Assessing the Potential of Untargeted SWATH Mass Spectrometry-Based Metabolomics to Differentiate Closely Related Exposures in Observational Studies.

Mass spectrometry (MS) is increasingly used in clinical studies to obtain molecular evidence of chemical exposures, such as tobacco smoke, alcohol, and drugs. This evidence can help verify clinical data retrieved through anamnesis or questionnaires and may provide insights into unreported exposures, for example those classified as the same despite small but possibly relevant chemical differences or due to contaminants in reported exposure compounds. Here, we aimed to explore the potential of untargeted SWATH metabolomics to differentiate such closely related exposures. This data-independent acquisition MS-based profiling technique was applied to urine samples of 316 liver and 570 kidney transplant recipients from the TransplantLines Biobank and Cohort Study (NCT03272841), where we focused on the immunosuppressive drug mycophenolate, which is either supplied as a morpholino-ester prodrug or as an enteric-coated product, the illicit drug cocaine, which is usually supplied as an adulterated product, and the proton pump inhibitors omeprazole and esomeprazole. Based on these examples, we found that untargeted SWATH metabolomics has considerable potential to identify different (unreported) exposure or co-exposure metabolites and may determine variations in their abundances. We also found that these signals alone may sometimes be unable to distinguish closely related exposures, and enhancement of differentiation, for example by integration with pharmacogenomics data, is needed.

Controlled Formation of Protonated and Radical Cation Precursor Ions by Atmospheric Pressure Photoionization with µLC-MS Enables Electron Ionization and MS/MS Library Search.

Atmospheric pressure photoionization (APPI) was developed as an alternative to electrospray ionization (ESI) for the generation of protonated molecules using liquid chromatography and optimized using dopants such as toluene, which predominantly forms protonated molecules, and chlorobenzene, which favors the formation of radical cations, although the latter has not been fully exploited. Based on 40 diverse low-molecular-weight compounds and micro liquid chromatography (µLC) coupled with APPI tandem mass spectrometry (APPI-MS/MS), the potential of radical cations was investigated. Chromatographic and ionization conditions were decoupled by post-column addition of methanol, allowing separate study and optimization. Due to the mass flow sensitive behavior of APPI, sensitivity is not affected by post-column dilution, and for 8 of 35 analytes, the radical cation response with µLC-APPI is better than for protonated molecules using µLC-ESI. Collision-induced fragmentation (CID) of radical cations produced within a collision energy range from 10-115 eV have, in the median, 65% of the fragments found in electron ionization (EI) spectra. This similarity allowed identification of 86% of the analytes using data-dependent acquisition (DDA) of radical cations and NIST EI library searches. We propose a workflow that uses multimodal DDA of protonated precursor molecules using ESI or APPI with toluene as a dopant, and radical cations produced by chlorobenzene-assisted µLC-APPI with post-column addition of methanol. This increases the confidence of molecular identification by allowing orthogonal library searches using MS/MS libraries for protonated precursor CID spectra and EI libraries for radical cation CID spectra.

Untargeted 'SWATH' mass spectrometry-based metabolomics for studying chronic and intermittent exposure to xenobiotics in cohort studies.

Humans are exposed to numerous chemicals daily, for example through nutrition, therapies, and lifestyle choices, which may exert beneficial or toxicological responses. In cohort studies, exposures are frequently assessed using questionnaires, although mass spectrometry-based metabolomics has recently emerged as complementary technique capable of yielding molecular evidence of exposures. Corresponding data processing workflows, however, have been mostly developed for detecting (omnipresent) endogenous metabolites, whereas detection of exogenous chemicals would benefit from fit-for-purpose strategies. In this work, we describe novel strategies for improved exposure detection and their application to data from an untargeted metabolomics study on urine samples from the TransplantLines Food and Nutrition Biobank and Cohort Study (NCT identifier 'NCT02811835'), which includes kidney transplant recipients, potential living kidney donors, and living kidney donors (post-donation). Specifically, we describe a reference spectra generation workflow using exposure-positive samples to detect more and also previously-undetected chronic exposures, and we present a novel approach to establish detection limits based on targeted signal extraction for more reliable and lower-level detection of intermittent exposures. These approaches can contribute to unlocking additional exposure-related information from small-molecule profiling datasets thus increasing data usefulness in metabolomics research and in environmental, food, clinical, and forensic toxicology.

