High resolution mass spectrometry based techniques at the crossroads of metabolic pathways

The dark side of technological advances in analysis of microbial ecosystems

Recent technological advances mean that samples from animal experiments may be analysed more cheaply, more easily and with a much greater return of data than previously. Research groups are frequently faced with a choice of continuing to use established technology in which they may have made a significant investment of time and resources, and have significant amounts of reference data, or switching to new technology where reference data may be limited. Apart from cost, the choice needs to be based on a comparison between the increase in data available from future experiments by switching and the value of comparison with reference data from historical experiments analysed with earlier technology. One approach to this problem is to ensure that sufficient quantity and variety of samples are taken from each experiment and appropriately stored to allow re-establishment of a sufficiently large reference set and to avoid the need to repeat animal experiments. The establishment of ‘biobanks’ of experimental material will require funding for infrastructure, consistent storage of metadata and, importantly, horizon-scanning to ensure that samples are taken appropriately for techniques which will become accessible in future. Such biobanks are a recognised resource in human medicine, where the value of samples increases as more analysis is carried out and added to the metadata.

Proposal for a chemically consistent way to annotate ions arising from the analysis of reference compounds under ESI conditions: A prerequisite to proper mass spectral database constitution in metabolomics

Nowadays, high-resolution mass spectrometry is widely used for metabolomic studies. Thanks to its high sensitivity, it enables the detection of a large range of metabolites. In metabolomics, the continuous quest for a metabolite identification as complete and accurate as possible has led during the last decade to an ever increasing development of public MS databases (including LC-MS data) concomitantly with bioinformatic tool expansion. To facilitate the annotation process of MS profiles obtained from biological samples, but also to ease data sharing, exchange, and exploitation, the standardization and harmonization of the way to describe and annotate mass spectra seemed crucial to us. Indeed, under electrospray (ESI) conditions, a single metabolite does not produce a unique ion corresponding to its protonated or deprotonated form but could lead to a complex mixture of signals. These MS signals result from the existence of different natural isotopologues of the same compound and also to the potential formation of adduct ions, homomultimeric and heteromultimeric ions, fragment ions resulting from "prompt" in-source dissociations. As a joint reflection process within the French Infrastructure for Metabolomics and Fluxomics (MetaboHUB) and with the purpose of developing a robust and exchangeable annotated MS database made from pure reference compounds (chemical standards) analysis, it appeared to us that giving the metabolomics community some clues to standardize and unambiguously annotate each MS feature was a prerequisite to data entry and further efficient querying of the mass spectral database. The use of a harmonized notation is also mandatory for interlaboratory MS data exchange. Additionally, thorough description of the variety of MS signals arising from the analysis of a unique metabolite might provide greater confidence on its annotation.

Hybrid model for efficient prediction of poly(A) signals in human genomic DNA

Polyadenylation signals (PAS) are found in most protein-coding and some non-coding genes in eukaryotes. Their accurate recognition improves understanding gene regulation mechanisms and recognition of the 3'-end of transcribed gene regions where premature or alternate transcription ends may lead to various diseases. Although different methods and tools for in-silico prediction of genomic signals have been proposed, the correct identification of PAS in genomic DNA remains challenging due to a vast number of non-relevant hexamers identical to PAS hexamers. In this study, we developed a novel method for PAS recognition. The method is implemented in a hybrid PAS recognition model (HybPAS), which is based on deep neural networks (DNNs) and logistic regression models (LRMs). One of such models is developed for each of the 12 most frequent human PAS hexamers. DNN models appeared the best for eight PAS types (including the two most frequent PAS hexamers), while LRM appeared best for the remaining four PAS types. The new models use different combinations of signal processing-based, statistical, and sequence-based features as input. The results obtained on human genomic data show that HybPAS outperforms the well-tuned state-of-the-art Omni-PolyA models, reducing the classification error for different PAS hexamers by up to 57.35% for 10 out of 12 PAS types, with Omni-PolyA models being better for two PAS types. For the most frequent PAS types, 'AATAAA' and 'ATTAAA', HybPAS reduced the error rate by 35.14% and 34.48%, respectively. On average, HybPAS reduces the error by 30.29%. HybPAS is implemented partly in Python and in MATLAB available at https://github.com/EMANG-KAUST/PolyA_Prediction_LRM_DNN.

Indole-3-Propionic Acid, a Tryptophan-Derived Bacterial Metabolite, Reduces Weight Gain in Rats

Recent evidence suggests that tryptophan, an essential amino acid, may exert biological effects by means of tryptophan-derived gut bacteria products. We evaluated the potential contribution of tryptophan-derived bacterial metabolites to body weight gain. The study comprised three experimental series performed on separate groups of male, Sprague-Dawley rats: (i) rats on standard laboratory diet treated with water solution of neomycin, an antibiotic, or tap water (controls-1); (ii) rats on standard diet (controls-2) or tryptophan-high (TH) or tryptophan-free (TF) diet; and (iii) rats treated with indole-3-propionic acid (I3P), a bacterial metabolite of tryptophan, or a vehicle (controls-3). (i) Rats treated with neomycin showed a significantly higher weight gain but lower stool and blood concentration of I3P than controls-1. (ii) The TH group showed significantly smaller increases in body weight but higher stool and plasma concentration of I3P than controls-2. In contrast, the TF group showed a decrease in body weight, decreased total serum protein and a significant increase in urine output. (iii) Rats treated with I3P showed significantly smaller weight gain than controls-3. Our study suggests that I3P, a gut bacteria metabolite of tryptophan, contributes to changes in body weight gain produced by antibiotics and tryptophan-rich diet.

Urine Nuclear Magnetic Resonance (NMR) Metabolomics in Age-Related Macular Degeneration

Biofluid biomarkers of age-related macular degeneration (AMD) are still lacking, and their identification is challenging. Metabolomics is well-suited to address this need, and urine is a valuable accessible biofluid. This study aimed to characterize the urinary metabolomic signatures of patients with different stages of AMD and a control group (>50 years). It was a prospective, cross-sectional study, where subjects from two cohorts were included: 305 from Coimbra, Portugal (AMD patients n = 252; controls n = 53) and 194 from Boston, United States (AMD patients n = 147; controls n = 47). For all participants, we obtained color fundus photographs (for AMD staging) and fasting urine samples, which were analyzed using 1H nuclear magnetic resonance (NMR) spectroscopy. Our results revealed that in both cohorts, urinary metabolomic profiles differed mostly between controls and late AMD patients, but important differences were also found between controls and subjects with early AMD. Analysis of the metabolites responsible for these separations revealed that, even though distinct features were observed for each cohort, AMD was in general associated with depletion of excreted citrate and selected amino acids at some stage of the disease, suggesting enhanced energy requirements. In conclusion, NMR metabolomics enabled the identification of urinary signals of AMD and its severity stages, which might represent potential metabolomic biomarkers of the disease.

