Correlation-Based Deconvolution (CorrDec) To Generate High-Quality MS2 Spectra from Data-Independent Acquisition in Multisample Studies

Combinatory data-independent acquisition and parallel reaction monitoring method for revealing the lipid metabolism biomarkers of coronary heart disease and its comorbidities

Disorders of lipid metabolism are a common cause of coronary heart disease (CHD) and its comorbidities. In this study, ultra-performance liquid chromatography-high-resolution mass spectrometry in data-independent acquisition (DIA) mode was applied to collect abundant tandem mass spectrometry data, which provided valuable information for lipid annotation. For the lipid isomers that could not be completely separated by chromatography, parallel reaction monitoring (PRM) mode was used for quantification. A total of 223 plasma lipid metabolites were annotated, and 116 of them were identified for their fatty acyl chain composition and location. In addition, 152 plasma lipids in patients with CHD and its comorbidities were quantitatively analyzed. Multivariate statistical analysis and metabolic pathway analysis demonstrated that glycerophospholipid and sphingolipid metabolism deserved more attention for CHD. This study proposed a method combining DIA and PRM for high-throughput characterization of plasma lipids. The results also improved our understanding of metabolic disorders of CHD and its comorbidities, which can provide valuable suggestions for medical intervention.

Electron-activated dissociation (EAD) for the complementary annotation of metabolites and lipids through data-dependent acquisition analysis and feature-based molecular networking, applied to the sentinel amphipod Gammarus fossarum

The past decades have marked the rise of metabolomics and lipidomics as the -omics sciences which reflect the most phenotypes in living systems. Mass spectrometry-based approaches are acknowledged for both quantification and identification of molecular signatures, the latter relying primarily on fragmentation spectra interpretation. However, the high structural diversity of biological small molecules poses a considerable challenge in compound annotation. Feature-based molecular networking (FBMN) combined with database searches currently sets the gold standard for annotation of large datasets. Nevertheless, FBMN is usually based on collision-induced dissociation (CID) data, which may lead to unsatisfying information. The use of alternative fragmentation methods, such as electron-activated dissociation (EAD), is undergoing a re-evaluation for the annotation of small molecules, as it gives access to additional fragmentation routes. In this study, we apply the performances of data-dependent acquisition mass spectrometry (DDA-MS) under CID and EAD fragmentation along with FBMN construction, to perform extensive compound annotation in the crude extracts of the freshwater sentinel organism Gammarus fossarum. We discuss the analytical aspects of the use of the two fragmentation modes, perform a general comparison of the information delivered, and compare the CID and EAD fragmentation pathways for specific classes of compounds, including previously unstudied species. In addition, we discuss the potential use of FBMN constructed with EAD fragmentation spectra to improve lipid annotation, compared to the classic CID-based networks. Our approach has enabled higher confidence annotations and finer structure characterization of 823 features, including both metabolites and lipids detected in G. fossarum extracts.

Human Metabolism and Excretion of Kawakawa (Piper excelsum) Leaf Chemicals

SCOPE

Piper excelsum (kawakawa) has a history of therapeutic use by Māori in Aotearoa New Zealand. It is currently widely consumed as a beverage and included as an ingredient in "functional" food product. Leaves contain compounds that are also found in a wide range of other spices, foods, and medicinal plants. This study investigates the human metabolism and excretion of kawakawa leaf chemicals.

METHODS AND RESULTS

Six healthy male volunteers in one study (Bioavailability of Kawakawa Tea metabolites in human volunteers [BOKA-T]) and 30 volunteers (15 male and 15 female) in a second study (Impact of acute Kawakawa Tea ingestion on postprandial glucose metabolism in healthy human volunteers [TOAST]) consume a hot water infusion of dried kawakawa leaves (kawakawa tea [KT]). Untargeted Liquid Chromatography-Tandem Mass spectrometry (LC-MS/MS) analyses of urine samples from BOKA-T identified 26 urinary metabolites that are significantly associated with KT consumption, confirmed by the analysis of samples from the independent TOAST study. Seven of the 26 metabolites are also detected in plasma. Thirteen of the 26 urinary compounds are provisionally identified as metabolites of specific compounds in KT, eight metabolites are identified as being derived from specific compounds in KT but without resolution of chemical structure, and five are of unknown origin.

CONCLUSIONS

Several kawakawa compounds that are also widely found in other plants are bioavailable and are modified by phase 1 and 2 metabolism.

A powerful two-dimensional chromatography method for the non-target analysis of depolymerised lignin

BACKGROUND

Lignin is an abundant natural polymer obtained as a by-product from the fractionation of lignocellulosic biomass. In the name of a circular economy, lignin should be valorised into valuable chemicals or biomaterials and utilised instead of petrochemicals. For the development of efficient valorisation processes, the structural characterisation of lignin can be highly beneficial. However, this is an arduous task, as the isolated (and sometimes processed) lignin mainly consists of various neutral monomers but also oligomers. In addition, the material contains isomers, which can be especially problematic to separate and identify.

