Using Skyline to Analyze Data-Containing Liquid Chromatography, Ion Mobility Spectrometry, and Mass Spectrometry Dimensions

Oxidation of chlortetracycline and its isomers by Botrytis aclada laccase in the absence of mediators: pH dependence and identification of transformation products by LC-MS

Tetracyclines are antibiotics considered emerging pollutants and currently, wastewater treatment plants are not able to remove them efficiently. Laccases are promising enzymes for bioremediation because they can oxidize a wide variety of substrates. The aim of this study was to evaluate the Botrytis aclada laccase for the oxidation of chlortetracycline and its isomers in the absence of a mediator molecule, at a pH range between 3.0 to 7.0, and to characterize the transformation products by LC-MS. Chlortetracycline and three isomers were detected in both, controls and reaction mixtures at 0 h and in controls after 48 h of incubation but in different proportions depending on pH. An additional isomer was also detected, but only in the presence of BaLac. Based on the transformation products identified in the enzymatic reactions and information from literature, we assembled a network of transformation pathways starting from chlortetracycline and its isomers. The spectrometric analysis of the products indicated the probable occurrence of oxygen insertion, dehydrogenation, demethylation and deamination reactions. Four new products were identified, and we also described a novel transformation product without the chloro group. We observed that increasing pH led to higher diversity of main products. This is the first study using the laccase from fungi Botrytis aclada to oxidate chlortetracycline and its isomers and it can be considered as an ecological alternative to be used in bioremediation processes such as wastewater.

Two DOT1 enzymes cooperatively mediate efficient ubiquitin-independent histone H3 lysine 76 tri-methylation in kinetoplastids

In higher eukaryotes, a single DOT1 histone H3 lysine 79 (H3K79) methyltransferase processively produces H3K79me2/me3 through histone H2B mono-ubiquitin interaction, while the kinetoplastid Trypanosoma brucei di-methyltransferase DOT1A and tri-methyltransferase DOT1B efficiently methylate the homologous H3K76 without H2B mono-ubiquitination. Based on structural and biochemical analyses of DOT1A, we identify key residues in the methyltransferase motifs VI and X for efficient ubiquitin-independent H3K76 methylation in kinetoplastids. Substitution of a basic to an acidic residue within motif VI (Gx6K) is essential to stabilize the DOT1A enzyme-substrate complex, while substitution of the motif X sequence VYGE by CAKS renders a rigid active-site loop flexible, implying a distinct mechanism of substrate recognition. We further reveal distinct methylation kinetics and substrate preferences of DOT1A (H3K76me0) and DOT1B (DOT1A products H3K76me1/me2) in vitro, determined by a Ser and Ala residue within motif IV, respectively, enabling DOT1A and DOT1B to mediate efficient H3K76 tri-methylation non-processively but cooperatively, and suggesting why kinetoplastids have evolved two DOT1 enzymes.

Acquisition and Analysis of DIA-Based Proteomic Data: A Comprehensive Survey in 2023

Data-independent acquisition (DIA) mass spectrometry (MS) has emerged as a powerful technology for high-throughput, accurate, and reproducible quantitative proteomics. This review provides a comprehensive overview of recent advances in both the experimental and computational methods for DIA proteomics, from data acquisition schemes to analysis strategies and software tools. DIA acquisition schemes are categorized based on the design of precursor isolation windows, highlighting wide-window, overlapping-window, narrow-window, scanning quadrupole-based, and parallel accumulation-serial fragmentation-enhanced DIA methods. For DIA data analysis, major strategies are classified into spectrum reconstruction, sequence-based search, library-based search, de novo sequencing, and sequencing-independent approaches. A wide array of software tools implementing these strategies are reviewed, with details on their overall workflows and scoring approaches at different steps. The generation and optimization of spectral libraries, which are critical resources for DIA analysis, are also discussed. Publicly available benchmark datasets covering global proteomics and phosphoproteomics are summarized to facilitate performance evaluation of various software tools and analysis workflows. Continued advances and synergistic developments of versatile components in DIA workflows are expected to further enhance the power of DIA-based proteomics.

Mapping microhabitats of lignocellulose decomposition by a microbial consortium

The leaf-cutter ant fungal garden ecosystem is a naturally evolved model system for efficient plant biomass degradation. Degradation processes mediated by the symbiotic fungus Leucoagaricus gongylophorus are difficult to characterize due to dynamic metabolisms and spatial complexity of the system. Herein, we performed microscale imaging across 12-µm-thick adjacent sections of Atta cephalotes fungal gardens and applied a metabolome-informed proteome imaging approach to map lignin degradation. This approach combines two spatial multiomics mass spectrometry modalities that enabled us to visualize colocalized metabolites and proteins across and through the fungal garden. Spatially profiled metabolites revealed an accumulation of lignin-related products, outlining morphologically unique lignin microhabitats. Metaproteomic analyses of these microhabitats revealed carbohydrate-degrading enzymes, indicating a prominent fungal role in lignocellulose decomposition. Integration of metabolome-informed proteome imaging data provides a comprehensive view of underlying biological pathways to inform our understanding of metabolic fungal pathways in plant matter degradation within the micrometer-scale environment.

Uncovering per- and polyfluoroalkyl substances (PFAS) with nontargeted ion mobility spectrometry-mass spectrometry analyses

Because of environmental and health concerns, legacy per- and polyfluoroalkyl substances (PFAS) have been voluntarily phased out, and thousands of emerging PFAS introduced as replacements. Traditional analytical methods target a limited number of mainly legacy PFAS; therefore, many species are not routinely assessed in the environment. Nontargeted approaches using high-resolution mass spectrometry methods have therefore been used to detect and characterize unknown PFAS. However, their ability to elucidate chemical structures relies on generation of informative fragments, and many low concentration species are not fragmented in typical data-dependent acquisition approaches. Here, a data-independent method leveraging ion mobility spectrometry (IMS) and size-dependent fragmentation was developed and applied to characterize aquatic passive samplers deployed near a North Carolina fluorochemical manufacturer. From the study, 11 PFAS structures for various per- and polyfluorinated ether sulfonic acids and multiheaded perfluorinated ether acids were elucidated in addition to 36 known PFAS. Eight of these species were previously unreported in environmental media, and three suspected species were validated.

