SWATH data independent acquisition mass spectrometry for metabolomics

Insights into the early-life chemical exposome of Nigerian infants and potential correlations with the developing gut microbiome

Early-life exposure to natural and synthetic chemicals can impact acute and chronic health conditions. Here, a suspect screening workflow anchored on high-resolution mass spectrometry was applied to elucidate xenobiotics in breast milk and matching stool samples collected from Nigerian mother-infant pairs (n = 11) at three time points. Potential correlations between xenobiotic exposure and the developing gut microbiome, as determined by 16S rRNA gene amplicon sequencing, were subsequently explored. Overall, 12,192 and 16,461 features were acquired in the breast milk and stool samples, respectively. Following quality control and suspect screening, 562 and 864 features remained, respectively, with 149 of these features present in both matrices. Taking advantage of 242 authentic reference standards measured for confirmatory purposes of food bio-actives and toxicants, 34 features in breast milk and 68 features in stool were identified and semi-quantified. Moreover, 51 and 78 features were annotated with spectral library matching, as well as 416 and 652 by in silico fragmentation tools in breast milk and stool, respectively. The analytical workflow proved its versatility to simultaneously determine a diverse panel of chemical classes including mycotoxins, endocrine-disrupting chemicals (EDCs), antibiotics, plasticizers, perfluorinated alkylated substances (PFAS), and pesticides, although it was originally optimized for polyphenols. Spearman rank correlation of the identified features revealed significant correlations between chemicals of the same classification such as polyphenols. One-way ANOVA and differential abundance analysis of the data obtained from stool samples revealed that molecules of plant-based origin elevated as complementary foods were introduced to the infants' diets. Annotated compounds in the stool, such as tricetin, positively correlated with the genus Blautia. Moreover, vulgaxanthin negatively correlated with Escherichia-Shigella. Despite the limited sample size, this exploratory study provides high-quality exposure data of matched biospecimens obtained from mother-infant pairs in sub-Saharan Africa and shows potential correlations between the chemical exposome and the gut microbiome.

Bone Proteomics Method Optimization for Forensic Investigations

The application of proteomic analysis to forensic skeletal remains has gained significant interest in improving biological and chronological estimations in medico-legal investigations. To enhance the applicability of these analyses to forensic casework, it is crucial to maximize throughput and proteome recovery while minimizing interoperator variability and laboratory-induced post-translational protein modifications (PTMs). This work compared different workflows for extracting, purifying, and analyzing bone proteins using liquid chromatography with tandem mass spectrometry (LC-MS)/MS including an in-StageTip protocol previously optimized for forensic applications and two protocols using novel suspension-trap technology (S-Trap) and different lysis solutions. This study also compared data-dependent acquisition (DDA) with data-independent acquisition (DIA). By testing all of the workflows on 30 human cortical tibiae samples, S-Trap workflows resulted in increased proteome recovery with both lysis solutions tested and in decreased levels of induced deamidations, and the DIA mode resulted in greater sensitivity and window of identification for the identification of lower-abundance proteins, especially when open-source software was utilized for data processing in both modes. The newly developed S-Trap protocol is, therefore, suitable for forensic bone proteomic workflows and, particularly when paired with DIA mode, can offer improved proteomic outcomes and increased reproducibility, showcasing its potential in forensic proteomics and contributing to achieving standardization in bone proteomic analyses for forensic applications.

Clinical advances in analytical profiling of signature lipids: implications for severe non-communicable and neurodegenerative diseases

BACKGROUND

Lipids play key roles in numerous biological processes, including energy storage, cell membrane structure, signaling, immune responses, and homeostasis, making lipidomics a vital branch of metabolomics that analyzes and characterizes a wide range of lipid classes. Addressing the complex etiology, age-related risk, progression, inflammation, and research overlap in conditions like Alzheimer's Disease, Parkinson's Disease, Cardiovascular Diseases, and Cancer poses significant challenges in the quest for effective therapeutic targets, improved diagnostic markers, and advanced treatments. Mass spectrometry is an indispensable tool in clinical lipidomics, delivering quantitative and structural lipid data, and its integration with technologies like Liquid Chromatography (LC), Magnetic Resonance Imaging (MRI), and few emerging Matrix-Assisted Laser Desorption Ionization- Imaging Mass Spectrometry (MALDI-IMS) along with its incorporation into Tissue Microarray (TMA) represents current advances. These innovations enhance lipidomics assessment, bolster accuracy, and offer insights into lipid subcellular localization, dynamics, and functional roles in disease contexts.

AIM OF THE REVIEW

The review article summarizes recent advancements in lipidomic methodologies from 2019 to 2023 for diagnosing major neurodegenerative diseases, Alzheimer's and Parkinson's, serious non-communicable cardiovascular diseases and cancer, emphasizing the role of lipid level variations, and highlighting the potential of lipidomics data integration with genomics and proteomics to improve disease understanding and innovative prognostic, diagnostic and therapeutic strategies.

KEY SCIENTIFIC CONCEPTS OF REVIEW

Clinical lipidomic studies are a promising approach to track and analyze lipid profiles, revealing their crucial roles in various diseases. This lipid-focused research provides insights into disease mechanisms, biomarker identification, and potential therapeutic targets, advancing our understanding and management of conditions such as Alzheimer's Disease, Parkinson's Disease, Cardiovascular Diseases, and specific cancers.

High-abundance peaks and peak clusters associate with pharmaceutical polymers and excipients in urinary untargeted clinical metabolomics data: exploration of their origin and possible impact on label-free quantification

Pharmaceutical polymers and excipients represent interesting but often overlooked chemical classes in clinical exposure and bioanalytical research. These chemicals may cause hypersensitivity reactions, they can be useful to confirm exposure to pharmaceuticals, and they may pose bioanalytical challenges, including ion suppression in liquid chromatography-mass spectrometry (LC-MS-)based workflows. In this work, we assessed these chemicals in light of a rather surprising finding presented in two previously published studies, namely that usage of cyclosporine A, an immunosuppressive drug which is known to be cleared through excretion in the bile, explained the largest amount of variance in principal component analysis of urinary LC-SWATH/MS small-molecule profiling data. Specifically, we examined the freely-accessible 24-hour urine metabolomics data of 570 kidney transplant recipients included in the TransplantLines Biobank and Cohort Study (NCT03272841). These data unveiled thousands of high-abundance polymer peaks in some samples, which were associated with the use of the macrogol (i.e., polyethylene glycol) 3350 oral laxative agent. In addition, we found multiple clusters of high-abundance peaks which were linked to the exposure to two pharmaceutical excipients, namely short-chain polyethylene glycol (molecular weight <1000 Da) and polyethoxylated castor oil (also known as Kolliphor® EL or Cremophor® EL). Respectively, these excipients are used in temazepam capsules and cyclosporine A capsules, and the latter provides a plausible explanation for the rather surprising finding that instigated our work. Moreover, such explanation and our findings in general put emphasis on taking into consideration these and other pharmaceutical polymers and excipients when exploring, processing, and interpreting clinical small-molecule profiling data.

LiLA: lipid lung-based ATLAS built through a comprehensive workflow designed for an accurate lipid annotation

Accurate lipid annotation is crucial for understanding the role of lipids in health and disease and identifying therapeutic targets. However, annotating the wide variety of lipid species in biological samples remains challenging in untargeted lipidomic studies. In this work, we present a lipid annotation workflow based on LC-MS and MS/MS strategies, the combination of four bioinformatic tools, and a decision tree to support the accurate annotation and semi-quantification of the lipid species present in lung tissue from control mice. The proposed workflow allowed us to generate a lipid lung-based ATLAS (LiLA), which was then employed to unveil the lipidomic signatures of the Mycobacterium tuberculosis infection at two different time points for a deeper understanding of the disease progression. This workflow, combined with manual inspection strategies of MS/MS data, can enhance the annotation process for lipidomic studies and guide the generation of sample-specific lipidome maps. LiLA serves as a freely available data resource that can be employed in future studies to address lipidomic alterations in mice lung tissue.