RESULTS

We present a powerful off-line comprehensive two-dimensional (2D) chromatography method combining liquid chromatography (LC), supercritical fluid chromatography (SFC), and high-resolution mass spectrometry for the non-target analysis of depolymerised lignin. The implementation of a 1-aminoanthracene column in the second dimension enabled a class separation of potential lignin monomers, dimers, trimers, and tetramers with an additional separation based on the number of hydroxyl groups and steric effects. The pentafluorophenyl column in the first dimension additionally improved the separation based on hydrophobicity. The comparison of off-line 2D LC × SFC to 1D SFC showed that besides the overall improved performance, the first method is also superior for the separation of isomers. Advanced data analysis methods (MS-DIAL, SIRIUS, and Feature-Based Molecular Network) were integrated into the non-target workflow to rapidly visualise and study the detected compounds, which proved to be especially beneficial for the characterisation of the separated isomers.

SIGNIFICANCE

The method yielded the first 2D LC plot demonstrating a classification of lignin compounds, which can be applied to compare various lignin sources and processing methods. In addition, the technique demonstrated improved separation of compounds, including isomers, which was especially beneficial as 77 % of the detected compounds had at least one isomer in the same lignin sample.

Enhanced efficiency of MS/MS all-ion fragmentation for non-targeted analysis of trace contaminants in surface water using multivariate curve resolution and data fusion

Data-independent acquisition-all-ion fragmentation (DIA-AIF) mode of mass spectrometry can facilitate wide-scope non-target analysis of contaminants in surface water due to comprehensive spectral identification. However, because of the complexity of the resulting MS2 AIF spectra, identifying unknown pollutants remains a significant challenge, with a significant bottleneck in translating non-targeted chemical signatures into environmental impacts. The present study proposes to process fused MS1 and MS2 data sets obtained from LC-HRMS/MS measurements in non-targeted AIF workflows on surface water samples using multivariate curve resolution-alternating least squares (MCR-ALS). This enables straightforward assignment between precursor ions obtained from resolved MS1 spectra and their corresponding MS2 spectra. The method was evaluated for two sets of tap water and surface water contaminated with 14 target chemicals as a proof of concept. The data set of surface water samples consisting of 3506 MS1 and 2170 MS2 AIF mass spectral features was reduced to 81 components via a fused MS1-MS2 MCR model that describes at least 98.8% of the data. Each component summarizes the distinct chromatographic elution of components together with their corresponding MS1 and MS2 spectra. MS2 spectral similarity of more than 82% was obtained for most target chemicals. This highlights the potential of this method for unraveling the composition of MS/MS complex data in a water environment. Ultimately, the developed approach was applied to the retrospective non-target analysis of an independent set of surface water samples.

PFΔScreen - an open-source tool for automated PFAS feature prioritization in non-target HRMS data

Per- and polyfluoroalkyl substances (PFAS) are a huge group of anthropogenic chemicals with unique properties that are used in countless products and applications. Due to the high stability of their C-F bonds, PFAS or their transformation products (TPs) are persistent in the environment, leading to ubiquitous detection in various samples worldwide. Since PFAS are industrial chemicals, the availability of authentic PFAS reference standards is limited, making non-target screening (NTS) approaches based on high-resolution mass spectrometry (HRMS) necessary for a more comprehensive characterization. NTS usually is a time-consuming process, since only a small fraction of the detected chemicals can be identified. Therefore, efficient prioritization of relevant HRMS signals is one of the most crucial steps. We developed PFΔScreen, a Python-based open-source tool with a simple graphical user interface (GUI) to perform efficient feature prioritization using several PFAS-specific techniques such as the highly promising MD/C-m/C approach, Kendrick mass defect analysis, diagnostic fragments (MS2), fragment mass differences (MS2), and suspect screening. Feature detection from vendor-independent MS raw data (mzML, data-dependent acquisition) is performed via pyOpenMS (or custom feature lists) with subsequent calculations for prioritization and identification of PFAS in both HPLC- and GC-HRMS data. The PFΔScreen workflow is presented on four PFAS-contaminated agricultural soil samples from south-western Germany. Over 15 classes of PFAS (more than 80 single compounds with several isomers) could be identified, including four novel classes, potentially TPs of the precursors fluorotelomer mercapto alkyl phosphates (FTMAPs). PFΔScreen can be used within the Python environment and is easily automatically installable and executable on Windows. Its source code is freely available on GitHub ( https://github.com/JonZwe/PFAScreen ).

