A complete workflow for high-resolution spectral-stitching nanoelectrospray direct-infusion mass-spectrometry-based metabolomics and lipidomics

The role of bile acid metabolism in bone and muscle: from analytics to mechanisms

Osteoporosis and sarcopenia are both common age-related disorders that are associated with increased morbidity and mortality. Bone and muscle are metabolically very active tissues that require large amounts of energy. Bile acids (BAs), a group of liver-derived steroid compounds, are primarily known as emulsifiers that facilitate the resorption of dietary fat and lipids. In addition, they have pleiotropic metabolic functions in lipoprotein and glucose metabolism, inflammation, and intestinal bacterial growth. Through these effects, they are related to metabolic diseases, such as diabetes, hypertriglyceridemia, atherosclerosis, and nonalcoholic steatohepatitis. BAs mediate their metabolic effects through receptor dependent and receptor-independent mechanisms. Emerging evidence suggests that BAs are also involved in bone and muscle metabolism. Under normal circumstances, BAs support bone health by shifting the delicate equilibrium of bone turnover toward bone formation. In contrast, low or excessive amounts of BAs promote bone resorption. In cholestatic liver disease, BAs accumulate in the liver, reach toxic concentrations in the circulation, and thus may contribute to bone loss and muscle wasting. In addition, the measurement of BAs is in rapid evolution with modern mass spectrometry techniques that allow for the detection of a continuously growing number of BAs. This review provides a comprehensive overview of the biochemistry, physiology and measurement of bile acids. Furthermore, it summarizes the existing literature regarding their role in bone and muscle.

An automated and high-throughput data processing workflow for PFAS identification in biota by direct infusion ultra-high resolution mass spectrometry

The increasing recognition of the health impacts from human exposure to per- and polyfluorinated alkyl substances (PFAS) has surged the need for sophisticated analytical techniques and advanced data analyses, especially for assessing exposure by food of animal origin. Despite the existence of nearly 15,000 PFAS listed in the CompTox chemicals dashboard by the US Environmental Protection Agency, conventional monitoring and suspect screening methods often fall short, covering only a fraction of these substances. This study introduces an innovative automated data processing workflow, named PFlow, for identifying PFAS in environmental samples using direct infusion Fourier transform ion cyclotron resonance mass spectrometry (DI-FT-ICR MS). PFlow's validation on a bream liver sample, representative of low-concentration biota, involves data pre-processing, annotation of PFAS based on their precursor masses, and verification through isotopologues. Notably, PFlow annotated 17 PFAS absent in the comprehensive targeted approach and tentatively identified an additional 53 compounds, thereby demonstrating its efficiency in enhancing PFAS detection coverage. From an initial dataset of 30,332 distinct m/z values, PFlow thoroughly narrowed down the candidates to 84 potential PFAS compounds, utilizing precise mass measurements and chemical logic criteria, underscoring its potential in advancing our understanding of PFAS prevalence and of human exposure.

Multi-omics bioactivity profile-based chemical grouping and read-across: a case study with Daphnia magna and azo dyes

Grouping/read-across is widely used for predicting the toxicity of data-poor target substance(s) using data-rich source substance(s). While the chemical industry and the regulators recognise its benefits, registration dossiers are often rejected due to weak analogue/category justifications based largely on the structural similarity of source and target substances. Here we demonstrate how multi-omics measurements can improve confidence in grouping via a statistical assessment of the similarity of molecular effects. Six azo dyes provided a pool of potential source substances to predict long-term toxicity to aquatic invertebrates (Daphnia magna) for the dye Disperse Yellow 3 (DY3) as the target substance. First, we assessed the structural similarities of the dyes, generating a grouping hypothesis with DY3 and two Sudan dyes within one group. Daphnia magna were exposed acutely to equi-effective doses of all seven dyes (each at 3 doses and 3 time points), transcriptomics and metabolomics data were generated from 760 samples. Multi-omics bioactivity profile-based grouping uniquely revealed that Sudan 1 (S1) is the most suitable analogue for read-across to DY3. Mapping ToxPrint structural fingerprints of the dyes onto the bioactivity profile-based grouping indicated an aromatic alcohol moiety could be responsible for this bioactivity similarity. The long-term reproductive toxicity to aquatic invertebrates of DY3 was predicted from S1 (21-day NOEC, 40 µg/L). This prediction was confirmed experimentally by measuring the toxicity of DY3 in D. magna. While limitations of this 'omics approach are identified, the study illustrates an effective statistical approach for building chemical groups.

Noise Filtering Algorithm Using Gaussian Mixture Models for High-Resolution Mass Spectra of Natural Organic Matter

High-resolution mass spectra of natural organic matter (NOM) contain a large number of noise signals. These signals interfere with the correct molecular composition estimation during nontargeted analysis because formula-assignment programs find empirical formulas for such peaks as well. Previously proposed noise filtering methods that utilize the profile of the intensity distribution of mass spectrum peaks rely on a histogram to calculate the intensity threshold value. However, the histogram profile can vary depending on the user settings. In addition, these algorithms are not automated, so they are handled manually. To overcome the mentioned drawbacks, we propose a new algorithm for noise filtering in mass spectra. This filter is based on Gaussian Mixture Models (GMMs), a machine learning method to find the intensity threshold value. The algorithm is completely data-driven and eliminates the need to work with a histogram. It has no customizable parameters and automatically determines the noise level for each individual mass spectrum. The algorithm performance was tested on mass spectra of natural organic matter obtained by averaging a different number of microscans (transients), and the results were compared with other noise filters proposed in the literature. Finally, the effect of this noise filtering approach on the fraction of peaks with assigned formulas was investigated. It was shown that there is always an increase in the identification rate, but the magnitude of the effect changes with the number of microscans averaged. The increase can be as high as 15%.

Ultra-high-throughput mass spectrometry in drug discovery: fundamentals and recent advances

INTRODUCTION

Ultra-high-throughput mass spectrometry, uHT-MS, is a technology that utilizes ionization and sample delivery technologies optimized to enable sampling from well plates at > 1 sample per second. These technologies do not need a chromatographic separation step and can be utilized in a wide variety of assays to detect a broad range of analytes including small molecules, lipids, and proteins.

AREAS COVERED

This manuscript provides a brief historical review of high-throughput mass spectrometry and the recently developed technologies that have enabled uHT-MS. The report also provides examples and references on how uHT-MS has been used in biochemical and chemical assays, nuisance compound profiling, protein analysis and high throughput experimentation for chemical synthesis.

EXPERT OPINION

The fast analysis time provided by uHT-MS is transforming how biochemical and chemical assays are performed in drug discovery. The potential to associate phenotypic responses produced by 1000's of compound treatments with changes in endogenous metabolite and lipid signals is becoming feasible. With the augmentation of simple, fast, high-throughput sample preparation, the scope of uHT-MS usage will increase. However, it likely will not supplant LC-MS for analyses that require low detection limits from complex matrices or characterization of complex biotherapeutics such as antibody-drug conjugates.

Chemo-Selective Single-Cell Metabolomics Reveals the Spatiotemporal Behavior of Exogenous Pollutants During Xenopus Laevis Embryogenesis

In-depth profiling of embryogenesis-associated endogenous and exogenous metabolic changes can reveal potential bio-effects resulting from human-made chemicals and underlying mechanisms. Due to the lack of potent tools for monitoring spatiotemporal distribution and bio-transformation behavior of dynamic metabolites at single-cell resolution, however, how and to what extent environmental chemicals may influence or interfere embryogenesis largely remain unclear. Herein, a zero-sample-loss micro-biopsy-based mass spectrometric platform is presented for quantitative, chemo-selective, high-coverage, and minimal-destructive profiling of development-associated cis-diol metabolites, which are critical for signal transduction and epigenome regulation, at both cellular level and tissue level of Xenopus laevis. Using this platform, three extraordinary findings that are otherwise hard to achieve are revealed: 1) there are characteristically different cis-diol metabolic signatures among oocytes, anterior and posterior part of tailbud-stage embryos; 2) halogenated cis-diols heavily accumulate at the posterior part of tailbud-stage embryos of Xenopus laevis; 3) dimethachlon, a kind of exogenous fungicide that is widely used as pesticide, may be bio-transformed and accumulated in vertebrate animals in environment. Thus, this study opens a new avenue to simultaneously monitoring intercellular and intraembryonic heterogeneity of endogenous and exogenous metabolites, providing new insights into metabolic remolding during embryogenesis and putting a warning on potential environmental risk.

