Complementary environmental analysis and functional characterization of lower glycolysis-gluconeogenesis in the diatom plastid.

Organic carbon fixed in chloroplasts through the Calvin-Benson-Bassham Cycle can be diverted towards different metabolic fates, including cyoplasmic and mitochondrial respiration, gluconeogenesis, and synthesis of diverse plastid metabolites via the pyruvate hub. In plants, pyruvate is principally produced via cytoplasmic glycolysis, although a plastid-targeted lower glycolytic pathway is known to exist in non-photosynthetic tissue. Here, we characterized a lower plastid glycolysis-gluconeogenesis pathway enabling the direct interconversion of glyceraldehyde-3-phosphate and phospho-enol-pyruvate in diatoms, ecologically important marine algae distantly related to plants. We show that two reversible enzymes required to complete diatom plastid glycolysis-gluconeogenesis, Enolase and bis-phospho-glycerate mutase (PGAM), originated through duplications of mitochondria-targeted respiratory isoforms. Through CRISPR-Cas9 mutagenesis, integrative 'omic analyses, and measured kinetics of expressed enzymes in the diatom Phaeodactylum tricornutum, we present evidence that this pathway diverts plastid glyceraldehyde-3-phosphate into the pyruvate hub, and may also function in the gluconeogenic direction. Considering experimental data, we show that this pathway has different roles dependent in particular on day length and environmental temperature, and show that the cpEnolase and cpPGAM genes are expressed at elevated levels in high latitude oceans where diatoms are abundant. Our data provide evolutionary, meta-genomic and functional insights into a poorly understood yet evolutionarily recurrent plastid metabolic pathway.

CLIPPER 2.0: Peptide-Level Annotation and Data Analysis for Positional Proteomics.

Positional proteomics methodologies have transformed protease research, and have brought mass spectrometry (MS)-based degradomics studies to the forefront of protease characterization and system-wide interrogation of protease signaling. Considerable advancements in both sensitivity and throughput of liquid chromatography (LC)-MS/MS instrumentation enable the generation of enormous positional proteomics datasets of natural and protein termini and neo-termini of cleaved protease substrates. However, concomitant progress has not been observed to the same extent in data analysis and post-processing steps, arguably constituting the largest bottleneck in positional proteomics workflows. Here, we present a computational tool, CLIPPER 2.0, that builds on prior algorithms developed for MS-based protein termini analysis, facilitating peptide-level annotation and data analysis. CLIPPER 2.0 can be used with several sample preparation workflows and proteomics search algorithms and enables fast and automated database information retrieval, statistical and network analysis, as well as visualization of terminomic datasets. We demonstrate the applicability of our tool by analyzing GluC and MMP9 cleavages in HeLa lysates. CLIPPER 2.0 is available at https://github.com/UadKLab/CLIPPER-2.0.

An Automated and Fast Sample Preparation Workflow for Laser Microdissection Guided Ultrasensitive Proteomics.

Spatial tissue proteomics integrating whole-slide imaging, laser microdissection, and ultrasensitive mass spectrometry is a powerful approach to link cellular phenotypes to functional proteome states in (patho)physiology. To be applicable to large patient cohorts and low sample input amounts, including single-cell applications, loss-minimized and streamlined end-to-end workflows are key. We here introduce an automated sample preparation protocol for laser microdissected samples utilizing the cellenONE robotic system, which has the capacity to process 192 samples in 3 h. Following laser microdissection collection directly into the proteoCHIP LF 48 or EVO 96 chip, our optimized protocol facilitates lysis, formalin de-crosslinking, and tryptic digest of low-input archival tissue samples. The seamless integration with the Evosep ONE LC system by centrifugation allows 'on-the-fly' sample clean-up, particularly pertinent for laser microdissection workflows. We validate our method in human tonsil archival tissue, where we profile proteomes of spatially-defined B-cell, T-cell, and epithelial microregions of 4000 µm2 to a depth of ∼2000 proteins and with high cell type specificity. We finally provide detailed equipment templates and experimental guidelines for broad accessibility.

Comprehensive investigation of pathway enrichment methods for functional interpretation of LC-MS global metabolomics data.

