comprehensive metabonomic databases and multivariate statistical models for prediction of toxicity, initially for liver and kidney toxicity in the rat and mouse, is reported. Additionally, both the analytical and biological variation which might arise through the use of metabonomics has been evaluated. An evaluation of intersite NMR analytical reproducibility has revealed a high degree of robustness. Second, a detailed comparison has been made of the ability of the six companies to provide consistent urine and serum samples using a study of the toxicity of hydrazine at two doses in the male rat, this study showing a high degree of consistency between samples from the various companies in terms of spectral patterns and biochemical composition. Differences between samples from the various companies were small compared to the biochemical effects of the toxin. A metabonomic model has been constructed for urine from control rats, enabling identification of outlier samples and the metabolic reasons for the deviation. Building on this success, and with the completion of studies on approximately 80 model toxins, first expert systems for prediction of liver and kidney toxicity have been generated.
The application of HPLC-MS combined with principal components analysis (PCA) to the metabonomic analysis of mouse urine is demonstrated. Urine samples from three strains of mouse were analysed by gradient HPLC-MS combined with positive and negative electrospray time-of-flight mass spectrometry. Analysis of the resulting data using PCA enabled the samples to be discriminated between on the basis of gender, strain and diurnal variation. These preliminary results suggest that HPLC-MS-based approaches may have a useful role in metabonomic analysis that complements existing approaches.
Medical systems biology has generated widespread interest because of its bold conception and exciting potential, but the field is still in its infancy. Although there has been tremendous progress achieved recently in generating, integrating and analysing data in the medical and pharmaceutical field, many challenges remain, especially with respect to the crucial core technologies required for analytical characterization. This review briefly summarizes these aspects for metabolomics, proteomics, data handling and multivariate biostatistics.
A large metabolomics study was performed on 600 plasma samples taken at four time points before and after a single intake of a high fat test meal by obese and lean subjects. All samples were analyzed by a liquid chromatography-mass spectrometry (LC-MS) lipidomic method for metabolic profiling. A pragmatic approach combining several well-established statistical methods was developed for processing this large data set in order to detect small differences in metabolic profiles in combination with a large biological variation. Such metabolomics studies require a careful analytical and statistical protocol. The strategy included data preprocessing, data analysis, and validation of statistical models. After several data preprocessing steps, partial least-squares discriminant analysis (PLS-DA) was used for finding biomarkers. To validate the found biomarkers statistically, the PLS-DA models were validated by means of a permutation test, biomarker models, and noninformative models. Univariate plots of potential biomarkers were used to obtain insight in up- or downregulation. The strategy proposed proved to be applicable for dealing with large-scale human metabolomics studies.
Metabonomic/metabolomic studies can involve the analysis of large numbers of samples for the detection of biomarkers and confidence in the analytical data, generated by methods such as GC and HPLC-MS, requires active measures on the part of the analyst. However, quality control for complex multi-component samples such as biofluids, where many of the components of interest in the sample are unknown prior to analysis, poses significant problems. Here the repeat analysis of a pooled sample throughout the run, thereby enabling the analysis to be monitored and controlled using targeted inspection of the data and pattern recognition, is advocated as a pragmatic solution to this problem.
Metabonomic approaches are believed to have the capability of revolutionizing diagnosis of diseases and assessment of patient conditions after medical interventions. In order to ensure comparability of metabonomic 1H NMR data from different studies, we suggest validated sample preparation guidelines for human urine based on a stability study that evaluates effects of storage time and temperature, freeze-drying, and the presence of preservatives. The results indicated that human urine samples should be stored at or below -25 degrees C, as no changes in the 1H NMR fingerprints have been observed during storage at this temperature for 26 weeks. Formation of acetate, presumably due to microbial contamination, was occasionally observed in samples stored at 4 degrees C without addition of a preservative. Addition of a preserving agent is not mandatory provided that the samples are stored at -25 degrees C. Thus, no differences were observed between 1H NMR spectra of nonpreserved urines and urines with added sodium azide and stored at -25 degrees C, whereas the presence of sodium fluoride caused a shift of especially citrate resonances. Freeze-drying of urine and reconstitution in D2O at pH 7.4 resulted in the disappearance of the creatinine CH2 signal at delta 4.06 due to deuteration. A study evaluating the effects of phosphate buffer concentration on signal variability and assessment of the probability of citrate or creatinine resonances crossing bucket border (a boundary between adjacent integrated regions) led to the conclusion that a minimum buffer concentration of 0.3 M is adequate for normal urines used in this study. However, final buffer concentration of 1 M will be required for very concentrated urines.
