Perspective: use and reuse of NMR-based metabolomics data: what works and what remains challenging.

BACKGROUND: The National Cancer Institute issued a Request for Information (RFI; NOT-CA-23-007) in October 2022, soliciting input on using and reusing metabolomics data. This RFI aimed to gather input on best practices for metabolomics data storage, management, and use/reuse. AIM OF REVIEW: The nuclear magnetic resonance (NMR) Interest Group within the Metabolomics Association of North America (MANA) prepared a set of recommendations regarding the deposition, archiving, use, and reuse of NMR-based and, to a lesser extent, mass spectrometry (MS)-based metabolomics datasets. These recommendations were built on the collective experiences of metabolomics researchers within MANA who are generating, handling, and analyzing diverse metabolomics datasets spanning experimental (sample handling and preparation, NMR/MS metabolomics data acquisition, processing, and spectral analyses) to computational (automation of spectral processing, univariate and multivariate statistical analysis, metabolite prediction and identification, multi-omics data integration, etc.) studies. KEY SCIENTIFIC CONCEPTS OF REVIEW: We provide a synopsis of our collective view regarding the use and reuse of metabolomics data and articulate several recommendations regarding best practices, which are aimed at encouraging researchers to strengthen efforts toward maximizing the utility of metabolomics data, multi-omics data integration, and enhancing the overall scientific impact of metabolomics studies.

ADAP-KDB: A Spectral Knowledgebase for Tracking and Prioritizing Unknown GC-MS Spectra in the NIH's Metabolomics Data Repository.

We report the development of a spectral knowledgebase named ADAP-KDB for tracking and prioritizing unknown gas chromatography-mass spectrometry (GC-MS) spectra in the NIH's Metabolomics Data Repository-a national and international repository for metabolomics data. ADAP-KDB consists of two parts. One part is a computational workflow that preprocesses raw mass spectrometry data and derives consensus mass spectra. The other part is a web portal for users to browse the consensus spectra and match query spectra against them. For each consensus spectrum, the Gini-Simpson diversity index and the p-value from χ2 goodness-of-fit test are calculated to measure its statistical significance, which enables prioritization of unknown mass spectra for subsequent costly compound identification.

A Practical Guide to Metabolomics Software Development.

A growing number of software tools have been developed for metabolomics data processing and analysis. Many new tools are contributed by metabolomics practitioners who have limited prior experience with software development, and the tools are subsequently implemented by users with expertise that ranges from basic point-and-click data analysis to advanced coding. This Perspective is intended to introduce metabolomics software users and developers to important considerations that determine the overall impact of a publicly available tool within the scientific community. The recommendations reflect the collective experience of an NIH-sponsored Metabolomics Consortium working group that was formed with the goal of researching guidelines and best practices for metabolomics tool development. The recommendations are aimed at metabolomics researchers with little formal background in programming and are organized into three stages: (i) preparation, (ii) tool development, and (iii) distribution and maintenance.

Aldh1l2 knockout mouse metabolomics links the loss of the mitochondrial folate enzyme to deregulation of a lipid metabolism observed in rare human disorder.