Unraveling the potential of segment scan mass spectral acquisition for chemical isotope labeling LC-MS-based metabolome analysis: Performance assessment across different types of biological samples

BACKGROUND

Chemical isotope labeling (CIL) LC-MS is a powerful tool for metabolome analysis with high metabolomic coverage and quantification accuracy. In CIL LC-MS, the overall metabolite detection efficiency using Orbitrap MS can be further improved by employing a segment scan method where the full m/z range is divided into multiple segments for spectral acquisition with a significant increase in the in-spectrum dynamic range. Considering the metabolic complexity in different types of biological samples (e.g., feces, urine, serum/plasma, cell/tissue extracts, saliva, etc.), we report the development and evaluation of the segment scan method for metabolome analysis of different sample types.

RESULTS

It was found that sample complexity significantly influenced the performance of the segment scan method. In metabolically complex samples such as feces and urine, the method yielded a substantial increase (up to 94 %) in detected peak pairs or metabolites, compared to conventional full scan. Conversely, less complex samples like saliva exhibited more modest gains (approximately 25 %). Based on the observations, a 120-m/z segment scan method was determined as a routine approach for CIL LC-Orbitrap-MS-based metabolomics with good compatibility with different types of biological samples. For this method, a further investigation on relative quantification accuracy was done. The peak area ratios of 12C-/13-labeled metabolites were slightly reduced with 72%-84 % of peak pairs falling within the ±25 % range of the anticipated peak ratio of 1.0 among different samples, as opposed to 81%-90 % in the full scan, which was attributed to the inclusion of more low-abundance peak pairs within the narrow MS segments. However, the overall peak ratio measurement precision was not significantly affected by the segment scan.

SIGNIFICANCE AND NOVELTY

The segment scan method was found to be useful for CIL LC-Orbitrap-MS-based metabolome analysis of different types of samples with significant improvement in metabolite detectability (25-94 % increase), compared to the conventional full scan method.

Database-assisted, globally optimized targeted secondary electrospray ionization high resolution mass spectrometry (dGOT-SESI-HRMS) and spectral stitching enhanced volatilomics analysis of bacterial metabolites

Secondary electrospray ionization high-resolution mass spectrometry (SESI-HRMS) is an innovative analytical technique for the rapid and non-invasive analysis of volatile organic compounds (VOCs). However, compound annotation and ion suppression in the SESI source has hindered feature detection, stability and reproducibility of SESI-HRMS in untargeted volatilomics. To address this, we have developed and optimized a novel pseudo-targeted approach, database-assisted globally optimized targeted (dGOT)-SESI-HRMS using the microbial-VOC (mVOC) database, and spectral stitching methods to enhance metabolite detection in headspace of anaerobic bacterial cultures. Headspace volatiles from representative bacteria strains were assessed using full scan with data dependent acquisition (DDA), conventional globally optimized targeted (GOT) method, and spectral stitching supported dGOT experiments based on a MS peaks list derived from mVOC. Our results indicate that spectral stitching supported dGOT-SESI-HRMS can proportionally fragment peaks with respect to different analysis windows, with a total of 109 VOCs fragmented from 306 targeted compounds. Of the collected spectra, 88 features were confirmed as culture derived volatiles with respect to media blanks. Annotation was also achieved with a total of 25 unique volatiles referenced to standard databases allowing for biological interpretation. Principal component analysis (PCA) summarizing the headspace volatile demonstrated improved separation of clusters when data was acquired using the dGOT method. Collectively, our dGOT-SESI-HRMS method afforded robust capability of capturing unique VOC profiles from different bacterial strains and culture conditions when compared to conventional GOT and DDA modes, suggesting the newly developed approach can serve as a more reliable analytical method for the sensitive monitoring of gut microbial metabolism.

Deep Characterization of Serum Metabolome Based on the Segment-Optimized Spectral-Stitching Direct-Infusion Fourier Transform Ion Cyclotron Resonance Mass Spectrometry Approach

Direct-infusion Fourier transform ion cyclotron resonance mass spectrometry (DI-FTICR MS) shows great promise for metabolomic analysis due to ultrahigh mass accuracy and resolution. However, most of the DI-FTICR MS approaches focused on high-throughput metabolomics analysis at the expense of sensitivity and resolution and the potential for metabolome characterization has not been fully explored. Here, we proposed a novel deep characterization approach of serum metabolome using a segment-optimized spectral-stitching DI-FTICR MS method integrated with high-confidence and database-independent formula assignments. With varied acquisition parameters for each segment, a highly efficient acquisition was achieved for the whole mass range with sub-ppm mass accuracy. In a pooled human serum sample, thousands of features were assigned with unambiguous formulas and possible candidates based on highly accurate mass measurements. Furthermore, a reaction network was used to select confidently unique formulas from possible candidates, which was constructed by unambiguous formulas and possible candidates connected by the formula differences resulting from biochemical and MS transformation. Compared with full-range and conventional segment acquisition, 8- and 1.2-fold increases in observed features were achieved, respectively. Assignment accuracy was 93-94% for both a standard mixture containing 190 metabolites and a spiked serum sample with the root mean square mass error of 0.15-0.16 ppm. In total, 3534 unequivocal neutral molecular formulas were assigned in the pooled serum sample, 35% of which are contained in the HMDB. This method offers great enhancement in the deep characterization of serum metabolome by DI-FTICR MS.

Unlocking the secrets of the microbiome: exploring the dynamic microbial interplay with humans through metabolomics and their manipulation for synthetic biology applications

Metabolomics is a powerful research discovery tool with the potential to measure hundreds to low thousands of metabolites. In this review, we discuss the application of GC-MS and LC-MS in discovery-based metabolomics research, we define metabolomics workflows and we highlight considerations that need to be addressed in order to generate robust and reproducible data. We stress that metabolomics is now routinely applied across the biological sciences to study microbiomes from relatively simple microbial systems to their complex interactions within consortia in the host and the environment and highlight this in a range of biological species and mammalian systems including humans. However, challenges do still exist that need to be overcome to maximise the potential for metabolomics to help us understanding biological systems. To demonstrate the potential of the approach we discuss the application of metabolomics in two broad research areas: (1) synthetic biology to increase the production of high-value fine chemicals and reduction in secondary by-products and (2) gut microbial interaction with the human host. While burgeoning in importance, the latter is still in its infancy and will benefit from the development of tools to detangle host-gut-microbial interactions and their impact on human health and diseases.

A Strategy for Uncovering the Serum Metabolome by Direct-Infusion High-Resolution Mass Spectrometry

Direct infusion nanoelectrospray high-resolution mass spectrometry (DI-nESI-HRMS) is a promising tool for high-throughput metabolomics analysis. However, metabolite assignment is limited by the inadequate mass accuracy and chemical space of the metabolome database. Here, a serum metabolome characterization method was proposed to make full use of the potential of DI-nESI-HRMS. Different from the widely used database search approach, unambiguous formula assignments were achieved by a reaction network combined with mass accuracy and isotopic patterns filter. To provide enough initial known nodes, an initial network was directly constructed by known metabolite formulas. Then experimental formula candidates were screened by the predefined reaction with the network. The effects of sources and scales of networks on assignment performance were investigated. Further, a scoring rule for filtering unambiguous formula candidates was proposed. The developed approach was validated by a pooled serum sample spiked with reference standards. The coverage and accuracy rates for the spiked standards were 98.9% and 93.6%, respectively. A total of 1958 monoisotopic features were assigned with unique formula candidates for the pooled serum, which is twice more than the database search. Finally, a case study of serum metabolomics in diabetes was carried out using the developed method.