SWATH-F: A Novel Nontarget Strategy Based on the SWATH-MS Deconvolution Method Assisting in Annotating PFAS Homologues in Multisample Studies

In order to identify emerging per- and polyfluoroalkyl substances (PFASs) and their alternatives in the environment or population, we need to perform extensive profiling of PFASs to determine their distribution in samples. The sequential window acquisition of all theoretical fragment-ion spectra (SWATH mode) is capable of obtaining a wide range of MS2 spectra but is difficult for direct identification of PFASs due to its complex MS2 spectra, and the nontarget screening method is difficult to identify due to its lack of a priori information. In this study, we demonstrated the great potential of SWATH-F, a nontarget fragment-based homologue screening method in combination with the SWATH-MS deconvolution, for detecting PFASs. We evaluated the application of SWATH-F to gradient spiked samples and real population serum samples, compared it with nontarget homologue screening in the information-dependent acquisition mode (IDA mode), and obtained better results for SWATH-F with 276% improvement (IDA:17 PFASs, SWATH-F: 64 PFASs) in identification. In addition, we automated the screening and identification process of SWATH-F to facilitate its use by researchers. SWATH-F is freely available on GitHub (https://github.com/njuIrene/SWATH-F) under an MIT license.

De Novo Cleaning of Chimeric MS/MS Spectra for LC-MS/MS-Based Metabolomics

The purity of tandem mass spectrometry (MS/MS) is essential to MS/MS-based metabolite annotation and unknown exploration. This work presents a de novo approach to cleaning chimeric MS/MS spectra generated in liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based metabolomics. The assumption is that true fragments and their precursors are well correlated across the samples in a study, while false or contamination fragments are rather independent. Using data simulation, this work starts with an investigation of the negative effects of chimeric MS/MS spectra on spectral similarity analysis and molecular networking. Next, the characteristics of true and false fragments in chimeric MS/MS spectra were investigated using MS/MS of chemical standards. We recognized three fragment peak attributes indicative of whether a peak is a false fragment, including (1) intensity ratio fluctuation, (2) appearance rate, and (3) relative intensity. Using these attributes, we tested three machine learning models and identified XGBoost as the best model to achieve an area under the precision-recall curve of 0.98 for a clear separation between true and false fragments. Based on the trained model, we constructed an automated bioinformatic platform, DNMS2Purifier (short for de novo MS2Purifier), for metabolic features from metabolomics studies. DNMS2Purifier recognizes and processes chimeric MS/MS spectra without additional sample analysis or library confirmation. DNMS2Purifer was evaluated on a metabolomics data set generated with different MS/MS precursor isolation windows. It successfully captured the increase in the number of false fragments from the increased isolation window. DNMS2Purifier was also compared to MS2Purifier, an existing MS/MS spectral cleaning tool based on the addition of data-independent acquisition (DIA) analysis. Results indicated that DNMS2Purifier uniquely recognizes false fragments, which complements the previous DIA-based approach. Finally, DNMS2Purifier was demonstrated using a real experimental metabolomics study, showing improved MS/MS spectral quality and leading to an improved spectral match ratio and molecular networking outcome.

Cumulative Neutral Loss Model for Fragment Deconvolution in Electrospray Ionization High-Resolution Mass Spectrometry Data

Clean high-resolution mass spectra (HRMS) are essential to a successful structural elucidation of an unknown feature during nontarget analysis (NTA) workflows. This is a crucial step, particularly for the spectra generated during data-independent acquisition or during direct infusion experiments. The most commonly available tools only take advantage of the time domain for spectral cleanup. Here, we present an algorithm that combines the time domain and mass domain information to perform spectral deconvolution. The algorithm employs a probability-based cumulative neutral loss (CNL) model for fragment deconvolution. The optimized model, with a mass tolerance of 0.005 Da and a scoreCNL threshold of 0.00, was able to achieve a true positive rate (TPr) of 95.0%, a false discovery rate (FDr) of 20.6%, and a reduction rate of 35.4%. Additionally, the CNL model was extensively tested on real samples containing predominantly pesticides at different concentration levels and with matrix effects. Overall, the model was able to obtain a TPr above 88.8% with FD rates between 33 and 79% and reduction rates between 9 and 45%. Finally, the CNL model was compared with the retention time difference method and peak shape correlation analysis, showing that a combination of correlation analysis and the CNL model was the most effective for fragment deconvolution, obtaining a TPr of 84.7%, an FDr of 54.4%, and a reduction rate of 51.0%.

IDSL.CSA: Composite Spectra Analysis for Chemical Annotation of Untargeted Metabolomics Datasets

Poor chemical annotation of high-resolution mass spectrometry data limits applications of untargeted metabolomics datasets. Our new software, the Integrated Data Science Laboratory for Metabolomics and Exposomics─Composite Spectra Analysis (IDSL.CSA) R package, generates composite mass spectra libraries from MS1-only data, enabling the chemical annotation of high-resolution mass spectrometry coupled with liquid chromatography peaks regardless of the availability of MS2 fragmentation spectra. We demonstrate comparable annotation rates for commonly detected endogenous metabolites in human blood samples using IDSL.CSA libraries versus MS/MS libraries in validation tests. IDSL.CSA can create and search composite spectra libraries from any untargeted metabolomics dataset generated using high-resolution mass spectrometry coupled to liquid or gas chromatography instruments. The cross-applicability of these libraries across independent studies may provide access to new biological insights that may be missed due to the lack of MS2 fragmentation data. The IDSL.CSA package is available in the R-CRAN repository at https://cran.r-project.org/package=IDSL.CSA. Detailed documentation and tutorials are provided at https://github.com/idslme/IDSL.CSA.