BACKGROUND: Mitochondrial folate enzyme ALDH1L2 (aldehyde dehydrogenase 1 family member L2) converts 10-formyltetrahydrofolate to tetrahydrofolate and CO2 simultaneously producing NADPH. We have recently reported that the lack of the enzyme due to compound heterozygous mutations was associated with neuro-ichthyotic syndrome in a male patient. Here, we address the role of ALDH1L2 in cellular metabolism and highlight the mechanism by which the enzyme regulates lipid oxidation. METHODS: We generated Aldh1l2 knockout (KO) mouse model, characterized its phenotype, tissue histology, and levels of reduced folate pools and applied untargeted metabolomics to determine metabolic changes in the liver, pancreas, and plasma caused by the enzyme loss. We have also used NanoString Mouse Inflammation V2 Code Set to analyze inflammatory gene expression and evaluate the role of ALDH1L2 in the regulation of inflammatory pathways. RESULTS: Both male and female Aldh1l2 KO mice were viable and did not show an apparent phenotype. However, H&E and Oil Red O staining revealed the accumulation of lipid vesicles localized between the central veins and portal triads in the liver of Aldh1l2-/- male mice indicating abnormal lipid metabolism. The metabolomic analysis showed vastly changed metabotypes in the liver and plasma in these mice suggesting channeling of fatty acids away from ß-oxidation. Specifically, drastically increased plasma acylcarnitine and acylglycine conjugates were indicative of impaired ß-oxidation in the liver. Our metabolomics data further showed that mechanistically, the regulation of lipid metabolism by ALDH1L2 is linked to coenzyme A biosynthesis through the following steps. ALDH1L2 enables sufficient NADPH production in mitochondria to maintain high levels of glutathione, which in turn is required to support high levels of cysteine, the coenzyme A precursor. As the final outcome, the deregulation of lipid metabolism due to ALDH1L2 loss led to decreased ATP levels in mitochondria. CONCLUSIONS: The ALDH1L2 function is important for CoA-dependent pathways including ß-oxidation, TCA cycle, and bile acid biosynthesis. The role of ALDH1L2 in the lipid metabolism explains why the loss of this enzyme is associated with neuro-cutaneous diseases. On a broader scale, our study links folate metabolism to the regulation of lipid homeostasis and the energy balance in the cell.

ADAP-GC 4.0: Application of Clustering-Assisted Multivariate Curve Resolution to Spectral Deconvolution of Gas Chromatography-Mass Spectrometry Metabolomics Data.

We report a multivariate curve resolution (MCR)-based spectral deconvolution workflow for untargeted gas chromatography-mass spectrometry metabolomics. As an essential step in preprocessing such data, spectral deconvolution computationally separates ions that are in the same mass spectrum but belong to coeluting compounds that are not resolved completely by chromatography. As a result of this computational separation, spectral deconvolution produces pure fragmentation mass spectra. Traditionally, spectral deconvolution has been achieved by using a model peak approach. We describe the fundamental differences between the model peak-based and the MCR-based spectral deconvolution and report ADAP-GC 4.0 that employs the latter approach while overcoming the associated computational complexity. ADAP-GC 4.0 has been evaluated using GC-TOF data sets from a 27-standards mixture at different dilutions and urine with the mixture spiked in, and GC Orbitrap data sets from mixtures of different standards. It produced the average matching scores 960, 959, and 926 respectively. Moreover, its performance has been compared against MS-DIAL, eRah, and ADAP-GC 3.2, and ADAP-GC 4.0 demonstrated a higher number of matched compounds and up to 6% increase of the average matching score.

ADAP-GC 3.2: Graphical Software Tool for Efficient Spectral Deconvolution of Gas Chromatography-High-Resolution Mass Spectrometry Metabolomics Data.

ADAP-GC is an automated computational workflow for extracting metabolite information from raw, untargeted gas chromatography-mass spectrometry metabolomics data. Deconvolution of coeluting analytes is a critical step in the workflow, and the underlying algorithm is able to extract fragmentation mass spectra of coeluting analytes with high accuracy. However, its latest version ADAP-GC 3.0 was not user-friendly. To make ADAP-GC easier to use, we have developed ADAP-GC 3.2 and describe here the improvements on three aspects. First, all of the algorithms in ADAP-GC 3.0 written in R have been replaced by their analogues in Java and incorporated into MZmine 2 to make the workflow user-friendly. Second, the clustering algorithm DBSCAN has replaced the original hierarchical clustering to allow faster spectral deconvolution. Finally, algorithms originally developed for constructing extracted ion chromatograms (EICs) and detecting EIC peaks from LC-MS data are incorporated into the ADAP-GC workflow, allowing the latter to process high mass resolution data. Performance of ADAP-GC 3.2 has been evaluated using unit mass resolution data from standard-mixture and urine samples. The identification and quantitation results were compared with those produced by ADAP-GC 3.0, AMDIS, AnalyzerPro, and ChromaTOF. Identification results for high mass resolution data derived from standard-mixture samples are presented as well.