Machine learning-empowered cis-diol metabolic fingerprinting enables precise diagnosis of primary liver cancer

Cis-diol metabolic reprogramming evolves during primary liver cancer (PLC) initiation and progression. However, owing to the low concentrations and highly structural heterogeneity of cis-diols in vivo, severe interference from complex biofluids and limited profiling coverage of existing methods, in-depth profiling of cis-diol metabolites and linking their specific changes with PLC remain challenging. Besides, due to the low specificity of widely used protein biomarkers, accurate classification of PLC from hepatitis still represents an unmet need in clinical diagnostics. Herein, to high-coverage profile cis-diols and explore the translational potential of them as biomarkers, a machine learning-empowered boronate affinity extraction-solvent evaporation assisted enrichment-mass spectrometry (MLE-BESE-MS) was developed. A single analytical platform integrated with multiple complementary functions, including pH-controlled boronate affinity extraction, solvent evaporation-assisted enrichment and nanoelectrospray ionization-based cis-diol identification, was constructed, which significantly improved the metabolite coverage. Meanwhile, by virtue of machine learning (principal components analysis, orthogonal partial least-squares discrimination analysis and random forest), collected cis-diols were statistically screened to extract efficient features for precise PLC diagnosis, and the results outperform the routinely used protein biomarker-based methods both in sensitivity (87.5% vs. less than 70%) and specificity (85.7% vs. ca. 80%). This machine learning-empowered integrated MS platform advanced the targeted metabolic analysis for early cancer diagnosis, rendering great promise for clinical translation.

Applications of lipidomics in goat meat products: Biomarkers, structure, nutrition interface and future perspectives

Goat meat, as a superior product including low lipids, low cholesterol contents and high-quality proteins, becomes the superior food for the national market. With the increasing demand for goat meat, the production, sensory quality and physicochemical properties of goat meat are also widely observed. Following significant discoveries on the mechanism determining goat meat quality, further research on complex and interactive factors leading to changes of goat meat quality is increasingly based on data-driven "omics" methods, such as lipidomics, which can rapidly identify and quantify >1000 lipid species at same time facilitating comprehensive analyses of lipids in tissues. Molecular mechanism and biomarkers indicating the changes of goat meat quality, authentication, meat analogue, nutrition and health by lipidomics are feasible. According to the analysis results of the classes and of different biomarkers lipids of goat meat quality, the main processes involved the biosynthesis of unsaturated fatty acids, associations with lipids and proteins, lipid oxidation, lipid hydrolysis, lipid degradation, lipid deposition and lipid denaturation, which have been translated into advanced technologies for identifying the goat meat adulteration and faux meat rapidly and accurately. SIGNIFICANCE: In this review, the research of lipidomics technology, past applications, recent findings and common on the recent advances of lipidomics in the quality assessment of mutton products by lipidomics with MS approaches have been summarized. The information reported in review can serve as a reference to characterize the lipids found in mutton, clarify the application of lipidomics to the field of mutton products and provide new perspectives in producing superior quality mutton products.

Metabolomics as a Critical Tool for Studying Clinical Surgery

Metabolomics enables the analysis of metabolites within an organism, which offers the closest direct measurement of the physiological activity of the organism, and has advanced efforts to characterize metabolic states, identify biomarkers, and investigate metabolic pathways. A high degree of innovation in analytical techniques has promoted the application of metabolomics, especially in the study of clinical surgery. Metabolomics can be employed as a clinical testing method to maximize therapeutic outcomes, and has been applied in rapid diagnosis of diseases, timely postoperative monitoring, prognostic assessment, and personalized medicine. This review focuses on the use of mass spectrometry and nuclear magnetic resonance-based metabolomics in clinical surgery, including identifying metabolic changes before and after surgery, finding disease-associated biomarkers, and exploring the potential of personalized therapy. Challenges and opportunities of metabolomics in organ transplantation are also discussed, with a particular emphasis on metabolomics in donor organ evaluation and protection, prognostic outcome prediction, as well as postoperative adverse reaction monitoring. In the end, current limitations of metabolomics in clinical surgery and future research directions are presented.

Derivation of metabolic point of departure using high-throughput in vitro metabolomics: investigating the importance of sampling time points on benchmark concentration values in the HepaRG cell line

Amongst omics technologies, metabolomics should have particular value in regulatory toxicology as the measurement of the molecular phenotype is the closest to traditional apical endpoints, whilst offering mechanistic insights into the biological perturbations. Despite this, the application of untargeted metabolomics for point-of-departure (POD) derivation via benchmark concentration (BMC) modelling is still a relatively unexplored area. In this study, a high-throughput workflow was applied to derive PODs associated with a chemical exposure by measuring the intracellular metabolome of the HepaRG cell line following treatment with one of four chemicals (aflatoxin B₁, benzo[a]pyrene, cyclosporin A, or rotenone), each at seven concentrations (aflatoxin B₁, benzo[a]pyrene, cyclosporin A: from 0.2048 μM to 50 μM; rotenone: from 0.04096 to 10 μM) and five sampling time points (2, 6, 12, 24 and 48 h). The study explored three approaches to derive PODs using benchmark concentration modelling applied to single features in the metabolomics datasets or annotated metabolites or lipids: (1) the 1st rank-ordered unannotated feature, (2) the 1st rank-ordered putatively annotated feature (using a recently developed HepaRG-specific library of polar metabolites and lipids), and (3) 25th rank-ordered feature, demonstrating that for three out of four chemical datasets all of these approaches led to relatively consistent BMC values, varying less than tenfold across the methods. In addition, using the 1st rank-ordered unannotated feature it was possible to investigate temporal trends in the datasets, which were shown to be chemical specific. Furthermore, a possible integration of metabolomics-driven POD derivation with the liver steatosis adverse outcome pathway (AOP) was demonstrated. The study highlights that advances in technologies enable application of in vitro metabolomics at scale; however, greater confidence in metabolite identification is required to ensure PODs are mechanistically anchored.

Blood Plasma Metabolome Profiling at Different Stages of Renal Cell Carcinoma

Early diagnostics significantly improves the survival of patients with renal cell carcinoma (RCC), which is the prevailing type of adult kidney cancer. However, the absence of clinically obvious symptoms and effective screening strategies at the early stages result to disease progression and survival rate reducing. The study was focused on revealing of potential low molecular biomarkers for early-stage RCC. The untargeted direct injection mass spectrometry-based metabolite profiling of blood plasma samples from 51 non-cancer volunteers (control) and 78 patients with different RCC subtypes and stages (early stages of clear cell RCC (ccRCC), papillary RCC (pRCC), chromophobe RCC (chrRCC) and advanced stages of ccRCC) was performed. Comparative analysis of the blood plasma metabolites between the control and cancer groups provided the detection of metabolites associated with different tumor stages. The designed model based on the revealed metabolites demonstrated high diagnostic power and accuracy. Overall, using the metabolomics approach the study revealed the metabolites demonstrating a high value for design of plasma-based test to improve early ccRCC diagnosis.

Balancing Trade-Offs Imposed by Growth Media and Mass Spectrometry for Bacterial Exometabolomics

The bacterial exometabolome consists of a vast array of specialized metabolites, many of which are only produced in response to specific environmental stimuli. For this reason, it is desirable to control the extracellular environment with a defined growth medium composed of pure ingredients. However, complex (undefined) media are expected to support the robust growth of a greater variety of microorganisms than defined media. Here, we investigate the trade-offs inherent to a range of complex and defined solid media for the growth of soil microorganisms, production of specialized metabolites, and detection of these compounds using direct infusion mass spectrometry. We find that complex media support growth of more soil microorganisms, as well as allowing for the detection of more previously discovered natural products as a fraction of total m/z features detected in each sample. However, the use of complex media often caused mass spectrometer injection failures and poor-quality mass spectra, which in some cases resulted in over a quarter of samples being removed from analysis. Defined media, while more limiting in growth, generated higher quality spectra and yielded more m/z features after background subtraction. These results inform future exometabolomic experiments requiring a medium that supports the robust growth of many soil microorganisms. IMPORTANCE Bacteria are capable of producing and secreting a rich diversity of specialized metabolites. Yet, much of their exometabolome remains hidden due to challenges associated with eliciting specialized metabolite production, labor-intensive sample preparation, and time-consuming analysis techniques. Using our versatile three-dimensional (3D)-printed culturing platform, SubTap, we demonstrate that rapid exometabolomic data collection from a diverse set of environmental bacteria is feasible. We optimized our platform by surveying Streptomyces isolated from soil on a variety of media types to assess viability, degree of specialized metabolite production, and compatibility with downstream LESA-DIMS analysis. Ultimately, this will enable data-rich experimentation, allowing for a better understanding of bacterial exometabolomes.