IDSL.CSA: Composite Spectra Analysis for Chemical Annotation of Untargeted Metabolomics Datasets

Poor chemical annotation of high-resolution mass spectrometry data limit applications of untargeted metabolomics datasets. Our new software, the Integrated Data Science Laboratory for Metabolomics and Exposomics - Composite Spectra Analysis (IDSL.CSA) R package, generates composite mass spectra libraries from MS1-only data, enabling the chemical annotation of LC/HRMS peaks regardless of the availability of MS2 fragmentation spectra. We demonstrate comparable annotation rates for commonly detected endogenous metabolites in human blood samples using IDSL.CSA libraries versus MS/MS libraries in validation tests. IDSL.CSA can create and search composite spectra libraries from any untargeted metabolomics dataset generated using high-resolution mass spectrometry coupled to liquid or gas chromatography instruments. The cross-applicability of these libraries across independent studies may provide access to new biological insights that may be missed due to the lack of MS2 fragmentation data. The IDSL.CSA package is available in the R CRAN repository at https://cran.r-project.org/package=IDSL.CSA . Detailed documentation and tutorials are provided at https://github.com/idslme/IDSL.CSA .

For Table of Contents Only

Regions of Interest Multivariate Curve Resolution Liquid Chromatography with Data-Independent Acquisition Tandem Mass Spectrometry

New data-independent acquisition (DIA) modes coupled to chromatographic separations are opening new perspectives in the processing of massive mass spectrometric (MS) data using chemometric methods. In this work, the application of the regions of interest multivariate curve resolution (ROIMCR) method is shown for the simultaneous analysis of MS1 and MS2 DIA raw data obtained by liquid chromatography coupled to quadrupole-time-of-flight MS analysis. The ROIMCR method proposed in this work relies on the intrinsic bilinear structure of the MS1 and MS2 experimental data which allows us for the fast direct resolution of the elution and spectral profiles of all sample constituents giving measurable MS signals, without needing any further data pretreatment such as peak matching, alignment, or modeling. Compound annotation and identification can be achieved directly by the comparison of the ROIMCR-resolved MS1 and MS2 spectra with those from standards or from mass spectral libraries. ROIMCR elution profiles of the resolved components can be used to build calibration curves for the prediction of their concentrations in complex unknown samples. The application of the proposed procedure is shown for the analysis of mixtures of per- and polyfluoroalkyl substances in standard mixtures, spiked hen eggs, and gull egg samples, where these compounds tend to accumulate.

BUDDY: molecular formula discovery via bottom-up MS/MS interrogation

A substantial fraction of metabolic features remains undetermined in mass spectrometry (MS)-based metabolomics, and molecular formula annotation is the starting point for unraveling their chemical identities. Here we present bottom-up tandem MS (MS/MS) interrogation, a method for de novo formula annotation. Our approach prioritizes MS/MS-explainable formula candidates, implements machine-learned ranking and offers false discovery rate estimation. Compared with the mathematically exhaustive formula enumeration, our approach shrinks the formula candidate space by 42.8% on average. Method benchmarking on annotation accuracy was systematically carried out on reference MS/MS libraries and real metabolomics datasets. Applied on 155,321 recurrent unidentified spectra, our approach confidently annotated >5,000 novel molecular formulae absent from chemical databases. Beyond the level of individual metabolic features, we combined bottom-up MS/MS interrogation with global optimization to refine formula annotations while revealing peak interrelationships. This approach allowed the systematic annotation of 37 fatty acid amide molecules in human fecal data. All bioinformatics pipelines are available in a standalone software, BUDDY ( https://github.com/HuanLab/BUDDY ).

Identifying Hair Biomarker Candidates for Alzheimer's Disease Using Three High Resolution Mass Spectrometry-Based Untargeted Metabolomics Strategies

High-resolution mass spectrometry (HRMS)-based untargeted metabolomics strategies have emerged as an effective tool for discovering biomarkers of Alzheimer's disease (AD). There are various HRMS-based untargeted metabolomics strategies for biomarker discovery, including the data-dependent acquisition (DDA) method, the combination of full scan and target MS/MS, and the all ion fragmentation (AIF) method. Hair has emerged as a potential biospecimen for biomarker discovery in clinical research since it might reflect the circulating metabolic profiles over several months, while the analytical performances of the different data acquisition methods for hair biomarker discovery have been rarely investigated. Here, the analytical performances of three data acquisition methods in HRMS-based untargeted metabolomics for hair biomarker discovery were evaluated. The human hair samples from AD patients (N = 23) and cognitively normal individuals (N = 23) were used as an example. The most significant number of discriminatory features was acquired using the full scan (407), which is approximately 10-fold higher than that using the DDA strategy (41) and 11% higher than that using the AIF strategy (366). Only 66% of discriminatory chemicals discovered in the DDA strategy were discriminatory features in the full scan dataset. Moreover, compared to the deconvoluted MS/MS spectra with coeluted and background ions from the AIF method, the MS/MS spectrum obtained from the targeted MS/MS approach is cleaner and purer. Therefore, an untargeted metabolomics strategy combining the full scan with the targeted MS/MS method could obtain most discriminatory features along with a high quality MS/MS spectrum for discovering the AD biomarkers.

