Simultaneous Proteomic Discovery and Targeted Monitoring using Liquid Chromatography, Ion Mobility Spectrometry, and Mass Spectrometry

Deciphering ApoE Genotype-Driven Proteomic and Lipidomic Alterations in Alzheimer's Disease Across Distinct Brain Regions

Alzheimer's disease (AD) is a neurodegenerative disease with a complex etiology influenced by confounding factors such as genetic polymorphisms, age, sex, and race. Traditionally, AD research has not prioritized these influences, resulting in dramatically skewed cohorts such as three times the number of Apolipoprotein E (APOE) ε4-allele carriers in AD relative to healthy cohorts. Thus, the resulting molecular changes in AD have previously been complicated by the influence of apolipoprotein E disparities. To explore how apolipoprotein E polymorphism influences AD progression, 62 post-mortem patients consisting of 33 AD and 29 controls (Ctrl) were studied to balance the number of ε4-allele carriers and facilitate a molecular comparison of the apolipoprotein E genotype. Lipid and protein perturbations were assessed across AD diagnosed brains compared to Ctrl brains, ε4 allele carriers (APOE4+ for those carrying 1 or 2 ε4s and APOE4- for non-ε4 carriers), and differences in ε3ε3 and ε3ε4 Ctrl brains across two brain regions (frontal cortex (FCX) and cerebellum (CBM)). The region-specific influences of apolipoprotein E on AD mechanisms showcased mitochondrial dysfunction and cell proteostasis at the core of AD pathophysiology in the post-mortem brains, indicating these two processes may be influenced by genotypic differences and brain morphology.

Evaluation of a Reference-Free Collision Cross Section Calibration Strategy for Proteomics Using SLIM-Based High-Resolution Ion Mobility Spectrometry-Mass Spectrometry

Ion mobility spectrometry (IMS) is a gas-phase analytical technique that separates ions with different sizes and shapes and is compatible with mass spectrometry (MS) to provide an additional separation dimension. The rapid nature of the IMS separation combined with the high sensitivity of MS-based detection and the ability to derive structural information on analytes in the form of the property collision cross section (CCS) makes IMS particularly well-suited for characterizing complex samples in -omics applications. In such applications, the quality of CCS from IMS measurements is critical to confident annotation of the detected components in the complex -omics samples. However, most IMS instrumentation in mainstream use requires calibration to calculate CCS from measured arrival times, with the most notable exception being drift tube IMS measurements using multifield methods. The strategy for calibrating CCS values, particularly selection of appropriate calibrants, has important implications for CCS accuracy, reproducibility, and transferability between laboratories. The conventional approach to CCS calibration involves explicitly defining calibrants ahead of data acquisition and crucially relies upon availability of reference CCS values. In this work, we present a novel reference-free approach to CCS calibration which leverages trends among putatively identified features and computational CCS prediction to conduct calibrations post-data acquisition and without relying on explicitly defined calibrants. We demonstrated the utility of this reference-free CCS calibration strategy for proteomics application using high-resolution structures for lossless ion manipulations (SLIM)-based IMS-MS. We first validated the accuracy of CCS values using a set of synthetic peptides and then demonstrated using a complex peptide sample from cell lysate.

Computational tools and algorithms for ion mobility spectrometry-mass spectrometry

Ion mobility spectrometry-mass spectrometry (IMS-MS or IM-MS) is a powerful analytical technique that combines the gas-phase separation capabilities of IM with the identification and quantification capabilities of MS. IM-MS can differentiate molecules with indistinguishable masses but different structures (e.g., isomers, isobars, molecular classes, and contaminant ions). The importance of this analytical technique is reflected by a staged increase in the number of applications for molecular characterization across a variety of fields, from different MS-based omics (proteomics, metabolomics, lipidomics, etc.) to the structural characterization of glycans, organic matter, proteins, and macromolecular complexes. With the increasing application of IM-MS there is a pressing need for effective and accessible computational tools. This article presents an overview of the most recent free and open-source software tools specifically tailored for the analysis and interpretation of data derived from IM-MS instrumentation. This review enumerates these tools and outlines their main algorithmic approaches, while highlighting representative applications across different fields. Finally, a discussion of current limitations and expectable improvements is presented.