A Direct Infusion Probe for Rapid Metabolomics of Low-Volume Samples

Targeted and nontargeted metabolomics has the potential to evaluate and detect global metabolite changes in biological systems. Direct infusion mass spectrometric analysis enables detection of all ionizable small molecules, thus simultaneously providing information on both metabolites and lipids in chemically complex samples. However, to unravel the heterogeneity of the metabolic status of cells in culture and tissue a low number of cells per sample should be analyzed with high sensitivity, which requires low sample volumes. Here, we present the design and characterization of the direct infusion probe, DIP. The DIP is simple to build and position directly in front of a mass spectrometer for rapid metabolomics of chemically complex biological samples using pneumatically assisted electrospray ionization at 1 μL/min flow rate. The resulting data is acquired in a square wave profile with minimal carryover between samples that enhances throughput and enables several minutes of uniform MS signal from 5 μL sample volumes. The DIP was applied to study the intracellular metabolism of insulin secreting INS-1 cells and the results show that exposure to 20 mM glucose for 15 min significantly alters the abundance of several small metabolites, amino acids, and lipids.

Spectral binning as an approach to post-acquisition processing of high resolution FIE-MS metabolome fingerprinting data

INTRODUCTION

Flow infusion electrospray high resolution mass spectrometry (FIE-HRMS) fingerprinting produces complex, high dimensional data sets which require specialist in-silico software tools to process the data prior to analysis.

OBJECTIVES

Present spectral binning as a pragmatic approach to post-acquisition procession of FIE-HRMS metabolome fingerprinting data.

METHODS

A spectral binning approach was developed that included the elimination of single scan m/z events, the binning of spectra and the averaging of spectra across the infusion profile. The modal accurate m/z was then extracted for each bin. This approach was assessed using four different biological matrices and a mix of 31 known chemical standards analysed by FIE-HRMS using an Exactive Orbitrap. Bin purity and centrality metrics were developed to objectively assess the distribution and position of accurate m/z within an individual bin respectively.

RESULTS

The optimal spectral binning width was found to be 0.01 amu. 80.8% of the extracted accurate m/z matched to predicted ionisation products of the chemical standards mix were found to have an error of below 3 ppm. The open-source R package binneR was developed as a user friendly implementation of the approach. This was able to process 100 data files using 4 Central Processing Units (CPU) workers in only 55 seconds with a maximum memory usage of 1.36 GB.

CONCLUSION

Spectral binning is a fast and robust method for the post-acquisition processing of FIE-HRMS data. The open-source R package binneR allows users to efficiently process data from FIE-HRMS experiments with the resources available on a standard desktop computer.

Preanalytical Pitfalls in Untargeted Plasma Nuclear Magnetic Resonance Metabolomics of Endocrine Hypertension

Despite considerable morbidity and mortality, numerous cases of endocrine hypertension (EHT) forms, including primary aldosteronism (PA), pheochromocytoma and functional paraganglioma (PPGL), and Cushing's syndrome (CS), remain undetected. We aimed to establish signatures for the different forms of EHT, investigate potentially confounding effects and establish unbiased disease biomarkers. Plasma samples were obtained from 13 biobanks across seven countries and analyzed using untargeted NMR metabolomics. We compared unstratified samples of 106 PHT patients to 231 EHT patients, including 104 PA, 94 PPGL and 33 CS patients. Spectra were subjected to a multivariate statistical comparison of PHT to EHT forms and the associated signatures were obtained. Three approaches were applied to investigate and correct confounding effects. Though we found signatures that could separate PHT from EHT forms, there were also key similarities with the signatures of sample center of origin and sample age. The study design restricted the applicability of the corrections employed. With the samples that were available, no biomarkers for PHT vs. EHT could be identified. The complexity of the confounding effects, evidenced by their robustness to correction approaches, highlighted the need for a consensus on how to deal with variabilities probably attributed to preanalytical factors in retrospective, multicenter metabolomics studies.

Challenges and opportunities for prevention and removal of unwanted variation in lipidomic studies

Large 'omics studies are of particular interest to population and clinical research as they allow elucidation of biological pathways that are often out of reach of other methodologies. Typically, these information rich datasets are produced from multiple coordinated profiling studies that may include lipidomics, metabolomics, proteomics or other strategies to generate high dimensional data. In lipidomics, the generation of such data presents a series of unique technological and logistical challenges; to maximize the power (number of samples) and coverage (number of analytes) of the dataset while minimizing the sources of unwanted variation. Technological advances in analytical platforms, as well as computational approaches, have led to improvement of data quality - especially with regard to instrumental variation. In the small scale, it is possible to control systematic bias from beginning to end. However, as the size and complexity of datasets grow, it is inevitable that unwanted variation arises from multiple sources, some potentially unknown and out of the investigators control. Increases in cohort sizes and complexity has led to new challenges in sample collection, handling, storage, and preparation stages. If not considered and dealt with appropriately, this unwanted variation may undermine the quality of the data and reliability of any subsequent analysis. Here we review the various experimental phases where unwanted variation may be introduced and review general strategies and approaches to handle this variation, specifically addressing issues relevant to lipidomics studies.

Targeted gut microbiota manipulation attenuates seizures in a model of infantile spasms syndrome

Infantile spasms syndrome (IS) is a devastating early-onset epileptic encephalopathy associated with poor neurodevelopmental outcomes. When first-line treatment options, including adrenocorticotropic hormone and vigabatrin, are ineffective, the ketogenic diet (KD) is often employed to control seizures. Since the therapeutic impact of the KD is influenced by the gut microbiota, we examined whether targeted microbiota manipulation, mimicking changes induced by the KD, would be valuable in mitigating seizures. Employing a rodent model of symptomatic IS, we show that both the KD and antibiotic administration reduce spasm frequency and are associated with improved developmental outcomes. Spasm reductions were accompanied by specific gut microbial alterations, including increases in Streptococcus thermophilus and Lactococcus lactis. Mimicking the fecal microbial alterations in a targeted probiotic, we administered these species in a 5:1 ratio. Targeted probiotic administration reduced seizures and improved locomotor activities in control diet–fed animals, similar to KD-fed animals, while a negative control (Ligilactobacillus salivarius) had no impact. Probiotic administration also increased antioxidant status and decreased proinflammatory cytokines. Results suggest that a targeted probiotic reduces seizure frequency, improves locomotor activity in a rodent model of IS, and provides insights into microbiota manipulation as a potential therapeutic avenue for pediatric epileptic encephalopathies.

Uncovering the interference from lipid fragments on the qualification and quantification of serum metabolites in matrix-assisted laser desorption/ionization time-of-flight mass spectrometric analysis

RATIONALE

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has exhibited great advantages in rapid analysis of metabolites. However, the influence of lipid fragments generated by in-source fragmentation (ISD) and/or post-source fragmentation (PSD) on the accurate qualification and quantification of metabolites has not been fully demonstrated.

METHODS

Phospholipid standards and serum extract were analyzed by MALDI MS with both TiO₂ nanoparticle (TiO₂ NP) and 2,5-DHB matrices to illustrate the structures of lipid fragments and their influence on the qualitative and quantitative analysis of metabolites in biological samples. Monophasic and biphasic extraction methods were also compared for their efficiency in removing potential interferents.