Combining native mass spectrometry and lipidomics to uncover specific membrane protein-lipid interactions from natural lipid sources

While it is known that lipids play an essential role in regulating membrane protein structure and function, it remains challenging to identify specific protein-lipid interactions. Here, we present an innovative approach that combines native mass spectrometry (MS) and lipidomics to identify lipids retained by membrane proteins from natural lipid extracts. Our results reveal that the bacterial ammonia channel (AmtB) enriches specific cardiolipin (CDL) and phosphatidylethanolamine (PE) from natural headgroup extracts. When the two extracts are mixed, AmtB retains more species, wherein selectivity is tuned to bias headgroup selection. Using a series of natural headgroup extracts, we show TRAAK, a two-pore domain K+ channel (K2P), retains specific acyl chains that is independent of the headgroup. A brain polar lipid extract was then combined with the K2Ps, TRAAK and TREK2, to understand lipid specificity. More than a hundred lipids demonstrated affinity for each protein, and both channels were found to retain specific fatty acids and lysophospholipids known to stimulate channel activity, even after several column washes. Natural lipid extracts provide the unique opportunity to not only present natural lipid diversity to purified membrane proteins but also identify lipids that may be important for membrane protein structure and function.

Ion mobility mass spectrometry for the study of mycobacterial mycolic acids

Lipids are highly structurally diverse molecules involved in a wide variety of biological processes. The involvement of lipids is even more pronounced in mycobacteria, including the human pathogen Mycobacterium tuberculosis, which produces a highly complex and diverse set of lipids in the cell envelope. These lipids include mycolic acids, which are among the longest fatty acids in nature and can contain up to 90 carbon atoms. Mycolic acids are ubiquitously found in mycobacteria and are alpha branched and beta hydroxylated lipids. Discrete modifications, such as alpha, alpha', epoxy, methoxy, keto, and carboxy, characterize mycolic acids at the species level. Here, we used high precision ion mobility-mass spectrometry to build a database including 206 mass-resolved collision cross sections (CCSs) of mycolic acids originating from the strict human pathogen M. tuberculosis, the opportunistic strains M. abscessus, M. marinum and M. avium, and the nonpathogenic strain M. smegmatis. Primary differences between the mycolic acid profiles could be observed between mycobacterial species. Acyl tail length and modifications were the primary structural descriptors determining CCS magnitude. As a resource for researchers, this work provides a detailed catalogue of the mass-resolved collision cross sections for mycolic acids along with a workflow to generate and analyse the dataset generated.

Genome mining of Pseudomonas spp. hints towards the production of under-pitched secondary metabolites

The recent advances in omics and computational analysis have enabled the capacity to identify the exclusive strain-specific metabolites and novel biosynthetic gene clusters. This study analyzed eight strains of P. aurantiaca including GS1, GS3, GS4, GS6, GS7, FS2, ARS38, PBSt2, one strain of P. chlororaphis RP4, one strain of P. aeruginosa (At1RP4), and one strain of P. fluorescens (RS1) for the production of rhamnolipids, quorum-sensing signals, and osmolytes. Seven rhamnolipid derivatives were variably detected in fluorescent pseudomonads. These rhamnolipids included Rha-C10-C8, Rha-Rha-C10-C10, Rha-C10-C12db, Rha-C10-C10, Rha-Rha-C10-C12, Rha-C10-C12, and Rha-Rha-C10-C12db. Pseudomonas spp. also showed the variable production of osmoprotectants including N-acetyl glutaminyl glutamine amide (NAGGN), betaine, ectoine, and trehalose. Betaine and ectoine were produced by all pseudomonads, however, NAGGN and trehalose were observed by five and three strains, respectively. Four strains including P. chlororaphis (RP4), P. aeruginosa (At1RP4), P. fluorescens (RS1), and P. aurantiaca (PBSt2) were exposed to 1- 4% NaCl concentrations and evaluated for the changes in phenazine production profile which were negligible. AntiSMASH 5.0 platform showed 50 biosynthetic gene clusters in PB-St2, of which 23 (45%) were classified as putative gene clusters with ClusterFinder algorithm, five (10%) were classified as non-ribosomal peptides synthetases (NRPS), five (10%) as saccharides, and four (8%) were classified as putative fatty acids. The genomic attributes and comprehensive insights into the metabolomic profile of these Pseudomonas spp. strains showcase their phytostimulatory, phyto-protective, and osmoprotective effects of diverse crops grown in normal and saline soils.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-023-03607-x.

PeakDecoder enables machine learning-based metabolite annotation and accurate profiling in multidimensional mass spectrometry measurements

Multidimensional measurements using state-of-the-art separations and mass spectrometry provide advantages in untargeted metabolomics analyses for studying biological and environmental bio-chemical processes. However, the lack of rapid analytical methods and robust algorithms for these heterogeneous data has limited its application. Here, we develop and evaluate a sensitive and high-throughput analytical and computational workflow to enable accurate metabolite profiling. Our workflow combines liquid chromatography, ion mobility spectrometry and data-independent acquisition mass spectrometry with PeakDecoder, a machine learning-based algorithm that learns to distinguish true co-elution and co-mobility from raw data and calculates metabolite identification error rates. We apply PeakDecoder for metabolite profiling of various engineered strains of Aspergillus pseudoterreus, Aspergillus niger, Pseudomonas putida and Rhodosporidium toruloides. Results, validated manually and against selected reaction monitoring and gas-chromatography platforms, show that 2683 features could be confidently annotated and quantified across 116 microbial sample runs using a library built from 64 standards.

Targeted MRM Quantification of Urinary Proteins in Chronic Kidney Disease Caused by Glomerulopathies

Glomerulopathies with nephrotic syndrome that are resistant to therapy often progress to end-stage chronic kidney disease (CKD) and require timely and accurate diagnosis. Targeted quantitative urine proteome analysis by mass spectrometry (MS) with multiple-reaction monitoring (MRM) is a promising tool for early CKD diagnostics that could replace the invasive biopsy procedure. However, there are few studies regarding the development of highly multiplexed MRM assays for urine proteome analysis, and the two MRM assays for urine proteomics described so far demonstrate very low consistency. Thus, the further development of targeted urine proteome assays for CKD is actual task. Herein, a BAK270 MRM assay previously validated for blood plasma protein analysis was adapted for urine-targeted proteomics. Because proteinuria associated with renal impairment is usually associated with an increased diversity of plasma proteins being present in urine, the use of this panel was appropriate. Another advantage of the BAK270 MRM assay is that it includes 35 potential CKD markers described previously. Targeted LC-MRM MS analysis was performed for 69 urine samples from 46 CKD patients and 23 healthy controls, revealing 138 proteins that were found in ≥2/3 of the samples from at least one of the groups. The results obtained confirm 31 previously proposed CKD markers. Combination of MRM analysis with machine learning for data processing was performed. As a result, a highly accurate classifier was developed (AUC = 0.99) that enables distinguishing between mild and severe glomerulopathies based on the assessment of only three urine proteins (GPX3, PLMN, and A1AT or SHBG).