Assessing Per- and Polyfluoroalkyl Substances (PFAS) in Fish Fillet Using Non-Targeted Analyses

Per- and polyfluoroalkyl substances (PFAS) are a class of thousands of man-made chemicals that are persistent and highly stable in the environment. Fish consumption has been identified as a key route of PFAS exposure for humans. However, routine fish monitoring targets only a handful of PFAS, and non-targeted analyses have largely only evaluated fish from heavily PFAS-impacted waters. Here, we evaluated PFAS in fish fillets from recreational and drinking water sources in central North Carolina to assess whether PFAS are present in these fillets that would not be detected by conventional targeted methods. We used liquid chromatography, ion mobility spectrometry, and mass spectrometry (LC-IMS-MS) to collect full scan feature data, performed suspect screening using an in-house library of 100 PFAS for high confidence feature identification, searched for additional PFAS features using non-targeted data analyses, and quantified perfluorooctane sulfonic acid (PFOS) in the fillet samples. A total of 36 PFAS were detected in the fish fillets, including 19 that would not be detected using common targeted methods, with a minimum of 6 and a maximum of 22 in individual fish. Median fillet PFOS levels were concerningly high at 11.6 to 42.3 ppb, and no significant correlation between PFOS levels and number of PFAS per fish was observed. Future PFAS monitoring in this region should target more of these 36 PFAS, and other regions not considered heavily PFAS contaminated should consider incorporating non-targeted analyses into ongoing fish monitoring studies.

Hybrid-DIA: intelligent data acquisition integrates targeted and discovery proteomics to analyze phospho-signaling in single spheroids

Achieving sufficient coverage of regulatory phosphorylation sites by mass spectrometry (MS)-based phosphoproteomics for signaling pathway reconstitution is challenging, especially when analyzing tiny sample amounts. To address this, we present a hybrid data-independent acquisition (DIA) strategy (hybrid-DIA) that combines targeted and discovery proteomics through an Application Programming Interface (API) to dynamically intercalate DIA scans with accurate triggering of multiplexed tandem mass spectrometry (MSx) scans of predefined (phospho)peptide targets. By spiking-in heavy stable isotope labeled phosphopeptide standards covering seven major signaling pathways, we benchmark hybrid-DIA against state-of-the-art targeted MS methods (i.e., SureQuant) using EGF-stimulated HeLa cells and find the quantitative accuracy and sensitivity to be comparable while hybrid-DIA also profiles the global phosphoproteome. To demonstrate the robustness, sensitivity, and biomedical potential of hybrid-DIA, we profile chemotherapeutic agents in single colon carcinoma multicellular spheroids and evaluate the phospho-signaling difference of cancer cells in 2D vs 3D culture.

Bioinformatics Tools and Knowledgebases to Assist Generating Targeted Assays for Plasma Proteomics

In targeted proteomics experiments, selecting the appropriate proteotypic peptides as surrogate for the target protein is a crucial pre-acquisition step. This step is largely a bioinformatics exercise that involves integrating information on the peptides and proteins and using various software tools and knowledgebases. We present here a few resources that automate and simplify the selection process to a great degree. These tools and knowledgebases were developed primarily to streamline targeted proteomics assay development and include PeptidePicker, PeptidePickerDB, MRMAssayDB, MouseQuaPro, and PeptideTracker. We have used these tools to develop and document thousands of targeted proteomics assays, many of them for plasma proteins with focus on human and mouse. An important aspect in all these resources is the integrative approach on which they are based. Using these tools in the first steps of designing a singleplexed or multiplexed targeted proteomic experiment can reduce the necessary experimental steps tremendously. All the tools and knowledgebases we describe here are Web-based and freely accessible so scientists can query the information conveniently from the browser. This chapter provides an overview of these software tools and knowledgebases, their content, and how to use them for targeted plasma proteomics. We further demonstrate how to use them with the results of the HUPO Human Plasma Proteome Project to produce a new database of 3.8 k targeted assays for known human plasma proteins. Upon experimental validation, these assays should help in the further quantitative characterizing of the plasma proteome.