Annotation of complex mass spectra by multi-layered analysis.

Annotating electrospray small molecule mass spectra remains a challenging problem due to the multiple processes occurring during ionization. Although an [M+H]+ is often present, ions can be formed by reactions with other cations, background compounds and co-eluting species, and by in-source fragmentation. Even single analytes can produce multiple ion forms, many of which remain unidentified and may appear to be different species, affecting reproducibility, quantification and precursor selection in DDA experiments. Annotation usually compares differences between peaks to known adducts and losses but fails if key peaks are missing or if the peaks are from unexpected adducts. Further, isotopes are often assumed to be due to 13C and removed prior to analysis which can leave 'orphan' peaks if unusual elements are present. Here we describe an alternative multi-layered approach (MLA) which successively matches spectra to calculated target ion lists and reprocesses the residual ions. This allows the analyst to focus on the unknown ions and to progressively increase target list complexity since explained ions are removed. Target ion lists can be calculated from expected or observed masses and potential adducts or can be pre-defined lists, for example common contaminants. Using this approach on spectra of known standards we identified adducts with Ca, Al, Fe, Ba and possibly Mg and Sr. We also detected several compounds and adducts in a spectrum of co-eluting species from an LC-MS analysis.

Improved metabolite characterization by liquid chromatography - Tandem mass spectrometry through electron impact type fragments from adduct ions.

Using a chimeric collision cell mounted on a quadrupole time-of-flight platform, collision induced dissociation (CID) and electron induced dissociation (EID) were investigated for the LC-MS analysis of low molecular weight compounds including drugs and endogenous metabolites. Compared to CID, EID fragmentation of the [M+H]+ species (10-20 eV) from standard compounds resulted in additional specific and informative fragments, mostly due to neutral losses and, in some cases due to ring openings. Some analytes, for example reserpine and vinpocetine, provided characteristic [M+H]•2+ species. For most analytes for sodium and potassium adducts and multimers a radical cation M•+ and electron impact type fragments were observed in the EID spectra, providing the opportunity to use EI libraries to support metabolite identification. EID opens the possibility to get structural information from adduct ions which is often not the case with CID. EID enabled the putative characterization of two metabolites in rat urine as glucuronides of 5,6-dihydroxyindole based on EID fragmentation of the potassium adducts.

Metabolomics data complemented drug use information in epidemiological databases: pilot study of potential kidney donors.

OBJECTIVE: The objective of this study was to investigate whether clinical metabolomics, which is increasingly applied in population-based and epidemiological studies, can be used to provide analytical evidence of exposures, and whether such information can be useful to strengthen and/or complement corresponding clinical database entries, taking drug use as an example. STUDY DESIGN AND SETTING: Liquid chromatography-mass spectrometry (LC-MS) metabolomics analyses were performed on urine from 100 randomly-selected control subjects (50% females) from the TransplantLines Food and Nutrition Biobank and Cohort Study (NCT identifier 'NCT02811835'), and drugs were identified through spectral library searching and targeted signal extraction. RESULTS: In 83 subjects for whom drug use information was available, 22 expected and 26 unexpected prescription-only drugs were identified, while 28 expected prescription-only drugs remained undetected. In addition, 7 prescription-only drugs were found in 17 subjects for whom drug use information was unavailable, and 58 over-the-counter drugs were identified in all 100 subjects. CONCLUSION: Molecular evidence for many drugs could be retrieved from LC-MS metabolomics data, which could be useful to complement and strengthen epidemiological databases given that considerable discrepancies were found between analytically-identified drugs and drugs listed in the available clinical database.