Simulated-to-real benchmarking of acquisition methods in untargeted metabolomics

Data-Dependent and Data-Independent Acquisition modes (DDA and DIA, respectively) are both widely used to acquire MS2 spectra in untargeted liquid chromatography tandem mass spectrometry (LC-MS/MS) metabolomics analyses. Despite their wide use, little work has been attempted to systematically compare their MS/MS spectral annotation performance in untargeted settings due to the lack of ground truth and the costs involved in running a large number of acquisitions. Here, we present a systematic in silico comparison of these two acquisition methods in untargeted metabolomics by extending our Virtual Metabolomics Mass Spectrometer (ViMMS) framework with a DIA module. Our results show that the performance of these methods varies with the average number of co-eluting ions as the most important factor. At low numbers, DIA outperforms DDA, but at higher numbers, DDA has an advantage as DIA can no longer deal with the large amount of overlapping ion chromatograms. Results from simulation were further validated on an actual mass spectrometer, demonstrating that using ViMMS we can draw conclusions from simulation that translate well into the real world. The versatility of the Virtual Metabolomics Mass Spectrometer (ViMMS) framework in simulating different parameters of both Data-Dependent and Data-Independent Acquisition (DDA and DIA) modes is a key advantage of this work. Researchers can easily explore and compare the performance of different acquisition methods within the ViMMS framework, without the need for expensive and time-consuming experiments with real experimental data. By identifying the strengths and limitations of each acquisition method, researchers can optimize their choice and obtain more accurate and robust results. Furthermore, the ability to simulate and validate results using the ViMMS framework can save significant time and resources, as it eliminates the need for numerous experiments. This work not only provides valuable insights into the performance of DDA and DIA, but it also opens the door for further advancements in LC-MS/MS data acquisition methods.

Deep Learning Based MS2 Feature Detection for Data-Independent Shotgun Proteomics

Accuracy of peptide identification in LC-MS analysis is crucial for information regarding the aspects of proteins that aid in biomarker discovery and the profiling of complex proteomes. The detection of peptide fragment ions in tandem mass spectrometry is still challenging given that current tools were not created or tested for the low-abundance, low-peak fragments of peptides found in MS2 data. Feature detection, a crucial pre-processing step in the LC-MS analysis pipeline that quantifies peptides by their mass-to-charge ratio, retention time, and intensity, is particularly challenging due to the overlapping nature of peptides and weak signals that are often indistinguishable from noises, thus creating a reliance on rigid mathematical structures and heuristics. In this study, we developed a deep-learning-based model with an innovative sliding window process that enables high-resolution processing of quantitative MS/MS data to conduct MS2 feature detection. Experimental results show that our model can produce more accurate values and identifications than existing feature detection tools, as well as a high rate of true positive features quantified. Therefore, we believe that our model illustrates the advantages of deep learning techniques applied towards computational proteomics.

DEIMoS: An Open-Source Tool for Processing High-Dimensional Mass Spectrometry Data

We present DEIMoS: Data Extraction for Integrated Multidimensional Spectrometry, a Python application programming interface (API) and command-line tool for high-dimensional mass spectrometry data analysis workflows that offers ease of development and access to efficient algorithmic implementations. Functionality includes feature detection, feature alignment, collision cross section (CCS) calibration, isotope detection, and MS/MS spectral deconvolution, with the output comprising detected features aligned across study samples and characterized by mass, CCS, tandem mass spectra, and isotopic signature. Notably, DEIMoS operates on N-dimensional data, largely agnostic to acquisition instrumentation; algorithm implementations simultaneously utilize all dimensions to (i) offer greater separation between features, thus improving detection sensitivity, (ii) increase alignment/feature matching confidence among data sets, and (iii) mitigate convolution artifacts in tandem mass spectra. We demonstrate DEIMoS with LC-IMS-MS/MS metabolomics data to illustrate the advantages of a multidimensional approach in each data processing step.