Potential of Single Pulse and Multiplexed Drift-Tube Ion Mobility Spectrometry Coupled to Micropillar Array Column for Proteomics Studies

Proteomics is one of the most significant methodologies to better understand the molecular pathways involved in diseases and to improve their diagnosis, treatment and follow-up. The investigation of the proteome of complex organisms is challenging from an analytical point of view, because of the large number of proteins present in a wide range of concentrations. In this study, nanofluidic chromatography, using a micropillar array column, was coupled to drift-tube ion mobility and time-of-flight mass spectrometry to identify as many proteins as possible in a protein digest standard of HeLa cells. Several chromatographic parameters were optimized. The high interest of drift-tube ion mobility to increase the number of identifications and to separate isobaric coeluting peptides was demonstrated. Multiplexed drift-tube ion mobility spectrometry was also investigated, to increase the sensitivity in proteomics studies. This innovative proteomics platform will be useful for analyzing patient samples to better understand unresolved disorders.

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.

Advances in High Throughput Proteomics Profiling in Establishing Potential Biomarkers for Gastrointestinal Cancer

Gastrointestinal cancers (GICs) remain the most diagnosed cancers and accounted for the highest cancer-related death globally. The prognosis and treatment outcomes of many GICs are poor because most of the cases are diagnosed in advanced metastatic stages. This is primarily attributed to the deficiency of effective and reliable early diagnostic biomarkers. The existing biomarkers for GICs diagnosis exhibited inadequate specificity and sensitivity. To improve the early diagnosis of GICs, biomarkers with higher specificity and sensitivity are warranted. Proteomics study and its functional analysis focus on elucidating physiological and biological functions of unknown or annotated proteins and deciphering cellular mechanisms at molecular levels. In addition, quantitative analysis of translational proteomics is a promising approach in enhancing the early identification and proper management of GICs. In this review, we focus on the advances in mass spectrometry along with the quantitative and functional analysis of proteomics data that contributes to the establishment of biomarkers for GICs including, colorectal, gastric, hepatocellular, pancreatic, and esophageal cancer. We also discuss the future challenges in the validation of proteomics-based biomarkers for their translation into clinics.

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.

Combining Isotopologue Workflows and Simultaneous Multidimensional Separations to Detect, Identify, and Validate Metabolites in Untargeted Analyses

While the combination of liquid chromatography and tandem mass spectrometry (LC-MS/MS) is commonly used for feature annotation in untargeted omics experiments, ensuring these prioritized features originate from endogenous metabolism remains challenging. Isotopologue workflows, such as isotopic ratio outlier analysis (IROA), mass isotopomer ratio analysis of U-¹³C labeled extracts (MIRACLE), and credentialing incorporate isotopic labels directly into metabolic precursors, guaranteeing that all features of interest are unequivocal byproducts of cellular metabolism. Furthermore, comprehensive separation and annotation of small molecules continue to challenge the metabolomics field, particularly for isomeric systems. In this paper, we evaluate the analytical utility of incorporating ion mobility spectrometry (IMS) as an additional separation mechanism into standard LC-MS/MS isotopologue workflows. Since isotopically labeled molecules codrift in the IMS dimension with their ¹²C versions, LC-IMS-CID-MS provides four dimensions (LC, IMS, MS, and MS/MS) to directly investigate the metabolic activity of prioritized untargeted features. Here, we demonstrate this additional selectivity by showcasing how a preliminary data set of 30 endogeneous metabolites are putatively annotated from isotopically labeled Escherichia coli cultures when analyzed by LC-IMS-CID-MS. Metabolite annotations were based on several molecular descriptors, including accurate mass measurement, carbon number, annotated fragmentation spectra, and collision cross section (CCS), collectively illustrating the importance of incorporating IMS into isotopologue workflows. Overall, our results highlight the enhanced separation space and increased annotation confidence afforded by IMS for metabolic characterization and provide a unique perspective for future developments in isotopically labeled MS experiments.