Adduct annotation in liquid chromatography/high-resolution mass spectrometry to enhance compound identification.

Annotation and interpretation of full scan electrospray mass spectra of metabolites is complicated by the presence of a wide variety of ions. Not only protonated, deprotonated, and neutral loss ions but also sodium, potassium, and ammonium adducts as well as oligomers are frequently observed. This diversity challenges automatic annotation and is often poorly addressed by current annotation tools. In many cases, annotation is integrated in metabolomics workflows and is based on specific chromatographic peak-picking tools. We introduce mzAdan, a nonchromatography-based multipurpose standalone application that was developed for the annotation and exploration of convolved high-resolution ESI-MS spectra. The tool annotates single or multiple accurate mass spectra using a customizable adduct annotation list and outputs a list of [M+H]+ candidates. MzAdan was first tested with a collection of 408 analytes acquired with flow injection analysis. This resulted in 402 correct [M+H]+ identifications and, with combinations of sodium, ammonium, and potassium adducts and water and ammonia losses within a tolerance of 10 mmu, explained close to 50% of the total ion current. False positives were monitored with mass accuracy and bias as well as chromatographic behavior which led to the identification of adducts with calcium instead of the expected potassium. MzAdan was then integrated in a workflow with XCMS for the untargeted LC-MS data analysis of a 52 metabolite standard mix and a human urine sample. The results were benchmarked against three other annotation tools, CAMERA, findMAIN, and CliqueMS: findMAIN and mzAdan consistently produced higher numbers of [M+H]+ candidates compared with CliqueMS and CAMERA, especially with co-eluting metabolites. Detection of low-intensity ions and correct grouping were found to be essential for annotation performance. Graphical abstract.

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.

SWATH data independent acquisition mass spectrometry for screening of xenobiotics in biological fluids: Opportunities and challenges for data processing.

SWATH data independent acquisition (DIA) mass spectrometry (MS) has become an established technique in MS-based 'omics' research and is increasingly used for the screening of xenobiotics (e.g. drugs, drug metabolites, pesticides, toxicants). Such xenobiotic screening methods are mostly applied for tentative compound identification purposes based on spectral library searching, while additional data processing techniques are scarcely used thereby leaving the full potential of these methods often unused. Here we present an analytical workflow for screening xenobiotics in human samples using SWATH/MS based on which we highlight opportunities for unlocking unused potential of these methods. The workflow was applied to urine samples from subjects who tested positive for THC and/or cocaine during roadside drug testing with the goal of confirming the positive roadside drug tests and identifying compounds that relate to illicit drug use (e.g. cutting agents, tobacco components) or associate with corresponding lifestyle choices (e.g. nasal decongestants, painkillers). These goals could only be reached by complementing spectral library search procedures with additional multivariate data analyses due to inherent incompleteness of the spectral library that was employed. Such incompleteness represents a common challenge for applications where limited or no metadata is available for study samples, for example in toxicology, doping control in sports, and workplace or roadside drug testing. It furthermore sets the stage for employing additional data processing techniques as is outlined in the presented work.

Hybrid SWATH/MS and HR-SRM/MS acquisition for phospholipidomics using QUAL/QUANT data processing.

A hybrid SWATH/MS and HR-SRM/MS acquisition approach using multiple unit mass windows and 100 u precursor selection windows has been developed to interface with a chromatographic lipid class separation. The method allows for the simultaneous monitoring of sum compositions in MS1 and up to 48 lipids in MS2 per lipid class. A total of 240 lipid sum compositions from five phospholipid classes could be monitored in MS2 (HR-SRM/MS) while there was no limitation in the number of analytes in MS1 (HR-SIM/MS). On average, 92 lipid sum compositions and 75 lipid species could be quantified in human plasma samples. The robustness and precision of the workflow has been assessed using technical triplicates of the subject samples. Lipid identification was improved using a combined qualitative and quantitative data processing based on prediction instead of library search. Lipid class specific extracted ion currents of precursors and the corresponding molecular species fragments were extracted based on the information obtained from lipid building blocks and a combinatorial strategy. The SWATH/MS approach with the post-acquisition processing is not limited to the analyzed phospholipid classes and can be applied to other analytes and samples of interest. Graphical abstract.