BACKGROUND: Global or untargeted metabolomics is widely used to comprehensively investigate metabolic profiles under various pathophysiological conditions such as inflammations, infections, responses to exposures or interactions with microbial communities. However, biological interpretation of global metabolomics data remains a daunting task. Recent years have seen growing applications of pathway enrichment analysis based on putative annotations of liquid chromatography coupled with mass spectrometry (LC-MS) peaks for functional interpretation of LC-MS-based global metabolomics data. However, due to intricate peak-metabolite and metabolite-pathway relationships, considerable variations are observed among results obtained using different approaches. There is an urgent need to benchmark these approaches to inform the best practices. RESULTS: We have conducted a benchmark study of common peak annotation approaches and pathway enrichment methods in current metabolomics studies. Representative approaches, including three peak annotation methods and four enrichment methods, were selected and benchmarked under different scenarios. Based on the results, we have provided a set of recommendations regarding peak annotation, ranking metrics and feature selection. The overall better performance was obtained for the mummichog approach. We have observed that a ~30% annotation rate is sufficient to achieve high recall (~90% based on mummichog), and using semi-annotated data improves functional interpretation. Based on the current platforms and enrichment methods, we further propose an identifiability index to indicate the possibility of a pathway being reliably identified. Finally, we evaluated all methods using 11 COVID-19 and 8 inflammatory bowel diseases (IBD) global metabolomics datasets.

Advances in targeted liquid chromatography-tandem mass spectrometry methods for endocannabinoid and N-acylethanolamine quantification in biological matrices: A systematic review.

Liquid chromatography paired with tandem mass spectrometry (LC-MS/MS) is the gold standard in measurement of endocannabinoid concentrations in biomatrices. We conducted a systematic review of literature to identify advances in targeted LC-MS/MS methods in the period 2017-2024. We found that LC-MS/MS methods for endocannabinoid quantification are relatively consistent both across time and across biomatrices. Recent advances have primarily been in three areas: (1) sample preparation techniques, specific to the chosen biomatrix; (2) the range of biomatrices tested, recently favoring blood matrices; and (3) the breadth of endocannabinoid and endocannabinoid-like analytes incorporated into assays. This review provides a summary of the recent literature and a guide for researchers looking to establish the best methods for quantifying endocannabinoids in a range of biomatrices.

HowDirty: An R package to evaluate molecular contaminants in LC-MS experiments.

Contaminants derived from consumables, reagents, and sample handling often negatively affect LC-MS data acquisition. In proteomics experiments, they can markedly reduce identification performance, reproducibility, and quantitative robustness. Here, we introduce a data analysis workflow combining MS1 feature extraction in Skyline with HowDirty, an R-markdown-based tool, that automatically generates an interactive report on the molecular contaminant level in LC-MS data sets. To facilitate the interpretation of the results, the HTML report is self-contained and self-explanatory, including plots that can be easily interpreted. The R package HowDirty is available from https://github.com/DavidGZ1/HowDirty. To demonstrate a showcase scenario for the application of HowDirty, we assessed the impact of ultrafiltration units from different providers on sample purity after filter-assisted sample preparation (FASP) digestion. This allowed us to select the filter units with the lowest contamination risk. Notably, the filter units with the lowest contaminant levels showed higher reproducibility regarding the number of peptides and proteins identified. Overall, HowDirty enables the efficient evaluation of sample quality covering a wide range of common contaminant groups that typically impair LC-MS analyses, facilitating corrective or preventive actions to minimize instrument downtime.

Lipidomics and mass spectrometry imaging unveil alterations in mice hippocampus lipid composition exposed to hypoxia.

Lipids are components of cytomembranes that are involved in various biochemical processes. High-altitude hypoxic environments not only affect the body's energy metabolism, but these environments can also cause abnormal lipid metabolism involved in the hypoxia-induced cognitive impairment. Thus, comprehensive lipidomic profiling of the brain tissue is an essential step toward understanding the mechanism of cognitive impairment induced by hypoxic exposure. In the present study, mice showed reduced new-object recognition and spatial memory when exposed to hypobaric hypoxia for 1 day. Histomorphological staining revealed significant morphological and structural damage to the hippocampal tissue, along with prolonged exposure to hypobaric hypoxia. Dynamic lipidomics of the mouse hippocampus showed a significant shift in both the type and distribution of phospholipids, as verified by spatial lipid mapping. Collectively, a diverse and dynamic lipid composition in mice hippocampus was uncovered, which deepens our understanding of biochemical changes during sustained hypoxic exposure and could provide new insights into the cognitive decline induced by high-altitude hypoxia exposure.