RESULTS

The fragment ions derived from the phosphocholine head group of phosphatidylcholines (PC) interfere with peaks of low molecular weight (LMW) metabolites at both the MS and MS² levels. The biphasic extraction system with methanol/chloroform very efficiently removed the interference from PC fragments, and the metabolites choline and carnitine in serum were directly and accurately quantified by MALDI MS by using this biphasic extraction.

CONCLUSIONS

The phospholipids could produce fragment ions through ISD and PSD in MALDI MS with both nanoparticle and organic matrices. The fragments exerted influence on the qualification and qualification of metabolites in serum. By choosing the proper extraction method, the interference from lipid fragments could be efficiently alleviated.

Liver metabolomics identifies bile acid profile changes at early stages of alcoholic liver disease in mice

Alcohol consumption is a global healthcare problem with enormous social, economic, and clinical consequences. The liver sustains the earliest and the greatest degree of tissue injury due to chronic alcohol consumption and it has been estimated that alcoholic liver disease (ALD) accounts for almost 50% of all deaths from cirrhosis in the world. In this study, we used a modified Lieber-DeCarli (LD) diet to treat mice with alcohol and simulate chronic alcohol drinking. Using an untargeted metabolomics approach, our aim was to identify the various metabolites and pathways that are altered in the early stages of ALD. Histopathology showed minimal changes in the liver after 6 weeks of alcohol consumption. However, untargeted metabolomics analyses identified 304 metabolic features that were either up- or down-regulated in the livers of ethanol-consuming mice. Pathway analysis revealed significant alcohol-induced alterations, the most significant of which was in the FXR/RXR activation pathway. Targeted metabolomics focusing on bile acid biosynthesis showed elevated taurine-conjugated cholic acid compounds in ethanol-consuming mice. In summary, we showed that the changes in the liver metabolome manifest very early in the development of ALD, and when minimal changes in liver histopathology have occurred. Although alterations in biochemical pathways indicate a complex pathology in the very early stages of alcohol consumption, bile acid changes may serve as biomarkers of the early onset of ALD.

Addition of Prebiotics to the Ketogenic Diet Improves Metabolic Profile but Does Not Affect Seizures in a Rodent Model of Infantile Spasms Syndrome

The ketogenic diet (KD) is an effective treatment for infantile spasms syndrome (IS). However, the KD has implications for somatic growth, development, and the gut microbiota. The impact of incorporating a prebiotic fiber (PRE, oligofructose-enriched inulin, 0.8 g/dL) into a KD diet on spasms, developmental milestones, fecal gut microbiota, metabolites, and hippocampal mitochondrial metabolism were examined. Following IS induction, animals were randomized to KD or KD + PRE diets. A third group without IS and suckled by dams was included as a normally developing reference group (R). PRE inclusion decreased ketones and increased circulating glucose levels but had no impact on spasms. In the liver, PRE increased triglyceride concentrations, decreased carnitine levels, and downregulated genes encoding enzymes responsible for ketogenesis. In the hippocampus, PRE increased glutathione levels but did not affect the maximal respiratory capacity of mitochondria. Analysis of the gut microbiota showed that KD + PRE increased microbial richness and the relative abundance of Bifidobacterium pseudolongum and Lactobacillus johnsonii. No differences in developmental milestones (i.e., surface righting, negative geotaxis, and open field behavior) were observed between KD and KD + PRE, except for ultrasonic vocalizations that were more frequent in KD + PRE. In summary, PRE did not impact spasms or developmental outcomes, but was effective in improving both metabolic parameters and gut microbiota diversity.

Metabolomics-based phenotypic screens for evaluation of drug synergy via direct-infusion mass spectrometry

Drugs used in combination can synergize to increase efficacy, decrease toxicity, and prevent drug resistance. While conventional high-throughput screens that rely on univariate data are incredibly valuable to identify promising drug candidates, phenotypic screening methodologies could be beneficial to provide deep insight into the molecular response of drug combination with a likelihood of improved clinical outcomes. We developed a high-content metabolomics drug screening platform using stable isotope-tracer direct-infusion mass spectrometry that informs an algorithm to determine synergy from multivariate phenomics data. Using a cancer drug library, we validated the drug screening, integrating isotope-enriched metabolomics data and computational data mining, on a panel of prostate cell lines and verified the synergy between CB-839 and docetaxel both in vitro (three-dimensional model) and in vivo. The proposed unbiased metabolomics screening platform can be used to rapidly generate phenotype-informed datasets and quantify synergy for combinatorial drug discovery.

Recent applications of metabolomics in plant breeding

Metabolites play a central role in maintaining organismal life and in defining crop phenotypes, such as nutritional value, fragrance, color, and stress resistance. Among the 'omes' in biology, the metabolome is the closest to the phenotype. Consequently, metabolomics has been applied to crop improvement as method for monitoring changes in chemical compositions, clarifying the mechanisms underlying cellular functions, discovering markers and diagnostics, and phenotyping for mQTL, mGWAS, and metabolite-genome predictions. In this review, 359 reports of the most recent applications of metabolomics to plant breeding-related studies were examined. In addition to the major crops, more than 160 other crops including rare medicinal plants were considered. One bottleneck associated with using metabolomics is the wide array of instruments that are used to obtain data and the ambiguity associated with metabolite identification and quantification. To further the application of metabolomics to plant breeding, the features and perspectives of the technology are discussed.

Seizure modulation by the gut microbiota and tryptophan-kynurenine metabolism in an animal model of infantile spasms

BACKGROUND

The infantile spasms syndrome is an early-onset epileptic encephalopathy presenting in the first 2 years of life, often with severe developmental consequences. The role of the gut microbiota and metabolism in infantile spasms remains unexplored.

METHODS

Employing a brain injury neonatal rat model of infantile spasms intractable to anticonvulsant medication treatments, we determined how the ketogenic diet and antibiotics affected specific microbial communities and the resultant circulating factors that confer spasms protection in the infantile spasms model. To confirm a role of kynurenine metabolism pathway in spasms protection, indoleamine 2,3-dioxygenase 1 was pharmacologically inhibited and comprehensive metabolomics was applied.

FINDINGS

We show that antibiotics reduced spasms and improved the effectiveness of the ketogenic diet when given in combination. Examination of the gut microbiota and metabolomics showed the downregulation of indoleamine 2,3-dioxygenase 1 and upregulation of hippocampal kynurenic acid, a metabolite with antiepileptic effects. To further test the involvement of indoleamine 2,3-dioxygenase 1, a specific antagonist 1-methyltryptophan and minocycline, an antibiotic and inhibitor of kynurenine formation from tryptophan, were administered, respectively. Both treatments were effective in reducing spasms and elevating hippocampal kynurenic acid. A fecal microbiota transplant experiment was then performed to examine the contribution of the gut microbiota on spasm mitigation. Transplant of feces of ketogenic diet animals into normal diet animals was effective in reducing spasms.

INTERPRETATION

These results highlight the importance of tryptophan-kynurenine metabolism in infantile spasms and provide evidence for new-targeted therapies such as indoleamine 2,3-dioxygenase 1 inhibition or microbiota manipulation to promote kynurenic acid production as a strategy to reduce spasms in infantile spasms.

FUNDING

This study was funded by the Alberta Children's Hospital Research Institute and the Owerko Centre.

Integrating in vitro metabolomics with a 96-well high-throughput screening platform

INTRODUCTION

High-throughput screening (HTS) is emerging as an approach to support decision-making in chemical safety assessments. In parallel, in vitro metabolomics is a promising approach that can help accelerate the transition from animal models to high-throughput cell-based models in toxicity testing.

OBJECTIVE

In this study we establish and evaluate a high-throughput metabolomics workflow that is compatible with a 96-well HTS platform employing 50,000 hepatocytes of HepaRG per well.

METHODS

Low biomass cell samples were extracted for metabolomics analyses using a newly established semi-automated protocol, and the intracellular metabolites were analysed using a high-resolution spectral-stitching nanoelectrospray direct infusion mass spectrometry (nESI-DIMS) method that was modified for low sample biomass.

