Review: Microfluidic applications in metabolomics and metabolic profiling

Metabolomics is an emerging area of research focused on measuring small molecules in biological samples. There are a number of different types of metabolomics, ranging from global profiling of all metabolites in a single sample to measurement of a selected group of analytes. Microfluidics and related technologies have been used in this research area with good success. The aim of this review article is to summarize the use of microfluidics in metabolomics. Direct application of microfluidics to the determination of small molecules is covered first. Next, important sample preparation methods developed for microfluidics and applicable to metabolomics are covered. Finally, a summary of metabolomic work as it relates to analysis of cellular events using microfluidics is covered.     

Assessing the suitability of capillary electrophoresis‐mass spectrometry for biomarker discovery in plasma‐based metabolomics

The actual utility of capillary electrophoresis‐mass spectrometry (CE‐MS) for biomarker discovery using metabolomics still needs to be assessed. Therefore, a simulated comparative metabolic profiling study for biomarker discovery by CE‐MS was performed, using pooled human plasma samples with spiked biomarkers. Two studies have been carried out in this work. Focus of study I was on comparing two sets of plasma samples, in which one set (class I) was spiked with five isotope‐labeled compounds, whereas another set (class II) was spiked with six different isotope‐labeled compounds. In study II, focus was also on comparing two sets of plasma samples, however, the isotope‐labeled compounds were spiked to both class I and class II samples but with concentrations which differ by a factor two between both classes (with one compound absent in each class). The aim was to determine whether CEMS‐based metabolomics could reveal the spiked biomarkers as the main classifiers, applying two different data analysis software tools (MetaboAnalyst and Matlab). Unsupervised analysis of the recorded metabolic profiles revealed a clear distinction between class I and class II plasma samples in both studies. This classification was mainly attributed to the spiked isotope‐labeled compounds, thereby emphasizing the utility of CE‐MS for biomarker discovery.     

Regularized MANOVA (rMANOVA) in untargeted metabolomics

Many advanced metabolomics experiments currently lead to data where a large number of response variables were measured while one or several factors were changed. Often the number of response variables vastly exceeds the sample size and well-established techniques such as multivariate analysis of variance (MANOVA) cannot be used to analyze the data.     

An untargeted metabolomics approach to contaminant analysis: pinpointing potential unknown compounds

This study deals with an automated data analysis strategy to pinpoint potential unknown compounds in full scan mass spectrometry (MS) experiments. Three examples of an untargeted metabolomics approach to contaminant analysis are given. By comparing a plant-oil based hormone cocktail to 90 plant oil samples ca. 25 compounds specific to the hormone cocktail could be detected. Five of these compounds were confirmed as steroid hormones. A comparison of a drink water sample from a farm to distillated water showed the presence of contaminants specific to this drink water sample. A grass sample, which was known to give a false positive result in a DR-CALUX bioassay, was unexpectedly shown to contain an abnormal level of chrysene, which was obviously not eliminated during clean-up.     

Data-processing strategies for metabolomics studies

Metabolomics studies aim at a better understanding of biochemical processes by studying relations between metabolites and between metabolites and other types of information (e.g., sensory and phenotypic features). The objectives of these studies are diverse, but the types of data generated and the methods for extracting information from the data and analysing the data are similar. Besides instrumental analysis tools, various data-analysis tools are needed to extract this relevant information. The entire data-processing workflow is complex and has many steps. For a comprehensive overview, we cover the entire workflow of metabolomics studies, starting from experimental design and sample-size determination to tools that can aid in biological interpretation. We include illustrative examples and discuss the problems that have to be dealt with in data analysis in metabolomics. We also discuss where the challenges are for developing new methods and tailor-made quantitative strategies.     

Improving global feature detectabilities through scan range splitting for untargeted metabolomics by high-performance liquid chromatography-Orbitrap mass spectrometry

Untargeted metabolomics aims at obtaining quantitative information on the highest possible number of low-molecular biomolecules present in a biological sample. Rather small changes in mass spectrometric spectrum acquisition parameters may have a significant influence on the detectabilities of metabolites in untargeted global-scale studies by means of high-performance liquid chromatography-mass spectrometry (HPLC-MS). Employing whole cell lysates of human renal proximal tubule cells, we present a systematic global-scale study of the influence of mass spectrometric scan parameters and post-acquisition data treatment on the number and intensity of metabolites detectable in whole cell lysates.     