Altered Metabolism during the Dark Period in Drosophila Short Sleep Mutants.

Sleep is regulated via circadian mechanisms, but effects of sleep disruption on physiological rhythms, in particular metabolic cycling, remain unclear. To examine this question, we probed diurnal metabolic alterations of two Drosophila short sleep mutants, fumin and sleepless. Samples were collected with high temporal sampling (every 2 h) over 24 h under a 12:12 light:dark cycle, and profiling was done using an ion-switching LCMS/MS method. Fewer metabolites with 24 h oscillations were noted with short sleep (50 and 46 in fumin and sleepless, BH. Q < 0.2 by RAIN analysis) compared to a wild-type control (iso31, 63 with BH. Q < 0.2), and peak phases of the sleep mutants were consolidated into two major phase peaks at mid-day and middle of night. Overall, altered nicotinate/nicotinamide, alanine/aspartate/glutamate, acetylcholine, glyoxylate/dicarboxylate, and TCA cycle metabolism were observed in the short sleep mutants, indicative of increased energetic demand and oxidative stress compared to wild type. Both changes in cycling and discriminant models suggest unique alterations in the dark period indicative of constrained metabolic networks. Thus, we conclude that sleep loss alters metabolic function uniquely throughout the day, and further examination of specific mechanisms is warranted.

Multicenter Collaborative Study to Optimize Mass Spectrometry Workflows of Clinical Specimens.

The foundation for integrating mass spectrometry (MS)-based proteomics into systems medicine is the development of standardized start-to-finish and fit-for-purpose workflows for clinical specimens. An essential step in this pursuit is to highlight the common ground in a diverse landscape of different sample preparation techniques and liquid chromatography-mass spectrometry (LC-MS) setups. With the aim to benchmark and improve the current best practices among the proteomics MS laboratories of the CLINSPECT-M consortium, we performed two consecutive round-robin studies with full freedom to operate in terms of sample preparation and MS measurements. The six study partners were provided with two clinically relevant sample matrices: plasma and cerebrospinal fluid (CSF). In the first round, each laboratory applied their current best practice protocol for the respective matrix. Based on the achieved results and following a transparent exchange of all lab-specific protocols within the consortium, each laboratory could advance their methods before measuring the same samples in the second acquisition round. Both time points are compared with respect to identifications (IDs), data completeness, and precision, as well as reproducibility. As a result, the individual performances of participating study centers were improved in the second measurement, emphasizing the effect and importance of the expert-driven exchange of best practices for direct practical improvements.

Online Mixed-Bed Ion Exchange Chromatography for Native Top-Down Proteomics of Complex Mixtures.

Native top-down mass spectrometry (nTDMS) allows characterization of protein structure and noncovalent interactions with simultaneous sequence mapping and proteoform characterization. The majority of nTDMS studies utilize purified recombinant proteins, with significant challenges hindering application to endogenous systems. To perform native top-down proteomics (nTDP), where endogenous proteins from complex biological systems are analyzed by nTDMS, it is essential to separate proteins under nondenaturing conditions. However, it remains difficult to achieve high resolution with MS-compatible online chromatography while preserving protein tertiary structure and noncovalent interactions. Herein, we report the use of online mixed-bed ion exchange chromatography (IEC) to enable separation of endogenous proteins from complex mixtures under nondenaturing conditions, preserving noncovalent interactions for nTDP analysis. We have successfully detected large proteins (>146 kDa) and identified endogenous metal-binding and oligomeric protein complexes in human heart tissue lysate. The use of a mixed-bed stationary phase allowed retention and elution of proteins over a wide range of isoelectric points without altering the sample or mobile phase pH. Overall, our method provides a simple online IEC-MS platform that can effectively separate proteins from complex mixtures under nondenaturing conditions and preserve higher-order structure for nTDP applications.

Offline Two-Dimensional Liquid Chromatography-Mass Spectrometry for Deep Annotation of the Fecal Metabolome Following Fecal Microbiota Transplantation.