RESULTS

The method was assessed with respect to sensitivity and repeatability of the entire workflow from cell culturing and sampling to measurement of the metabolic phenotype, demonstrating sufficient sensitivity (> 3000 features in hepatocyte extracts) and intra- and inter-plate repeatability for polar nESI-DIMS assays (median relative standard deviation < 30%). The assays were employed for a proof-of-principle toxicological study with a model toxicant, cadmium chloride, revealing changes in the metabolome across five sampling times in the 48-h exposure period. To allow the option for lipidomics analyses, the solvent system was extended by establishing separate extraction methods for polar metabolites and lipids.

CONCLUSIONS

Experimental, analytical and informatics workflows reported here met pre-defined criteria in terms of sensitivity, repeatability and ability to detect metabolome changes induced by a toxicant and are ready for application in metabolomics-driven toxicity testing to complement HTS assays.

Automated Sample Preparation and Data Collection Workflow for High-Throughput In Vitro Metabolomics

Regulatory bodies have started to recognise the value of in vitro screening and metabolomics as two types of new approach methodologies (NAMs) for chemical risk assessments, yet few high-throughput in vitro toxicometabolomics studies have been reported. A significant challenge is to implement automated sample preparation of the low biomass samples typically used for in vitro screening. Building on previous work, we have developed, characterised and demonstrated an automated sample preparation and analysis workflow for in vitro metabolomics of HepaRG cells in 96-well microplates using a Biomek i7 Hybrid Workstation (Beckman Coulter) and Orbitrap Elite (Thermo Scientific) high-resolution nanoelectrospray direct infusion mass spectrometry (nESI-DIMS), across polar metabolites and lipids. The experimental conditions evaluated included the day of metabolite extraction, order of extraction of samples in 96-well microplates, position of the 96-well microplate on the instrument's deck and well location within a microplate. By using the median relative standard deviation (mRSD (%)) of spectral features, we have demonstrated good repeatability of the workflow (final mRSD < 30%) with a low percentage of features outside the threshold applied for statistical analysis. To improve the quality of the automated workflow further, small method modifications were made and then applied to a large cohort study (4860 sample infusions across three nESI-DIMS assays), which confirmed very high repeatability of the whole workflow from cell culturing to metabolite measurements, whilst providing a significant improvement in sample throughput. It is envisioned that the automated in vitro metabolomics workflow will help to advance the application of metabolomics (as a part of NAMs) in chemical safety, primarily as an approach for high throughput screening and prioritisation.

An integrated host-microbiome response to atrazine exposure mediates toxicity in Drosophila

The gut microbiome produces vitamins, nutrients, and neurotransmitters, and helps to modulate the host immune system-and also plays a major role in the metabolism of many exogenous compounds, including drugs and chemical toxicants. However, the extent to which specific microbial species or communities modulate hazard upon exposure to chemicals remains largely opaque. Focusing on the effects of collateral dietary exposure to the widely used herbicide atrazine, we applied integrated omics and phenotypic screening to assess the role of the gut microbiome in modulating host resilience in Drosophila melanogaster. Transcriptional and metabolic responses to these compounds are sex-specific and depend strongly on the presence of the commensal microbiome. Sequencing the genomes of all abundant microbes in the fly gut revealed an enzymatic pathway responsible for atrazine detoxification unique to Acetobacter tropicalis. We find that Acetobacter tropicalis alone, in gnotobiotic animals, is sufficient to rescue increased atrazine toxicity to wild-type, conventionally reared levels. This work points toward the derivation of biotic strategies to improve host resilience to environmental chemical exposures, and illustrates the power of integrative omics to identify pathways responsible for adverse health outcomes.

On-Water Selectivity Switch in Microdroplets in the 1,2,3-Triazole Synthesis from Bromoethenesulfonyl Fluoride

Water profoundly affects many organic reactions by accelerating them or changing their selectivity. Performing reactions "on-water" offers an intriguing opportunity to influence chemical reactivity. A nebulizer plume is an efficient way of generating microdroplets─the uniquely complex reaction environment which opens alternative possibilities that are not readily accessible in bulk emulsions. We describe the on-water switch of chemoselectivity in the formation of triazoles controlled by the on-water environment in dual spray. These conditions facilitate elimination of H-SO₂F from the triazoline intermediate, whereas the reaction in organic solvents results in the exclusive HBr elimination. The influence of two-phase conditions was investigated to obtain the best reaction efficiency, and the crucial importance of the water/organic interface interactions was verified by pH variation and D₂O use.

Metabolomics as a potential tool for the diagnosis of growth hormone deficiency (GHD): a review

Metabolomics uses several analytical tools to identify the chemical diversity of metabolites present in organisms. These metabolites are low molecular weight molecules (<1500 Da) classified as a final or intermediary product of metabolic processes. The application of this omics technology has become prominent in inferring physiological conditions through reporting on the phenotypic state; therefore, the introduction of metabolomics into clinical studies has been growing in recent years due to its efficiency in discriminating pathophysiological states. Regarding endocrine diseases, there is a great interest in verifying comprehensive and individualized physiological scenarios, in particular for growth hormone deficiency (GHD). The current GHD diagnostic tests are laborious and invasive and there is no exam with ideal reproducibility and sensitivity for diagnosis neither standard GH cut-off point. Therefore, this review was focussed on articles that applied metabolomics in the search for new biomarkers for GHD. The present work shows that the applications of metabolomics in GHD are still limited, since the little complementarily of analytical techniques, a low number of samples, GHD combined to other deficiencies, and idiopathic diagnosis shows a lack of progress. The results of the research are relevant and similar; however, their results do not provide an application for clinical practice due to the lack of multidisciplinary actions that would be needed to mediate the translation of the knowledge produced in the laboratory, if transferred to the medical setting.

Novel Stable Isotope-Resolved Metabolomics Method for a Small Number of Cells Using Chip-Based Nanoelectrospray Mass Spectrometry

Stable isotope-resolved metabolomics (SIRM) can provide metabolic conversion information of specific targets; it is a powerful tool for cell metabolism studies. The common analytical platform for SIRM is chromatography-mass spectrometry, which requires a large number of cells and is not suitable for precious rare cell analysis. To study a small number of cells, we established a novel SIRM method using chip-based nanoelectrospray mass spectrometry (MS). ¹³C-glutamine was taken as an example; the unlabeled and ¹³C-labeled cells were cultured and extracted in a 96-well plate and then directly injected into MS and analyzed in full scan mode and parallel reaction monitoring (PRM) mode targeting 44 glutamine-derived metabolites and their isotopologues. To define focused metabolite-related MS2 fragments produced in the PRM, a new strategy was proposed including MS2 exact m/z matching, MS2 false positive filtering, and MS2 fragment grouping to remove the interfering MS2 ions. In total, 292 and 349 pairs of paired MS2 ions were obtained in positive and negative ionization modes, respectively. By searching spectra databases, 31 targeted metabolites with their isotopologues were identified and their characteristic product ions were confirmed for MS2 quantification. The relative quantification was achieved by MS2 quantification, which showed better sensitivity and accuracy than common MS1-based quantification. Finally, this method was applied to isocitrate dehydrogenase I-mutated glioma cells for revealing the effects of triptolide on glioma cell metabolism using U-¹³C-glutamine as a labeling substrate.