Windows Scanning Multiomics: Integrated Metabolomics and Proteomics

Metabolomics and proteomics offer significant advantages in understanding biological mechanisms at two hierarchical levels. However, conventional single omics analysis faces challenges due to the high demand for specimens and the complexity of intrinsic associations. To obtain comprehensive and accurate system biological information, we developed a multiomics analytical method called Windows Scanning Multiomics (WSM). In this method, we performed simultaneous extraction of metabolites and proteins from the same sample, resulting in a 10% increase in the coverage of the identified biomolecules. Both metabolomics and proteomics analyses were conducted by using ultrahigh-performance liquid chromatography mass spectrometry (UPLC-MS), eliminating the need for instrument conversions. Additionally, we designed an R-based program (WSM.R) to integrate mathematical and biological correlations between metabolites and proteins into a correlation network. The network created from simultaneously extracted biomolecules was more focused and comprehensive compared to those from separate extractions. Notably, we excluded six pairs of false-positive relationships between metabolites and proteins in the network established using simultaneously extracted biomolecules. In conclusion, this study introduces a novel approach for multiomics analysis and data processing that greatly aids in bioinformation mining from multiomics results. This method is poised to play an indispensable role in systems biology research.

Structure-activity relationship studies and biological properties evaluation of peptidic NRP-1 ligands: Investigation of N-terminal cysteine importance

Neuropilin-1 (NRP-1) is a major co-receptor of vascular endothelial growth factor receptor-2 (VEGFR-2). It may also stimulate tumour growth and metastasis independently of VEGF-A165. These functions make VEGF-A165/NRP-1 complex formation and its inhibition of great interest, where NRP-1 is the target for which effective ligands are sought. Design of peptide-like inhibitors represent a strategy with great potential in the treatment of NRP-1-related disorders. Here, we present the synthesis, molecular modelling, structure-activity relationship studies as well as biological evaluation of peptides with the branched sequences H2N-X-Lys(hArg)-Dab-Oic-Arg-OH and H2N-Lys(X-hArg)-Dab-Oic-Arg-OH. Two of the designed peptides, in which Cys was inserted in X position, expressed high affinity (∼40 nM value) for NRP-1 and were resistant to enzymatic digestion in human serum. Moreover, peptide/NRP-1 complex promoted fast intracytoplasmic protein trafficking towards the plasma membrane in breast cancer cells. Our results suggest that these compounds might be good candidates for further development of VEGF-A165/NRP-1 inhibitors.

Screening strategy for 1210 exogenous chemicals in serum by two-dimensional liquid chromatography-mass spectrometry

Humans are at risk of exogenous exposure to exogenous chemicals. Challenges exist for the comprehensive monitoring of residues with different physical and chemical properties in serum. Here, an on-line two-dimensional liquid chromatography (2D-LC) - high resolution mass spectrometry system (HRMS) was developed, expanding the range of the partition coefficient in octanol/water of the residue analysis from -8 to 12. A high-coverage serum residue screening strategy was further designed by integrating 2D-LC system with HRMS full MS/data independent acquisition and automatic spectral library searching. This strategy enables to simultaneously screen 1210 pesticides, veterinary/human drugs, other chemical pollutants and their metabolites in serum with a single analysis. Method validation showed 92% and 81% of 1022 residues spiked in serum could be detected at 50 ng/mL and 5 ng/mL, respectively. The developed method was applied to the analysis of 24 separately pooled serum samples, 58 suspect residues were found, some of them were detected at high frequencies over than 50%. Among them, 4,6-Dinitro-O-cresol and probable carcinogenic folpet are highly toxic, and cimaterol is banned in China. Collectively, this study developed a 2D-LC-HRMS -based screening strategy for screening pesticides, veterinary/human drugs, and other chemical pollutants in serum, it is helpful for studying the effect of exogenous exposures on human health.

Liquid chromatography and differential mobility spectrometry-data-independent mass spectrometry for comprehensive multidimensional separations in metabolomics

The benefits of combining drift time ion mobility (DTIMS) with liquid chromatography-high-resolution mass spectrometry (HRMS) have been reported for metabolomics but the use of differential time mobility spectrometry (DMS) is less obvious due to the need for rapid scanning of the DMS cell. Drift DTIMS provides additional precursor ion selectivity and collisional cross-section information but the separation resolution between analytes remains cell- and component-dependent. With DMS, the addition of 2-propanol modifier can improve the selectivity but on cost of analyte MS response. In the present work, we investigate the liquid chromatography-mass spectrometry (LC-MS) analysis of a mix of 50 analytes, representative for urine and plasma metabolites, using scanning DMS with the single modifiers cyclohexane (Ch), toluene (Tol), acetonitrile (ACN), ethanol (EtOH), and 2-propanol (IPA), and a binary modifier mixture (cyclohexane/2-propanol) with emphasis on selectivity and signal sensitivity. 1.5% IPA in the N₂ stream was found to suppress the signal of 50% of the analytes which could be partially recovered with the use of IPA to 0.05% as a Ch/IPA mixture. The potential to use the separation voltage/compensation voltage/modifier (SV/CoV/Mod) feature as an additional analyte identifier for qualitative analysis is also presented and applied to a data-independent LCxDMS-SWATH-MS workflow for the analysis of endogenous metabolites and drugs of abuse in human urine samples from traffic control.

Metabolomics: Going Deeper, Going Broader, Going Further

Metabolomics is a continuously dynamic field of research that is driven by demanding research questions and technological advances alike. In this review we highlight selected recent and ongoing developments in the area of mass spectrometry-based metabolomics. The field of view that can be seen through the metabolomics lens can be broadened by adoption of separation techniques such as hydrophilic interaction chromatography and ion mobility mass spectrometry (going broader). For a given biospecimen, deeper metabolomic analysis can be achieved by resolving smaller entities such as rare cell populations or even single cells using nano-LC and spatially resolved metabolomics or by extracting more useful information through improved metabolite identification in untargeted metabolomic experiments (going deeper). Integration of metabolomics with other (omics) data allows researchers to further advance in the understanding of the complex metabolic and regulatory networks in cells and model organisms (going further). Taken together, diverse fields of research from mechanistic studies to clinics to biotechnology applications profit from these technological developments.

Eight key rules for successful data-dependent acquisition in mass spectrometry-based metabolomics

In recent years, metabolomics has emerged as a pivotal approach for the holistic analysis of metabolites in biological systems. The rapid progress in analytical equipment, coupled to the rise of powerful data processing tools, now provides unprecedented opportunities to deepen our understanding of the relationships between biochemical processes and physiological or phenotypic conditions in living organisms. However, to obtain unbiased data coverage of hundreds or thousands of metabolites remains a challenging task. Among the panel of available analytical methods, targeted and untargeted mass spectrometry approaches are among the most commonly used. While targeted metabolomics usually relies on multiple-reaction monitoring acquisition, untargeted metabolomics use either data-independent acquisition (DIA) or data-dependent acquisition (DDA) methods. Unlike DIA, DDA offers the possibility to get real, selective MS/MS spectra and thus to improve metabolite assignment when performing untargeted metabolomics. Yet, DDA settings are more complex to establish than DIA settings, and as a result, DDA is more prone to errors in method development and application. Here, we present a tutorial which provides guidelines on how to optimize the technical parameters essential for proper DDA experiments in metabolomics applications. This tutorial is organized as a series of rules describing the impact of the different parameters on data acquisition and data quality. It is primarily intended to metabolomics users and mass spectrometrists that wish to acquire both theoretical background and practical tips for developing effective DDA methods.