Post-Deconvolution MS/MS Spectra Extraction with Data-Independent Acquisition for Comprehensive Profiling of Urinary Glucuronide-Conjugated Metabolome

Conjugation reactions are of critical significance in human metabolism. Identification of these conjugated metabolites is still challenging. Here, we propose a strategy, post-deconvolution MS/MS spectra extraction with data-independent acquisition (PDMS2E-DIA), to comprehensively profile the glucuronide-conjugated metabolome. PDMS2E-DIA enables the identification of conjugated and unconjugated metabolite pairs through neutral loss filtering combined with a significant change in abundance after the deconjugation reaction. Purified DIA MS/MS spectra were constructed by extracting MS/MS fragments shared between spectra derived from conjugated and unconjugated metabolites. The feasibility of this approach was first demonstrated by the identification of two glucuronide-conjugated metabolite standards spiked in urine samples. For human urine samples, 479 features were structurally annotated as potential glucuronide-conjugated metabolites, resulting in the identification of 211 metabolites. Fragment peaks derived from interferents were found to be removed by PDMS2E-DIA, which increased about 6 times the number of identified urine metabolites compared with those calculated from raw DIA deconvoluted MS/MS spectra. This approach was found to have great potential for identifying glucuronide-conjugated metabolites, as well as other kinds of chemical conjugations.

Mass-Spectrometry-Based Functional Proteomic and Phosphoproteomic Technologies and Their Application for Analyzing Ex Vivo and In Vitro Models of Hypertrophic Cardiomyopathy

Hypertrophic cardiomyopathy (HCM) is an autosomal dominant disease thought to be principally caused by mutations in sarcomeric proteins. Despite extensive genetic analysis, there are no comprehensive molecular frameworks for how single mutations in contractile proteins result in the diverse assortment of cellular, phenotypic, and pathobiological cascades seen in HCM. Molecular profiling and system biology approaches are powerful tools for elucidating, quantifying, and interpreting dynamic signaling pathways and differential macromolecule expression profiles for a wide range of sample types, including cardiomyopathy. Cutting-edge approaches combine high-performance analytical instrumentation (e.g., mass spectrometry) with computational methods (e.g., bioinformatics) to study the comparative activity of biochemical pathways based on relative abundances of functionally linked proteins of interest. Cardiac research is poised to benefit enormously from the application of this toolkit to cardiac tissue models, which recapitulate key aspects of pathogenesis. In this review, we evaluate state-of-the-art mass-spectrometry-based proteomic and phosphoproteomic technologies and their application to in vitro and ex vivo models of HCM for global mapping of macromolecular alterations driving disease progression, emphasizing their potential for defining the components of basic biological systems, the fundamental mechanistic basis of HCM pathogenesis, and treating the ensuing varied clinical outcomes seen among affected patient cohorts.

Rapid discovery of potential ADR compounds from injection of total saponins from Panax notoginseng using data-independent acquisition untargeted metabolomics

Injection of total saponins from Panax notoginseng (ISPN) is a modern preparation derived from traditional Chinese medicine (TCM) and is widely applied in the treatment of cardiovascular, cerebrovascular, ophthalmology, and endocrine system diseases. With the increase in the clinical application of ISPN, its adverse drug reactions (ADRs) and related safety issues have attracted much attention. In the present study, a data-independent acquisition (DIA) strategy was proposed to comprehensively characterize the saponins contained in ISPN based on the ultra-high-performance liquid chromatography/quadrupole-Orbitrap MS (UHPLC/Q-Orbitrap MS) platform. As many as 276 saponins were detected, and 250 compounds were identified or tentatively identified based on the retention times and MS/MS data. Furthermore, a metabolomic strategy was utilized to discover the discriminative saponins between normal and ADR batches. The results showed that six saponins, including ginsenoside Rh4, ginsenoside Rk3, ginsenoside Rg5, ginsenoside Rk1, ginsenoside Rg6, and 20(S)-ginsenoside Rh2, were significantly different between the two groups. According to cytotoxicity analysis and degranulation detection of RBL-2H3 cells, ginsenoside Rg5, ginsenoside Rk1, and 20(S)-ginsenoside Rh2 were considered the potential compounds responsible for clinical ADRs, ultimately. In addition, the quantitative analysis showed that the content of these three compounds in ISPN samples with ADRs was generally higher than that in samples without ADRs. This study demonstrated that it is advisable to screen out potential markers related to ADRs for developing the quality standard of ISPN by the integration of untargeted metabolomic analysis and cell biology study, and thus reduce its ADRs in the clinic.

Defining the Scope of Exposome Studies and Research Needs from a Multidisciplinary Perspective

The concept of the exposome was introduced over 15 years ago to reflect the important role that the environment exerts on health and disease. While originally viewed as a call-to-arms to develop more comprehensive exposure assessment methods applicable at the individual level and throughout the life course, the scope of the exposome has now expanded to include the associated biological response. In order to explore these concepts, a workshop was hosted by the Gunma University Initiative for Advanced Research (GIAR, Japan) to discuss the scope of exposomics from an international and multidisciplinary perspective. This Global Perspective is a summary of the discussions with emphasis on (1) top-down, bottom-up, and functional approaches to exposomics, (2) the need for integration and standardization of LC- and GC-based high-resolution mass spectrometry methods for untargeted exposome analyses, (3) the design of an exposomics study, (4) the requirement for open science workflows including mass spectral libraries and public databases, (5) the necessity for large investments in mass spectrometry infrastructure in order to sequence the exposome, and (6) the role of the exposome in precision medicine and nutrition to create personalized environmental exposure profiles. Recommendations are made on key issues to encourage continued advancement and cooperation in exposomics.