Metabolomics and Isotope Tracing

Great strides have been made over the past decade toward comprehensive study of metabolism. Mass spectrometry (MS) has played a central role by enabling measurement of many metabolites simultaneously. Tracking metabolite labeling from stable isotope tracers can in addition reveal pathway activities. Here, we describe the basics of metabolite measurement by MS, including sample preparation, metabolomic analysis, and data interpretation. In addition, drawing on examples of successful experiments, we highlight the ways in which metabolomics and isotope tracing can illuminate biology.

Exploiting High-Resolution Mass Spectrometry for Targeted Metabolite Quantification and 13C-Labeling Metabolism Analysis

Quantification of targeted metabolites, especially trace metabolites and structural isomers, in complex biological materials is an ongoing challenge for metabolomics. In this chapter, we summarize high-resolution mass spectrometry-based approaches mainly used for targeted metabolite and metabolomics analysis, and then introduce an MS1/MS2-combined PRM workflow for quantification of central carbon metabolism intermediates, amino acids, and shikimate pathway-related metabolites. Major steps in the workflow, including cell culture, metabolite extraction, LC-MS analysis and data processing, are described. Furthermore, we adapt this new approach to a dynamic ¹³C-labeling experiment and demonstrate its unique advantage in capturing and correcting isotopomer labeling curves to facilitate nonstationary ¹³C-labeling metabolism analysis.

Correcting for natural isotope abundance and tracer impurity in MS-, MS/MS- and high-resolution-multiple-tracer-data from stable isotope labeling experiments with IsoCorrectoR

Experiments with stable isotope tracers such as ¹³C and ¹⁵N are increasingly used to gain insights into metabolism. However, mass spectrometric measurements of stable isotope labeling experiments should be corrected for the presence of naturally occurring stable isotopes and for impurities of the tracer substrate. Here, we analyzed the effect that such correction has on the data: omitting correction or performing invalid correction can result in largely distorted data, potentially leading to misinterpretation. IsoCorrectoR is the first R-based tool to offer said correction capabilities. It is easy-to-use and comprises all correction features that comparable tools can offer in a single solution: correction of MS and MS/MS data for natural stable isotope abundance and tracer impurity, applicability to any tracer isotope and correction of multiple-tracer data from high-resolution measurements. IsoCorrectoR’s correction performance agreed well with manual calculations and other available tools including Python-based IsoCor and Perl-based ICT. IsoCorrectoR can be downloaded as an R-package from: http://bioconductor.org/packages/release/bioc/html/IsoCorrectoR.html.

Characterization of Red Wine Proanthocyanidins Using a Putative Proanthocyanidin Database, Amide Hydrophilic Interaction Liquid Chromatography (HILIC), and Time-of-Flight Mass Spectrometry

Proanthocyanidins are complex polymers of flavan-3-ol monomers and play a key sensory and health role in foods and beverages. We describe here a novel method for characterizing wine proanthocyanidins using a theoretical database comprised of the chemical formula and exact mass of 996 compounds. The database was constructed using the four primary grape and wine proanthocyanidin monomers: (epi)catechin, (epi)catechin-3-O-gallate, (epi)gallocatechin, and (epi)gallocatechin-3-O-gallate, each combined in all possible combinations up to a polymerization of 10. The database was queried against spectra collected using ultrahigh performance liquid chromatography (UHLPC) with a hydrophilic interaction liquid chromatography (HILIC) column and coupled to a high-resolution accurate mass quadrupole time-of-flight mass spectrometer (Q-TOF MS). Two wine samples produced with different post fermentation maceration were analyzed using the presented method to demonstrate application for analysis of diverse proanthocyanidins. The first sample was pressed immediately at the end of fermentation when all sugar had been utilized and the second received eight weeks of post fermentation maceration. The HILIC column combined with high resolution tandem mass spectrometry and database matching provided tentative identification of 89 compounds with excellent resolution and without the need for two-dimensional separations. The identified compounds were visualized with Kendrick mass analysis, a simple technique allowing for rapid visualization of which compounds are present in a given sample.

Application of omics technologies for a deeper insight into quali-quantitative production traits in broiler chickens: A review

The poultry industry is continuously facing substantial and different challenges such as the increasing cost of feed ingredients, the European Union's ban of antibiotic as growth promoters, the antimicrobial resistance and the high incidence of muscle myopathies and breast meat abnormalities. In the last decade, there has been an extraordinary development of many genomic techniques able to describe global variation of genes, proteins and metabolites expression level. Proper application of these cutting-edge omics technologies (mainly transcriptomics, proteomics and metabolomics) paves the possibility to understand much useful information about the biological processes and pathways behind different complex traits of chickens. The current review aimed to highlight some important knowledge achieved through the application of omics technologies and proteo-genomics data in the field of feed efficiency, nutrition, meat quality and disease resistance in broiler chickens.

Antioxidant response of cucumber (Cucumis sativus) exposed to nano copper pesticide: Quantitative determination via LC-MS/MS

Targeted metabolomics aims to provide a new approach to investigate metabolites and gather both qualitative and quantitative information. We describe a protocol for extraction and analysis of plant metabolites, specifically 13 secondary metabolites (antioxidants) using liquid chromatography coupled to triple quadrupole mass spectrometry (LC-MS/MS), with high linearity (R2 > 0.99) and reproducibility (0.23-6.23 R%) with low limits of detection (>0.001 ng/mL) and quantification (>0.2 ng/mL). The protocol was applied to study the antioxidant response of cucumber plants exposed to nanocopper pesticide. Dose-dependent changes in antioxidant concentrations were found, and 10 antioxidants were significantly consumed to scavenge reactive oxygen species, protecting plants from damage. Levels of three antioxidants were up-regulated, as a response to the depletion of the other antioxidants, signaling activation of the defense system. We demonstrated that the reported LC-MS/MS method provides a quantitative analysis of antioxidants in plant tissues, for example to investigate interactions between plants and nanomaterials.