Metabolomic spectral libraries for data-independent SWATH liquid chromatography mass spectrometry acquisition.

High-quality mass spectral libraries have become crucial in mass spectrometry-based metabolomics. Here, we investigate a workflow to generate accurate mass discrete and composite spectral libraries for metabolite identification and for SWATH mass spectrometry data processing. Discrete collision energy (5-100 eV) accurate mass spectra were collected for 532 metabolites from the human metabolome database (HMDB) by flow injection analysis and compiled into composite spectra over a large collision energy range (e.g., 10-70 eV). Full scan response factors were also calculated. Software tools based on accurate mass and predictive fragmentation were specially developed and found to be essential for construction and quality control of the spectral library. First, elemental compositions constrained by the elemental composition of the precursor ion were calculated for all fragments. Secondly, all possible fragments were generated from the compound structure and were filtered based on their elemental compositions. From the discrete spectra, it was possible to analyze the specific fragment form at each collision energy and it was found that a relatively large collision energy range (10-70 eV) gives informative MS/MS spectra for library searches. From the composite spectra, it was possible to characterize specific neutral losses as radical losses using in silico fragmentation. Radical losses (generating radical cations) were found to be more prominent than expected. From 532 metabolites, 489 provided a signal in positive mode [M+H]+ and 483 in negative mode [M-H]-. MS/MS spectra were obtained for 399 compounds in positive mode and for 462 in negative mode; 329 metabolites generated suitable spectra in both modes. Using the spectral library, LC retention time, response factors to analyze data-independent LC-SWATH-MS data allowed the identification of 39 (positive mode) and 72 (negative mode) metabolites in a plasma pool sample (total 92 metabolites) where 81 previously were reported in HMDB to be found in plasma. Graphical abstract Library generation workflow for LC-SWATH MS, using collision energy spread, accurate mass, and fragment annotation.

Liquid chromatography/mass spectrometry for the detection of ash tree metabolites following Emerald Ash Borer infestation.

RATIONALE: The Emerald Ash Borer (EAB), Agrilus planipennis, an invasive insect detected in the USA and Canada in 2002, is a threat to ash trees with both ecological and economic implications. Early detection of EAB-infestation is difficult due to lack of visible signs and symptoms in the early stages of attack, but is essential to prevent ash mortality. An efficient and reliable tool for the early detection of EAB-infestation would be advantageous. METHODS: A mass spectrometry based metabolomics approach, using liquid chromatography/mass spectrometry (LC/MS), has been used to investigate the leaf metabolites of both healthy and EAB-infested trees. RESULTS: Leaves from 40 healthy and 40 EAB-infested trees were extracted and analyzed using LC/MS. Resulting data were examined to differentiate between foliage from healthy and EAB-infested trees. Possible biomarkers of EAB attack have been detected. Twenty-one metabolites with increased average ion intensity in EAB-infested ash tree samples and nine metabolites with increased average ion intensity in healthy ash tree samples were identified. CONCLUSIONS: Results of this study indicate that metabolomic screening of leaf samples using LC/MS can be useful as a potential tool for the early detection of EAB-infestation.

SWATH acquisition mode for drug metabolism and metabolomics investigations.