Targeted Blood Plasma Proteomics and Hemostasis Assessment of Post COVID-19 Patients with Acute Myocardial Infarction

The molecular mechanisms underlying cardiovascular complications after the SARS-CoV-2 infection remain unknown. The goal of our study was to analyze the features of blood coagulation, platelet aggregation, and plasma proteomics in COVID-19 convalescents with AMI. The study included 66 AMI patients and 58 healthy volunteers. The groups were divided according to the anti-N IgG levels (AMI post-COVID (n = 44), AMI control (n = 22), control post-COVID (n = 31), and control (n = 27)). All participants underwent rotational thromboelastometry, thrombodynamics, impedance aggregometry, and blood plasma proteomics analysis. Both AMI groups of patients demonstrated higher values of clot growth rates, thrombus size and density, as well as the elevated levels of components of the complement system, proteins modifying the state of endothelium, acute-phase and procoagulant proteins. In comparison with AMI control, AMI post-COVID patients demonstrated decreased levels of proteins connected to inflammation and hemostasis (lipopolysaccharide-binding protein, C4b-binding protein alpha-chain, plasma protease C1 inhibitor, fibrinogen beta-chain, vitamin K-dependent protein S), and altered correlations between inflammation and fibrinolysis. A new finding is that AMI post-COVID patients opposite the AMI control group, are characterized by a less noticeable growth of acute-phase proteins and hemostatic markers that could be explained by prolonged immune system alteration after COVID-19.

Lipid Biogeochemistry and Modern Lipidomic Techniques

Lipids are structurally diverse biomolecules that serve multiple roles in cells. As such, they are used as biomarkers in the modern ocean and as paleoproxies to explore the geological past. Here, I review lipid geochemistry, biosynthesis, and compartmentalization; the varied uses of lipids as biomarkers; and the evolution of analytical techniques used to measure and characterize lipids. Advancements in high-resolution accurate-mass mass spectrometry have revolutionized the lipidomic and metabolomic fields, both of which are quickly being integrated into marine meta-omic studies. Lipidomics allows us to analyze tens of thousands of features, providing an open analytical window and the ability to quantify unknown compounds that can be structurally elucidated later. However, lipidome annotation is not a trivial matter and represents one of the biggest challenges for oceanographers, owing in part to the lack of marine lipids in current in silico databases and data repositories. A case study reveals the gaps in our knowledge and open opportunities to answer fundamental questions about molecular-level control of chemical reactions and global-scale patterns in the lipidscape.

Ion mobility mass spectrometry in the omics era: Challenges and opportunities for metabolomics and lipidomics

Researchers worldwide are taking advantage of novel, commercially available, technologies, such as ion mobility mass spectrometry (IM-MS), for metabolomics and lipidomics applications in a variety of fields including life, biomedical, and food sciences. IM-MS provides three main technical advantages over traditional LC-MS workflows. Firstly, in addition to mass, IM-MS allows collision cross-section values to be measured for metabolites and lipids, a physicochemical identifier related to the chemical shape of an analyte that increases the confidence of identification. Second, IM-MS increases peak capacity and the signal-to-noise, improving fingerprinting as well as quantification, and better defining the spatial localization of metabolites and lipids in biological and food samples. Third, IM-MS can be coupled with various fragmentation modes, adding new tools to improve structural characterization and molecular annotation. Here, we review the state-of-the-art in IM-MS technologies and approaches utilized to support metabolomics and lipidomics applications and we assess the challenges and opportunities in this growing field.

Evaluating Peptide Fragment Ion Detection Using Traveling Wave Ion Mobility Spectrometry with Signal-Enhanced MSE (SEMSE)

The inherent poor sampling of fragment ions in time-of-flight mass analyzers was recently improved for data-dependent acquisition (DDA) by considering their drift times in traveling wave ion mobility spectrometry (TWIMS). Here, we extend this TWIMS-DDA approach to the data-independent acquisition (DIA) mode MS^E to improve the signal intensities of fragment ions by providing improved ion beam sampling efficiency, which we termed therefore signal-enhanced MS^E (SEMS^E). The theoretical expectation that SEMS^E improves the number of identified peptides, the number of quantifiable peptides, and the lower limit of quantitation in wideband DIA was evaluated on an electrospray ionisation-ion mobility spectrometry-quadrupole-time-of-flight-MS (ESI-IMS-Q-TOF-MS) (Synapt G2-Si) in comparison to five established TWIMS-DDA and TWIMS-MS^E methods with respect to the number of peptide identifications, the spectral quality of supporting peptide spectra matches, and (most importantly) fragment ion signal sensitivity. A comparison of the fragment signals clearly indicated that SEMS^E provides 6.8- to 11.5-fold larger peak areas than established MS^E techniques. While this clearly shows the advantages of SEMS^E, the inherent limitations of the current software tools do not allow using all benefits in routine analyses. As the simultaneous fragmentation of co-eluting peptides limited peptide identification, DDA and MS^E data sets were integrated using Skyline.

Prognosis of Alzheimer’s Disease Using Quantitative Mass Spectrometry of Human Blood Plasma Proteins and Machine Learning

Early recognition of the risk of Alzheimer’s disease (AD) onset is a global challenge that requires the development of reliable and affordable screening methods for wide-scale application. Proteomic studies of blood plasma are of particular relevance; however, the currently proposed differentiating markers are poorly consistent. The targeted quantitative multiple reaction monitoring (MRM) assay of the reported candidate biomarkers (CBs) can contribute to the creation of a consistent marker panel. An MRM-MS analysis of 149 nondepleted EDTA–plasma samples (MHRC, Russia) of patients with AD (n = 47), mild cognitive impairment (MCI, n = 36), vascular dementia (n = 8), frontotemporal dementia (n = 15), and an elderly control group (n = 43) was performed using the BAK 125 kit (MRM Proteomics Inc., Canada). Statistical analysis revealed a significant decrease in the levels of afamin, apolipoprotein E, biotinidase, and serum paraoxonase/arylesterase 1 associated with AD. Different training algorithms for machine learning were performed to identify the protein panels and build corresponding classifiers for the AD prognosis. Machine learning revealed 31 proteins that are important for AD differentiation and mostly include reported earlier CBs. The best-performing classifiers reached 80% accuracy, 79.4% sensitivity and 83.6% specificity and were able to assess the risk of developing AD over the next 3 years for patients with MCI. Overall, this study demonstrates the high potential of the MRM approach combined with machine learning to confirm the significance of previously identified CBs and to propose consistent protein marker panels.

Evaluation of acquisition modes for semi-quantitative analysis by targeted and untargeted mass spectrometry

RATIONALE

Analyte quantitation by mass spectrometry underpins a diverse range of scientific endeavors. The fast-growing field of mass spectrometer development has resulted in several targeted and untargeted acquisition modes suitable for these applications. By characterizing the acquisition methods available on an ion mobility (IM)-enabled orthogonal acceleration time-of-flight (oa-ToF) instrument, the optimum modes for analyte semi-quantitation can be deduced.