Characterization of degradation products of macitentan under various stress conditions using liquid chromatography/mass spectrometry

RATIONALE

Stress testing of a drug candidate is an important step in the drug discovery and development process. The presence of degradation products in a drug affects the quality as well as the safety and efficacy of drug formulation. Hence, it is essential to develop an efficient analytical method which could be useful for the separation, identification and characterization of all possible degradation products (DPs) of a drug. Macitentan (MT) is an endothelin receptor antagonist (ERA) drug used to treat high blood pressure in the lungs. Comprehensive stress testing of MT was carried out as per ICH guidelines to understand the degradation profile of the drug.

METHODS

MT was subjected to various stress conditions such as acidic, basic, neutral hydrolysis, oxidation, photolysis and thermal conditions; and the resulting degradation products were investigated using liquid chromatography/diode-array detection/electrospray ionization high-resolution mass spectrometry (LC/DAD/ESI-HRMS) and tandem mass spectrometry (MS/MS) techniques. An efficient and simple ultra-high-performance liquid chromatography (UHPLC) method has been developed using an Accucore C18 column (4.6 × 150 mm, 2.6 μm) using a gradient elution of 5 mM ammonium formate and acetonitrile as mobile phases.

RESULTS

MT was found to degrade under acid and base hydrolysis stress conditions; whereas it was stable under oxidation, neutral hydrolysis, thermal and photolytic conditions. MT formed nine DPs (DP1 to DP9) and one DP (DP10) under acidic and basic hydrolytic conditions, respectively. All the degradation products (DP1 to DP10) were identified and characterized by LC/MS/MS in positive ion mode with accurate mass measurements.

CONCLUSIONS

MT was found to be labile under hydrolytic conditions. The structures of the DPs were characterized by appropriate mechanisms. The proposed method can be effectively used for the characterization of MT and its DPs.

Fully-automated systems and the need for global approaches should exhort clinical labs to reinvent routine MS analysis?

Today, many LC–high-resolution MS instruments have become affordable, easy-to-use, sensitive and quantitative. Meanwhile, there is an increased need for more comprehensive approaches. However, omics analyses are still restricted to specialists whereas, in hospitals, routine analyses are targeted and quantitative and represent the main and heavy tasks. But the availability of fully automated LC–MS instruments that can handle independently from sample extraction to result reporting, as well as the increasing biomedical interest for global approaches, clinical analytical workflow should be reorganized. Bioanalysts are now in the position to develop/implement clinical metabolomics or proteomics as routine analyses. In this article, this coming evolution and the reasons to implement global/omics determinations as routine analysis, is described.

Identification of small molecules using accurate mass MS/MS search

Tandem mass spectral library search (MS/MS) is the fastest way to correctly annotate MS/MS spectra from screening small molecules in fields such as environmental analysis, drug screening, lipid analysis, and metabolomics. The confidence in MS/MS-based annotation of chemical structures is impacted by instrumental settings and requirements, data acquisition modes including data-dependent and data-independent methods, library scoring algorithms, as well as post-curation steps. We critically discuss parameters that influence search results, such as mass accuracy, precursor ion isolation width, intensity thresholds, centroiding algorithms, and acquisition speed. A range of publicly and commercially available MS/MS databases such as NIST, MassBank, MoNA, LipidBlast, Wiley MSforID, and METLIN are surveyed. In addition, software tools including NIST MS Search, MS-DIAL, Mass Frontier, SmileMS, Mass++, and XCMS2 to perform fast MS/MS search are discussed. MS/MS scoring algorithms and challenges during compound annotation are reviewed. Advanced methods such as the in silico generation of tandem mass spectra using quantum chemistry and machine learning methods are covered. Community efforts for curation and sharing of tandem mass spectra that will allow for faster distribution of scientific discoveries are discussed.

Paradigm shift - Metabolic transformation of docosahexaenoic and eicosapentaenoic acids to bioactives exemplify the promise of fatty acid drug discovery

Fatty acid drug discovery (FADD) is defined as the identification of novel, specialized bioactive mediators that are derived from fatty acids and have precise pharmacological/therapeutic potential. A number of reports indicate that dietary intake of omega-3 fatty acids and limited intake of omega-6 promotes overall health benefits. In 1929, Burr and Burr indicated the significant role of essential fatty acids for survival and functional health of many organs. In reference to specific dietary benefits of differential omega-3 fatty acids, docosahexaenoic and eicosapentaenoic acids (DHA and EPA) are transformed to monohydroxy, dihydroxy, trihydroxy, and other complex mediators during infection, injury, and exercise to resolve inflammation. The presented FADD approach describes the metabolic transformation of DHA and EPA in response to injury, infection, and exercise to govern uncontrolled inflammation. Metabolic transformation of DHA and EPA into a number of pro-resolving molecules exemplifies a novel, inexpensive approach compared to traditional, expensive drug discovery. DHA and EPA have been recommended for prevention of cardiovascular disease since 1970. Therefore, the FADD approach is relevant to cardiovascular disease and resolution of inflammation in many injury models. Future research demands identification of novel action targets, receptors for biomolecules, mechanism(s), and drug-interactions with resolvins in order to maintain homeostasis.

LC-HRMS Metabolomics for Untargeted Diagnostic Screening in Clinical Laboratories: A Feasibility Study

Today’s high-resolution mass spectrometers (HRMS) allow bioanalysts to perform untargeted/global determinations that can reveal unexpected compounds or concentrations in a patient’s sample. This could be performed for preliminary diagnosis attempts when usual diagnostic processes and targeted determinations fail. We have evaluated an untargeted diagnostic screening (UDS) procedure. UDS is a metabolome analysis that compares one sample (e.g., a patient) with control samples (a healthy population). Using liquid chromatography (LC)-HRMS full-scan analysis of human serum extracts and unsupervised data treatment, we have compared individual samples that were spiked with one xenobiotic or a higher level of one endogenous compound with control samples. After the use of different filters that drastically reduced the number of metabolites detected, the spiked compound was eventually revealed in each test sample and ranked. The proposed UDS procedure appears feasible and reliable to reveal unexpected xenobiotics (toxicology) or higher concentrations of endogenous metabolites. HRMS-based untargeted approaches could be useful as preliminary diagnostic screening when canonical processes do not reveal disease etiology nor establish a clear diagnosis and could reduce misdiagnosis. On the other hand, the risk of overdiagnosis of this approach should be reduced with mandatory biomedical interpretation of the patient’s UDS results and with confirmatory targeted and quantitative determinations.