AIM: Sequential window acquisition of all theoretical fragment-ion spectra (SWATH) has recently emerged as a powerful high resolution mass spectrometric data independent acquisition technique. In the present work, the potential and challenges of an integrated strategy based on LC-SWATH/MS for simultaneous drug metabolism and metabolomics studies was investigated. METHODOLOGY: The richness of SWATH data allows numerous data analysis approaches, including: detection of metabolites by prediction; metabolite detection by mass defect filtering; quantification from high-resolution MS precursor chromatograms or fragment chromatograms. Multivariate analysis can be applied to the data from the full scan or SWATH windows and allows changes in endogenous metabolites as well as xenobiotic metabolites, to be detected. Principal component variable grouping detects intersample variable correlation and groups variables with similar profiles which simplifies interpretation and highlights related ions and fragments. Principal component variable grouping can extract product ion spectra from the data collected by fragmenting a wide precursor ion window. CONCLUSION: It was possible to characterize 28 vinpocetine metabolites in urine, mostly mono- and di-hydroxylated forms, and detect endogenous metabolite expression changes in urine after the administration of a single dose of a model drug (vinpocetine) to rats.

Development of a highly automated and multiplexed targeted proteome pipeline and assay for 112 rat brain synaptic proteins.

We present a comprehensive workflow for large scale (>1000 transitions/run) label-free LC-MRM proteome assays. Innovations include automated MRM transition selection, intelligent retention time scheduling that improves S/N by twofold, and automatic peak modeling. Improvements to data analysis include a novel Q/C metric, normalized group area ratio, MLR normalization, weighted regression analysis, and data dissemination through the Yale protein expression database. As a proof of principle we developed a robust 90 min LC-MRM assay for mouse/rat postsynaptic density fractions which resulted in the routine quantification of 337 peptides from 112 proteins based on 15 observations per protein. Parallel analyses with stable isotope dilution peptide standards (SIS), demonstrate very high correlation in retention time (1.0) and protein fold change (0.94) between the label-free and SIS analyses. Overall, our method achieved a technical CV of 11.4% with >97.5% of the 1697 transitions being quantified without user intervention, resulting in a highly efficient, robust, and single injection LC-MRM assay.

Mapping differential interactomes by affinity purification coupled with data-independent mass spectrometry acquisition.

Characterizing changes in protein-protein interactions associated with sequence variants (e.g., disease-associated mutations or splice forms) or following exposure to drugs, growth factors or hormones is critical to understanding how protein complexes are built, localized and regulated. Affinity purification (AP) coupled with mass spectrometry permits the analysis of protein interactions under near-physiological conditions, yet monitoring interaction changes requires the development of a robust and sensitive quantitative approach, especially for large-scale studies in which cost and time are major considerations. We have coupled AP to data-independent mass spectrometric acquisition (sequential window acquisition of all theoretical spectra, SWATH) and implemented an automated data extraction and statistical analysis pipeline to score modulated interactions. We used AP-SWATH to characterize changes in protein-protein interactions imparted by the HSP90 inhibitor NVP-AUY922 or melanoma-associated mutations in the human kinase CDK4. We show that AP-SWATH is a robust label-free approach to characterize such changes and propose a scalable pipeline for systems biology studies.

Label-free quantitative proteomics trends for protein-protein interactions.

Understanding protein interactions within the complexity of a living cell is challenging, but techniques coupling affinity purification and mass spectrometry have enabled important progress to be made in the past 15 years. As identification of protein-protein interactions is becoming easier, the quantification of the interaction dynamics is the next frontier. Several quantitative mass spectrometric approaches have been developed to address this issue that vary in their strengths and weaknesses. While isotopic labeling approaches continue to contribute to the identification of regulated interactions, techniques that do not require labeling are becoming increasingly used in the field. Here, we describe the major types of label-free quantification used in interaction proteomics, and discuss the relative merits of data dependent and data independent acquisition approaches in label-free quantification. This article is part of a Special Issue entitled: From protein structures to clinical applications.

Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis.