METHODS

Serial dilutions of commercial metabolite, peptide, or cross-linked peptide analytes were prepared in matrices of human urine or Escherichia coli digest. Each analyte dilution was introduced into an IM separation-enabled oa-ToF mass spectrometer by reversed-phase liquid chromatography and electrospray ionization. Data were acquired for each sample in duplicate using nine different acquisition modes, including four IM-enabled acquisitions modes, available on the mass spectrometer.

RESULTS

Five (metabolite) or seven (peptide/cross-linked peptide) point calibration curves were prepared for analytes across each of the acquisition modes. A nonlinear response was observed at high concentrations for some modes, attributed to saturation effects. Two correction methods, one MS1 isotope-correction and one MS2 ion intensity-correction, were applied to address this observation, resulting in an up to twofold increase in dynamic range. By averaging the semi-quantitative results across analyte classes, two parameters, linear dynamic range (LDR) and lower limit of quantification (LLOQ), were determined to evaluate each mode.

CONCLUSION

A comparison of the acquisition modes revealed that data-independent acquisition and parallel reaction monitoring methods are most robust for semi-quantitation when considering achievable LDR and LLOQ. IM-enabled modes exhibited sensitivity increases, but a simultaneous reduction in dynamic range required correction methods to recover. These findings will assist users in identifying the optimum acquisition mode for their analyte quantitation needs, supporting a diverse range of applications and providing guidance for future acquisition mode developments.

Utilizing Skyline to analyze lipidomics data containing liquid chromatography, ion mobility spectrometry and mass spectrometry dimensions

Lipidomics studies suffer from analytical and annotation challenges because of the great structural similarity of many of the lipid species. To improve lipid characterization and annotation capabilities beyond those afforded by traditional mass spectrometry (MS)-based methods, multidimensional separation methods such as those integrating liquid chromatography, ion mobility spectrometry, collision-induced dissociation and MS (LC-IMS-CID-MS) may be used. Although LC-IMS-CID-MS and other multidimensional methods offer valuable hydrophobicity, structural and mass information, the files are also complex and difficult to assess. Thus, the development of software tools to rapidly process and facilitate confident lipid annotations is essential. In this Protocol Extension, we use the freely available, vendor-neutral and open-source software Skyline to process and annotate multidimensional lipidomic data. Although Skyline ( https://skyline.ms/skyline.url ) was established for targeted processing of LC-MS-based proteomics data, it has since been extended such that it can be used to analyze small-molecule data as well as data containing the IMS dimension. This protocol uses Skyline's recently expanded capabilities, including small-molecule spectral libraries, indexed retention time and ion mobility filtering, and provides a step-by-step description for importing data, predicting retention times, validating lipid annotations, exporting results and editing our manually validated 500+ lipid library. Although the time required to complete the steps outlined here varies on the basis of multiple factors such as dataset size and familiarity with Skyline, this protocol takes ~5.5 h to complete when annotations are rigorously verified for maximum confidence.

Recent developments in data acquisition, treatment and analysis with ion mobility-mass spectrometry for lipidomics

Lipids are involved in many biological processes and their study is constantly increasing. To identify a lipid among thousand requires of reliable methods and techniques. Ion Mobility (IM) can be coupled with Mass Spectrometry (MS) to increase analytical selectivity in lipid analysis of lipids. IM-MS has experienced an enormous development in several aspects, including instrumentation, sensitivity, amount of information collected and lipid identification capabilities. This review summarizes the latest developments in IM-MS analyses for lipidomics and focusses on the current acquisition modes in IM-MS, the approaches for the pre-treatment of the acquired data and the subsequent data analysis. Methods and tools for the calculation of Collision Cross Section (CCS) values of analytes are also reviewed. CCS values are commonly studied to support the identification of lipids, providing a quasi-orthogonal property that increases the confidence level in the annotation of compounds and can be matched in CCS databases. The information contained in this review might be of help to new users of IM-MS to decide the adequate instrumentation and software to perform IM-MS experiments for lipid analyses, but also for other experienced researchers that can reconsider their routines and protocols. This article is protected by copyright. All rights reserved.

Analysis of per- and polyfluoroalkyl substances in Houston Ship Channel and Galveston Bay following a large-scale industrial fire using ion-mobility-spectrometry-mass spectrometry

Per- and polyfluoroalkyl substances (PFAS) are persistent organic pollutants of concern because of their ubiquitous presence in surface and ground water; analytical methods that can be used for rapid comprehensive exposure assessment and fingerprinting of PFAS are needed. Following the fires at the Intercontinental Terminals Company (ITC) in Deer Park, TX in 2019, large quantities of PFAS-containing firefighting foams were deployed. The release of these substances into the Houston Ship Channel/Galveston Bay (HSC/GB) prompted concerns over the extent and level of PFAS contamination. A targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based study of temporal and spatial patterns of PFAS associated with this incident revealed presence of 7 species; their levels gradually decreased over a 6-month period. Because the targeted LC-MS/MS analysis was focused on about 30 PFAS molecules, it may have missed other PFAS compounds present in firefighting foams. Therefore, we utilized untargeted LC-ion mobility spectrometry-mass spectrometry (LC-IMS-MS)-based analytical approach for a more comprehensive characterization of PFAS in these water samples. We analyzed 31 samples from 9 sites in the HSC/GB that were collected over 5 months after the incident. Our data showed that additional 19 PFAS were detected in surface water of HSC/GB, most of them decreased gradually after the incident. PFAS features detected by LC-MS/MS correlated well in abundance with LC-IMS-MS data; however, LC-IMS-MS identified a number of additional PFAS, many known to be components of firefighting foams. These findings therefore illustrate that untargeted LC-IMS-MS improved our understanding of PFAS presence in complex environmental samples.

Utilizing Pine Needles to Temporally and Spatially Profile Per- and Polyfluoroalkyl Substances (PFAS)

As concerns over exposure to per- and polyfluoroalkyl substances (PFAS) are continually increasing, novel methods to monitor their presence and modifications are greatly needed, as some have known toxic and bioaccumulative characteristics while most have unknown effects. This task however is not simple, as the Environmental Protection Agency (EPA) CompTox PFAS list contains more than 9000 substances as of September 2020 with additional substances added continually. Nontargeted analyses are therefore crucial to investigating the presence of this immense list of possible PFAS. Here, we utilized archived and field-sampled pine needles as widely available passive samplers and a novel nontargeted, multidimensional analytical method coupling liquid chromatography, ion mobility spectrometry, and mass spectrometry (LC-IMS-MS) to evaluate the temporal and spatial presence of numerous PFAS. Over 70 PFAS were detected in the pine needles from this study, including both traditionally monitored legacy perfluoroalkyl acids (PFAAs) and their emerging replacements such as chlorinated derivatives, ultrashort chain PFAAs, perfluoroalkyl ether acids including hexafluoropropylene oxide dimer acid (HFPO-DA, "GenX") and Nafion byproduct 2, and a cyclic perfluorooctanesulfonic acid (PFOS) analog. Results from this study provide critical insight related to PFAS transport, contamination, and reduction efforts over the past six decades.