Cutting-edge strategies and critical advancements in characterization and quantification of metabolites concerning translational metabolomics

Despite remarkable progress in drug discovery strategies, significant challenges are still remaining in translating new insights into clinical applications. Scientists are devising creative approaches to bridge the gap between scientific and translational research. Metabolomics is a unique field among other omics techniques for identifying novel metabolites and biomarkers. Fortunately, characterization and quantification of metabolites are becoming faster due to the progress in the field of orthogonal analytical techniques. This review detailed the advancement in the progress of sample preparation, and data processing techniques including data mining tools, database, and their quality control (QC). Advances in data processing tools make it easier to acquire unbiased data that includes a diverse set of metabolites. In addition, novel breakthroughs including, miniaturization as well as their integration with other devices, metabolite array technology, and crystalline sponge-based method have led to faster, more efficient, cost-effective, and holistic metabolomic analysis. The use of cutting-edge techniques to identify the human metabolite, including biomarkers has proven to be advantageous in terms of early disease identification, tracking the progression of illness, and possibility of personalized treatments. This review addressed the constraints of current metabolomics research, which are impeding the facilitation of translation of research from bench to bedside. Nevertheless, the possible way out from such constraints and future direction of translational metabolomics has been conferred.

A Novel Approach of SWATH-Based Metabolomics Analysis Using the Human Metabolome Database Spectral Library

Metabolomics is a potential approach to paving new avenues for clinical diagnosis, molecular medicine, and therapeutic drug monitoring and development. The conventional metabolomics analysis pipeline depends on the data-independent acquisition (DIA) technique. Although powerful, it still suffers from stochastic, non-reproducible ion selection across samples. Despite the presence of different metabolomics workbenches, metabolite identification remains a tedious and time-consuming task. Consequently, sequential windowed acquisition of all theoretical MS (SWATH) acquisition has attracted much attention to overcome this limitation. This article aims to develop a novel SWATH platform for data analysis with a generation of an accurate mass spectral library for metabolite identification using SWATH acquisition. The workflow was validated using inclusion/exclusion compound lists. The false-positive identification was 3.4% from the non-endogenous drugs with 96.6% specificity. The workflow has proven to overcome background noise despite the complexity of the SWATH sample. From the Human Metabolome Database (HMDB), 1282 compounds were tested in various biological samples to demonstrate the feasibility of the workflow. The current study identified 377 compounds in positive and 303 in negative modes with 392 unique non-redundant metabolites. Finally, a free software tool, SASA, was developed to analyze SWATH-acquired samples using the proposed pipeline.

High-Performance Data Processing Workflow Incorporating Effect-Directed Analysis for Feature Prioritization in Suspect and Nontarget Screening

Effect-directed analysis (EDA) aims at the detection of bioactive chemicals of emerging concern (CECs) by combining toxicity testing and high-resolution mass spectrometry (HRMS). However, consolidation of toxicological and chemical analysis techniques to identify bioactive CECs remains challenging and laborious. In this study, we incorporate state-of-the-art identification approaches in EDA and propose a robust workflow for the high-throughput screening of CECs in environmental and human samples. Three different sample types were extracted and chemically analyzed using a single high-performance liquid chromatography HRMS method. Chemical features were annotated by suspect screening with several reference databases. Annotation quality was assessed using an automated scoring system. In parallel, the extracts were fractionated into 80 micro-fractions each covering a couple of seconds from the chromatogram run and tested for bioactivity in two bioassays. The EDA workflow prioritized and identified chemical features related to bioactive fractions with varying levels of confidence. Confidence levels were improved with the in silico software tools MetFrag and the retention time indices platform. The toxicological and chemical data quality was comparable between the use of single and multiple technical replicates. The proposed workflow incorporating EDA for feature prioritization in suspect and nontarget screening paves the way for the routine identification of CECs in a high-throughput manner.

Analysis of illicit pills and drugs of abuse in urine samples using a 3D-printed open port probe hyphenated with differential mobility spectrometry-mass spectrometry

The present work describes the application of an in-house developed 3D-printed open port probe (3DP-OPP) with differential ion mobility spectrometry (DMS) mass spectrometry.

Adaptive defence and sensing responses of host plant roots to fungal pathogen attack revealed by transcriptome and metabolome analyses

Plant root-produced constitutive and inducible defences inhibit pathogenic microorganisms within roots and in the rhizosphere. However, regulatory mechanisms underlying host responses during root-pathogen interactions are largely unexplored. Using the model species Brachypodium distachyon (Bd), we studied transcriptional and metabolic responses altered in Bd roots following challenge with Fusarium graminearum (Fg), a fungal pathogen that causes diseases in diverse organs of cereal crops. Shared gene expression patterns were found between Bd roots and spikes during Fg infection associated with the mycotoxin deoxynivalenol (DON). Overexpression of BdMYB78, an up-regulated transcription factor, significantly increased root resistance during Fg infection. We show that Bd roots recognize encroaching Fg prior to physical contact by altering transcription of genes associated with multiple cellular processes such as reactive oxygen species and cell development. These changes coincide with altered levels of secreted host metabolites detected by an untargeted metabolomic approach. The secretion of Bd metabolites was suppressed by Fg as enhanced levels of defence-associated metabolites were found in roots during pre-contact with a Fg mutant defective in host perception and the ability to cause disease. Our results help to understand root defence strategies employed by plants, with potential implications for improving the resistance of cereal crops to soil pathogens.

CRB-SWATH: A Method for Enhancing Untargeted Precursor Ion Extraction and Automatically Constructing Their Tandem Mass Spectra from SWATH Datasets by Chromatographic Retention Behaviors

Sequential window acquisition of all theoretical spectra (SWATH) as a typical data-independent acquisition (DIA) strategy is favorable for untargeted metabolomics. It could theoretically acquire product ions of all precursor ions, including precursor ions showing chromatographic peaks of rather poor qualities. However, existing data processing methods present limited capabilities in capturing poor-quality peaks of precursor ions. Thus, although their product ions could be acquired, their precursor ions are absent. Here, we present a new strategy, chromatographic retention behavior-SWATH (CRB-SWATH), that could unbiasedly capture poor-quality peaks and provide high resolutions of multiplexed mass spectroscopy (MS/MS) spectra in SWATH datasets. CRB-SWATH monitors CRBs of SWATH-MS signals under a series of altered elution gradients. As signals of compounds differ from noise by showing CRBs, both the precursor and fragment ions are captured, while ignoring their peak qualities. Moreover, CRB-SWATH offers good chances to resolve highly multiplexed MS/MS spectra in SWATH datasets because precursor ions coeluted in a single elution gradient often present different CRBs. In the untargeted metabolic analysis of Hela cell extracts, CRB-SWATH showed the advantage in exclusively capturing 2645 ions of poor-quality peaks (i.e., tiny peaks, discontinuous ion traces, tailing peaks, zigzag peaks, etc.), accounting for 34.4% of all the untargeted precursor ions extracted. Therein, it is noteworthy that among 2116 negative ions detected in hydrophilic interaction liquid chromatography (HILIC) mode, 1284 poor-quality ion peaks (>60%) were exclusively captured by CRB-SWATH. As CRB-SWATH automatically captures a large sum of true ion peaks of poor qualities, extracts MS/MS spectra of high purities, and provides chromatographic retention behaviors of untargeted metabolites for identification and classification, it could be a useful metabolomics tool for understanding biological phenomena better.