Biological interpretation of untargeted LC-MS-based metabolomics data depends on accurate compound identification, but current techniques fall short of identifying most features that can be detected. The human fecal metabolome is complex, variable, incompletely annotated, and serves as an ideal matrix to evaluate novel compound identification methods. We devised an experimental strategy for compound annotation using multidimensional chromatography and semiautomated feature alignment and applied these methods to study the fecal metabolome in the context of fecal microbiota transplantation (FMT) for recurrent C. difficile infection. Pooled fecal samples were fractionated using semipreparative liquid chromatography and analyzed by an orthogonal LC-MS/MS method. The resulting spectra were searched against commercial, public, and local spectral libraries, and annotations were vetted using retention time alignment and prediction. Multidimensional chromatography yielded more than a 2-fold improvement in identified compounds compared to conventional LC-MS/MS and successfully identified several rare and previously unreported compounds, including novel fatty-acid conjugated bile acid species. Using an automated software-based feature alignment strategy, most metabolites identified by the new approach could be matched to features that were detected but not identified in single-dimensional LC-MS/MS data. Overall, our approach represents a powerful strategy to enhance compound identification and biological insight from untargeted metabolomics data.

Cross-Validation of Metabolic Phenotypes in SARS-CoV-2 Infected Subpopulations Using Targeted Liquid Chromatography-Mass Spectrometry (LC-MS).

To ensure biological validity in metabolic phenotyping, findings must be replicated in independent sample sets. Targeted workflows have long been heralded as ideal platforms for such validation due to their robust quantitative capability. We evaluated the capability of liquid chromatography-mass spectrometry (LC-MS) assays targeting organic acids and bile acids to validate metabolic phenotypes of SARS-CoV-2 infection. Two independent sample sets were collected: (1) Australia: plasma, SARS-CoV-2 positive (n = 20), noninfected healthy controls (n = 22) and COVID-19 disease-like symptoms but negative for SARS-CoV-2 infection (n = 22). (2) Spain: serum, SARS-CoV-2 positive (n = 33) and noninfected healthy controls (n = 39). Multivariate modeling using orthogonal projections to latent structures discriminant analyses (OPLS-DA) classified healthy controls from SARS-CoV-2 positive (Australia; R2 = 0.17, ROC-AUC = 1; Spain R2 = 0.20, ROC-AUC = 1). Univariate analyses revealed 23 significantly different (p < 0.05) metabolites between healthy controls and SARS-CoV-2 positive individuals across both cohorts. Significant metabolites revealed consistent perturbations in cellular energy metabolism (pyruvic acid, and 2-oxoglutaric acid), oxidative stress (lactic acid, 2-hydroxybutyric acid), hypoxia (2-hydroxyglutaric acid, 5-aminolevulinic acid), liver activity (primary bile acids), and host-gut microbial cometabolism (hippuric acid, phenylpropionic acid, indole-3-propionic acid). These data support targeted LC-MS metabolic phenotyping workflows for biological validation in independent sample sets.

Multiomics Characterization of a Less Invasive Microfluidic-Based Cell Sorting Technique.

Fluorescence-activated cell sorting (FACS) is a specialized technique to isolate specific cell subpopulations with a high level of recovery and accuracy. However, the cell sorting procedure can impact the viability and metabolic state of cells. Here, we performed a comparative study and evaluated the impact of traditional high-pressure charged droplet-based and microfluidic chip-based sorting on the metabolic and phosphoproteomic profile of different cell types. While microfluidic chip-based sorted cells more closely resembled the unsorted control group for most cell types tested, the droplet-based sorted cells showed significant metabolic and phosphoproteomic alterations. In particular, greater changes in redox and energy status were present in cells sorted with the droplet-based cell sorter along with larger shifts in proteostasis. 13C-isotope tracing analysis on cells recovering postsorting revealed that the sorter-induced suppression of mitochondrial TCA cycle activity recovered faster in the microfluidic chip-based sorted group. Apart from this, amino acid and lipid biosynthesis pathways were suppressed in sorted cells, with minimum impact and faster recovery in the microfluidic chip-based sorted group. These results indicate microfluidic chip-based sorting has a minimum impact on metabolism and is less disruptive compared to droplet-based sorting.

Variability of human Alpha-1-acid glycoprotein N-glycome in a Caucasian population.