A Preprocessing Tool for Enhanced Ion Mobility-Mass Spectrometry-Based Omics Workflows

The ability to improve the data quality of ion mobility-mass spectrometry (IM-MS) measurements is of great importance for enabling modular and efficient computational workflows and gaining better qualitative and quantitative insights from complex biological and environmental samples. We developed the PNNL PreProcessor, a standalone and user-friendly software housing various algorithmic implementations to generate new MS-files with enhanced signal quality and in the same instrument format. Different experimental approaches are supported for IM-MS based on Drift-Tube (DT) and Structures for Lossless Ion Manipulations (SLIM), including liquid chromatography (LC) and infusion analyses. The algorithms extend the dynamic range of the detection system, while reducing file sizes for faster and memory-efficient downstream processing. Specifically, multidimensional smoothing improves peak shapes of poorly defined low-abundance signals, and saturation repair reconstructs the intensity profile of high-abundance peaks from various analyte types. Other functionalities are data compression and interpolation, IM demultiplexing, noise filtering by low intensity threshold and spike removal, and exporting of acquisition metadata. Several advantages of the tool are illustrated, including an increase of 19.4% in lipid annotations and a two-times faster processing of LC-DT IM-MS data-independent acquisition spectra from a complex lipid extract of a standard human plasma sample. The software is freely available at https://omics.pnl.gov/software/pnnl-preprocessor.

Mass spectrometry and proteome analysis to identify SARS-CoV-2 protein from COVID-19 patient swab samples

With new emerging SARS-CoV-2 strains and their increased pathogenicity, diagnosis has become more challenging. Molecular diagnosis often involves the use of nasopharyngeal swabs and subsequent real-time PCR-based tests. Although this test is the gold standard, it has several limitations; therefore, more complementary assays are required. This protocol describes how to identify SARS-CoV-2 protein from patients' nasopharyngeal swab samples. We first introduce the approach of label-free quantitative proteomics. We then detail target verification by triple quadrupole mass spectrometry (MS)-based targeted proteomics.

For complete details on the use and execution of this profile, please refer to Bankar et al. (2021).

•

A protocol for identification of SARS-CoV-2 protein from patients' swab samples

•

Sequential steps involved in proteomic sample preparation are elaborated

•

Detailed procedure for MS-based targeted proteomic verification is presented

•

A detailed presentation of workflow for label-free and targeted data analyses

Combining Micropunch Histology and Multidimensional Lipidomic Measurements for In-Depth Tissue Mapping

While decades of technical and analytical advancements have been utilized to discover novel lipid species, increase speciation, and evaluate localized lipid dysregulation at subtissue, cellular, and subcellular levels, many challenges still exist. One limitation is that the acquisition of both in-depth spatial information and comprehensive lipid speciation is extremely difficult, especially when biological material is limited or lipids are at low abundance. In neuroscience, for example, it is often desired to focus on only one brain region or subregion, which can be well under a square millimeter for rodents. Herein, we evaluate a micropunch histology method where cortical brain tissue at 2.0, 1.25, 1.0, 0.75, 0.5, and 0.25 mm diameter sizes and 1 mm depth was collected and analyzed with multidimensional liquid chromatography, ion mobility spectrometry, collision induced dissociation, and mass spectrometry (LC-IMS-CID-MS) measurements. Lipid extraction was optimized for the small sample sizes, and assessment of lipidome coverage for the 2.0 to 0.25 mm diameter sizes showed a decline from 304 to 198 lipid identifications as validated by all 4 analysis dimensions (~35% loss in coverage for ~88% less tissue). While losses were observed, the ~200 lipids and estimated 4630 neurons contained within the 0.25 punch still provided in-depth characterization of the small tissue region. Furthermore, while localization routinely achieved by mass spectrometry imaging (MSI) and single cell analyses is greater, this diameter is sufficiently small to isolate subcortical, hypothalamic, and other brain regions in adult rats, thereby increasing the coverage of lipids within relevant spatial windows without sacrificing speciation. Therefore, micropunch histology enables in-depth, region-specific lipid evaluations, an approach that will prove beneficial to a variety of lipidomic applications.

Utilizing liquid chromatography, ion mobility spectrometry, and mass spectrometry to assess INLIGHT™ derivatized N-linked glycans in biological samples

Glycosylation is a ubiquitous co- and post-translational modification involved in the sorting, folding, and trafficking of proteins in biological systems; in humans, >50% of gene products are glycosylated with the cellular machinery of glycosylation compromising ~2% of the genome. Perturbations in glycosylation have been implicated in a variety of diseases including neurodegenerative diseases and certain types of cancer. However, understanding the relationship between a glycan and its biological role is often difficult due to the numerous glycan isomers that exist. To address this challenge, nanoflow liquid chromatography, ion mobility spectrometry, and mass spectrometry (nLC-IMS-MS) were combined with the Individuality Normalization when Labeling with the Isotopic Glycan Hydrazide Tags (INLIGHT™) strategy to study a series of glycan standards and those enzymatically released from the glycoproteins horseradish peroxidase, fetuin, and pooled human plasma. The combination of IMS and the natural (NAT) and stable-isotope label (SIL) in the INLIGHT™ strategy provided additional confidence for each glycan identification due to the mobility aligned NAT- and SIL-labeled glycans and further capabilities for isomer examinations. Additionally, molecular trend lines based on the IMS and MS dimensions were investigated for the INLIGHT™ derivatized glycans, facilitating rapid identification of putative glycans in complex biological samples.