Evaluation of the chronic intoxication of fluoride on human serum metabolome using untargeted metabolomics

Drinking water is the main source of fluoride intake for the human body and its regulated consumption helps in decreasing dental caries. However, excessive fluoride consumption over a prolonged time period causes fluorosis disease which adversely affects many tissues and organs of the body. This paper describes the evaluation of chronic intoxication of fluoride on human serum metabolome. The untargeted metabolomics approach using UPLC-QTOF-MS/MS is applied for metabolomic profiling, whereas the estimation of fluoride in serum samples was carried out using the ion-selective electrode (ISE). Fluoride concentration was found to be 0.16–1.25 mg/L in serum samples of 39 fluorosis patients and 0.008–0.045 mg/L in 20 healthy samples. A total of 47 metabolites were identified based on the high-resolution mass spectrometry analysis. A volcano plot was generated to discriminate features that are significantly different between the fluorosis and healthy groups at the probability of 0.05 and fold change ≥ 2. Among all identified metabolites, intensities of ten differential identified metabolites including inosine, α-linolenic acid, guanosine, octanoyl-L-carnitine, His-Trp, phytosphingosine, lauroyl-L-carnitine, hydrocortisone, deoxyinosine and dodecanedioic acid have been found altered in disease samples compared to healthy controls. Major pathways identified based on these metabolites include energy metabolism, fatty acid oxidation, purine degradation pathway, elevated protein degradation, and increased ω-6 fatty acid linoleate signatures were observed.     

Holistic quality evaluation of Saposhnikoviae Radix (Saposhnikovia divaricata) by reversed-phase ultra-high performance liquid chromatography and hydrophilic interaction chromatography coupled with ion mobility quadrupole time-of-flight mass spectrometry-based untargeted metabolomics

Untargeted metabolomics more suits the quality evaluation of TCM because of its holistic property. To assess the holistic quality difference of Saposhnikoviae Radix (the roots of Saposhnikovia divaricata), we integrate ultra-high-performance liquid chromatography coupled with ion mobility/quadrupole time-of-flight mass spectrometry (UHPLC/IM-QTOF-MS)-based untargeted metabolomics and quantitative assay. A BEH C18 column in the reversed-phase mode and a BEH Amide column in Hydrophilic Interaction Chromatography (HILIC) mode were utilized for metabolites profiling, which enabled high coverage of the non-polar to polar components in Saposhnikoviae Radix. Moreover, the application of major components knockout strategy enlarged the exposure of those minor components. Integrated use of high-definition MS^E (HDMS^E) and data-dependent acquisition (DDA) could enhance the metabolites characterization by providing reliable fragmentation information and collision cross section values. Computational in-house library-driven automated peak annotation of the HDMS^E and DDA data assisted to characterize 104 components from Saposhnikoviae Radix. Chemometric analyses of the commercial Saposhnikoviae Radix samples (64 batches collected from 11 cultivars aging from 1 to 4 years), based on the positive MS^E data, in general could indicate large discrimination between Guan-Fang-Feng (from Heilongjiang) and the others, but negligible difference among Saposhnikoviae Radix from the other ten provinces of China and with different ages. Quantitative assays of prim-O-glucosylcimifugin and 4′-O-β-D-glucosyl-5-O-methylvisamminol, by a rapid and fully validated UHPLC-UV method, could primarily deduce that Guan-Fang-Feng aging 2 and 3 years exhibited better quality. The methods established can holistically assess the quality of TCM with wide spans of plant metabolites covered.     

Missing value imputation strategies for metabolomics data

The origin of missing values can be caused by different reasons and depending on these origins missing values should be considered differently and dealt with in different ways. In this research, four methods of imputation have been compared with respect to revealing their effects on the normality and variance of data, on statistical significance and on the approximation of a suitable threshold to accept missing data as truly missing. Additionally, the effects of different strategies for controlling familywise error rate or false discovery and how they work with the different strategies for missing value imputation have been evaluated. Missing values were found to affect normality and variance of data and k‐means nearest neighbour imputation was the best method tested for restoring this. Bonferroni correction was the best method for maximizing true positives and minimizing false positives and it was observed that as low as 40% missing data could be truly missing. The range between 40 and 70% missing values was defined as a “gray area” and therefore a strategy has been proposed that provides a balance between the optimal imputation strategy that was k‐means nearest neighbor and the best approximation of positioning real zeros.     

Optimization of a liquid chromatography ion mobility-mass spectrometry method for untargeted metabolomics using experimental design and multivariate data analysis

High-resolution mass spectrometry coupled with pattern recognition techniques is an established tool to perform comprehensive metabolite profiling of biological datasets. This paves the way for new, powerful and innovative diagnostic approaches in the post-genomic era and molecular medicine. However, interpreting untargeted metabolomic data requires robust, reproducible and reliable analytical methods to translate results into biologically relevant and actionable knowledge. The analyses of biological samples were developed based on ultra-high performance liquid chromatography (UHPLC) coupled to ion mobility - mass spectrometry (IM-MS). A strategy for optimizing the analytical conditions for untargeted UHPLC-IM-MS methods is proposed using an experimental design approach. Optimization experiments were conducted through a screening process designed to identify the factors that have significant effects on the selected responses (total number of peaks and number of reliable peaks). For this purpose, full and fractional factorial designs were used while partial least squares regression was used for experimental design modeling and optimization of parameter values. The total number of peaks yielded the best predictive model and is used for optimization of parameters setting.     