An Extensive Metabolomics Workflow to Discover Cardiotoxin-Induced Molecular Perturbations in Microtissues

Discovering modes of action and predictive biomarkers of drug-induced structural cardiotoxicity offers the potential to improve cardiac safety assessment of lead compounds and enhance preclinical to clinical translation during drug development. Cardiac microtissues are a promising, physiologically relevant, in vitro model, each composed of ca. 500 cells. While untargeted metabolomics is capable of generating hypotheses on toxicological modes of action and discovering metabolic biomarkers, applying this technology to low-biomass microtissues in suspension is experimentally challenging. Thus, we first evaluated a filtration-based approach for harvesting microtissues and assessed the sensitivity and reproducibility of nanoelectrospray direct infusion mass spectrometry (nESI-DIMS) measurements of intracellular extracts, revealing samples consisting of 28 pooled microtissues, harvested by filtration, are suitable for profiling the intracellular metabolome and lipidome. Subsequently, an extensive workflow combining nESI-DIMS untargeted metabolomics and lipidomics of intracellular extracts with ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS/MS) analysis of spent culture medium, to profile the metabolic footprint and quantify drug exposure concentrations, was implemented. Using the synthetic drug and model cardiotoxin sunitinib, time-resolved metabolic and lipid perturbations in cardiac microtissues were investigated, providing valuable data for generating hypotheses on toxicological modes of action and identifying putative biomarkers such as disruption of purine metabolism and perturbation of polyunsaturated fatty acid levels.

SubTap, a Versatile 3D Printed Platform for Eavesdropping on Extracellular Interactions

Communication within the microbiome occurs through an immense diversity of small molecules. Capturing these microbial interactions is a significant challenge due to the complexity of the exometabolome and its sensitivity to environmental stimuli. Traditional methods for acquiring exometabolomic data from interacting microorganisms are limited by their low throughput or lack of sampling depth. To address this challenge, we introduce subtapping (short for substrate tapping), a technique for tapping into extracellular metabolites that are being transferred through the growth substrate during coculture. High-throughput subtapping is made possible by a new coculturing platform, named SubTap, that we engineered to resemble a 96-well plate. The three-dimensional (3D) printed SubTap platform captures the exometabolome in an agar compartment that connects physically separated growth chambers, which permits cell growth without competition for space. We show how SubTap facilitates replicable and quick detection of exometabolites via direct infusion mass spectrometry analysis. Using bacterial isolates from the soil, we apply SubTap to characterize the effects of growth medium, growth duration, and mixed versus unmixed coculturing on the exometabolome. Finally, we demonstrate SubTap's versatility by interrogating microbial interactions in multicultures with up to four strains. IMPORTANCE Improvements in experimental techniques and instrumentation have led to the discovery that the microbiome plays an essential role in human and environmental health. Nevertheless, there remain major impediments to conducting large-scale interrogations of the microbiome in a high-throughput manner, particularly in the field of exometabolomics. Existing methods to coculture microorganisms and interrogate their interactions are labor-intensive and low throughput. This inspired us to develop a solution for coculturing that was (i) open source, (ii) inexpensive, (iii) scalable, (iv) customizable, and (v) compatible with existing mass spectrometry instrumentation. Here, we present SubTap-a 3D printed coculturing platform that permits tapping directly into the growth substrate between physically separated, but interconnected, growth compartments. SubTap allows multiculture (with up to four distinct growth compartments) in spatially mixed or unmixed configurations and enables repeatable results with mass spectrometry, as shown by our validation with known compounds and cultures of one to four organisms.

The Combined Treatment With the FLT3-Inhibitor AC220 and the Complex I Inhibitor IACS-010759 Synergistically Depletes Wt- and FLT3-Mutated Acute Myeloid Leukemia Cells

Acute myeloid leukemia (AML) is an aggressive hematologic malignancy with a high mortality rate and relapse risk. Although progress on the genetic and molecular understanding of this disease has been made, the standard of care has changed minimally for the past 40 years and the five-year survival rate remains poor, warranting new treatment strategies. Here, we applied a two-step screening platform consisting of a primary cell viability screening and a secondary metabolomics-based phenotypic screening to find synergistic drug combinations to treat AML. A novel synergy between the oxidative phosphorylation inhibitor IACS-010759 and the FMS-like tyrosine kinase 3 (FLT3) inhibitor AC220 (quizartinib) was discovered in AML and then validated by ATP bioluminescence and apoptosis assays. In-depth stable isotope tracer metabolic flux analysis revealed that IACS-010759 and AC220 synergistically reduced glucose and glutamine enrichment in glycolysis and the TCA cycle, leading to impaired energy production and de novo nucleotide biosynthesis. In summary, we identified a novel drug combination, AC220 and IACS-010759, which synergistically inhibits cell growth in AML cells due to a major disruption of cell metabolism, regardless of FLT3 mutation status.

Comparison of Flow Injection-MS/MS and LC-MS/MS for the Determination of Ochratoxin A

Two methods for measuring ochratoxin A in corn, oat, and grape juice were developed and compared. Flow injection (FI) and on-line liquid chromatography (LC) performances were evaluated separately, with both methods using a triple quadrupole tandem mass spectrometer (MS/MS) for quantitation. Samples were fortified with ¹³C uniformly labeled ochratoxin A as the internal standard (¹³C-IS) and prepared by dilution and filtration, followed by FI- and LC-MS/MS analysis. For the LC-MS/MS method, which had a 10 min run time/sample, recoveries of ochratoxin A fortified at 1, 5, 20, and 100 ppb in corn, oat, red grape juice, and white grape juice ranged from 100% to 117% with RSDs < 9%. The analysis time of the FI-MS/MS method was <60 s/sample, however, the method could not detect ochratoxin A at the lowest fortification concentration, 1 ppb, in all tested matrix sources. At 5, 20, and 100 ppb, recoveries by FI-MS/MS ranged from 79 to 117% with RSDs < 15%. The FI-MS/MS method also had ~5× higher solvent and matrix-dependent instrument detection limits (0.12–0.35 ppb) compared to the LC-MS/MS method (0.02–0.06 ppb). In the analysis of incurred corn and oat samples, both methods generated comparable results within ±20% of reference values, however, the FI-MS/MS method failed to determine ochratoxin A in two incurred wheat flour samples due to co-eluted interferences due to the lack of chromatographic separation.

Progress towards an OECD reporting framework for transcriptomics and metabolomics in regulatory toxicology

Omics methodologies are widely used in toxicological research to understand modes and mechanisms of toxicity. Increasingly, these methodologies are being applied to questions of regulatory interest such as molecular point-of-departure derivation and chemical grouping/read-across. Despite its value, widespread regulatory acceptance of omics data has not yet occurred. Barriers to the routine application of omics data in regulatory decision making have been: 1) lack of transparency for data processing methods used to convert raw data into an interpretable list of observations; and 2) lack of standardization in reporting to ensure that omics data, associated metadata and the methodologies used to generate results are available for review by stakeholders, including regulators. Thus, in 2017, the Organisation for Economic Co-operation and Development (OECD) Extended Advisory Group on Molecular Screening and Toxicogenomics (EAGMST) launched a project to develop guidance for the reporting of omics data aimed at fostering further regulatory use. Here, we report on the ongoing development of the first formal reporting framework describing the processing and analysis of both transcriptomic and metabolomic data for regulatory toxicology. We introduce the modular structure, content, harmonization and strategy for trialling this reporting framework prior to its publication by the OECD.

Statistical analysis in metabolic phenotyping

Metabolic phenotyping is an important tool in translational biomedical research. The advanced analytical technologies commonly used for phenotyping, including mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy, generate complex data requiring tailored statistical analysis methods. Detailed protocols have been published for data acquisition by liquid NMR, solid-state NMR, ultra-performance liquid chromatography (LC-)MS and gas chromatography (GC-)MS on biofluids or tissues and their preprocessing. Here we propose an efficient protocol (guidelines and software) for statistical analysis of metabolic data generated by these methods. Code for all steps is provided, and no prior coding skill is necessary. We offer efficient solutions for the different steps required within the complete phenotyping data analytics workflow: scaling, normalization, outlier detection, multivariate analysis to explore and model study-related effects, selection of candidate biomarkers, validation, multiple testing correction and performance evaluation of statistical models. We also provide a statistical power calculation algorithm and safeguards to ensure robust and meaningful experimental designs that deliver reliable results. We exemplify the protocol with a two-group classification study and data from an epidemiological cohort; however, the protocol can be easily modified to cover a wider range of experimental designs or incorporate different modeling approaches. This protocol describes a minimal set of analyses needed to rigorously investigate typical datasets encountered in metabolic phenotyping.