Improving the Speed and Selectivity of Newborn Screening Using Ion Mobility Spectrometry-Mass Spectrometry

Detection and diagnosis of congenital disorders is the principal aim of newborn screening (NBS) programs worldwide. Mass spectrometry (MS) has become the preferred primary testing method for high-throughput NBS sampling because of its speed and selectivity. However, the ever-increasing list of NBS biomarkers included in expanding panels creates unique analytical challenges for multiplexed MS assays due to isobaric/isomeric overlap and chimeric fragmentation spectra. Since isobaric and isomeric systems limit the diagnostic power of current methods and require costly follow-up exams due to many false-positive results, here, we explore the utility of ion mobility spectrometry (IMS) to enhance the accuracy of MS assays for primary (tier 1) screening. Our results suggest that ∼400 IMS resolving power would be required to confidently assess most NBS biomarkers of interest in dried blood spots (DBSs) that currently require follow-up testing. While this level of selectivity is unobtainable with most commercially available platforms, the separations detailed here for a commercially available drift tube IMS (Agilent 6560 with high-resolution demultiplexing, HRdm) illustrate the unique capabilities of IMS to separate many diagnostic NBS biomarkers from interferences. Furthermore, to address the need for increased speed of NBS analyses, we utilized an automated solid-phase extraction (SPE) system for ∼10 s sampling of simulated NBS samples prior to IMS-MS. This proof-of-concept work demonstrates the unique capabilities of SPE-IMS-MS for high-throughput sample introduction and enhanced separation capacity conducive for increasing speed and accuracy for NBS.

Development and Application of Multidimensional Lipid Libraries to Investigate Lipidomic Dysregulation Related to Smoke Inhalation Injury Severity

The implication of lipid dysregulation in diseases, toxic exposure outcomes, and inflammation has brought great interest to lipidomic studies. However, lipids have proven to be analytically challenging due to their highly isomeric nature and vast concentration ranges in biological matrices. Therefore, multidimensional techniques such as those integrating liquid chromatography, ion mobility spectrometry, collision-induced dissociation, and mass spectrometry (LC-IMS-CID-MS) have been implemented to separate lipid isomers as well as provide structural information and increased identification confidence. These data sets are however extremely large and complex, resulting in challenges for data processing and annotation. Here, we have overcome these challenges by developing sample-specific multidimensional lipid libraries using the freely available software Skyline. Specifically, the human plasma library developed for this work contains over 500 unique lipids and is combined with adapted Skyline functions such as indexed retention time (iRT) for retention time prediction and IMS drift time filtering for enhanced selectivity. For comparison with other studies, this database was used to annotate LC-IMS-CID-MS data from a NIST SRM 1950 extract. The same workflow was then utilized to assess plasma and bronchoalveolar lavage fluid (BALF) samples from patients with varying degrees of smoke inhalation injury to identify lipid-based patient prognostic and diagnostic markers.

Revealing Fatty Acid Heterogeneity in Staphylococcal Lipids with Isotope Labeling and RPLC-IM-MS

Up to 80% of the fatty acids in Staphylococcus aureus membrane lipids are branched, rather than straight-chain, fatty acids. The branched fatty acids (BCFAs) may have either an even or odd number of carbons, and the branch position may be at the penultimate carbon (iso) or the antepenultimate (anteiso) carbon of the tail. This results in two sets of isomeric fatty acid species with the same number of carbons that cannot be resolved by mass spectrometry. The isomer/isobar challenge is further complicated when the mixture of BCFAs and straight-chain fatty acids (SCFAs) are esterified into diacylated lipids such as the phosphatidylglycerol (PG) species of the S. aureus membrane. No conventional chromatographic method has been able to resolve diacylated lipids containing mixtures of SCFAs, anteiso-odd, iso-odd, and iso-even BCFAs. A major hurdle to method development in this area is the lack of relevant analytical standards for lipids containing BCFA isomers. The diversity of the S. aureus lipidome and its naturally high levels of BCFAs present an opportunity to explore the potential of resolving diacylated lipids containing BCFAs and SFCAs. Using our knowledge of lipid and fatty acid biosynthesis in S. aureus, we have used a stable-isotope-labeling strategy to develop and validate a 30 min C18 reversed-phase liquid chromatography method combined with traveling-wave ion mobility-mass spectrometry to provide resolution of diacylated lipids based on the number of BCFAs that they contain.

Targeted and Untargeted Mass Spectrometry Reveals the Impact of High-Fat Diet on Peripheral Amino Acid Regulation in a Mouse Model of Alzheimer's Disease

Recent research regarding amino acid metabolism has shown that there may be a link between obesity and Alzheimer's disease (AD). This work reports a metabolomics study using targeted and untargeted mass spectrometry-based metabolomic strategies to investigate this link. Targeted hydrophilic interaction liquid chromatography-triple quadrupole mass spectrometry and untargeted reversed-phase liquid chromatography-high resolution tandem mass spectrometry assays were developed to analyze the metabolic changes that occur in AD and obesity. APP_Swe/PS1_ΔE9 (APP/PSEN1) transgenic mice (to represent familial or early-onset AD) and wild-type littermate controls were fed either a high-fat diet (HFD, 60% kcal from lard) or a low-fat diet (LFD, 10% kcal from lard) from 2 months of age or a reversal diet (HFD, followed by LFD from 9.5 months). For targeted analyses, we applied the guidelines outlined in the Clinical and Laboratory Standards Institute (CLSI) LC-MS C62-A document and the U.S. Food and Drug Administration (FDA) bioanalytical method validation guidance for industry to evaluate the figures of merit of the assays. Our targeted and untargeted metabolomics results suggest that numerous peripheral pathways, specifically amino acid metabolism and fatty acid metabolism, were significantly affected by AD and diet. Multiple amino acids (including alanine, glutamic acid, leucine, isoleucine, and phenylalanine), carnitines, and members of the fatty acid oxidation pathway were significantly increased in APP/PSEN1 mice on HFD compared to those on LFD. More substantial effects and changes were observed in the APP/PSEN1 mice than in the WT mice, suggesting that they were more sensitive to an HFD. These dysregulated peripheral pathways include numerous amino acid pathways and fatty acid beta oxidation and suggest that obesity combined with AD further enhances cognitive impairment, possibly through aggravated mitochondrial dysfunction. Furthermore, partial reversibility of many altered pathways was observed, which highlights that diet change can mitigate the metabolic effects of AD. The same trends in individual amino acids were observed in both strategies, highlighting the biological validity of the results.

Current status and future prospects for ion-mobility mass spectrometry in the biopharmaceutical industry

Detailed characterization of protein reagents and biopharmaceuticals is key in defining successful drug discovery campaigns, aimed at bringing molecules through different discovery stages up to development and commercialization. There are many challenges in this process, with complex and detailed analyses playing paramount roles in modern industry. Mass spectrometry (MS) has become an essential tool for characterization of proteins ever since the onset of soft ionization techniques and has taken the lead in quality assessment of biopharmaceutical molecules, and protein reagents, used in the drug discovery pipeline. MS use spans from identification of correct sequences, to intact molecule analyses, protein complexes and more recently epitope and paratope identification. MS toolkits could be incredibly diverse and with ever evolving instrumentation, increasingly novel MS-based techniques are becoming indispensable tools in the biopharmaceutical industry. Here we discuss application of Ion Mobility MS (IMMS) in an industrial setting, and what the current applications and outlook are for making IMMS more mainstream.