Targeting human urinary metabolome by LC-MS/MS: a review

Urine is a biological matrix that contains hundreds of metabolic end products which constitute the urinary metabolome. The development and advances on LC-MS/MS have revolutionized the analytical study of biomolecules by enabling their accurate identification and quantification in an unprecedented manner. Nowadays, LC-MS/MS is helping to unveil the complexity of urine metabolome, and the results obtained have multiple biomedical applications. This review focuses on the targeted LC-MS/MS analysis of the urine metabolome. In the first part, we describe general considerations (from sample collection to quantitation) required for a proper targeted metabolic analysis. In the second part, we address the urinary analysis and recent applications of four relevant families: amino acids, catecholamines, lipids and steroids.

Human Plasma Metabolomics Study across All Stages of Age-Related Macular Degeneration Identifies Potential Lipid Biomarkers

PURPOSE

To characterize the plasma metabolomic profile of patients with age-related macular degeneration (AMD) using mass spectrometry (MS).

DESIGN

Cross-sectional observational study.

PARTICIPANTS

We prospectively recruited participants with a diagnosis of AMD and a control group (>50 years of age) without any vitreoretinal disease.

METHODS

All participants underwent color fundus photography, used for AMD diagnosis and staging, according to the Age-Related Eye Disease Study classification scheme. Fasting blood samples were collected and plasma was analyzed by Metabolon, Inc. (Durham, NC), using ultrahigh-performance liquid chromatography (UPLC) and high-resolution MS. Metabolon's hardware and software were used to identify peaks and control quality. Principal component analysis and multivariate regression were performed to assess differences in the metabolomic profiles of AMD patients versus controls, while controlling for potential confounders. For biological interpretation, pathway enrichment analysis of significant metabolites was performed using MetaboAnalyst.

MAIN OUTCOME MEASURES

The primary outcome measures were levels of plasma metabolites in participants with AMD compared with controls and among different AMD severity stages.

RESULTS

We included 90 participants with AMD (30 with early AMD, 30 with intermediate AMD, and 30 with late AMD) and 30 controls. Using UPLC and MS, 878 biochemicals were identified. Multivariate logistic regression identified 87 metabolites with levels that differed significantly between AMD patients and controls. Most of these metabolites (82.8%; n = 72), including the most significant metabolites, belonged to the lipid pathways. Analysis of variance revealed that of the 87 metabolites, 48 (55.2%) also were significantly different across the different stages of AMD. A significant enrichment of the glycerophospholipids pathway was identified (P = 4.7 × 10-9) among these metabolites.

CONCLUSIONS

Participants with AMD have altered plasma metabolomic profiles compared with controls. Our data suggest that the most significant metabolites map to the glycerophospholipid pathway. These findings have the potential to improve our understanding of AMD pathogenesis, to support the development of plasma-based metabolomics biomarkers of AMD, and to identify novel targets for treatment of this blinding disease.

Metabolomics as a Tool to Understand Pathophysiological Processes

Multiple diseases have a strong metabolic component, and metabolomics as a powerful phenotyping technology, in combination with orthogonal biological and clinical approaches, will undoubtedly play a determinant role in accelerating the understanding of mechanisms that underlie these complex diseases determined by a set of genetic, lifestyle, and environmental exposure factors. Here, we provide several examples of valuable findings from metabolomics-led studies in diabetes and obesity metabolism, neurodegenerative disorders, and cancer metabolism and offer a longer term vision toward personalized approach to medicine, from population-based studies to pharmacometabolomics.

Serial-omics of P53-/-, Brca1-/- Mouse Breast Tumor and Normal Mammary Gland

This study demonstrates a liquid-liquid extraction for the sequential tandem mass spectrometry (LC-MS/MS) analysis of non-polar lipids, polar metabolites, proteins and phosphorylation sites from a single piece of tissue. Extraction of 10 mg BRCA-/-, p53-/- breast tumor tissue or normal mammary gland tissue with methyl-tert-butyl ether (MTBE) results in three phases: an upper non-polar phase containing 1,382 lipids, a lower polar phase with 805 metabolites and a precipitated protein pellet with 4,792 proteins with 1,072 phosphorylation sites. Comparative analysis revealed an activated AKT-mTOR pathway in tumors. Tumors also showed a reduction of phosphorylation sites involved in transcription and RNA splicing and decreased abundance of enzymes in lipid synthesis. Analysis of polar metabolites revealed a reduction in glycolysis, pentose phosphate pathway, polyamines and nucleotides, but an increase in TCA and urea cycle intermediates. Analysis of lipids revealed a shift from high triglycerides in mammary gland to high phospholipid levels in tumors. The data were integrated into a model showing breast tumors exhibit features on the proteomic, lipidomic and metabolomic level that are distinct from normal breast tissue. Our integrative technique lends itself to samples such as tumor biopsies, dried blood spots and fluids including urine and CSF to develop biomarkers of disease.

Data acquisition workflows in liquid chromatography coupled to high resolution mass spectrometry-based metabolomics: Where do we stand?

Typical mass spectrometry (MS) based untargeted metabolomics protocols are tedious as well as time- and sample-consuming. In particular, they often rely on "full-scan-only" analyses using liquid chromatography (LC) coupled to high resolution mass spectrometry (HRMS) from which metabolites of interest are first highlighted, and then tentatively identified by using targeted MS/MS experiments. However, this situation is evolving with the emergence of integrated HRMS based-data acquisition protocols able to perform multi-event acquisitions. Most of these protocols, referring to as data dependent and data independent acquisition (DDA and DIA, respectively), have been initially developed for proteomic applications and have recently demonstrated their applicability to biomedical studies. In this context, the aim of this article is to take stock of the progress made in the field of DDA- and DIA-based protocols, and evaluate their ability to change conventional metabolomic and lipidomic data acquisition workflows, through a review of HRMS instrumentation, DDA and DIA workflows, and also associated informatics tools.