One Step Forward for Reducing False Positive and False Negative Compound Identifications from Mass Spectrometry Metabolomics Data: New Algorithms for Constructing Extracted Ion Chromatograms and Detecting Chromatographic Peaks.

False positive and false negative peaks detected from extracted ion chromatograms (EIC) are an urgent problem with existing software packages that preprocess untargeted liquid or gas chromatography-mass spectrometry metabolomics data because they can translate downstream into spurious or missing compound identifications. We have developed new algorithms that carry out the sequential construction of EICs and detection of EIC peaks. We compare the new algorithms to two popular software packages XCMS and MZmine 2 and present evidence that these new algorithms detect significantly fewer false positives. Regarding the detection of compounds known to be present in the data, the new algorithms perform at least as well as XCMS and MZmine 2. Furthermore, we present evidence that mass tolerance in m/z should be favored rather than mass tolerance in ppm in the process of constructing EICs. The mass tolerance parameter plays a critical role in the EIC construction process and can have immense impact on the detection of EIC peaks.

Detailed Investigation and Comparison of the XCMS and MZmine 2 Chromatogram Construction and Chromatographic Peak Detection Methods for Preprocessing Mass Spectrometry Metabolomics Data.

XCMS and MZmine 2 are two widely used software packages for preprocessing untargeted LC/MS metabolomics data. Both construct extracted ion chromatograms (EICs) and detect peaks from the EICs, the first two steps in the data preprocessing workflow. While both packages have performed admirably in peak picking, they also detect a problematic number of false positive EIC peaks and can also fail to detect real EIC peaks. The former and latter translate downstream into spurious and missing compounds and present significant limitations with most existing software packages that preprocess untargeted mass spectrometry metabolomics data. We seek to understand the specific reasons why XCMS and MZmine 2 find the false positive EIC peaks that they do and in what ways they fail to detect real compounds. We investigate differences of EIC construction methods in XCMS and MZmine 2 and find several problems in the XCMS centWave peak detection algorithm which we show are partly responsible for the false positive and false negative compound identifications. In addition, we find a problem with MZmine 2's use of centWave. We hope that a detailed understanding of the XCMS and MZmine 2 algorithms will allow users to work with them more effectively and will also help with future algorithmic development.

ADAP-GC 3.0: Improved Peak Detection and Deconvolution of Co-eluting Metabolites from GC/TOF-MS Data for Metabolomics Studies.

ADAP-GC is an automated computational pipeline for untargeted, GC/MS-based metabolomics studies. It takes raw mass spectrometry data as input and carries out a sequence of data processing steps including construction of extracted ion chromatograms, detection of chromatographic peak features, deconvolution of coeluting compounds, and alignment of compounds across samples. Despite the increased accuracy from the original version to version 2.0 in terms of extracting metabolite information for identification and quantitation, ADAP-GC 2.0 requires appropriate specification of a number of parameters and has difficulty in extracting information on compounds that are in low concentration. To overcome these two limitations, ADAP-GC 3.0 was developed to improve both the robustness and sensitivity of compound detection. In this paper, we report how these goals were achieved and compare ADAP-GC 3.0 against three other software tools including ChromaTOF, AnalyzerPro, and AMDIS that are widely used in the metabolomics community.

Protein Structural Analysis via Mass Spectrometry-Based Proteomics.

Modern mass spectrometry (MS) technologies have provided a versatile platform that can be combined with a large number of techniques to analyze protein structure and dynamics. These techniques include the three detailed in this chapter: (1) hydrogen/deuterium exchange (HDX), (2) limited proteolysis, and (3) chemical crosslinking (CX). HDX relies on the change in mass of a protein upon its dilution into deuterated buffer, which results in varied deuterium content within its backbone amides. Structural information on surface exposed, flexible or disordered linker regions of proteins can be achieved through limited proteolysis, using a variety of proteases and only small extents of digestion. CX refers to the covalent coupling of distinct chemical species and has been used to analyze the structure, function and interactions of proteins by identifying crosslinking sites that are formed by small multi-functional reagents, termed crosslinkers. Each of these MS applications is capable of revealing structural information for proteins when used either with or without other typical high resolution techniques, including NMR and X-ray crystallography.