Trend analysis of time-series data: A novel method for untargeted metabolite discovery

A new strategy for biomarker discovery is presented that uses time-series metabolomics data. Data sets from samples analysed at different time points after an intervention are searched for compounds that show a meaningful trend following the intervention. Obviously, this requires new data-analytical tools to distinguish such compounds from those showing only random variation. Two univariate methods, autocorrelation and curve-fitting, are used either as stand-alone methods or in combination to discover unknown metabolites in data sets originating from target-compound analysis. Both techniques reduce the long list of detected compounds in the kinetic sample set to include only those having a pre-defined interesting time profile. Thus, new metabolites may be discovered within data structures that are usually only used for target-compound analysis.The new strategy is tested on a sample set obtained from a gut fermentation study of a polyphenol-rich diet. For this study, the initial list of over 9000 potentially interesting features was reduced to less than 150, thus significantly reducing the expensive and time-consuming manual examination.     

Untargeted metabolomics analysis to unveil the chemical markers for the differentiation among three Gleditsia sinensis-derived herbal medicines by ultra-high performance liquid chromatography/quadrupole time-of-flight mass spectrometry

Precise identification and differentiation among those congeneric Traditional Chinese Medicines (TCMs) or derived from the same plant trend to be more challenging, particularly in the absence of appearance characteristics. Three TCMs, involving Gleditsiae Sinensis Fructus (GSF), Gleditsiae Fructus Abnormalis (GFA), and Gleditsiae Spina (GS), recorded in Chinese Pharmacopoeia (2020 edition) are derived from Gleditsia sinensis, but prescribed for different clinical uses. The documents aimed to compare their chemical differences are rare, to date. An untargeted metabolomics approach, based on ultra-high performance liquid chromatography/quadrupole time-of-flight mass spectrometry (UHPLC/QTOF-MS), was elaborated to unveil the potential chemical markers to differentiate among GSF, GFA, and GS. Good chromatographic separation of all the GSF/GFA/GS components was achieved within 33 min by utilizing a BEH C18 column, while data-independent MS^E in the positive mode was selected for profiling the metabolic features. Notably, the high-mass saponins (1300–2500 Da) gave unique protonated precursors ([M + H]⁺) in the positive ESI mode, compared with those complicated ion species occurring in the negative mode. Pattern recognition chemometrics analysis of 45 batches of G. sinensis samples could unveil 70 significantly altered ions assigned as 46 potentially differential components. The positive/negative high-accuracy MS² data analysis, phytochemical isolation/NMR analysis, and searching of an in-house library of G. sinensis, were utilized for structural elucidation. Three compounds (saikachinoside A, locustoside A, and locustoside B) rich in GSF could be the markers to differentiate from GFA/GS, while four components were characteristic for GS. These results obtained can greatly benefit the quality control of TCMs derived from G. sinensis.     

Ultra-high performance liquid chromatography/ion mobility time-of-flight mass spectrometry-based untargeted metabolomics combined with quantitative assay unveiled the metabolic difference among the root,leaf, and flower bud of Panax notoginseng

Despite Panax notoginseng (Sanchi: the root and rhizome) is globally popular serving as the source of food additives, health-care products, and traditional Chinese medicines (TCMs), the saponin difference between the root (PNR) and two aerial parts (leaf, PNL; flower bud, PNF) that can be vicariously used, remains unclear. Authentication of Sanchi, particular from the Chinese patent medicines (CPMs), poses great challenges. Ultra-high performance liquid chromatography/ion mobility-quadrupole time-of-flight mass spectrometry (UHPLC/IM-QTOF-MS)-based untargeted metabolomics and quantitative assay by UHPLC-UV were utilized to establish the “Identification Markers” for Sanchi. Targeted monitoring of multiple identification markers was performed for authenticating Sanchi simultaneously from 15 different CPMs. Dimension-enhanced profiling by UHPLC/IM-QTOF-MS in the negative high-definition MS^E (HDMS^E) mode and in-house library-driven peak annotation could characterize totally 328 ginsenosides (133 from PNR, 125 from PNL, and 161 from PNF). Multivariate statistical analysis of the PNR/PNL/PNF samples (45 batches) identified 27 potential markers. Five major markers (notoginsenoside R1, ginsenosides Rg1, -Rb1, -Rb2, and -Rb3) thereof were quantitatively assayed by a fully validated UHPLC-UV (detected at 203 nm) approach. The application of selective ion monitoring (SIM) of 12 differential saponins coupled with UHPLC separation could precisely identify Sanchi from 15 different CPMs (45 batches). Holistic difference in ginsenosides among three parts of P. notoginseng was unveiled, and the markers deduced may assist to identify the illicit substitution of leaf or flower as the root in the TCM compound formulae. Conclusively, the integration of untargeted metabolomics and quantitative analysis can provide reliable information enabling the precise authentication of TCM.     