Serum Metabolic Alterations upon Zika Infection

Zika virus (ZIKV) infection has recently emerged as a major concern worldwide due to its strong association with nervous system malformation (microcephaly) of fetuses in pregnant women infected by the virus. Signs and symptoms of ZIKV infection are often mistaken with other common viral infections. Since transmission may occur through biological fluids exchange and coitus, in addition to mosquito bite, this condition is an important infectious disease. Thus, understanding the mechanism of viral infection has become an important research focus, as well as providing potential targets for assertive clinical diagnosis and quality screening for hemoderivatives. Within this context, the present work analyzed blood plasma from 79 subjects, divided as a control group and a ZIKV-infected group. Samples underwent direct-infusion mass spectrometry and statistical analysis, where eight markers related to the pathophysiological process of ZIKV infection were elected and characterized. Among these, Angiotensin (1-7) and Angiotensin I were upregulated under infection, showing an attempt to induce autophagy of the infected cells. However, this finding is concerning about hypertensive individuals under treatment with inhibitors of the Renin-Angiotensin System (RAS), which could reduce this response against the virus and exacerbate the symptoms of the infection. Moreover, one of the most abundant glycosphingolipids in the nervous tissue, Ganglioside GM2, was also elected in the present study as an infection biomarker. Considered an important pathogen receptor at membrane's outer layer, this finding represents the importance of gangliosides for ZIKV infection and its association with brain tropism. Furthermore, a series of phosphatidylinositols were also identified as biomarkers, implying a significant role of the PI3K-AKT-mTOR Pathway in this mechanism. Finally, these pathways may also be understood as potential targets to be considered in pharmacological intervention studies on ZIKV infection management.

MALDI-FT-ICR-MS for archaeological lipid residue analysis

Soft-ionization methods are currently at the forefront of developing novel methods for analysing degraded archaeological organic residues. Here, we present little-used soft ionization method of matrix assisted laser desorption/ionization-Fourier transform-ion cyclotron resonance-mass spectrometry (MALDI-FT-ICR-MS) for the identification of archaeological lipid residues. It is a high-resolution and sensitive method with low limits of detection capable of identifying lipid compounds in small concentrations, thus providing a highly potential new technique for the analysis of degraded lipid components. A thorough methodology development for analysing cooked and degraded food remains from ceramic vessels was carried out, and the most efficient sample preparation protocol is described. The identified components, also controlled by independent parallel analysis by gas chromatography-mass spectrometry (GC-MS) and gas chromatography-combustion-isotope ratio mass spectrometry (GC-C-IRMS), demonstrate its capability of identifying very different food residues including dairy, adipose fats as well as lipids of aquatic origin. The results obtained from experimentally cooked and original archaeological samples prove the suitability of MALDI-FT-ICR-MS for analysing archaeological organic residues. Sample preparation protocol and identification of compounds provide future reference for analysing various aged and degraded lipid residues in different organic and mineral matrices.

Using omics approaches to understand pulmonary diseases

Omics approaches are high-throughput unbiased technologies that provide snapshots of various aspects of biological systems and include: 1) genomics, the measure of DNA variation; 2) transcriptomics, the measure of RNA expression; 3) epigenomics, the measure of DNA alterations not involving sequence variation that influence RNA expression; 4) proteomics, the measure of protein expression or its chemical modifications; and 5) metabolomics, the measure of metabolite levels. Our understanding of pulmonary diseases has increased as a result of applying these omics approaches to characterize patients, uncover mechanisms underlying drug responsiveness, and identify effects of environmental exposures and interventions. As more tissue- and cell-specific omics data is analyzed and integrated for diverse patients under various conditions, there will be increased identification of key mechanisms that underlie pulmonary biological processes, disease endotypes, and novel therapeutics that are efficacious in select individuals. We provide a synopsis of how omics approaches have advanced our understanding of asthma, chronic obstructive pulmonary disease (COPD), acute respiratory distress syndrome (ARDS), idiopathic pulmonary fibrosis (IPF), and pulmonary arterial hypertension (PAH), and we highlight ongoing work that will facilitate pulmonary disease precision medicine.

Metabolite Measurement: Pitfalls to Avoid and Practices to Follow

Metabolites are the small biological molecules involved in energy conversion and biosynthesis. Studying metabolism is inherently challenging due to metabolites' reactivity, structural diversity, and broad concentration range. Herein, we review the common pitfalls encountered in metabolomics and provide concrete guidelines for obtaining accurate metabolite measurements, focusing on water-soluble primary metabolites. We show how seemingly straightforward sample preparation methods can introduce systematic errors (e.g., owing to interconversion among metabolites) and how proper selection of quenching solvent (e.g., acidic acetonitrile:methanol:water) can mitigate such problems. We discuss the specific strengths, pitfalls, and best practices for each common analytical platform: liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS), nuclear magnetic resonance (NMR), and enzyme assays. Together this information provides a pragmatic knowledge base for carrying out biologically informative metabolite measurements.

A Web Service Framework for Interactive Analysis of Metabolomics Data

Analyzing mass spectrometry-based metabolomics data presents a major challenge to metabolism researchers, as it requires downloading and processing large data volumes through complex "pipelines", even in cases where only a single metabolite or peak is of interest. This presents a significant hurdle for data sharing, reanalysis, or meta-analysis of existing data sets, whether locally stored or available from public repositories. Here we introduce mzAccess, a software system that provides interactive, online access to primary mass spectrometry data in real-time via a Web service protocol, circumventing the need for bulk data processing. mzAccess allows querying instrument data for spectra, chromatograms, or two-dimensional MZ-RT areas in either profile or centroid modes through a simple, uniform interface that is independent of vendor or instrument type. Using a cache mechanism, mzAccess achieves response times in the millisecond range for typical liquid chromatography-mass spectrometry (LC-MS) peaks, enabling real-time browsing of large data sets with hundreds or even thousands of samples. By simplifying access to metabolite data, we hope that this system will help enable data sharing and reanalysis in the metabolomics field.

Proposed Confidence Scale and ID Score in the Identification of Known-Unknown Compounds Using High Resolution MS Data

High-resolution (HR) MS instruments recording HR-full scan allow analysts to go further beyond pre-acquisition choices. Untargeted acquisition can reveal unexpected compounds or concentrations and can be performed for preliminary diagnosis attempt. Then, revealed compounds will have to be identified for interpretations. Whereas the need of reference standards is mandatory to confirm identification, the diverse information collected from HRMS allows identifying unknown compounds with relatively high degree of confidence without reference standards injected in the same analytical sequence. However, there is a necessity to evaluate the degree of confidence in putative identifications, possibly before further targeted analyses. This is why a confidence scale and a score in the identification of (non-peptidic) known-unknown, defined as compounds with entries in database, is proposed for (LC-) HRMS data. The scale is based on two representative documents edited by the European Commission (2007/657/EC) and the Metabolomics Standard Initiative (MSI), in an attempt to build a bridge between the communities of metabolomics and screening labs. With this confidence scale, an identification (ID) score is determined as [a number, a letter, and a number] (e.g., 2D3), from the following three criteria: I, a General Identification Category (1, confirmed, 2, putatively identified, 3, annotated compounds/classes, and 4, unknown); II, a Chromatography Class based on the relative retention time (from the narrowest tolerance, A, to no chromatographic references, D); and III, an Identification Point Level (1, very high, 2, high, and 3, normal level) based on the number of identification points collected. Three putative identification examples of known-unknown will be presented. Graphical Abstract ᅟ.