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.

Mapping specificity, cleavage entropy, allosteric changes and substrates of blood proteases in a high-throughput screen

Proteases are among the largest protein families and critical regulators of biochemical processes like apoptosis and blood coagulation. Knowledge of proteases has been expanded by the development of proteomic approaches, however, technology for multiplexed screening of proteases within native environments is currently lacking behind. Here we introduce a simple method to profile protease activity based on isolation of protease products from native lysates using a 96FASP filter, their analysis in a mass spectrometer and a custom data analysis pipeline. The method is significantly faster, cheaper, technically less demanding, easy to multiplex and produces accurate protease fingerprints. Using the blood cascade proteases as a case study, we obtain protease substrate profiles that can be used to map specificity, cleavage entropy and allosteric effects and to design protease probes. The data further show that protease substrate predictions enable the selection of potential physiological substrates for targeted validation in biochemical assays.

Characterizing proteases in their native environment is still challenging. Here, the authors develop a proteomics workflow for analyzing protease-specific peptides from cell lysates in 96-well format, providing mechanistic insights into blood proteases and enabling the prediction of protease substrates.

A Workflow towards the Reproducible Identification and Quantitation of Protein Carbonylation Sites in Human Plasma

Protein carbonylation, a marker of excessive oxidative stress, has been studied in the context of multiple human diseases related to oxidative stress. The variety of post-translational carbonyl modifications (carbonyl PTMs) and their low concentrations in plasma challenge their reproducible identification and quantitation. However, carbonyl-specific biotinylated derivatization tags (e.g., aldehyde reactive probe, ARP) allow for targeting carbonyl PTMs by enriching proteins and peptides carrying these modifications. In this study, an oxidized human serum albumin protein model (OxHSA) and plasma from a healthy donor were derivatized with ARP, digested with trypsin, and enriched using biotin-avidin affinity chromatography prior to nano reversed-phase chromatography coupled online to electrospray ionization tandem mass spectrometry with travelling wave ion mobility spectrometry (nRPC-ESI-MS/MS-TWIMS). The presented workflow addresses several analytical challenges by using ARP-specific fragment ions to reliably identify ARP peptides. Furthermore, the reproducible recovery and relative quantitation of ARP peptides were validated. Human serum albumin (HSA) in plasma was heavily modified by a variety of direct amino acid oxidation products and adducts from reactive carbonyl species (RCS), with most RCS modifications being detected in six hotspots, i.e., Lys10, Lys190, Lys199, Lys281, Lys432, and Lys525 of mature HSA.

GlycoHunter: An Open-Source Software for the Detection and Relative Quantification of INLIGHT-Labeled N-Linked Glycans

Glycans are responsible for many biological activities; however, their structures are incredibly diverse and complex, often rendering the field of glycomics unsolvable by a single analytical technique. The development of multiple chemical derivatization strategies and bioinformatic software is responsible for some of the greatest analytical gains in the field of glycomics. The INLIGHT strategy is a chemical derivatization technique using hydrazide chemistry to derivatize the reducing end of N-linked glycans and incorporates either a natural (NAT, ¹²C₆) or a stable-isotope label (SIL, ¹³C₆) to carry out relative quantification. Here we present GlycoHunter, a user-friendly software created in MATLAB that enables researchers to accurately and efficiently process MS1 glycomics data where a NAT and SIL pair is generated for relative quantification, including but not limited to, INLIGHT. GlycoHunter accepts the commonly used data file formats imzML and mzXML and effectively identifies all peak pairs associated with NAT- and SIL-labeled N-linked glycans using MS1 data. It also includes the ability to tailor the search parameters and export the results for further analysis using Skyline or Excel.

Recent applications of ion mobility spectrometry in natural product research

Ion mobility spectrometry (IMS) is a rapid separation technique capable of extracting complementary structural information to chromatography and mass spectrometry (MS). IMS, especially in combination with MS, has experienced inordinate growth in recent years as an analytical technique, and elicited intense interest in many research fields. In natural product analysis, IMS shows promise as an additional tool to enhance the performance of analytical methods used to identify promising drug candidates. Potential benefits of the incorporation of IMS into analytical workflows currently used in natural product analysis include the discrimination of structurally similar secondary metabolites, improving the quality of mass spectral data, and the use of mobility-derived collision cross-section (CCS) values as an additional identification criterion in targeted and untargeted analyses. This review aims to provide an overview of the application of IMS to natural product analysis over the last six years. Instrumental aspects and the fundamental background of IMS will be briefly covered, and recent applications of the technique for natural product analysis will be discussed to demonstrate the utility of the technique in this field.

Identity confirmation of anthocyanins in berries by LC-DAD-IM-QTOFMS

Rugged analytical methods for the screening and identity confirmation of anthocyanins require a dedicated sample preparation, chromatographic setup, and the reliable generation of multiple identification points to confirm identity against the wide range of phenolic compounds typically present in food, beverage, and plant material samples. To this end, combinations of spectroscopic and mass spectrometric detection are frequently employed for this application to provide higher confidence in the absence of authentic standards. In the present work, low‐field drift tube ion mobility (DTIM) separation is evaluated for this task using a LC–DAD–DTIM–QTOFMS method. DTIM‐MS allows accurate determination of collision cross sections (^DTCCS) for all analysed compounds as well as a precise alignment tool for reconciling fragment and precursor ions in data independent acquisition mode. The presented approach thereby allows for an anthocyanin screening method taking true advantage of all dimensions of the analytical platform: relative retention (RPLC), UV/VIS absorption spectrum, accurate mass, ^DTCCS_N2, and confirmed high‐resolution fragment ions. From the analysis of authentic standards and several berry samples primarily from the Vaccinium genus, Level 1 confirmation data for six anthocyanins from the cyanidin family, and Level 2 confirmation for a further 29 anthocyanins confirmed in berry samples is provided. The method and accompanying dataset provided as part of this work provides a means to develop anthocyanin screening methods using the ion mobility dimension as an additional alignment and filtering parameter in data independent analysis acquisition across any LC–IM–MS platform.

Ion Mobility-Mass Spectrometry Imaging Workflow

Mass spectrometry imaging (MSI) is a powerful technique for the label-free spatially resolved analysis of biological tissues. Coupling ion mobility (IM) separation with MSI allows for separation of isobars in the mobility dimension and increases confidence of peak assignments. Recently, imaging experiments have been implemented on several commercially available and custom-designed ion mobility instruments, making IM-MSI experiments more broadly accessible to the MS community. However, the absence of open access data analysis software for IM-MSI systems presents a bottleneck. Herein, we present an imaging workflow to visualize IM-MSI data produced on the Agilent 6560 ion mobility quadrupole time-of-flight system. Specifically, we have developed a Python script, the ion mobility-mass spectrometry image creation script (IM-MSIC), which interfaces Agilent's Mass Hunter Mass Profiler software with the MacCoss lab's Skyline software and generates drift time and mass-to-charge-selected ion images. In the workflow, Mass Profiler is used for an untargeted feature detection. The IM-MSIC script mediates user input of data, extracts ion chronograms utilizing Skyline's command-line interface, and then proceeds toward ion image generation within a single user interface. Ion image postprocessing is subsequently performed using different tools implemented in accompanying scripts. Though the current work only showcases Agilent IM-MSI data, this workflow can be readily adapted for use with most major instrument vendors.