To perform metabonomics investigations, it is necessary to generate comprehensive metabolite profiles for complex samples such as biofluids and tissue/tissue extracts. Analytical technologies that can be used to achieve this aim are constantly evolving, and new developments are changing the way in which such profiles' metabolite profiles can be generated. Here, the utility of various analytical techniques for global metabolite profiling, such as, e.g., 1H NMR, MS, HPLC-MS, and GC-MS, are explored and compared.
While for 1H NMR techniques there already exist common analytical and reporting standards, this does not apply to LC-MS metabolic profiling approaches. These standards are the more recommended when applying metabonomics to human biofluids, particularly urine samples, due to the high degree of biological variation compared to animals. A control study was performed, and urine samples of 30 healthy male and female human subjects were collected at intervals of 8 h twice a day for three consecutive days. Using selective multiple reaction monitoring in combination with a column-switching tool for the analysis of the mercapturate pattern, samples were screened for time and gender differences, the most common confounders. Data preprocessing parameters, alignment, scaling to internal standards, and normalization techniques were optimized by PCA, PLS-DA, and OPLS models. Great care was taken in the validation process of both analytical and chemometric protocols. Additionally, a problem of LC-MS, the combination of "different-batch" data to "one-batch" data could be solved by a batchwise scaling procedure. Based on these results, the use of metabolic profiling via mercapturates will be feasible for the detection of disease or toxicity markers in the future since mercapturates are important biomarkers of reactive metabolites known to be involved in many toxic processes.
1H NMR spectroscopy potentially provides a robust approach for high-throughput metabolic screening of biofluids such as urine and plasma, but sample handling and preparation need careful optimization to ensure that spectra accurately report biological status or disease state. We have investigated the effects of storage temperature and time on the 1H NMR spectral profiles of human urine from two participants, collected three times a day on four different days. These were analyzed using modern chemometric methods. Analytical and preparation variation (tested between -40 degrees C and room temperature) and time of storage (to 24 h) were found to be much less influential than biological variation in sample classification. Statistical total correlation spectroscopy and discriminant function methods were used to identify the specific metabolites that were hypervariable due to preparation and biology. Significant intraindividual variation in metabolite profiles were observed even for urine collected on the same day and after at least 6 h fasting. The effect of long-term storage at different temperatures was also investigated, showing urine is stable if frozen for at least 3 months and that storage at room temperature for long periods (1-3 months) results in a metabolic profile explained by bacterial activity. Presampling (e.g., previous day) intake of food and medicine can also strongly influence the urinary metabolic profiles indicating that collective detailed participant historical meta data are important for interpretation of metabolic phenotypes and for avoiding false biomarker discovery.
Self-evidently, research in areas supporting "systems biology" such as genomics, proteomics, and metabonomics are critically dependent on the generation of sound analytical data. Metabolic phenotyping using LC-MS-based methods is currently at a relatively early stage of development, and approaches to ensure data quality are still developing. As part of studies on the application of LC-MS in metabonomics, the within-day reproducibility of LC-MS, with both positive and negative electrospray ionization (ESI), has been investigated using a standard "quality control" (QC) sample. The results showed that the first few injections on the system were not representative, and should be discarded, and that reproducibility was critically dependent on signal intensity. On the basis of these findings, an analytical protocol for the metabonomic analysis of human urine has been developed with proposed acceptance criteria based on a step-by-step assessment of the data. Short-term sample stability for human urine was also assessed. Samples were stable for at least 20 h at 4 degrees C in the autosampler while queuing for analysis. Samples stored at either -20 or -80 degrees C for up to 1 month were indistinguishable on subsequent LC-MS analysis. Overall, by careful monitoring of the QC data, it is possible to demonstrate that the "within-day" reproducibility of LC-MS is sufficient to ensure data quality in global metabolic profiling applications.