A relative quantitative positive/negative ion switching method for untargeted lipidomics via high resolution LC-MS/MS from any biological source

INTRODUCTION

Advances in high-resolution mass spectrometry have created renewed interest for studying global lipid biochemistry in disease and biological systems.

OBJECTIVES

Here, we present an untargeted 30 min. LC-MS/MS platform that utilizes positive/negative polarity switching to perform unbiased data dependent acquisitions (DDA) via higher energy collisional dissociation (HCD) fragmentation to profile more than 1000-1500 lipid ions mainly from methyl-tert-butyl ether (MTBE) or chloroform:methanol extractions.

METHODS

The platform uses C18 reversed-phase chromatography coupled to a hybrid QExactive Plus/HF Orbitrap mass spectrometer and the entire procedure takes ~10 h from lipid extraction to identification/quantification for a data set containing 12 samples (~4 h for a single sample). Lipids are identified by both accurate precursor ion mass and fragmentation features and quantified using Lipid-Search and Elements software.

RESULTS

Using this approach, we are able to profile intact lipid ions from up to 18 different main lipid classes and 66 subclasses. We show several studies from different biological sources, including cultured cancer cells, resected tissues from mice such as lung and breast tumors and biological fluids such as plasma and urine.

CONCLUSIONS

Using mouse embryonic fibroblasts, we showed that TSC2-/- KD significantly abrogates lipid biosynthesis and that rapamycin can rescue triglyceride (TG) lipids and we show that SREBP-/- shuts down lipid biosynthesis significantly via mTORC1 signaling pathways. We show that in mouse EGFR driven lung tumors, a large number of TGs and phosphatidylmethanol (PMe) lipids are elevated while some phospholipids (PLs) show some of the largest decrease in lipid levels from ~ 2000 identified lipid ions. In addition, we identified more than 1500 unique lipid species from human blood plasma.

Constant Ion Loss Method for the Untargeted Detection of Bis-sulfate Metabolites

The untargeted detection of phase II metabolites is a key issue for the study of drug metabolism in biological systems. Sensitive and selective mass spectrometric (MS) techniques coupled to ultrahigh performance liquid chromatographic (UHPLC) systems are the most effective for this purpose. In this study, we evaluate different MS approaches with a triple quadrupole instrument for the untargeted detection of bis-sulfate metabolites. Bis-sulfates of 23 steroid metabolites were synthesized and their MS behavior was comprehensively studied. Bis-sulfates ionized preferentially as the dianion ([M - 2H]^2-) with a small contribution of the monoanion ([M - H]^-). Product ion spectra generated from the [M - 2H]^2- precursor ions were dominated by the loss of HSO₄^- to generate two product ions, that is, the ion at m/z 97 (HSO₄^-) and the ion corresponding to the remaining monosulfate fragment. Other product ions were found to be specific for some structures. As an example, the loss of [CH₃ + SO₃]^- was found to be important for several compounds with unsaturation adjacent to the sulfate. On the basis of the common behavior of the bis-sulfate metabolites two alternatives were evaluated for the untargeted detection of bis-sulfate metabolites (i) a precursor ion scan method using the ion at m/z 97 and (ii) a constant ion loss (CIL) method using the loss of HSO₄^-. Both methods allowed for the untargeted detection of the model compounds. Eight steroid bis-sulfates were synthesized in high purity in order to quantitatively evaluate the developed strategies. Lower limits of detection (2-20 ng/mL) were obtained using the CIL method. Additionally, the CIL method was found to be more specific in the detection of urinary bis-sulfates. The applicability of the CIL approach was demonstrated by determining progestogens altered during pregnancy and by detecting the bis-sulfate metabolites of tibolone.

In Situ Metabolomics in Cancer by Mass Spectrometry Imaging

Metabolomics is a rapidly evolving and a promising research field with the expectation to improve diagnosis, therapeutic treatment prediction, and prognosis of particular diseases. Among all techniques used to assess the metabolome in biological systems, mass spectrometry imaging is the method of choice to qualitatively and quantitatively analyze metabolite distribution in tissues with a high spatial resolution, thus providing molecular data in relation to cancer histopathology. The technique is ideally suited to study tissues molecular content and is able to provide molecular biomarkers or specific mass signatures which can be used in classification or the prognostic evaluation of tumors. Recently, it was shown that FFPE tissue samples are also suitable for metabolic analyses. This progress in methodology allows access to a highly valuable resource of tissues believed to widen and strengthen metabolic discovery-driven studies.

Microfluidic approaches to synchrotron radiation-based Fourier transform infrared (SR-FTIR) spectral microscopy of living biosystems

A long-standing desire in biological and biomedical sciences is to be able to probe cellular chemistry as biological processes are happening inside living cells. Synchrotron radiation-based Fourier transform infrared (SR-FTIR) spectral microscopy is a label-free and nondestructive analytical technique that can provide spatiotemporal distributions and relative abundances of biomolecules of a specimen by their characteristic vibrational modes. Despite great progress in recent years, SR-FTIR imaging of living biological systems remains challenging because of the demanding requirements on environmental control and strong infrared absorption of water. To meet this challenge, microfluidic devices have emerged as a method to control the water thickness while providing a hospitable environment to measure cellular processes and responses over many hours or days. This paper will provide an overview of microfluidic device development for SR-FTIR imaging of living biological systems, provide contrast between the various techniques including closed and open-channel designs, and discuss future directions of development within this area. Even as the fundamental science and technological demonstrations develop, other ongoing issues must be addressed; for example, choosing applications whose experimental requirements closely match device capabilities, and developing strategies to efficiently complete the cycle of development. These will require imagination, ingenuity and collaboration.