AIM: Alpha-1-acid glycoprotein (AGP) is a highly glycosylated protein in human plasma and one of the most abundant acute phase proteins in humans. Glycosylation plays a crucial role in its biological functions, and alterations in AGP N-glycome have been associated with various diseases and inflammatory conditions. However, large-scale studies of AGP N-glycosylation in the general population are lacking. METHODS: Using recently developed high-throughput glycoproteomic workflow for site-specific AGP N-glycosylation analysis, 803 individuals from the Croatian island of Korcula were analyzed and their AGP N-glycome data associated with biochemical and physiological traits, as well as different environmental factors. RESULTS: After regression analysis, we found that AGP N-glycosylation is strongly associated with sex, somewhat less with age, along with multiple biochemical and physiological traits (e.g. BMI, triglycerides, uric acid, glucose, smoking status, fibrinogen). CONCLUSION: For the first time we have extensively explored the inter-individual variability of AGP N-glycome in a general human population, demonstrating its changes with sex, age, biochemical, and physiological status of individuals, providing the baseline for future population and clinical studies.

Changes in the amount of nucleotide sugars in aged mouse tissues.

Aging affects tissue glycan profiles, which may alter cellular functions and increase the risk of age-related diseases. Glycans are biosynthesized by glycosyltransferases using the corresponding nucleotide sugar, and the availability of nucleotide sugars affects glycosylation efficiency. However, the effects of aging on nucleotide sugar profiles and contents are yet to be elucidated. Therefore, this study aimed to investigate the effects of aging on nucleotide sugars using a new LC-MS/MS method. Specifically, the new method was used to determine the nucleotide sugar contents of various tissues (brain, liver, heart, skeletal muscle, kidney, lung, and colon) of male C57BL/6NCr mice (7- or 26-month-old). Characteristic age-associated nucleotide sugar changes were observed in each tissue sample. Particularly, there was a significant decrease in UDP-glucuronic acid content in the kidney of aged mice and a decrease in the contents of several nucleotide sugars, including UDP-N-acetylgalactosamine, in the brain of aged mice. Additionally, there were variations in nucleotide sugar profiles among the tissues examined regardless of the age. The kidneys had the highest concentration of UDP-glucuronic acid among the seven tissues. In contrast, the skeletal muscle had the lowest concentration of total nucleotide sugars among the tissues; however, CMP-N-acetylneuraminic acid and CDP-ribitol were relatively enriched. Conclusively, these findings may contribute to the understanding of the roles of glycans in tissue aging.

Salivary metabolomic biomarkers for esophageal and gastric cancers by liquid chromatography-mass spectrometry.

Early detection of esophageal and gastric cancers is essential for patients' prognosis; however, optimal noninvasive screening tests are currently not available. Saliva is a biofluid that is readily available, allowing for frequent screening tests. Thus, we explored salivary diagnostic biomarkers for esophageal and gastric cancers using metabolomic analyses. Saliva samples were collected from patients with esophageal (n = 50) and gastric cancer (n = 63), and patients without cancer as controls (n = 20). Salivary metabolites were analyzed by liquid chromatography-mass spectrometry to identify salivary biomarkers. We also examined the metabolic profiles of gastric cancer tissues and compared them with the salivary biomarkers. The sensitivity of the diagnostic models based on salivary biomarkers was assessed by comparing it with that of serum tumor markers. Additionally, using postoperative saliva samples collected from patients with gastric cancer, we analyzed the changes in the biomarkers' concentrations before and after surgery. Cytosine was detected as a salivary biomarker for gastric cancer, and cytosine, 2-oxoglutarate, and arginine were detected as salivary biomarkers for esophageal cancer. Cytidine, a cytosine nucleotide, showed decreased concentrations in gastric cancer tissues. The sensitivity of the diagnostic models for esophageal and gastric cancers was 66.0% and 47.6%, respectively, while that of serum tumor markers was 40%. Salivary cytosine concentration increased significantly postoperatively relative to the preoperative value. In summary, we identified salivary biomarkers for esophageal and gastric cancers, which showed diagnostic sensitivity at least comparable to that of serum tumor markers. Salivary metabolomic tests could be promising screening tests for these types of cancer.

Predictive analysis of breast cancer response to neoadjuvant chemotherapy through plasma metabolomics.