Untargeted metabolomics reveal signatures of a healthy lifestyle.

This cross-sectional study investigated differences in the plasma metabolome in two groups of adults that were of similar age but varied markedly in body composition and dietary and physical activity patterns. Study participants included 52 adults in the lifestyle group (LIFE) (28 males, 24 females) and 52 in the control group (CON) (27 males, 25 females). The results using an extensive untargeted ultra high-performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS) metabolomics analysis with 10,535 metabolite peaks identified 486 important metabolites (variable influence on projections scores of VIP ≥ 1) and 16 significantly enriched metabolic pathways that differentiated LIFE and CON groups. A novel metabolite signature of positive lifestyle habits emerged from this analysis highlighted by lower plasma levels of numerous bile acids, an amino acid profile characterized by higher histidine and lower glutamic acid, glutamine, ß-alanine, phenylalanine, tyrosine, and proline, an elevated vitamin D status, higher levels of beneficial fatty acids and gut microbiome catabolism metabolites from plant substrates, and reduced levels of N-glycan degradation metabolites and environmental contaminants. This study established that the plasma metabolome is strongly associated with body composition and lifestyle habits. The robust lifestyle metabolite signature identified in this study is consistent with an improved life expectancy and a reduced risk for chronic disease.

Decoding the exposome: data science methodologies and implications in exposome-wide association studies (ExWASs).

This paper explores the exposome concept and its role in elucidating the interplay between environmental exposures and human health. We introduce two key concepts critical for exposomics research. Firstly, we discuss the joint impact of genetics and environment on phenotypes, emphasizing the variance attributable to shared and nonshared environmental factors, underscoring the complexity of quantifying the exposome's influence on health outcomes. Secondly, we introduce the importance of advanced data-driven methods in large cohort studies for exposomic measurements. Here, we introduce the exposome-wide association study (ExWAS), an approach designed for systematic discovery of relationships between phenotypes and various exposures, identifying significant associations while controlling for multiple comparisons. We advocate for the standardized use of the term "exposome-wide association study, ExWAS," to facilitate clear communication and literature retrieval in this field. The paper aims to guide future health researchers in understanding and evaluating exposomic studies. Our discussion extends to emerging topics, such as FAIR Data Principles, biobanked healthcare datasets, and the functional exposome, outlining the future directions in exposomic research. This abstract provides a succinct overview of our comprehensive approach to understanding the complex dynamics of the exposome and its significant implications for human health.

Reproducible mass spectrometry data processing and compound annotation in MZmine 3.

Untargeted mass spectrometry (MS) experiments produce complex, multidimensional data that are practically impossible to investigate manually. For this reason, computational pipelines are needed to extract relevant information from raw spectral data and convert it into a more comprehensible format. Depending on the sample type and/or goal of the study, a variety of MS platforms can be used for such analysis. MZmine is an open-source software for the processing of raw spectral data generated by different MS platforms. Examples include liquid chromatography-MS, gas chromatography-MS and MS-imaging. These data might typically be associated with various applications including metabolomics and lipidomics. Moreover, the third version of the software, described herein, supports the processing of ion mobility spectrometry (IMS) data. The present protocol provides three distinct procedures to perform feature detection and annotation of untargeted MS data produced by different instrumental setups: liquid chromatography-(IMS-)MS, gas chromatography-MS and (IMS-)MS imaging. For training purposes, example datasets are provided together with configuration batch files (i.e., list of processing steps and parameters) to allow new users to easily replicate the described workflows. Depending on the number of data files and available computing resources, we anticipate this to take between 2 and 24 h for new MZmine users and nonexperts. Within each procedure, we provide a detailed description for all processing parameters together with instructions/recommendations for their optimization. The main generated outputs are represented by aligned feature tables and fragmentation spectra lists that can be used by other third-party tools for further downstream analysis.