Getting more out of FLAG-Tag co-immunoprecipitation mass spectrometry experiments using FAIMS

Co-immunoprecipitation of proteins coupled to mass spectrometry is critical for the understanding of protein interaction networks. In instances where a suitable antibody is not available, it is common to graft synthetic tags onto a target protein sequence thereby allowing the use of commercially available antibodies for affinity purification. A common approach is through FLAG-Tag co-immunoprecipitation. To allow the selective elution of protein complexes, competitive displacement using a large molar excess of the tag peptides is often carried out. Yet, this creates downstream challenges for the mass spectrometry analysis due to the presence of large quantities of these peptides. Here, we demonstrate that Field Asymmetric Ion Mobility Spectrometry (FAIMS), a gas phase ion separation device prior to mass spectrometry analysis can be applied to FLAG-Tag co-immunoprecipitation experiments to increase the depth of protein coverage. By excluding these abundant tag peptides, we were able to observe deeper coverage of interacting proteins and as a result, deeper biological insights, without the need for additional sample handling or altering sample preparation protocols. SIGNIFICANCE: We have shown that application of FAIMS separation in the gas phase can increase the proteome coverage of Flag-Tagged co-immunoprecipitation mass spectrometry experiments versus one without FAIMS. We were able to observe deeper coverage of interacting proteins and as a result, deeper biological insights, without additional sample handling, fractionation, machine run time or modifying the sample preparation protocol.

An Update on MRMAssayDB: A Comprehensive Resource for Targeted Proteomics Assays in the Community

Precise multiplexed quantification of proteins in biological samples can be achieved by targeted proteomics using multiple or parallel reaction monitoring (MRM/PRM). Combined with internal standards, the method achieves very good repeatability and reproducibility enabling excellent protein quantification and allowing longitudinal and cohort studies. A laborious part of performing such experiments lies in the preparation steps dedicated to the development and validation of individual protein assays. Several public repositories host information on targeted proteomics assays, including NCI’s Clinical Proteomic Tumor Analysis Consortium assay portals, PeptideAtlas SRM Experiment Library, SRMAtlas, PanoramaWeb, and PeptideTracker, with all offering varying levels of details. We introduced MRMAssayDB in 2018 as an integrated resource for targeted proteomics assays. The Web-based application maps and links the assays from the repositories, includes comprehensive up-to-date protein and sequence annotations, and provides multiple visualization options on the peptide and protein level. We have extended MRMAssayDB with more assays and extensive annotations. Currently it contains >828 000 assays covering >51 000 proteins from 94 organisms, of which >17 000 proteins are present in >2400 biological pathways, and >48 000 mapping to >21 000 Gene Ontology terms. This is an increase of about four times the number of assays since introduction. We have expanded annotations of interaction, biological pathways, and disease associations. A newly added visualization module for coupled molecular structural annotation browsing allows the user to interactively examine peptide sequence and any known PTMs and disease mutations, and map all to available protein 3D structures. Because of its integrative approach, MRMAssayDB enables a holistic view of suitable proteotypic peptides and commonly used transitions in empirical data. Availability: http://mrmassaydb.proteincentre.com.

Hybrid QconCAT-Based Targeted Absolute and Data-Independent Acquisition-Based Label-Free Quantification Enables In-Depth Proteomic Characterization of Wheat Amylase/Trypsin Inhibitor Extracts

Wheat amylase/trypsin inhibitors (ATIs) have gained significant relevance as inducers of intestinal and extra-intestinal inflammation. In this study, we present a novel hybrid data-independent acquisition (DIA) liquid chromatography-mass spectrometry (LC-MS) approach, combining QconCAT technology with short microflow LC gradients and DIA and apply the method toward the quantitative proteome analysis of ATI extracts. The presented method is fast, robust, and reproducible and provides precise QconCAT-based absolute quantification of major ATI proteins while simultaneously quantifying the proteome by label-free quantification (LFQ). We analyzed extracts of 60 varieties of common wheat grown in replication and evaluated the reproducibility and precision of the workflow for the quantification of ATIs. Applying the method to analyze different wheat species (i.e., common wheat, spelt, durum wheat, emmer, and einkorn) and comparing the results to published data, we validated inter-laboratory and cross-methodology reproducibility of ATI quantification, which is essential in the context of large-scale breeding projects. Additionally, we applied our workflow to assess environmental effects on ATI expression, analyzing ATI content and proteome of same varieties grown at different locations. Finally, we explored the potential of combining QconCAT-based absolute quantification with DIA-based LFQ proteome analysis for the generation of new hypotheses or assay development.