Caffeoylquinic acids: chemistry, biosynthesis, occurrence, analytical challenges, and bioactivity

Caffeoylquinic acids (CQAs) are specialized plant metabolites we encounter in our daily life. Humans consume CQAs in mg-to-gram quantities through dietary consumption of plant products. CQAs are considered beneficial for human health, mainly due to their anti-inflammatory and antioxidant properties. Recently, new biosynthetic pathways via a peroxidase-type p-coumaric acid 3-hydroxylase enzyme were discovered. More recently, a new GDSL lipase-like enzyme able to transform monoCQAs into diCQA was identified in Ipomoea batatas. CQAs were recently linked to memory improvement; they seem to be strong indirect antioxidants via Nrf2 activation. However, there is a prevalent confusion in the designation and nomenclature of different CQA isomers. Such inconsistencies are critical and complicate bioactivity assessment since different isomers differ in bioactivity and potency. A detailed explanation regarding the origin of such confusion is provided, and a recommendation to unify nomenclature is suggested. Furthermore, for studies on CQA bioactivity, plant-based laboratory animal diets contain CQAs, which makes it difficult to include proper control groups for comparison. Therefore, a synthetic diet free of CQAs is advised to avoid interferences since some CQAs may produce bioactivity even at nanomolar levels. Biotransformation of CQAs by gut microbiota, the discovery of new enzymatic biosynthetic and metabolic pathways, dietary assessment, and assessment of biological properties with potential for drug development are areas of active, ongoing research. This review is focused on the chemistry, biosynthesis, occurrence, analytical challenges, and bioactivity recently reported for mono-, di-, tri-, and tetraCQAs.

Identification of Food-Derived Isoflavone Sulfates as Inhibition Markers for Intestinal Breast Cancer Resistance Proteins

Potential inhibition of the breast cancer resistance protein (BCRP), a drug efflux transporter, is a key issue during drug development, and the use of its physiologic substrates as biomarkers can be advantageous to assess inhibition. In this study, we aimed to identify BCRP substrates by an untargeted metabolomic approach. Mice were orally administered lapatinib to inhibit BCRP in vivo, and plasma samples were assessed by liquid chromatography/time of flight/mass spectrometry with all-ion fragmentation acquisition and quantified by liquid chromatography with tandem mass spectrometry. A differential metabolomic analysis was also performed for plasma from Bcrp ^-/- and wild-type mice. Plasma peaks of food-derived isoflavone metabolites, daidzein sulfate (DS), and genistein sulfate (GS) increased after lapatinib administration and in Bcrp ^-/- mice. Administration of lapatinib and another BCRP inhibitor febuxostat increased the area under the plasma concentration-time curve (AUC) of DS, GS, and equol sulfate (ES) by 3.6- and 1.8-, 5.6- and 4.1-, and 1.6- and 4.8-fold, respectively. BCRP inhibitors also increased the AUC and maximum plasma concentration of DS and ES after coadministration with each parent compound. After adding parent compounds to the apical side of induced pluripotent stem cell-derived small intestinal epithelial-like cells, DS, GS, and ES in the basal compartment significantly increased in the presence of lapatinib and febuxostat, suggesting the inhibition of intestinal BCRP. ATP-dependent uptake of DS and ES in BCRP-expressing membrane vesicles was reduced by both inhibitors, indicating inhibition of BCRP-mediated DS and ES transport. Thus, we propose the first evidence of surrogate markers for BCRP inhibition. SIGNIFICANCE STATEMENT: This study performed untargeted metabolomics to identify substrates of BCRP/ABCG2 to assess changes in its transport activity in vivo by BCRP/ABCG2 inhibitors. Food-derived isoflavone sulfates were identified as useful markers for evaluating changes in BCRP-mediated transport in the small intestine by its inhibitors.

Current Challenges and Recent Developments in Mass Spectrometry-Based Metabolomics

High-resolution mass spectrometry (MS) has advanced the study of metabolism in living systems by allowing many metabolites to be measured in a single experiment. Although improvements in mass detector sensitivity have facilitated the detection of greater numbers of analytes, compound identification strategies, feature reduction software, and data sharing have not kept up with the influx of MS data. Here, we discuss the ongoing challenges with MS-based metabolomics, including de novo metabolite identification from mass spectra, differentiation of metabolites from environmental contamination, chromatographic separation of isomers, and incomplete MS databases. Because of their popularity and sensitive detection of small molecules, this review focuses on the challenges of liquid chromatography-mass spectrometry-based methods. We then highlight important instrumentational, experimental, and computational tools that have been created to address these challenges and how they have enabled the advancement of metabolomics research.

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.