Discovery of mouse spleen signaling responses to anthrax using label-free quantitative phosphoproteomics via mass spectrometry.

Inhalational anthrax is caused by spores of the bacterium Bacillus anthracis (B. anthracis), and is an extremely dangerous disease that can kill unvaccinated victims within 2 weeks. Modern antibiotic-based therapy can increase the survival rate to ∼50%, but only if administered presymptomatically (within 24-48 h of exposure). To discover host signaling responses to presymptomatic anthrax, label-free quantitative phosphoproteomics via liquid chromatography coupled to mass spectrometry was used to compare spleens from uninfected and spore-challenged mice over a 72 h time-course. Spleen proteins were denatured using urea, reduced using dithiothreitol, alkylated using iodoacetamide, and digested into peptides using trypsin, and the resulting phosphopeptides were enriched using titanium dioxide solid-phase extraction and analyzed by nano-liquid chromatography-Linear Trap Quadrupole-Orbitrap-MS(/MS). The fragment ion spectra were processed using DeconMSn and searched using both Mascot and SEQUEST resulting in 252,626 confident identifications of 6248 phosphopeptides (corresponding to 5782 phosphorylation sites). The precursor ion spectra were deisotoped using Decon2LS and aligned using MultiAlign resulting in the confident quantitation of 3265 of the identified phosphopeptides. ANOVAs were used to produce a q-value ranked list of host signaling responses. Late-stage (48-72 h postchallenge) Sterne strain (lethal) infections resulted in global alterations to the spleen phosphoproteome. In contrast, ΔSterne strain (asymptomatic; missing the anthrax toxin) infections resulted in 188 (5.8%) significantly altered (q<0.05) phosphopeptides. Twenty-six highly tentative phosphorylation responses to early-stage (24 h postchallenge) anthrax were discovered (q<0.5), and ten of these originated from eight proteins that have known roles in the host immune response. These tentative early-anthrax host response signaling events within mouse spleens may translate into presymptomatic diagnostic biomarkers of human anthrax detectable within circulating immune cells, and could aid in the identification of pathogenic mechanisms and therapeutic targets.

Development of integrated smartphone/resistive biosensor for on-site rapid environmental monitoring of organophosphate pesticides in food and water.

Organophosphate (OP) pesticides remain a worldwide health concern due to their acute or chronic poisoning and widespread use in agriculture around the world. There is a need for robust and field-deployable tools for onsite detection of OP pesticides in food and water. Herein, we present an integrated smartphone/resistive biosensor for simple, rapid, reagentless, and sensitive monitoring of OP pesticides in food and environmental water. The biosensor leverages the hydrolytic activity of acetylcholinesterase (AChE) to its substrate, acetylcholine (ACh), and unique transport properties of polyaniline nanofibers (PAnNFs) of chitosan/AChE/PAnNF/carbon nanotube (CNT) nanocomposite film on a gold interdigitated electrode. The principle of the sensor relies on OP inhibiting AChE, thus, reducing the rate of ACh hydrolysis and consequently decreasing the rate of protons doping the PAnNFs. Such resulted decrease in conductance of PAnNF can be used to quantify OP pesticides in a sample. A mobile app for the biosensor was developed for analyzing measurement data and displaying and sharing testing results. Under optimal conditions, the biosensor demonstrated a wide linear range (1 ppt-100 ppb) with a low detection limit (0.304 ppt) and high reproducibility (RSD <5%) for Paraoxon-Methyl (PM), a model analyte. Furthermore, the biosensor was successfully applied for analyzing PM spiked food/water samples with an average recovery rate of 98.3% and provided comparable results with liquid chromatography-mass spectrometry. As such, the nanosensing platform provides a promising tool for onsite rapid and sensitive detection of OP pesticides in food and environmental water.

The unknown lipids project: harmonized methods improve compound identification and data reproducibility in an inter-laboratory untargeted lipidomics study.