Most proteomic studies use liquid chromatography coupled to tandem mass spectrometry to identify and quantify the peptides generated by the proteolysis of a biological sample. However, with the current methods it remains challenging to rapidly, consistently, reproducibly, accurately, and sensitively detect and quantify large fractions of proteomes across multiple samples. Here we present a new strategy that systematically queries sample sets for the presence and quantity of essentially any protein of interest. It consists of using the information available in fragment ion spectral libraries to mine the complete fragment ion maps generated using a data-independent acquisition method. For this study, the data were acquired on a fast, high resolution quadrupole-quadrupole time-of-flight (TOF) instrument by repeatedly cycling through 32 consecutive 25-Da precursor isolation windows (swaths). This SWATH MS acquisition setup generates, in a single sample injection, time-resolved fragment ion spectra for all the analytes detectable within the 400-1200 m/z precursor range and the user-defined retention time window. We show that suitable combinations of fragment ions extracted from these data sets are sufficiently specific to confidently identify query peptides over a dynamic range of 4 orders of magnitude, even if the precursors of the queried peptides are not detectable in the survey scans. We also show that queried peptides are quantified with a consistency and accuracy comparable with that of selected reaction monitoring, the gold standard proteomic quantification method. Moreover, targeted data extraction enables ad libitum quantification refinement and dynamic extension of protein probing by iterative re-mining of the once-and-forever acquired data sets. This combination of unbiased, broad range precursor ion fragmentation and targeted data extraction alleviates most constraints of present proteomic methods and should be equally applicable to the comprehensive analysis of other classes of analytes, beyond proteomics.

Selected reaction monitoring mass spectrometry reveals the dynamics of signaling through the GRB2 adaptor.

Signaling pathways are commonly organized through inducible protein-protein interactions, mediated by adaptor proteins that link activated receptors to cytoplasmic effectors. However, we have little quantitative data regarding the kinetics with which such networks assemble and dissolve to generate specific cellular responses. To address this deficiency, we designed a mass spectrometry method, affinity purification-selected reaction monitoring (AP-SRM), which we used to comprehensively and quantitatively investigate changes in protein interactions with GRB2, an adaptor protein that participates in a remarkably diverse set of protein complexes involved in multiple aspects of cellular function. Our data reliably define context-specific and time-dependent networks that form around GRB2 after stimulation, and reveal core and growth factor-selective complexes comprising 90 proteins identified as interacting with GRB2 in HEK293T cells. Capturing a key hub protein and dissecting its interactions by SRM should be equally applicable to quantifying signaling dynamics for a range of hubs in protein interaction networks.

Methodological considerations in the development of HPLC-MS methods for the analysis of rodent plasma for metabonomic studies.

A study of the factors involved in obtaining valid global metabolite profiles from the HPLC-MS of rat or mouse plasma for the purposes of metabonomic analysis has been undertaken. Plasma proteins were precipitated with three volumes of either methanol or acetonitrile. Chromatographic separations were performed on a C18-bonded stationary phase using 3.5 and 5 mum particles packed into 2.1 and 4.6 mm i.d. formats, respectively, and on a C8 phase using 3.5 mum particles and a 2.1 mm i.d. column. Three reversed-phase gradient solvent systems, based on acidified water-acetonitrile, acidified water-methanol and acidified water-methanol-acetonitrile mixtures, were investigated. The column eluent was analysed with both positive and negative electrospray ionisation using a quadrupole-linear ion trap mass spectrometer. These studies revealed that while accurate classification of sample type can be made, there are a number of methodological problems associated with the analysis of plasma with respect to factors such as repeatability and column longevity. In particular, special care has to be taken to ensure that the analytical system is properly "conditioned" by the repeated injection of matrix samples. The use of biological quality control (QC) samples provided an important means of monitoring method performance. Finally, the source of the plasma (Zucker wild-type or (fa/fa) rat or mouse tumour model) also appeared to have an effect on the repeatability of the methodology.