Variable Data Independent Acquisition and Data Mining Exploring Feature-Based Molecular Networking Analysis for Untargeted Screening of Synthetic Cannabinoids in Oral Fluid

Novel psychoactive substances (NPS) are constantly emerging in the drug market, and synthetic cannabinoids (SCs) are included in this NPS family. Forensic laboratories often struggle with these continually emerging SCs, forcing them to develop an untargeted workflow to incorporate these psychoactive drugs in their procedures. Usually, forensic laboratories select analytical methods based on targeted mass spectrometry (MS) technologies for strictly tracking already known NPS. The appropriate way to tackle unknown substances is to develop pipelines for untargeted analysis that include LC-HRMS analytical methods and data analysis. Once established, this strategy would allow drug testing laboratories to be always one step ahead of the new trends concerning the "designer drugs" market. To address this challenge an untargeted workflow based on mass spectrometry data acquisition and data analysis was developed to detect SCs in oral fluid (OF) samples at a low concentration range. The samples were extracted by mixed-mode solid-phase extraction and analyzed by Liquid Chromatography - High-Resolution Mass Spectrometry (LC-HRMS). Tandem mass spectra (MS2) were recorded performing a variable isolation width across a mass range of all theoretical precursor ions (vDIA) after the chromatographic separation. After raw data processing with the MSDial software, the deconvoluted features were sent to GNPS for Feature-Based Molecular Networking (FBMN) construction for nontargeted data mining. The FBMN analysis created a unique integrated network for most of the SCs assessed in the OF at a low level (20 ng/mL). These results demonstrate the potential of an untargeted approach to detect different derivatives of SCs at trace levels for forensic applications.

Untargeted Metabolomics in Forensic Toxicology: A New Approach for the Detection of Fentanyl Intake in Urine Samples

The misuse of fentanyl, and novel synthetic opioids (NSO) in general, has become a public health emergency, especially in the United States. The detection of NSO is often challenged by the limited diagnostic time frame allowed by urine sampling and the wide range of chemically modified analogues, continuously introduced to the recreational drug market. In this study, an untargeted metabolomics approach was developed to obtain a comprehensive “fingerprint” of any anomalous and specific metabolic pattern potentially related to fentanyl exposure. In recent years, in vitro models of drug metabolism have emerged as important tools to overcome the limited access to positive urine samples and uncertainties related to the substances actually taken, the possible combined drug intake, and the ingested dose. In this study, an in vivo experiment was designed by incubating HepG2 cell lines with either fentanyl or common drugs of abuse, creating a cohort of 96 samples. These samples, together with 81 urine samples including negative controls and positive samples obtained from recent users of either fentanyl or “traditional” drugs, were subjected to untargeted analysis using both UHPLC reverse phase and HILIC chromatography combined with QTOF mass spectrometry. Data independent acquisition was performed by SWATH in order to obtain a comprehensive profile of the urinary metabolome. After extensive processing, the resulting datasets were initially subjected to unsupervised exploration by principal component analysis (PCA), yielding clear separation of the fentanyl positive samples with respect to both controls and samples positive to other drugs. The urine datasets were then systematically investigated by supervised classification models based on soft independent modeling by class analogy (SIMCA) algorithms, with the end goal of identifying fentanyl users. A final single-class SIMCA model based on an RP dataset and five PCs yielded 96% sensitivity and 74% specificity. The distinguishable metabolic patterns produced by fentanyl in comparison to other opioids opens up new perspectives in the interpretation of the biological activity of fentanyl.

Analyzing Assay Specificity in Metabolomics Using Unique Ion Signature Simulations

Targeted, untargeted, and data-independent acquisition (DIA) metabolomics workflows are often hampered by ambiguous identification based on either MS1 information alone or relatively few MS2 fragment ions. While DIA methods have been popularized in proteomics, it is less clear whether they are suitable for metabolomics workflows due to their large precursor isolation windows and complex coisolation patterns. Here, we quantitatively investigate the conditions necessary for unique metabolite detection in complex backgrounds using precursor and fragment ion mass-to-charge (m/z) separation, comparing three benchmarked mass spectrometry (MS) methods [MS1, MRM (multiple reaction monitoring), and DIA]. Our simulations show that DIA outperformed MS1-only and MRM-based methods with regards to specificity by factors of ∼2.8-fold and ∼1.8-fold, respectively. Additionally, we show that our results are not dependent on the number of transitions used or the complexity of the background matrix. Finally, we show that collision energy is an important factor in unambiguous detection and that a single collision energy setting per compound cannot achieve optimal pairwise differentiation of compounds. Our analysis demonstrates the power of using both high-resolution precursor and high-resolution fragment ion m/z for unambiguous compound detection. This work also establishes DIA as an emerging MS acquisition method with high selectivity for metabolomics, outperforming both data-dependent acquisition (DDA) and MRM with regards to unique compound identification potential.

Untargeted UHPLC-ESI-QTOF-MS/MS analysis with targeted feature extraction at precursor and fragment level for profiling of the platelet lipidome with ex vivo thrombin-activation

Lipids play a major role in platelet signaling and activation. In this study, we analyzed the platelet lipidome in an untargeted manner by reversed-phase UHPLC for lipid species separation coupled to high-resolution QTOF-MS/MS in data-independent acquisition (DIA) mode with sequential window acquisition of all theoretical fragment ion mass spectra (SWATH) for compound detection. Lipid identification and peak picking was supported by the characteristic regular elution pattern of lipids differing in carbon and double bond numbers. It was primarily based on post-acquisition targeted feature extraction from the SWATH data. Multiple extracted ion chromatograms (EICs) from SWATH data of diagnostic ions on MS1 and MS2 level from both positive and negative ion mode allowed to distinguish between poorly resolved isomeric lipids based on their distinct fragment ions, which were used for relative quantification at a molecular lipid species level. It supports assay specificity for relative lipid quantitation via multiple quantifiably ions unlike to data-dependent acquisition methods which rely on precursor ions only. This approach was used to analyze human platelet samples. 457 lipids were annotated. Concentrations of lipids were estimated by stable isotope-labelled lipid class-specific internal standards as surrogate calibrants. Heatmaps of lipid concentrations in dependence on carbon and double bond numbers for the distinct lipid classes revealed a snapshot of the platelet lipidome in the resting state with lipid species distributions within classes supporting some functional interpretations. As expected, activation of the platelets by thrombin has led to significant alterations in the platelet lipidome as proven by univariate (volcano plot) and multivariate (PLS-DA) statistics. Several lipids were significantly up-regulated (lysophosphatidylinositols, oxylipins such as thromboxane B2 (TXB2), hydroxyheptadecatrienoic acid (HHT), hydroxyeicosatetraenoic acid (HETE), hydroxyoctadecadienoic acid (HODE), sphingoid-bases, (very) long chain saturated fatty acids) or down-regulated (lysophosphatidylethanolamines, polyunsaturated fatty acids, phosphatidylinositols). Several of them are well known as biomarkers of platelet activation while others may provide some further insights into pathways of platelet activation and platelet metabolism.

DecoID improves identification rates in metabolomics through database-assisted MS/MS deconvolution

Chimeric MS/MS spectra contain fragments from multiple precursor ions and therefore hinder compound identification in metabolomics. Historically, deconvolution of these chimeric spectra has been challenging and relied on specific experimental methods that introduce variation in the ratios of precursor ions between multiple tandem mass spectrometry (MS/MS) scans. DecoID provides a complementary, method-independent approach where database spectra are computationally mixed to match an experimentally acquired spectrum by using LASSO regression. We validated that DecoID increases the number of identified metabolites in MS/MS datasets from both data-independent and data-dependent acquisition without increasing the false discovery rate. We applied DecoID to publicly available data from the MetaboLights repository and to data from human plasma, where DecoID increased the number of identified metabolites from data-dependent acquisition data by over 30% compared to direct spectral matching. DecoID is compatible with any user-defined MS/MS database and provides automated searching for some of the largest MS/MS databases currently available.