Protein Biomarker Quantification by Immunoaffinity Liquid Chromatography–Tandem Mass Spectrometry: Current State and Future Vision

Immunoaffinity–mass spectrometry (IA-MS) is an emerging analytical genre with several advantages for profiling and determination of protein biomarkers. Because IA-MS combines affinity capture, analogous to ligand binding assays (LBAs), with mass spectrometry (MS) detection, this platform is often described using the term hybrid methods. The purpose of this report is to provide an overview of the principles of IA-MS and to demonstrate, through application, the unique power and potential of this technology. By combining target immunoaffinity enrichment with the use of stable isotope-labeled internal standards and MS detection, IA-MS achieves high sensitivity while providing unparalleled specificity for the quantification of protein biomarkers in fluids and tissues. In recent years, significant uptake of IA-MS has occurred in the pharmaceutical industry, particularly in the early stages of clinical development, enabling biomarker measurement previously considered unattainable. By comparison, IA-MS adoption by CLIA laboratories has occurred more slowly. Current barriers to IA-MS use and opportunities for expanded adoption are discussed. The path forward involves identifying applications for which IA-MS is the best option compared with LBA or MS technologies alone. IA-MS will continue to benefit from advances in reagent generation, more sensitive and higher throughput MS technologies, and continued growth in use by the broader analytical community. Collectively, the pursuit of these opportunities will secure expanded long-term use of IA-MS for clinical applications.

Proteomic studies of bone and skeletal health outcomes

Proteins are an essential part of essentially all biological processes, and there is enormous variation in protein forms and concentrations that is not reflected in DNA or RNA. Recently there have been rapid advances in the ability to measure protein sequence, modification and concentration, particularly with methods based in mass spectrometry. Global measures of proteins in tissues or in the circulation provide a broad assessment of the proteome that can be extremely useful for discovery, and targeted proteomic measures can yield specific and sensitive assessments of specific peptides and proteins. While most proteomic measures are directed at the detection of consensus peptide sequences, mass spectrometry based proteomic methods also allow a detailed examination of the peptide sequence differences that result from genetic variants and that may have important effects on protein function. In evaluating proteomic data, a number of analytical considerations are important, including an understanding of missing data, the challenge of multiple testing and replication, and the use of rapidly evolving methods in systems biology. While proteomics has not yet had a major impact in skeletal research, interesting recent research has used these approaches in the study of bone cell biology and the discovery of biomarkers of skeletal disorders. Proteomics can be expected to have an increasing influence in the study of bone biology and pathophysiology.

Determination of Gas-Phase Ion Mobility Coefficients Using Voltage Sweep Multiplexing

In a standard single averaged, drift tube ion mobility spectrometry (IMS) experiment, typically less than 1% of the ions are utilized, with the rest of the ions neutralizing on a closed ion gate or ion optic element. Though some efforts at lower pressures (e.g., 4 Torr) have been made to address this issue by concentrating ions prior to release into a drift cell, the ion current reaching the detector during an IMS experiment is often diminished due to this lower duty cycle. Additionally, when considering the temporal nature of the drift tube IMS experiment and the trajectory of IMS towards higher resolution separations and lower duty cycles, increased detector sampling rates are another factor also which further necessitates new modes of conducting the IMS experiment. Placing this trend in context with ion mobility-mass spectrometry instruments (IM-MS), there are numerous types of mass spectrometers that are simply incompatible with the single averaged ion mobility spectrometry experiments due to timing incompatibilities (i.e., ion traps are an order of magnitude slower than the IMS experiment). However, by utilizing a dual gate ion mobility spectrometer for ion multiplexing, ion utilization efficiency can be significantly increased while creating a measurement signal that can be recorded at low sampling rates. In this work, we present the fundamental theory and first results from proof-of-concept measurements using a new type of ion multiplexing that relies on changing the electric field within the drift cell during the course of an experiment while simultaneously opening the ion gates at a constant frequency. For brevity, this mode is termed voltage sweep multiplexing (VSM). Key variables for this type of experiment are discussed and verified with measurements from traditional signal averaged experiments. Graphical Abstract .