Human Plasma Metabolic Profiles of Coronary Heart Disease by Gas Chromatography-Mass Spectrometry with Monte Carlo Tree Approach

Metabolomics is a useful approach to explore systemic metabolic variation and to elucidate disease mechanisms. In this study, human plasma metabolic profiles of coronary heart disease (CHD) patients and healthy controls were obtained by gas chromatography-mass spectrometry (GC-MS). A relatively new pattern recognition method, the Monte Carlo tree (MCTree) approach, was used to explore metabolic differences between CHD patients and healthy controls. In this way, CHD patients with different severity of coronary atherosclerosis were classified by the corresponding metabolic profiles. Furthermore, important metabolites contributing to the classification were screened and identified by their mass spectra. Several potential biomarkers were discussed in some detail. The results demonstrated that the proposed method might be a useful tool for discovering metabolic abnormalities and potential biomarkers for diseases.     

Urinary exposure marker discovery for toxicants using ultra-high pressure liquid chromatography coupled with Orbitrap high resolution mass spectrometry and three untargeted metabolomics approaches

Human biomonitoring is the assessment of actual internal contamination of chemicals by measuring exposure markers, chemicals or their metabolites, in human urine, blood, serum, and other body fluids. However, the metabolism of chemicals within an organism is extremely complex. Therefore, the identification of metabolites is often difficult and laborious. Several untargeted metabolomics methods have been developed to perform objective searching/filtering of accurate-mass-based LC-MS data to facilitate metabolite identification. In this study, three metabolomics data processing approaches were used for chemical exposure marker discovery in urine with an LTQ-Orbitrap high-resolution mass spectrometry (HRMS) dataset; di-isononyl phthalate (DINP) was used as an example. The data processing techniques included the SMAIT, mass defect filtering (MDF), and XCMS Online. Sixteen, 83, and 139 probable DINP metabolite signals were obtained using the SMAIT, MDF, and XCMS procedures, respectively. Fourteen probable metabolite signals mined simultaneously by the three metabolomics approaches were confirmed as DINP metabolites by structural information provided by LC-MS/MS. Among them, 13 probable metabolite signals were validated as exposure-related markers in a rat model. Six (m/z 319.155, 361.127, 373.126, 389.157, 437.112 and 443.130) of the 13 exposure-related DINP metabolite signals have not previously been reported in the literature. Our data indicate that SMAIT provided an efficient method to discover effectively and systematically urinary exposure markers of toxicant. The DINP metabolism information can provide valuable information for further investigations of DINP toxicity, toxicokinetics, exposure assessment, and human health effects.     

The correspondence problem for metabonomics datasets

In metabonomics it is difficult to tell which peak is which in datasets with many samples. This is known as the correspondence problem. Data from different samples are not synchronised, i.e., the peak from one metabolite does not appear in exactly the same place in all samples. For datasets with many samples, this problem is nontrivial, because each sample contains hundreds to thousands of peaks that shift and are identified ambiguously. Statistical analysis of the data assumes that peaks from one metabolite are found in one column of a data table. For every error in the data table, the statistical analysis loses power and the risk of missing a biomarker increases. It is therefore important to solve the correspondence problem by synchronising samples and there is no method that solves it once and for all. In this review, we analyse the correspondence problem, discuss current state-of-the-art methods for synchronising samples, and predict the properties of future methods.     

From differentiating metabolites to biomarkers

The current developments in metabolomics and metabolic profiling technologies have led to the discovery of several new metabolic biomarkers. Finding metabolites present in significantly different levels between sample sets, however, does not necessarily make these metabolites useful biomarkers. The route to valid and applicable biomarkers (biomarker qualification) is long and demands a significant amount of work. In this overview, we critically discuss the current state-of-the-art of metabolic biomarker discovery, with highlights and shortcomings, and suggest a pathway to clinical usefulness.

Combined untargeted and targeted fingerprinting with comprehensive two-dimensional chromatography for volatiles and ripening indicators in olive oil

Comprehensive two-dimensional gas chromatography (GC × GC) is the most effective multidimensional separation technique for in-depth investigations of complex samples of volatiles (VOC) in food. However, each analytical run produces dense, multi-dimensional data, so elaboration and interpretation of chemical information is challenging.