Isomer Selective Comprehensive Lipidomics Analysis of Phosphoinositides in Biological Samples by Liquid Chromatography with Data Independent Acquisition Tandem Mass Spectrometry

Phosphoinositides (PIPx) play central roles in membrane dynamics and signal transduction of key functions like cellular growth, proliferation, differentiation, migration, and adhesion. They are highly regulated through a network of distinct phosphatidylinositol phosphates consisting of seven groups and three regioisomers in two groups (PIP and PIP2), which arise from phosphorylation at 3', 4', and 5' positions of the inositol ring. Numerous studies have revealed the importance of both fatty acyl chains, degree of phosphorylation, and phosphorylation positions under physiological and pathological states. However, a comprehensive analytical method that allows differentiation of all regioisomeric forms with different acyl side chains and degrees of phosphorylation is still lacking. Here, we present an integrated comprehensive workflow of PIPx analysis utilizing a chiral polysaccharide stationary phase coupled with electrospray ionization high-resolution mass spectrometry with a data independent acquisition technique using the SWATH technology. Correspondingly, a targeted data mining strategy in the untargeted comprehensively acquired MS and MS/MS data was developed. This powerful highly selective method gives a full picture of PIPx profiles in biological samples. Herein, we present for the first time the full PIPx profiles of NIST SRM1950 plasma, Pichia pastoris lipid extract, and HeLa cell extract, including profile changes upon treatment with potential PI3K inhibitor wortmannin. We also illustrate using this inhibitor that measurements of the PIPx profile averaged over the distinct regioisomers by analytical procedures, which cannot differentiate between the individual PIPx isomers, can easily lead to biased conclusions.

Discovery of Polyhydroxyalkyl Pyrazine Generation upon Coffee Roasting by In-Bean Labeling Experiments

The major non-volatile reaction products formed from free amino acids during the early stage of coffee roasting were investigated using biomimetic in-bean experiments with labeled and unlabeled free amino acids. Comprehensive untargeted screening by ultra-high performance liquid chromatography-electrospray-ionization-quadrupole time-of-flight-tandem mass spectrometry (UHPLC-ESI-QToF-MS) in data-independent acquisition (DIA) mode was carried out and modeling by orthogonal partial least-squares discriminant analysis (OPLS-DA) helped in revealing 11 pyrazine structures identified in coffee for the first time. 2-(2',3',4'-Trihydroxybutyl)-(5/6)-methyl-pyrazine (1) and 2,(5/6)-bis(2',3',4'-trihydroxybutyl)-pyrazine (2) were the most prominent compounds, while 2-(3',4'-dihydroxybutyl)-(5/6)-methyl-pyrazine (5) and 2-(2',3',4'-trihydroxybutyl)-(5/6)-(2'-hydroxyethyl)-pyrazine (10) were not even previously identified in other food matrices. The structures could be verified by means of additional biomimetic in-bean experiments with labeled sucrose leveraging the carbon module labeling (CAMOLA) approach. Based on these results, plausible formation pathways could be formulated fitting into the known Maillard reaction mechanisms. Sucrose was highlighted as the predominant precursor of the carbon backbone of all identified pyrazines butonly 33-55% of the nitrogen atoms originated from free amino acids.

Novel acquisition strategies for metabolomics using drift tube ion mobility-quadrupole resolved all ions time-of-flight mass spectrometry (IM-QRAI-TOFMS)

The focus of this work was the implementation of ion mobility (IM) and a prototype quadrupole driver within data independent acquisition (DIA) using a drift tube IM-QTOFMS aiming to improve the level of confidence in identity confirmation workflows for non-targeted metabolomics. In addition to conventional all ions (IM-AI) acquisition, quadrupole resolved all ions (IM-QRAI) acquisition allows a drift time-directed precursor ion isolation in DIA using sequential isolation of precursor ions using mass windows of up to 100 Da which can be rapidly ramped across single ion mobility transients (i.e., <100 ms) according to the arrival times of precursor ions. Both IM-AI and IM-QRAI approaches were used for identity confirmation and relative quantification of metabolites in cellular extracts of the cell factory host Pichia pastoris. Samples were spiked with a uniformly ¹³C-labeled (U¹³C) internal standard and LC with low-field drift tube IM separation was used in combination with IM-AI and IM-QRAI. Combining excellent hardware performance and correlation of IM arrival times of natural (^natC) and U¹³C metabolites enabled alignment of signals in the arrival time domain (^DTCCS_N2 differences ≤0.3%), and, in the case of IM-QRAI operation, maintenance of quantitative signals in comparison to IM-AI. The combination of tailored IM-QRAI methods for precursor ion isolation and IM separation also minimized the occurrence of spectral interferences in complex DIA datasets. Combined use of the software tools MS-DIAL, MS-Finder and Skyline for peak picking, feature alignment, reconciliation of ^natC and U¹³C isotopologue pairs, deconvolution of fragment spectra from DIA data, identity confirmation (including ^DTCCS_N2) and targeted re-extraction of datafiles were employed for the data processing workflow. Overall, the combined new acquisition and data processing approaches enabled 87 metabolites to be identified between Level 1 (identified by standard compound) and Level 3.2 (accurate mass spectrum and number of carbons confirmed). The developed methods constitute promising metabolomics discovery tools and can be used to elucidate the number of carbon atoms present in unknown metabolites in stable isotope-supported metabolomics.

Untargeted In Silico Compound Classification-A Novel Metabolomics Method to Assess the Chemodiversity in Bryophytes

In plant ecology, biochemical analyses of bryophytes and vascular plants are often conducted on dried herbarium specimen as species typically grow in distant and inaccessible locations. Here, we present an automated in silico compound classification framework to annotate metabolites using an untargeted data independent acquisition (DIA)–LC/MS–QToF-sequential windowed acquisition of all theoretical fragment ion mass spectra (SWATH) ecometabolomics analytical method. We perform a comparative investigation of the chemical diversity at the global level and the composition of metabolite families in ten different species of bryophytes using fresh samples collected on-site and dried specimen stored in a herbarium for half a year. Shannon and Pielou’s diversity indices, hierarchical clustering analysis (HCA), sparse partial least squares discriminant analysis (sPLS-DA), distance-based redundancy analysis (dbRDA), ANOVA with post-hoc Tukey honestly significant difference (HSD) test, and the Fisher’s exact test were used to determine differences in the richness and composition of metabolite families, with regard to herbarium conditions, ecological characteristics, and species. We functionally annotated metabolite families to biochemical processes related to the structural integrity of membranes and cell walls (proto-lignin, glycerophospholipids, carbohydrates), chemical defense (polyphenols, steroids), reactive oxygen species (ROS) protection (alkaloids, amino acids, flavonoids), nutrition (nitrogen- and phosphate-containing glycerophospholipids), and photosynthesis. Changes in the composition of metabolite families also explained variance related to ecological functioning like physiological adaptations of bryophytes to dry environments (proteins, peptides, flavonoids, terpenes), light availability (flavonoids, terpenes, carbohydrates), temperature (flavonoids), and biotic interactions (steroids, terpenes). The results from this study allow to construct chemical traits that can be attributed to biogeochemistry, habitat conditions, environmental changes and biotic interactions. Our classification framework accelerates the complex annotation process in metabolomics and can be used to simplify biochemical patterns. We show that compound classification is a powerful tool that allows to explore relationships in both molecular biology by “zooming in” and in ecology by “zooming out”. The insights revealed by our framework allow to construct new research hypotheses and to enable detailed follow-up studies.