PURPOSE: Preoperative chemotherapy is a critical component of breast cancer management, yet its effectiveness is not uniform. Moreover, the adverse effects associated with chemotherapy necessitate the identification of a patient subgroup that would derive the maximum benefit from this treatment. This study aimed to establish a method for predicting the response to neoadjuvant chemotherapy in breast cancer patients utilizing a metabolomic approach. METHODS: Plasma samples were obtained from 87 breast cancer patients undergoing neoadjuvant chemotherapy at our facility, collected both before the commencement of the treatment and before the second treatment cycle. Metabolite analysis was conducted using capillary electrophoresis-mass spectrometry (CE-MS) and liquid chromatography-mass spectrometry (LC-MS). We performed comparative profiling of metabolite concentrations by assessing the metabolite profiles of patients who achieved a pathological complete response (pCR) against those who did not, both in initial and subsequent treatment cycles. RESULTS: Significant variances were observed in the metabolite profiles between pCR and non-pCR cases, both at the onset of preoperative chemotherapy and before the second cycle. Noteworthy distinctions were also evident between the metabolite profiles from the initial and the second neoadjuvant chemotherapy courses. Furthermore, metabolite profiles exhibited variations associated with intrinsic subtypes at all assessed time points. CONCLUSION: The application of plasma metabolomics, utilizing CE-MS and LC-MS, may serve as a tool for predicting the efficacy of neoadjuvant chemotherapy in breast cancer in the future after all necessary validations have been completed.

RNase H-based analysis of synthetic mRNA 5' cap incorporation.

Advances in mRNA synthesis and lipid nanoparticles technologies have helped make mRNA therapeutics and vaccines a reality. The 5' cap structure is a crucial modification required to functionalize synthetic mRNA for efficient protein translation in vivo and evasion of cellular innate immune responses. The extent of 5' cap incorporation is one of the critical quality attributes in mRNA manufacturing. RNA cap analysis involves multiple steps: generation of predefined short fragments from the 5' end of the kilobase-long synthetic mRNA molecules using RNase H, a ribozyme or a DNAzyme, enrichment of the 5' cleavage products, and LC-MS intact mass analysis. In this paper, we describe (1) a framework to design site-specific RNA cleavage using RNase H; (2) a method to fluorescently label the RNase H cleavage fragments for more accessible readout methods such as gel electrophoresis or high-throughput capillary electrophoresis; (3) a simplified method for post-RNase H purification using desthiobiotinylated oligonucleotides and streptavidin magnetic beads followed by elution using water. By providing a design framework for RNase H-based RNA 5' cap analysis using less resource-intensive analytical methods, we hope to make RNA cap analysis more accessible to the scientific community.

Protein turnover models for LC-MS data of heavy water metabolic labeling.

Protein turnover is vital for cellular functioning and is often associated with the pathophysiology of a variety of diseases. Metabolic labeling with heavy water followed by liquid chromatography coupled to mass spectrometry is a powerful tool to study in vivo protein turnover in high throughput and large scale. Heavy water is a cost-effective and easy to use labeling agent. It labels all nonessential amino acids. Due to its toxicity in high concentrations (20% or higher), small enrichments (8% or smaller) of heavy water are used with most organisms. The low concentration results in incomplete labeling of peptides/proteins. Therefore, the data processing is more challenging and requires accurate quantification of labeled and unlabeled forms of a peptide from overlapping mass isotopomer distributions. The work describes the bioinformatics aspects of the analysis of heavy water labeled mass spectral data, available software tools and current challenges and opportunities.

Recent advances in LC-MS-based metabolomics for clinical biomarker discovery.

The employment of liquid chromatography-mass spectrometry (LC-MS) untargeted and targeted metabolomics has led to the discovery of novel biomarkers and improved the understanding of various disease mechanisms. Numerous strategies have been reported to expand the metabolite coverage in LC-MS-untargeted and targeted metabolomics. To improve the sensitivity of low-abundance or poor-ionized metabolites for reducing the amount of clinical sample, chemical derivatization methods are used to target different functional groups. Proper sample preparation is beneficial for reducing the matrix effect, maintaining the stability of the LC-MS system, and increasing the metabolite coverage. Machine learning has recently been integrated into the workflow of LC-MS metabolomics to accelerate metabolite identification and data-processing automation, and increase the accuracy of disease classification and clinical outcome prediction. Due to the rapidly growing utility of LC-MS metabolomics in discovering disease markers, this review will address the recent advances in the field and offer perspectives on various strategies for expanding metabolite coverage, chemical derivatization, sample preparation, clinical disease markers, and machining learning for disease modeling.