Mass Spectrometry of Human Transporters

Transporters are key to understanding how an individual will respond to a particular dose of a drug. Two patients with similar systemic concentrations may have quite different local concentrations of a drug at the required site. The transporter profile of any individual depends upon a variety of genetic and environmental factors, including genotype, age, and diet status. Robust models (virtual patients) are therefore required and these models are data hungry. Necessary data include quantitative transporter profiles at the relevant organ. Liquid chromatography with tandem mass spectrometry (LC-MS/MS) is currently the most powerful method available for obtaining this information. Challenges include sourcing the tissue, isolating the hydrophobic membrane-embedded transporter proteins, preparing the samples for MS (including proteolytic digestion), choosing appropriate quantification methodology, and optimizing the LC-MS/MS conditions. Great progress has been made with all of these, especially within the last few years, and is discussed here.

Alpha-synuclein fragments trigger distinct aggregation pathways

Aggregation of alpha-synuclein (αSyn) is a crucial event underlying the pathophysiology of synucleinopathies. The existence of various intracellular and extracellular αSyn species, including cleaved αSyn, complicates the quest for an appropriate therapeutic target. Hence, to develop efficient disease-modifying strategies, it is fundamental to achieve a deeper understanding of the relevant spreading and toxic αSyn species. Here, we describe comparative and proof-of-principle approaches to determine the involvement of αSyn fragments in intercellular spreading. We demonstrate that two different αSyn fragments (1–95 and 61–140) fulfill the criteria of spreading species. They efficiently instigate formation of proteinase-K-resistant aggregates from cell-endogenous full-length αSyn, and drive it into different aggregation pathways. The resulting aggregates induce cellular toxicity. Strikingly, these aggregates are only detectable by specific antibodies. Our results suggest that αSyn fragments might be relevant not only for spreading, but also for aggregation-fate determination and differential strain formation.

Drift-Tube Ion Mobility-Mass Spectrometry for Nontargeted 'Omics

This chapter describes the developments in drift-tube ion mobility-mass spectrometry (DTIM-MS) that have driven application development in 'omics analyses. Harnessing the additional, orthogonal separation that DTIM provides increased confidence in compound identifications as the mass spectral complexity can be reduced and mobility-derived parameters (most prominently the collision cross section, CCS) used to support identity confirmation goals for a variety of 'omics application areas. Presented within this contribution is a methodology for improving the transmission and maintaining accurate determination of drift time-derived CCS (^DTCCS) for low molecular weight compounds for a typical nontargeted 'omics (metabolomics) workflow using liquid chromatography in combination with DTIM-MS.

An Analytical Perspective on Protein Analysis and Discovery Proteomics by Ion Mobility-Mass Spectrometry

Ion mobility combined with mass spectrometry (IM-MS) is a powerful technique for the analysis of biomolecules and complex mixtures. This chapter reviews the current state-of-the-art in ion mobility technology and its application to biology, protein analysis, and quantitative discovery proteomics in particular, from an analytical perspective. IM-MS can be used as a technique to separate mixtures, to determine structural information (rotationally averaged cross-sectional area) and to enhance MS duty cycle and sensitivity. Moreover, IM-MS is ideally suited for hyphenating with liquid chromatography, or other front-end separation techniques such as, GC, microcolumn LC, capillary electrophoresis, and direct analysis, including MALDI and DESI, providing an semiorthogonal layer of separation, which affords the more unambiguous and confident detection of a wide range of analytes. To illustrate these enhancements, as well as recent developments, the principle of in-line IM separation and hyphenation to orthogonal acceleration time-of-flight mass spectrometers are discussed, in addition to the enhancement of biophysical MS-based analysis using typical proteomics and related application examples.

Ion Mobility Spectrometry and the Omics: Distinguishing Isomers, Molecular Classes and Contaminant Ions in Complex Samples

Ion mobility spectrometry (IMS) is a widely used analytical technique providing rapid gas phase separations. IMS alone is useful, but its coupling with mass spectrometry (IMS-MS) and various front-end separation techniques has greatly increased the molecular information achievable from different omic analyses. IMS-MS analyses are specifically gaining attention for improving metabolomic, lipidomic, glycomic, proteomic and exposomic analyses by increasing measurement sensitivity (e.g. S/N ratio), reducing the detection limit, and amplifying peak capacity. Numerous studies including national security-related analyses, disease screenings and environmental evaluations are illustrating that IMS-MS is able to extract information not possible with MS alone. Furthermore, IMS-MS has shown great utility in salvaging molecular information for low abundance molecules of interest when high concentration contaminant ions are present in the sample by reducing detector suppression. This review highlights how IMS-MS is currently being used in omic analyses to distinguish structurally similar molecules, isomers, molecular classes and contaminant ions.

Collision cross section compendium to annotate and predict multi-omic compound identities

Ion mobility mass spectrometry (IM-MS) expands the analyte coverage of existing multi-omic workflows by providing an additional separation dimension as well as a parameter for characterization and identification of molecules - the collision cross section (CCS). This work presents a large, Unified CCS compendium of >3800 experimentally acquired CCS values obtained from traceable molecular standards and measured with drift tube ion mobility-mass spectrometers. An interactive visualization of this compendium along with data analytic tools have been made openly accessible. Represented in the compendium are 14 structurally-based chemical super classes, consisting of a total of 80 classes and 157 subclasses. Using this large data set, regression fitting and predictive statistics have been performed to describe mass-CCS correlations specific to each chemical ontology. These structural trends provide a rapid and effective filtering method in the traditional untargeted workflow for identification of unknown biochemical species. The utility of the approach is illustrated by an application to metabolites in human serum, quantified trends of which were used to assess the probability of an unknown compound belonging to a given class. CCS-based filtering narrowed the chemical search space by 60% while increasing the confidence in the remaining isomeric identifications from a single class, thus demonstrating the value of integrating predictive analyses into untargeted experiments to assist in identification workflows. The predictive abilities of this compendium will improve in specificity and expand to more chemical classes as additional data from the IM-MS community is contributed. Instructions for data submission to the compendium and criteria for inclusion are provided.