Lipidomics: Techniques, Applications, and Outcomes Related to Biomedical Sciences

Lipidomics is a newly emerged discipline that studies cellular lipids on a large scale based on analytical chemistry principles and technological tools, particularly mass spectrometry. Recently, techniques have greatly advanced and novel applications of lipidomics in the biomedical sciences have emerged. This review provides a timely update on these aspects. After briefly introducing the lipidomics discipline, we compare mass spectrometry-based techniques for analysis of lipids and summarize very recent applications of lipidomics in health and disease. Finally, we discuss the status of the field, future directions, and advantages and limitations of the field.

Mass spectrometry-based plant metabolomics: Metabolite responses to abiotic stress

Metabolomics is one omics approach that can be used to acquire comprehensive information on the composition of a metabolite pool to provide a functional screen of the cellular state. Studies of the plant metabolome include analysis of a wide range of chemical species with diverse physical properties, from ionic inorganic compounds to biochemically derived hydrophilic carbohydrates, organic and amino acids, and a range of hydrophobic lipid-related compounds. This complexitiy brings huge challenges to the analytical technologies employed in current plant metabolomics programs, and powerful analytical tools are required for the separation and characterization of this extremely high compound diversity present in biological sample matrices. The use of mass spectrometry (MS)-based analytical platforms to profile stress-responsive metabolites that allow some plants to adapt to adverse environmental conditions is fundamental in current plant biotechnology research programs for the understanding and development of stress-tolerant plants. In this review, we describe recent applications of metabolomics and emphasize its increasing application to study plant responses to environmental (stress-) factors, including drought, salt, low oxygen caused by waterlogging or flooding of the soil, temperature, light and oxidative stress (or a combination of them). Advances in understanding the global changes occurring in plant metabolism under specific abiotic stress conditions are fundamental to enhance plant fitness and increase stress tolerance. © 2015 Wiley Periodicals, Inc. Mass Spec Rev 35:620-649, 2016.

A New Strategy for Analyzing Time-Series Data Using Dynamic Networks: Identifying Prospective Biomarkers of Hepatocellular Carcinoma

Time-series metabolomics studies can provide insight into the dynamics of disease development and facilitate the discovery of prospective biomarkers. To improve the performance of early risk identification, a new strategy for analyzing time-series data based on dynamic networks (ATSD-DN) in a systematic time dimension is proposed. In ATSD-DN, the non-overlapping ratio was applied to measure the changes in feature ratios during the process of disease development and to construct dynamic networks. Dynamic concentration analysis and network topological structure analysis were performed to extract early warning information. This strategy was applied to the study of time-series lipidomics data from a stepwise hepatocarcinogenesis rat model. A ratio of lyso-phosphatidylcholine (LPC) 18:1/free fatty acid (FFA) 20:5 was identified as the potential biomarker for hepatocellular carcinoma (HCC). It can be used to classify HCC and non-HCC rats, and the area under the curve values in the discovery and external validation sets were 0.980 and 0.972, respectively. This strategy was also compared with a weighted relative difference accumulation algorithm (wRDA), multivariate empirical Bayes statistics (MEBA) and support vector machine-recursive feature elimination (SVM-RFE). The better performance of ATSD-DN suggests its potential for a more complete presentation of time-series changes and effective extraction of early warning information.

Large-Scale Metabolite Analysis of Standards and Human Serum by Laser Desorption Ionization Mass Spectrometry from Silicon Nanopost Arrays

The unique challenges presented by metabolomics have driven the development of new mass spectrometry (MS)-based techniques for small molecule analysis. We have previously demonstrated silicon nanopost arrays (NAPA) to be an effective substrate for laser desorption ionization (LDI) of small molecules for MS. However, the utility of NAPA-LDI-MS for a wide range of metabolite classes has not been investigated. Here we apply NAPA-LDI-MS to the large-scale acquisition of high-resolution mass spectra and tandem mass spectra from a collection of metabolite standards covering a range of compound classes including amino acids, nucleotides, carbohydrates, xenobiotics, lipids, and other classes. In untargeted analysis of metabolite standard mixtures, detection was achieved for 374 compounds and useful MS/MS spectra were obtained for 287 compounds, without individual optimization of ionization or fragmentation conditions. Metabolite detection was evaluated in the context of 31 metabolic pathways, and NAPA-LDI-MS was found to provide detection for 63% of investigated pathway metabolites. Individual, targeted analysis of the 20 common amino acids provided detection of 100% of the investigated compounds, demonstrating that improved coverage is possible through optimization and targeting of individual analytes or analyte classes. In direct analysis of aqueous and organic extracts from human serum samples, spectral features were assigned to a total of 108 small metabolites and lipids. Glucose and amino acids were quantitated within their physiological concentration ranges. The broad coverage demonstrated by this large-scale screening experiment opens the door for use of NAPA-LDI-MS in numerous metabolite analysis applications.

High-mass-resolution MALDI mass spectrometry imaging of metabolites from formalin-fixed paraffin-embedded tissue

Formalin-fixed and paraffin-embedded (FFPE) tissue specimens are the gold standard for histological examination, and they provide valuable molecular information in tissue-based research. Metabolite assessment from archived tissue samples has not been extensively conducted because of a lack of appropriate protocols and concerns about changes in metabolite content or chemical state due to tissue processing. We present a protocol for the in situ analysis of metabolite content from FFPE samples using a high-mass-resolution matrix-assisted laser desorption/ionization fourier-transform ion cyclotron resonance mass spectrometry imaging (MALDI-FT-ICR-MSI) platform. The method involves FFPE tissue sections that undergo deparaffinization and matrix coating by 9-aminoacridine before MALDI-MSI. Using this platform, we previously detected ∼1,500 m/z species in the mass range m/z 50-1,000 in FFPE samples; the overlap compared with fresh frozen samples is 72% of m/z species, indicating that metabolites are largely conserved in FFPE tissue samples. This protocol can be reproducibly performed on FFPE tissues, including small samples such as tissue microarrays and biopsies. The procedure can be completed in a day, depending on the size of the sample measured and raster size used. Advantages of this approach include easy sample handling, reproducibility, high throughput and the ability to demonstrate molecular spatial distributions in situ. The data acquired with this protocol can be used in research and clinical practice.