Targeting unique biological signals on the fly to improve MS/MS coverage and identification efficiency in metabolomics

When using liquid chromatography/mass spectrometry (LC/MS) to perform untargeted metabolomics, it is common to detect thousands of features from a biological extract. Although it is impractical to collect non-chimeric MS/MS data for each in a single chromatographic run, this is generally unnecessary because most features do not correspond to unique metabolites of biological relevance. Here we show that relatively simple data-processing strategies that can be applied on the fly during acquisition of data with an Orbitrap ID-X, such as blank subtraction and well-established adduct or isotope calculations, decrease the number of features to target for MS/MS analysis by up to an order of magnitude for various types of biological matrices. We demonstrate that annotating these non-biological contaminants and redundancies in real time during data acquisition enables comprehensive MS/MS data to be acquired on each remaining feature at a single collision energy. To ensure that an appropriate collision energy is applied, we introduce a method using a series of hidden ion-trap scans in an Orbitrap ID-X to find an optimal value for each feature that can then be applied in a subsequent high-resolution Orbitrap scan. Data from 100 metabolite standards indicate that this real-time optimization of collision energies leads to more informative MS/MS patterns compared to using a single fixed collision energy alone. As a benchmark to evaluate the overall workflow, we manually annotated unique biological features by independently subjecting E. coli samples to a credentialing analysis. While credentialing led to a more rigorous reduction in feature number, on-the-fly annotation with blank subtraction on an Orbitrap ID-X did not inappropriately discard unique biological metabolites. Taken together, our results reveal that optimal fragmentation data can be obtained in a single LC/MS/MS run for >90% of the unique biological metabolites in a sample when features are annotated during acquisition and collision energies are selected by using parallel mass spectrometry detection.

An enhanced strategy integrating offline superimposed two-dimensional separation with mass defect filter and diagnostic ion filter: Comprehensive characterization of steroid alkaloids in Fritillariae Pallidiflorae Bulbus as a case study

The inherent complexity of traditional Chinese medicines necessitates the application of multi-dimensional information to accomplish comprehensive profiling and confirmative identification of their chemical components. In this study, we display an enhanced strategy by integrating offline superimposed two-dimensional separation (S-2D-LC) with mass defect filter and diagnostic ion filter to comprehensively characterize the alkaloid composition of Fritillariae Pallidiflorae Bulbus (FPB). The superimposed HILIC × RP and UPCC × RP offline two-dimensional liquid chromatography system was constructed with superior orthogonality (R²=0.004 and R²=0.001) for chromatographic separation. In total, 70 fractions were collected after the first-dimensional chromatographic separation (HILIC and UPCC) and then analyzed by the second-dimensional reversed phase (RP) liquid chromatography coupled with Q-TOF/MS/MS in FAST DDA acquisition mode. A four-step interpretation strategy combining mass defect filter with diagnostic ion filter was developed to rapidly characterize alkaloids in Fritillaria species. Ultimately, a sum of 529 Fritillaria alkaloids were characterized from two botanical origins of FPB. The integrated strategy is practical to efficiently expose and comprehensively characterize more trace and isomeric components in complex herbal medicines.

Data-Independent Identification of Suspected Organic Pollutants Using Gas Chromatography-Atmospheric Pressure Chemical Ionization-Mass Spectrometry

The identity of an unknown environmental pollutant is reflected by the mass and dissociation chemistry of its (quasi)molecular ion. Gas chromatography-atmospheric pressure chemical ionization-mass spectrometry (GC-APCI-MS) increases the yield of molecular ions (compared to conventional electron ionization) by collisional cooling. Scanning quadrupole data-independent acquisition (SQDIA) permits unbiased, unattended selection of (quasi)molecular ions and acquisition of structure-diagnostic collision-induced dissociation mass spectra, while minimizing interferences, by sequentially cycling a quadrupole isolation window through the m/z range. This study reports on the development of a suspect screening method based on industrial compounds with bioaccumulation potential. A comparison of false and correct identifications in a mixed standard containing 30 analytes suggests that SQDIA results in a markedly lower false-positive rate than standard DIA: 5 for SQDIA and 82 for DIA. Electronic waste dust was analyzed using GC and quadrupole time-of-flight MS with APCI and SQDIA acquisition. A total of 52 brominated, chlorinated, and organophosphorus compounds were identified by suspect screening; 15 unique elemental compositions were identified using nontargeted screening; 17 compounds were confirmed using standards and others identified to confidence levels 2, 3, or 4. SQDIA reduced false-positive identifications, compared to experiments without quadrupole isolation. False positives also varied by class: 20% for Br, 37% for Cl, 75% for P, and >99% for all other classes. The structure proposal of a previously reported halogenated compound was revisited. The results underline the utility of GC-SQDIA experiments that provide information on both the (quasi)molecular ions and its dissociation products for a more confident structural assignment.

An integrated data-dependent and data-independent acquisition method for hazardous compounds screening in foods using a single UHPLC-Q-Orbitrap run

Thousands of hazardous compounds that contaminate foods and feeds pose potential risks for human and animal health. However, it remains a challenge to perform a fast monitoring for safety surveillance. Herein we report a novel approach, integrated data-dependent and data-independent acquisition (DDIA) method, to efficiently screen for hundreds of chemicals in a single run using ultra-high-performance liquid chromatography coupled with quadrupole Orbitrap high-resolution mass spectrometry (UHPLC-Q-Orbitrap). This method was successfully applied to analyze 180 veterinary drugs in milk, 220 pesticides in tomato and 50 mycotoxins in maize, respectively. Compared with the widely used approaches of data-dependent acquisition (DDA) or data-independent acquisition (DIA), the obtained results indicate that DDIA-based method combines the advantages of both DDA and DIA, since it achieves higher reproducibility of identification, lower false results for targeted compounds. Notably, the advantage of DDIA approach is that it enables better date retroactivity for untargeted compounds, such as metabolites and decomposition products. With the improvement in high-resolution mass spectrometry (HRMS) as well as data-mining techniques, we believe that DDIA data acquisition approach based on LC-HRMS will be widely applied in various fields in the near future, especially in compound screening and omics field, such as metabolomics and proteomics.

Urea-Peptide Hybrids as VEGF-A165/NRP-1 Complex Inhibitors with Improved Receptor Affinity and Biological Properties

Neuropilin-1 (NRP-1), the major co-receptor of vascular endothelial growth factor receptor-2 (VEGFR-2), may also independently act with VEGF-A₁₆₅ to stimulate tumour growth and metastasis. Therefore, there is great interest in compounds that can block VEGF-A165/NRP-1 interaction. Peptidomimetic type inhibitors represent a promising strategy in the treatment of NRP-1-related disorders. Here, we present the synthesis, affinity, enzymatic stability, molecular modeling and in vitro binding evaluation of the branched urea–peptide hybrids, based on our previously reported Lys(hArg)-Dab-Oic-Arg active sequence, where the Lys(hArg) branching has been modified by introducing urea units to replace the peptide bond at various positions. One of the resulting hybrids increased the affinity of the compound for NRP-1 more than 10-fold, while simultaneously improving resistance for proteolytic stability in serum. In addition, ligand binding to NRP-1 induced rapid protein stock exocytotic trafficking to the plasma membrane in breast cancer cells. Examined properties characterize this compound as a good candidate for further development of VEGF₁₆₅/NRP-1 inhibitors.

Use of information dependent acquisition mass spectra and sequential window acquisition of all theoretical fragment-ion mass spectra for fruit juices metabolomics and authentication

INTRODUCTION

LC-MS based untargeted metabolomics are the main untargeted methods used for juice metabolomics to solve the authentication problem faced in fruit juice industry.

OBJECTIVES

To evaluate the performances of different untargeted metabolomics methods on fruit juices metabolomics and authentication, orange and apple fruit juices were selected for this study.

METHODS

IDA-MS and SWATH-MS based on UHPLC-QTOF were used for the metabolomics and authenticity determination of apple and orange juices, including the lab-made samples of oranges (Citrus sinensis Osb.) from Jiangxi Province, apples (Malus domestica Borkh) from Shandong Province, and different brands of commercial orange and apple juice samples from markets.