Untargeted lipidomics allows analysis of a broader range of lipids than targeted methods and permits discovery of unknown compounds. Previous ring trials have evaluated the reproducibility of targeted lipidomics methods, but inter-laboratory comparison of compound identification and unknown feature detection in untargeted lipidomics has not been attempted. To address this gap, five laboratories analyzed a set of mammalian tissue and biofluid reference samples using both their own untargeted lipidomics procedures and a common chromatographic and data analysis method. While both methods yielded informative data, the common method improved chromatographic reproducibility and resulted in detection of more shared features between labs. Spectral search against the LipidBlast in silico library enabled identification of over 2,000 unique lipids. Further examination of LC-MS/MS and ion mobility data, aided by hybrid search and spectral networking analysis, revealed spectral and chromatographic patterns useful for classification of unknown features, a subset of which were highly reproducible between labs. Overall, our method offers enhanced compound identification performance compared to targeted lipidomics, demonstrates the potential of harmonized methods to improve inter-site reproducibility for quantitation and feature alignment, and can serve as a reference to aid future annotation of untargeted lipidomics data.

ADAP-GC 2.0: deconvolution of coeluting metabolites from GC/TOF-MS data for metabolomics studies.

ADAP-GC 2.0 has been developed to deconvolute coeluting metabolites that frequently exist in real biological samples of metabolomics studies. Deconvolution is based on a chromatographic model peak approach that combines five metrics of peak qualities for constructing/selecting model peak features. Prior to deconvolution, ADAP-GC 2.0 takes raw mass spectral data as input, extracts ion chromatograms for all the observed masses, and detects chromatographic peak features. After deconvolution, it aligns components across samples and exports the qualitative and quantitative information of all of the observed components. Centered on the deconvolution, the entire data analysis workflow is fully automated. ADAP-GC 2.0 has been tested using three different types of samples. The testing results demonstrate significant improvements of ADAP-GC 2.0, compared to the previous ADAP 1.0, to identify and quantify metabolites from gas chromatography/time-of-flight mass spectrometry (GC/TOF-MS) data in untargeted metabolomics studies.

Memory-Efficient Searching of Gas-Chromatography Mass Spectra Accelerated by Prescreening.

The number of metabolomics studies and spectral libraries for compound annotation (i.e., assigning possible compound identities to a fragmentation spectrum) has been growing steadily in recent years. Accompanying this growth is the number of mass spectra available for searching through those libraries. As the size of spectral libraries grows, accurate and fast compound annotation becomes more challenging. We herein report a prescreening algorithm that was developed to address the speed of spectral search under the constraint of low memory requirements. This prescreening has been incorporated into the Automated Data Analysis Pipeline Spectral Knowledgebase (ADAP-KDB) and can be applied to compound annotation by searching other spectral libraries as well. Performance of the prescreening algorithm was evaluated for different sets of parameters and compared to the original ADAP-KDB spectral search and the MSSearch software. The comparison has demonstrated that the new algorithm is about four-times faster than the original when searching for low-resolution mass spectra, and about as fast as the original when searching for high-resolution mass spectra. However, the new algorithm is still slower than MSSearch due to the relational database design of the former. The new search workflow can be tried out at the ADAP-KDB web portal.

Xlink-identifier: an automated data analysis platform for confident identifications of chemically cross-linked peptides using tandem mass spectrometry.

Chemical cross-linking combined with mass spectrometry provides a powerful method for identifying protein-protein interactions and probing the structure of protein complexes. A number of strategies have been reported that take advantage of the high sensitivity and high resolution of modern mass spectrometers. Approaches typically include synthesis of novel cross-linking compounds, and/or isotopic labeling of the cross-linking reagent and/or protein, and label-free methods. We report Xlink-Identifier, a comprehensive data analysis platform that has been developed to support label-free analyses. It can identify interpeptide, intrapeptide, and deadend cross-links as well as underivatized peptides. The software streamlines data preprocessing, peptide scoring, and visualization and provides an overall data analysis strategy for studying protein-protein interactions and protein structure using mass spectrometry. The software has been evaluated using a custom synthesized cross-linking reagent that features an enrichment tag. Xlink-Identifier offers the potential to perform large-scale identifications of protein-protein interactions using tandem mass spectrometry.