Metabolomics: Applications and Promise in Mycobacterial Disease

Until recently, the study of mycobacterial diseases was trapped in culture-based technology that is more than a century old. The use of nucleic acid amplification is changing this, and powerful new technologies are on the horizon. Metabolomics, which is the study of sets of metabolites of both the bacteria and host, is being used to clarify mechanisms of disease, and can identify changes leading to better diagnosis, treatment, and prognostication of mycobacterial diseases. Metabolomic profiles are arrays of biochemical products of genes in their environment. These complex patterns are biomarkers that can allow a more complete understanding of cell function, dysfunction, and perturbation than genomics or proteomics. Metabolomics could herald sweeping advances in personalized medicine and clinical trial design, but the challenges in metabolomics are also great. Measured metabolite concentrations vary with the timing within a condition, the intrinsic biology, the instruments, and the sample preparation. Metabolism profoundly changes with age, sex, variations in gut microbial flora, and lifestyle. Validation of biomarkers is complicated by measurement accuracy, selectivity, linearity, reproducibility, robustness, and limits of detection. The statistical challenges include analysis, interpretation, and description of the vast amount of data generated. Despite these drawbacks, metabolomics provides great opportunity and the potential to understand and manage mycobacterial diseases.

Using Dicationic Ion-Pairing Compounds To Enhance the Single Cell Mass Spectrometry Analysis Using the Single-Probe: A Microscale Sampling and Ionization Device

A unique mass spectrometry (MS) method has been developed to determine the negatively charged species in live single cells using the positive ionization mode. The method utilizes dicationic ion-pairing compounds through the miniaturized multifunctional device, the single-probe, for reactive MS analysis of live single cells under ambient conditions. In this study, two dicationic reagents, 1,5-pentanediyl-bis(1-butylpyrrolidinium) difluoride (C5(bpyr)2F2) and 1,3-propanediyl-bis(tripropylphosphonium) difluoride (C3(triprp)2F2), were added in the solvent and introduced into single cells to extract cellular contents for real-time MS analysis. The negatively charged (1- charged) cell metabolites, which form stable ion-pairs (1+ charged) with dicationic compounds (2+ charged), were detected in positive ionization mode with a greatly improved sensitivity. We have tentatively assigned 192 and 70 negatively charged common metabolites as adducts with (C5(bpyr)2F2) and (C3(triprp)2F2), respectively, in three separate SCMS experiments in the positive ion mode. The total number of tentatively assigned metabolites is 285 for C5(bpyr)2F2 and 143 for C3(triprp)2F2. In addition, the selectivity of dicationic compounds in the complex formation allows for the discrimination of overlapped ion peaks with low abundances. Tandem (MS/MS) analyses at the single cell level were conducted for selected adduct ions for molecular identification. The utilization of the dicationic compounds in the single-probe MS technique provides an effective approach to the detection of a broad range of metabolites at the single cell level.

Advanced Multidimensional Separations in Mass Spectrometry: Navigating the Big Data Deluge

Hybrid analytical instrumentation constructed around mass spectrometry (MS) is becoming the preferred technique for addressing many grand challenges in science and medicine. From the omics sciences to drug discovery and synthetic biology, multidimensional separations based on MS provide the high peak capacity and high measurement throughput necessary to obtain large-scale measurements used to infer systems-level information. In this article, we describe multidimensional MS configurations as technologies that are big data drivers and review some new and emerging strategies for mining information from large-scale datasets. We discuss the information content that can be obtained from individual dimensions, as well as the unique information that can be derived by comparing different levels of data. Finally, we summarize some emerging data visualization strategies that seek to make highly dimensional datasets both accessible and comprehensible.

Mass spectrometric approaches for the identification of anthracycline analogs produced by actinobacteria

Anthracyclines are a well-known chemical class produced by actinobacteria used effectively in cancer treatment; however, these compounds are usually produced in few amounts because of being toxic against their producers. In this work, we successfully explored the mass spectrometry versatility to detect 18 anthracyclines in microbial crude extract. From collision-induced dissociation and nuclear magnetic resonance spectra, we proposed structures for five new and identified three more anthracyclines already described in the literature, nocardicyclins A and B and nothramicin. One new compound 8 (4-[4-(dimethylamino)-5-hydroxy-4,6-dimethyloxan-2-yl]oxy-2,5,7,12-tetrahydroxy-3,10-dimethoxy-2-methyl-3,4-dihydrotetracene-1,6,11-trione) was isolated and had its structure confirmed by (1) H nuclear magnetic resonance. The anthracyclines identified in this work show an interesting aminoglycoside, poorly found in natural products, 3-methyl-rhodosamine and derivatives. This fact encouraged to develop a focused method to identify compounds with aminoglycosides (rhodosamine, m/z 158; 3-methyl-rhodosamine, m/z 172; 4'-O-acethyl-3-C-methyl-rhodosamine, m/z 214). This method allowed the detection of four more anthracyclines. This focused method can also be applied in the search of these aminoglycosides in other microbial crude extracts. Additionally, it was observed that nocardicyclin A, nothramicin and compound 8 were able to interact to DNA through a DNA-binding study by mass spectrometry, showing its potential as anticancer drugs. Copyright © 2016 John Wiley & Sons, Ltd.

Applications of Fourier Transform Ion Cyclotron Resonance (FT-ICR) and Orbitrap Based High Resolution Mass Spectrometry in Metabolomics and Lipidomics

Metabolomics, along with other "omics" approaches, is rapidly becoming one of the major approaches aimed at understanding the organization and dynamics of metabolic networks. Mass spectrometry is often a technique of choice for metabolomics studies due to its high sensitivity, reproducibility and wide dynamic range. High resolution mass spectrometry (HRMS) is a widely practiced technique in analytical and bioanalytical sciences. It offers exceptionally high resolution and the highest degree of structural confirmation. Many metabolomics studies have been conducted using HRMS over the past decade. In this review, we will explore the latest developments in Fourier transform mass spectrometry (FTMS) and Orbitrap based metabolomics technology, its advantages and drawbacks for using in metabolomics and lipidomics studies, and development of novel approaches for processing HRMS data.

Metabolomics in epidemiology: from metabolite concentrations to integrative reaction networks

Metabolomics is becoming feasible for population-scale studies of human disease. In this review, we survey epidemiological studies that leverage metabolomics and multi-omics to gain insight into disease mechanisms. We outline key practical, technological and analytical limitations while also highlighting recent successes in integrating these data. The use of multi-omics to infer reaction rates is discussed as a potential future direction for metabolomics research, as a means of identifying biomarkers as well as inferring causality. Furthermore, we highlight established analysis approaches as well as simulation-based methods currently used in single- and multi-cell levels in systems biology.