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.

Moonshot Objectives: Catalyze New Scientific Breakthroughs-Proteogenomics

Breaking down the silos between disciplines to accelerate the pace of cancer research is a key paradigm for the Cancer Moonshot. Molecular analyses of cancer biology have tended to segregate between a focus on nucleic acids-DNA, RNA, and their modifications-and a focus on proteins and protein function. Proteogenomics represents a fusion of those two approaches, leveraging the strengths of each to provide a more integrated vision of the flow of information from DNA to RNA to protein and eventually function at the molecular level. Proteogenomic studies have been incorporated into multiple activities associated with the Cancer Moonshot, demonstrating substantial added value. Innovative study designs integrating genomic, transcriptomic, and proteomic data, particularly those using clinically relevant samples and involving clinical trials, are poised to provide new insights regarding cancer risk, progression, and response to therapy.

Proteomic Analysis of Extracellular Vesicles for Cancer Diagnostics

Extracellular vesicles (EVs) including exosomes and microvesicles are lipid bilayer-encapsulated nanoparticles released by cells, ranging from 40 nm to several microns in diameter. Biological cargoes including proteins, RNAs, and DNAs can be ferried by EVs to neighboring and distant cells via biofluids, serving as a means of cell-to-cell communication under normal and pathological conditions, especially cancers. On the other hand, EVs have been investigated as a novel "information capsule" for early disease detection and monitoring via liquid biopsy. This review summarizes current advancements in EV subtype characterization, cancer EV capture, proteomic analysis technologies, as well as possible EV-based multiomics for cancer diagnostics.

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

Recent advances in ion mobility spectrometry (IMS) have illustrated its power in determining the structural characteristics of a molecule, especially when coupled with other separations dimensions such as liquid chromatography (LC) and mass spectrometry (MS). However, these three separation techniques together greatly complicate data analyses, making better informatics tools essential for assessing the resulting data. In this manuscript, Skyline was adapted to analyze LC-IMS-CID-MS data from numerous instrument vendor datasets and determine the effect of adding the IMS dimension into the normal LC-MS molecular pipeline. For the initial evaluation, a tryptic digest of bovine serum albumin (BSA) was spiked into a yeast protein digest at seven different concentrations, and Skyline was able to rapidly analyze the MS and CID-MS data for 38 of the BSA peptides. Calibration curves for the precursor and fragment ions were assessed with and without the IMS dimension. In all cases, addition of the IMS dimension removed noise from co-eluting peptides with close m/z values, resulting in calibration curves with greater linearity and lower detection limits. This study presents an important informatics development since to date LC-IMS-CID-MS data from the different instrument vendors is often assessed manually and cannot be analyzed quickly. Because these evaluations require days for the analysis of only a few target molecules in a limited number of samples, it is unfeasible to evaluate hundreds of targets in numerous samples. Thus, this study showcases Skyline's ability to work with the multidimensional LC-IMS-CID-MS data and provide biological and environmental insights rapidly. Graphical Abstract ᅟ.

Improved Sensitivity and Separations for Phosphopeptides using Online Liquid Chromotography Coupled with Structures for Lossless Ion Manipulations Ion Mobility-Mass Spectrometry