RESULTS

IDA-MS and SWATH-MS could both acquire numerous MS1 features and MS2 information of juice components, while SWATH-MS excels at the acquisition rate of MS2. Distinctive separation between authentic orange juice and not authentic orange juice could be seen from principal component analysis and hierarchical clustering analysis based on both IDA-MS and SWATH-MS. After analysis of variance, fold change analysis and orthogonal projection to latent structures discriminant mode, 53 and 46 potential markers were defined by IDA-MS and SWATH-MS (with 77.4% and 100% MS2 acquisition rate) separately. Subsequently, these potential markers were putatively annotated using general chemical databases with 6 more annotated by SWATH-MS. Furthermore, 7 of the potential markers, l-asparagine, umbelliferone, glucosamine, phlorin, epicatechin, phytosphingosine and chlorogenic acid, were identified with standards. For the consideration of model simplicity, two determined makers (umbelliferone and chlorogenic acid) were selected to construct the DD-SIMCA model in commercial samples because of their good correlation with apple adulteration proportion, and the sensitivity and specificity of the model were 100% and 95%.

CONCLUSION

SWATH-MS excels at the MS2 acquisition of juice components and potential markers. This study provides an overall performance comparison between IDA-MS and SWATH-MS, and guidance for the method selection on fruit juice metabolomics and juice authenticity determination. Two of the potential markers determined, umbelliferone and chlorogenic acid, could be used as apple juice indicators in orange juice.

A Critical Review of Bottom-Up Proteomics: The Good, the Bad, and the Future of this Field

Proteomics is the field of study that includes the analysis of proteins, from either a basic science prospective or a clinical one. Proteins can be investigated for their abundance, variety of proteoforms due to post-translational modifications (PTMs), and their stable or transient protein–protein interactions. This can be especially beneficial in the clinical setting when studying proteins involved in different diseases and conditions. Here, we aim to describe a bottom-up proteomics workflow from sample preparation to data analysis, including all of its benefits and pitfalls. We also describe potential improvements in this type of proteomics workflow for the future.

SWATH-MS for metabolomics and lipidomics: critical aspects of qualitative and quantitative analysis

INTRODUCTION

While liquid chromatography coupled to mass spectrometric detection in the selected reaction monitoring detection mode offers the best quantification sensitivity for omics, the number of target analytes is limited, must be predefined and specific methods developed. Data independent acquisition (DIA), including SWATH using quadrupole time of flight or orbitrap mass spectrometers and generic acquisition methods, has emerged as a powerful alternative technique for quantitative and qualitative analyses since it can cover a wide range of analytes without predefinition.

OBJECTIVES

Here we review the current state of DIA, SWATH-MS and highlight novel acquisition strategies for metabolomics and lipidomics and opportunities for data analysis tools.

METHOD

Different databases were searched for papers that report developments and applications of DIA and in particular SWATH-MS in metabolomics and lipidomics.

RESULTS

DIA methods generate digital sample records that can be mined retrospectively as further knowledge is gained and, with standardized acquisition schemes, used in multiple studies. The different chemical spaces of metabolites and lipids require different specificities, hence different acquisition and data processing approaches must be considered for their analysis.

CONCLUSIONS

Although the hardware and acquisition modes are well defined for SWATH-MS, a major challenge for routine use remains the lack of appropriate software tools capable of handling large datasets and large numbers of analytes.

Comparative Evaluation of Data Dependent and Data Independent Acquisition Workflows Implemented on an Orbitrap Fusion for Untargeted Metabolomics

Constant improvements to the Orbitrap mass analyzer, such as acquisition speed, resolution, dynamic range and sensitivity have strengthened its value for the large-scale identification and quantification of metabolites in complex biological matrices. Here, we report the development and optimization of Data Dependent Acquisition (DDA) and Sequential Window Acquisition of all THeoretical fragment ions (SWATH-type) Data Independent Acquisition (DIA) workflows on a high-field Orbitrap Fusion^TM Tribrid^TM instrument for the robust identification and quantification of metabolites in human plasma. By using a set of 47 exogenous and 72 endogenous molecules, we compared the efficiency and complementarity of both approaches. We exploited the versatility of this mass spectrometer to collect meaningful MS/MS spectra at both high- and low-mass resolution and various low-energy collision-induced dissociation conditions under optimized DDA conditions. We also observed that complex and composite DIA-MS/MS spectra can be efficiently exploited to identify metabolites in plasma thanks to a reference tandem spectral library made from authentic standards while also providing a valuable data resource for further identification of unknown metabolites. Finally, we found that adding multi-event MS/MS acquisition did not degrade the ability to use survey MS scans from DDA and DIA workflows for the reliable absolute quantification of metabolites down to 0.05 ng/mL in human plasma.

Partially overlapping sequential window acquisition of all theoretical mass spectra: A methodology to improve the spectra quality of veterinary drugs present at low concentrations in highly complex biological matrices

RATIONALE

Residues of veterinary drugs in food matrices have to be detected, identified and confirmed at low concentrations. Data-independent acquisition (DIA) methods such as the sequential window acquisition of all theoretical spectra (SWATH) permit the extraction of relatively clean spectra out of complex matrices. Such spectra can be significantly improved by using a modified SWATH algorithm which provides several times narrower mass isolation windows without affecting the cycle time.

METHODS

A quadrupole-time-of-flight mass spectrometer was operated in a partially overlapping SWATH mode. Unlike in conventional SWATH, acquisition sequences are not identically repeated, but each sequence is initiated with a mass intercept (mass shift). Pearson correlation is used to assign HRMS-resolved product ions to the precursor mass of interest. Trace analytes (veterinary drugs) in a complex matrix extract (bovine liver) were investigated.

RESULTS

Utilizing identical cycle times, the partially overlapping SWATH mode produced for the investigated small molecules a significantly higher selectivity than the conventional SWATH acquisition mode. The acquisition strategy enables long ion accumulation times and therefore the required high sensitivity of detection. The study investigates the quality of the obtained product ion spectra and compares it with that in conventional acquisition modes.

CONCLUSIONS

The modified SWATH mode permits high duty cycles in combination with narrow virtual mass windows. The technique is a further step toward harvesting a HRMS product ion spectrum out of a data-independent acquisition which moves closer towards the quality of a dedicated precursor unit isolation product ion spectrum.

Applicability of Supercritical fluid chromatography-Mass spectrometry to metabolomics. II-Assessment of a comprehensive library of metabolites and evaluation of biological matrices

In this work, the impact of biological matrices, such as plasma and urine, was evaluated under SFCHRMS in the field of metabolomics. For this purpose, a representative set of 49 metabolites were selected. The assessment of the matrix effects (ME), the impact of biological fluids on the quality of MS/MS spectra and the robustness of the SFCHRMS method were each taken into consideration. The results have highlighted a limited presence of ME in both plasma and urine, with 30% of the metabolites suffering from ME in plasma and 25% in urine, demonstrating a limited sensitivity loss in the presence of matrices. Subsequently, the MS/MS spectra evaluation was performed for further peak annotation. Their analyses have highlighted three different scenarios: 63% of the tested metabolites did not suffer from any interference regardless of the matrix; 21% were negatively impacted in only one matrix and the remaining 16% showed the presence of matrix-belonging compounds interfering in both urine and plasma. Finally, the assessment of retention times stability in the biological samples, has brought into evidence a remarkable robustness of the SFCHRMS method. Average RSD (%) values of retention times for spiked metabolites were equal or below 0.5%, in the two biological fluids over a period of three weeks. In the second part of the work, the evaluation of the Sigma Mass Spectrometry Metabolite Library of Standards containing 597 metabolites, under SFCHRMS conditions was performed. A total detectability of the commercial library up to 66% was reached. Among the families of detected metabolites, large percentages were met for some of them. Highly polar metabolites such as amino acids (87%), nucleosides (85%) and carbohydrates (71%) have demonstrated important success rates, equally for hydrophobic analytes such as steroids (78%) and lipids (71%). On the negative side, very poor performance was found for phosphorylated metabolites, namely phosphate-containing compounds (14%) and nucleotides (31%).