Untargeted LC-MS Metabolomics for the Analysis of Micro-scaled Extracellular Metabolites from Hepatocytes

Metabolome analysis in micro physiological models is a challenge due to the low volume of the cell culture medium (CCM). Here, we report a LC-MS-based untargeted metabolomics protocol for the detection of hepatocyte extracellular metabolites from micro-scale samples of CCM. Using a single LC-MS method we have detected 57 metabolites of which 27 showed >2-fold shifts after 72-hour incubation. We demonstrate that micro-scale CCM samples can be used for modelling micro-physiological temporal dynamics in metabolite intensities.

Spatiotemporal determination of metabolite activities in the corneal epithelium on a chip

The corneal epithelial barrier maintains the metabolic activities of the ocular surface by regulating membrane transporters and metabolic enzymes responsible for the homeostasis of the eye as well as the pharmacokinetic behavior of drugs. Despite its importance, no established biomimetic in vitro methods are available to perform the spatiotemporal investigation of metabolism and determine the transportation of endogenous and exogenous molecules across the corneal epithelium barrier. This study introduces multiple corneal epitheliums on a chip namely, Corneal Epithelium on a Chip (CEpOC), which enables the spatiotemporal collection as well as analysis of micro-scaled extracellular metabolites from both the apical and basolateral sides of the barriers. Longitudinal samples collected during 48 h period were analyzed using untargeted liquid chromatography-mass spectrometry metabolomics method, and 104 metabolites were annotated. We observed the spatiotemporal secretion of biologically relevant metabolites (i.e., antioxidant, glutathione and uric acid) as well as the depletion of essential nutrients such as amino acids and vitamins mimicking the in vivo molecules trafficking across the human corneal epithelium. Through the shifts of extracellular metabolites and quantitative analysis of mRNA associated with transporters, we were able to investigate the secretion and transportation activities across the polarized barrier in a correlation with the expression of corneal transporters. Thus, CEpOC can provide a non-invasive, simple, yet effectively informative method to determine pharmacokinetics and pharmacodynamics as well as to discover novel biomarkers for drug toxicological and safety tests as advanced experimental model of the human corneal epithelium.

High-Precision Automated Workflow for Urinary Untargeted Metabolomic Epidemiology

Urine is a noninvasive biofluid that is rich in polar metabolites and well suited for metabolomic epidemiology. However, because of individual variability in health and hydration status, the physiological concentration of urine can differ >15-fold, which can pose major challenges in untargeted liquid chromatography-mass spectrometry (LC-MS) metabolomics. Although numerous urine normalization methods have been implemented (e.g., creatinine, specific gravity-SG), most are manual and, therefore, not practical for population-based studies. To address this issue, we developed a method to measure SG in 96-well-plates using a refractive index detector (RID), which exhibited accuracy within 85-115% and <3.4% precision. Bland-Altman statistics showed a mean deviation of -0.0001 SG units (limits of agreement: -0.0014 to 0.0011) relative to a hand-held refractometer. Using this RID-based SG normalization, we developed an automated LC-MS workflow for untargeted urinary metabolomics in a 96-well-plate format. The workflow uses positive and negative ionization HILIC chromatography and acquires mass spectra in data-independent acquisition (DIA) mode at three collision energies. Five technical internal standards (tISs) were used to monitor data quality in each method, all of which demonstrated raw coefficients of variation (CVs) < 10% in the quality controls (QCs) and < 20% in the samples for a small cohort (n = 87 urine samples, n = 22 QCs). Application in a large cohort (n = 842 urine samples, n = 248 QCs) demonstrated CVQC < 5% and CVsamples < 16% for 4/5 tISs after signal drift correction by cubic spline regression. The workflow identified >540 urinary metabolites including endogenous and exogenous compounds. This platform is suitable for performing urinary untargeted metabolomic epidemiology and will be useful for applications in population-based molecular phenotyping.

Multi-dimensional characterization and identification of sterols in untargeted LC-MS analysis using all ion fragmentation technology

Sterols are an important type of lipids, and play many important roles in physiological and pathological processes. However, comprehensive analysis of sterols especially identification of unknown sterols is challenging. In this work, LC-MS with all ion fragmentation (AIF) technology was developed for untargeted analysis of sterols in biological samples. AIF technology provided holistic and multi-dimensional characterization for both knowns and unknowns sterols, including accurate m/z, isotope pattern, retention time (RT), and co-eluted peak profiles between MS1 and MS2 ions in one analysis. We further developed an analysis strategy by integrating the multi-dimensional properties to support unambiguous identification of sterols, including distinguishing sterol isomers. The developed strategy enabled to identify a total of 23 sterols in mouse samples, and quantified 19 sterols in mouse liver tissues. More importantly, we demonstrated that AIF based multi-dimensional analysis provided a possibility to identify sterols without chemical standards and facilitated to discover novel compounds with sterol-like structures in biological samples. In summary, we employed the LC-MS based AIF technology to develop multi-dimensional characterization and identification of both known and unknown sterols in complex biological samples. The comprehensive analysis of sterols facilitates to provide molecular insights to many physiological and pathological activities in biology.