Strategy for Nontargeted Metabolomic Annotation and Quantitation Using a High-Resolution Spectral-Stitching Nanoelectrospray Direct-Infusion Mass Spectrometry with Data-Independent Acquisition

Direct-infusion nanoelectrospray ionization high-resolution mass spectrometry (DI-nESI-HRMS) is an alternative approach to chromatography-MS-based techniques for nontargeted metabolomics, offering a high sample throughout. However, its annotation accuracy of analytes is still full of challenges. In this study, we proposed a strategy for the annotation and quantitation of nontargeted metabolomic data using a spectral-stitching DI-nESI-HRMS with data-independent acquisition. The metabolite annotation strategy included the isotopic distribution, MS/MS spectrum similarity, and precursor and product ion correlation as well as matching of the extracted metabolite features along with the targeted metabolite precursors. Two groups of mixed standard solutions containing 40 and 79 metabolites were, respectively, used to establish the metabolite annotation strategy and validate its reliability. The results showed that the detected standards could be well annotated at top three explanations and total qualitative percentages were 100% (40 of 40) for the standard solution and 94.9% (74 of 78) for the standards spiked into the serum matrix. The intensity of the precursor ions was used for quantitation except for isomers, which were quantified by the intensities of the characteristic product ions if available. Finally, the strategy was applied to study serum metabolomics in diabetes, and the results demonstrated that it is promising for a large-scale cohort metabolomic study.

Lipid Profiling of 20 Mammalian Cells by Capillary Microsampling Combined with High-Resolution Spectral Stitching Nanoelectrospray Ionization Direct-Infusion Mass Spectrometry

Studies of cellular metabolism can provide profound insights into the underlying molecular mechanisms and metabolic function. To date, the majority of cellular metabolism studies based on chromatography-mass spectrometry (MS) require population cells to obtain informative metabolome. These methods are not only time-consuming but also not suitable for amount-limited cell samples such as circulating tumor cells, stem cells, and neurons. Therefore, it is extremely essential to develop analytical methods enabling to detect metabolome from tens of cells in a high-throughput and high-sensitivity way. In this work, a novel platform for rapid and sensitive detection of lipidome in 20 mammalian cells was proposed using capillary microsampling combined with high-resolution spectral stitching nanoelectrospray ionization direct-infusion MS. It can be used to collect cells rapidly and accurately via the capillary microprobe, extract lipids directly in a 96-well plate using a spray solvent, and detect more than 500 lipids covering 19 lipid subclasses within 3 min. This novel platform was successfully applied to study the lipid features of different cancer cell types and subtypes as well as target cells from tissue samples. This study provides a strategy for determining the lipid species with rich information in tens of cells and demonstrates great potential for clinical applications.

Acoustic Mist Ionization Mass Spectrometry for Ultrahigh-Throughput Metabolomics Screening

Incorporating safety data early in the drug discovery pipeline is key to reducing costly lead candidate failures. For a single drug development project, we estimate that several thousand samples per day require screening (<10 s per acquisition). While chromatography-based metabolomics has proven value at predicting toxicity from metabolic biomarker profiles, it lacks sufficiently high sample throughput. Acoustic mist ionization mass spectrometry (AMI-MS) is an atmospheric pressure ionization approach that can measure metabolites directly from 384-well plates with unparalleled speed. We sought to implement a signal processing and data analysis workflow to produce high-quality AMI-MS metabolomics data and to demonstrate its application to drug safety screening. An existing direct infusion mass spectrometry workflow was adapted, extended, optimized, and tested, utilizing three AMI-MS data sets acquired from technical and biological replicates of metabolite standards and HepG2 cell lysates and a toxicity study. Driven by criteria to minimize variance and maximize feature counts, an algorithm to extract the pulsed scan data was designed; parameters for signal-to-noise-ratio, replicate filter, sample filter, missing value filter, and RSD filter were all optimized; normalization and batch correction strategies were adapted; and cell phenotype filtering was implemented to exclude high cytotoxicity samples. The workflow was demonstrated using a highly replicated HepG2 toxicity data set, comprising 2772 samples from exposures to 16 drugs across 9 concentrations and generated in under 5 h, revealing metabolic phenotypes and individual metabolite changes that characterize specific modes of action. This AMI-MS workflow opens the door to ultrahigh-throughput metabolomics screening, increasing the rate of sample analysis by approximately 2 orders of magnitude.

HIGH RESOLUTION MASS SPECTROMETRY IN LIPIDOMICS

The boost of research output in lipidomics during the last decade is tightly linked to improved instrumentation in mass spectrometry. Associated with this trend is the shift from low resolution-toward high-resolution lipidomics platforms. This review article summarizes the state of the art in the lipidomics field with a particular focus on the merits of high mass resolution. Following some theoretical considerations on the benefits of high mass resolution in lipidomics, it starts with a historical perspective on lipid analysis by sector instruments and moves further to today's instrumental approaches, including shotgun lipidomics, liquid chromatography-mass spectrometry, matrix-assisted laser desorption ionization-time-of-flight, and imaging lipidomics. Subsequently, several data processing and data analysis software packages are critically evaluated with all their pros and cons. Finally, this article emphasizes the importance and necessity of quality standards as the field evolves from its pioneering phase into a mature and robust omics technology and lists various initiatives for improving the applicability of lipidomics. © 2020 The Authors. Mass Spectrometry Reviews published by John Wiley & Sons Ltd. Mass Spec Rev.

Pharmacometabonomics: data processing and statistical analysis

Individual variations in drug efficacy, side effects and adverse drug reactions are still challenging that cannot be ignored in drug research and development. The aim of pharmacometabonomics is to better understand the pharmacokinetic properties of drugs and monitor the drug effects on specific metabolic pathways. Here, we systematically reviewed the recent technological advances in pharmacometabonomics for better understanding the pathophysiological mechanisms of diseases as well as the metabolic effects of drugs on bodies. First, the advantages and disadvantages of all mainstream analytical techniques were compared. Second, many data processing strategies including filtering, missing value imputation, quality control-based correction, transformation, normalization together with the methods implemented in each step were discussed. Third, various feature selection and feature extraction algorithms commonly applied in pharmacometabonomics were described. Finally, the databases that facilitate current pharmacometabonomics were collected and discussed. All in all, this review provided guidance for researchers engaged in pharmacometabonomics and metabolomics, and it would promote the wide application of metabolomics in drug research and personalized medicine.

Development of a chromatography-free method for high-throughput MS-based bioanalysis of therapeutic monoclonal antibodies

Aim: Our objective was to test the feasibility of developing an LC-free, MS-based approach for high-throughput bioanalysis of humanized therapeutic monoclonal antibodies. Methodology: A universal tryptic peptide from human IgG1, IgG3 and IgG4 was selected as the surrogate peptide for quantitation. After tryptic digestion, the surrogate peptide was fractionated via solid-phase extraction before being subjected to direct infusion-based MS/MS analysis. A high-resolution, multiplexed (MSX = 2) parallel reaction monitoring method was developed for data acquisition. Results & conclusion: This proof-of-concept study demonstrated the feasibility of achieving high-throughput MS-based bioanalysis of monoclonal antibodies using an LC-free workflow with sensitivity comparable to conventional LC-MS/MS-based methods.

Microbial Metabolomics: From Methods to Translational Applications

Most microbe-associated infectious diseases severely affect human health. However, clinical diagnosis of pathogenic diseases remains challenging due to the lack of specific and highly reliable methods. To better understand the diagnosis, pathogenesis, and treatment of these diseases, systems biology-driven metabolomics goes beyond the annotated phenotype and better targets the functions than conventional approaches. As a novel strategy for analysis of metabolomes in microbes, microbial metabolomics has been recently used to study many diseases, such as obesity, urinary tract infection (UTI), and hepatitis C. In this chapter, we attempt to introduce various microbial metabolomics methods to better interpret the microbial metabolism underlying a diversity of infectious diseases and inspire scientists to pay more attention to microbial metabolomics, enabling broadly and efficiently its translational applications to infectious diseases, from molecular diagnosis to therapeutic discovery.