Lipidomic profiling of non-mineralized dental plaque and biofilm by untargeted UHPLC-QTOF-MS/MS and SWATH acquisition

Dental plaque is a structurally organized biofilm which consists of diverse microbial colonies and extracellular matrix. Its composition may change when pathogenic microorganisms become dominating. Therefore, dental biofilm or plaque has been frequently investigated in the context of oral health and disease. Furthermore, its potential as an alternative matrix for analytical purposes has also been recognized in other disciplines like archeology, food sciences, and forensics. Thus, a careful in-depth characterization of dental plaque is worthwhile. Most of the conducted studies focused on the screening of microbial populations in dental plaque. Their lipid membranes, on the other hand, may significantly impact substance (metabolite) exchange within microbial colonies as well as xenobiotics uptake and incorporation into teeth. Under this umbrella, a comprehensive lipidomic profiling for determination of lipid compositions of in vivo dental plaque samples and of in vitro cultivated biofilm as surrogate matrix to be used for analytical purposes has been performed in this work. An untargeted lipidomics workflow utilizing a ultra-high-performance liquid chromatography (UHPLC)-quadrupole-time-of-flight (QTOF) platform together with comprehensive SWATH (sequential window acquisition of all theoretical fragment ion mass spectra) acquisition and compatible software (MS-DIAL) that comprises a vast lipid library has been adopted to establish an extensive lipidomic fingerprint of dental plaque. The main lipid components in dental plaque were identified as triacylglycerols, followed by cholesterol, cholesteryl esters as well as diacylglycerols, and various phospholipid classes. In vivo plaque is a rare matrix which is usually available in very low amounts. When higher quantities for specific research assays are required, efficient ways to produce an appropriate surrogate matrix are mandatory. A potential surrogate matrix substituting dental plaque was prepared by cultivation of in vitro biofilm from saliva and similarities and differences in the lipidomics profile to in vivo plaque were mapped by statistical evaluation post-analysis. It was discovered that most lipid classes were highly elevated in the in vitro biofilm samples, in particular diacylglycerols, phosphatidylglycerols, and phosphatidylethanolamines (PEs). Furthermore, an overall shift from even-chain lipid species to odd-chain lipids was observed in the cultivated biofilms. On the other hand, even-chain phosphatidylcholines (PCs), lysoPCs, cholesteryl esters, and cholesterol-sulfate were shown to be specifically increased in plaque samples.

DecoMetDIA: Deconvolution of Multiplexed MS/MS Spectra for Metabolite Identification in SWATH-MS-Based Untargeted Metabolomics

SWATH-MS-based data-independent acquisition mass spectrometry (DIA-MS) technology has been recently developed for untargeted metabolomics due to its capability to acquire all MS2 spectra with high quantitative accuracy. However, software tools for deconvolving multiplexed MS/MS spectra from SWATH-MS with high efficiency and high quality are still lacking in untargeted metabolomics. Here, we developed a new software tool, namely, DecoMetDIA, to deconvolve multiplexed MS/MS spectra for metabolite identification and support the SWATH-based untargeted metabolomics. In DecoMetDIA, multiple model peaks are selected to model the coeluted and unresolved chromatographic peaks of fragment ions in multiplexed spectra and decompose them into a linear combination of the model peaks. DecoMetDIA enabled us to reconstruct the MS2 spectra of metabolites from a variety of different biological samples with high coverages. We also demonstrated that the deconvolved MS2 spectra from DecoMetDIA were of high accuracy through comparison to the experimental MS2 spectra from data-dependent acquisition (DDA). Finally, about 90% of deconvolved MS2 spectra in various biological samples were successfully annotated using software tools such as MetDNA and Sirius. The results demonstrated that the deconvolved MS2 spectra obtained from DecoMetDIA were accurate and valid for metabolite identification and structural elucidation. The comparison of DecoMetDIA to other deconvolution software such as MS-DIAL demonstrated that it performs very well for small polar metabolites. DecoMetDIA software is freely available at https://github.com/ZhuMSLab/DecoMetDIA .

Mass Spectrometry Data Repository Enhances Novel Metabolite Discoveries with Advances in Computational Metabolomics

Mass spectrometry raw data repositories, including Metabolomics Workbench and MetaboLights, have contributed to increased transparency in metabolomics studies and the discovery of novel insights in biology by reanalysis with updated computational metabolomics tools. Herein, we reanalyzed the previously published lipidomics data from nine algal species, resulting in the annotation of 1437 lipids achieving a 40% increase in annotation compared to the previous results. Specifically, diacylglyceryl-carboxyhydroxy-methylcholine (DGCC) in Pavlova lutheri and Pleurochrysis carterae, glucuronosyldiacylglycerol (GlcADG) in Euglena gracilis, and P. carterae, phosphatidylmethanol (PMeOH) in E. gracilis, and several oxidized phospholipids (oxidized phosphatidylcholine, OxPC; phosphatidylethanolamine, OxPE; phosphatidylglycerol, OxPG; phosphatidylinositol, OxPI) in Chlorella variabilis were newly characterized with the enriched lipid spectral databases. Moreover, we integrated the data from untargeted and targeted analyses from data independent tandem mass spectrometry (DIA-MS/MS) acquisition, specifically the sequential window acquisition of all theoretical fragment-ion MS/MS (SWATH-MS/MS) spectra, to increase the lipidomic annotation coverage. After the creation of a global library of precursor and diagnostic ions of lipids by the MS-DIAL untargeted analysis, the co-eluted DIA-MS/MS spectra were resolved in MRMPROBS targeted analysis by tracing the specific product ions involved in acyl chain compositions. Our results indicated that the metabolite quantifications based on DIA-MS/MS chromatograms were somewhat inferior to the MS¹-centric quantifications, while the annotation coverage outperformed those of the untargeted analysis of the data dependent and DIA-MS/MS data. Consequently, integrated analyses of untargeted and targeted approaches are necessary to extract the maximum amount of metabolome information, and our results showcase the value of data repositories for the discovery of novel insights in lipid biology.

Advancing untargeted metabolomics using data-independent acquisition mass spectrometry technology

Metabolomics quantitatively measures metabolites in a given biological system and facilitates the understanding of physiological and pathological activities. With the recent advancement of mass spectrometry (MS) technology, liquid chromatography-mass spectrometry (LC-MS) with data-independent acquisition (DIA) has been emerged as a powerful technology for untargeted metabolomics due to its capability to acquire all MS2 spectra and high quantitative accuracy. In this trend article, we first introduced the basic principles of several common DIA techniques including MS^E, all ion fragmentation (AIF), SWATH, and MSX. Then, we summarized and compared the data analysis strategies to process DIA-based untargeted metabolomics data, including metabolite identification and quantification. We think the advantages of the DIA technique will enable its broad application in untargeted metabolomics.

A combined targeted/untargeted LC-MS/MS-based screening approach for mammalian cell lines treated with ionic liquids: Toxicity correlates with metabolic profile

This work presents the development and validation of a quantitative HILIC UHPLC-ESI-QTOF-MS/MS method for amino acids combined with untargeted metabolic profiling of human corneal epithelial (HCE) cells after treatment with ionic liquids. The work included a preliminary metabotoxicity screening of 14 different ionic liquids, of which 9 carefully selected ionic liquids were chosen for a metabolomics study. This study is focused on the correlation between the toxicity of the ionic liquids and their metabolic profiles. The method development included the comparison of different MS/MS acquisition modes. A sequential window acquisition of all theoretical fragment ion mass spectra (SWATH) method with variable Q1 window widths and narrow Q1 target windows of 5 Da for most of the amino acids was selected as the optimal acquisition mode. Due to the absence of a true blank matrix, ¹³C,¹⁵N-isotopically labelled amino acids were utilized as surrogate calibrants, instead of proteinogenic amino acids. Partial least squares (PLS) analysis of the median effective concentrations (EC₅₀) of 9 selected ionic liquids showed a correlation with their metabolic profile measured by the untargeted screening.