Phosphoproteomics greatly augments proteomics and holds tremendous potential for insights into the modulation of biological systems for various disease states. However, numerous challenges hinder conventional methods in terms of measurement sensitivity, throughput, quantification, and capabilities for confident phosphopeptide and phosphosite identification. In this work, we report the first example of integrating structures for lossless ion manipulations ion mobility-mass spectrometry (SLIM IM-MS) with online reversed-phase liquid chromatography (LC) to evaluate its potential for addressing the aforementioned challenges. A mixture of 51 heavy-labeled phosphopeptides was analyzed with a SLIM IM module having integrated ion accumulation and long-path separation regions. The SLIM IM-MS provided limits of detection as low as 50-100 pM (50-100 amol/μL) for several phosphopeptides, with the potential for significant further improvements. In addition, conventionally problematic phosphopeptide isomers could be resolved following an 18 m SLIM IM separation. The 2-D LC-IM peak capacity was estimated as ∼9000 for a 90 min LC separation coupled to an 18 m SLIM IM separation, considerably higher than LC alone and providing a basis for both improved identification and quantification, with additional gains projected with the future use of longer path SLIM IM separations. Thus, LC-SLIM IM-MS offers great potential for improving the sensitivity, separation, and throughput of phosphoproteomics analyses.

Towards Discovery and Targeted Peptide Biomarker Detection Using nanoESI-TIMS-TOF MS

In the present work, the potential of trapped ion mobility spectrometry coupled to TOF mass spectrometry (TIMS-TOF MS) for discovery and targeted monitoring of peptide biomarkers from human-in-mouse xenograft tumor tissue was evaluated. In particular, a TIMS-MS workflow was developed for the detection and quantification of peptide biomarkers using internal heavy analogs, taking advantage of the high mobility resolution (R = 150-250) prior to mass analysis. Five peptide biomarkers were separated, identified, and quantified using offline nanoESI-TIMS-CID-TOF MS; the results were in good agreement with measurements using a traditional LC-ESI-MS/MS proteomics workflow. The TIMS-TOF MS analysis permitted peptide biomarker detection based on accurate mobility, mass measurements, and high sequence coverage for concentrations in the 10-200 nM range, while simultaneously achieving discovery measurements of not initially targeted peptides as markers from the same proteins and, eventually, other proteins. Graphical Abstract ᅟ.

A structural examination and collision cross section database for over 500 metabolites and xenobiotics using drift tube ion mobility spectrometry

The confident identification of metabolites and xenobiotics in biological and environmental studies is an analytical challenge due to their immense dynamic range, vast chemical space and structural diversity. Ion mobility spectrometry (IMS) is widely used for small molecule analyses since it can separate isomeric species and be easily coupled with front end separations and mass spectrometry for multidimensional characterizations. However, to date IMS metabolomic and exposomic studies have been limited by an inadequate number of accurate collision cross section (CCS) values for small molecules, causing features to be detected but not confidently identified. In this work, we utilized drift tube IMS (DTIMS) to directly measure CCS values for over 500 small molecules including primary metabolites, secondary metabolites and xenobiotics. Since DTIMS measurements do not need calibrant ions or calibration like some other IMS techniques, they avoid calibration errors which can cause problems in distinguishing structurally similar molecules. All measurements were performed in triplicate in both positive and negative polarities with nitrogen gas and seven different electric fields, so that relative standard deviations (RSD) could be assessed for each molecule and structural differences studied. The primary metabolites analyzed to date have come from key metabolism pathways such as glycolysis, the pentose phosphate pathway and the tricarboxylic acid cycle, while the secondary metabolites consisted of classes such as terpenes and flavonoids, and the xenobiotics represented a range of molecules from antibiotics to polycyclic aromatic hydrocarbons. Different CCS trends were observed for several of the diverse small molecule classes and when urine features were matched to the database, the addition of the IMS dimension greatly reduced the possible number of candidate molecules. This CCS database and structural information are freely available for download at http://panomics.pnnl.gov/metabolites/ with new molecules being added frequently.

Global Proteomic Analysis of Human Liver Microsomes: Rapid Characterization and Quantification of Hepatic Drug-Metabolizing Enzymes

Many genetic and environmental factors lead to interindividual variations in the metabolism and transport of drugs, profoundly affecting efficacy and toxicity. Precision dosing, that is, targeting drug dose to a well characterized subpopulation, is dependent on quantitative models of the profiles of drug-metabolizing enzymes (DMEs) and transporters within that subpopulation, informed by quantitative proteomics. We report the first use of ion mobility-mass spectrometry for this purpose, allowing rapid, robust, label-free quantification of human liver microsomal (HLM) proteins from distinct individuals. Approximately 1000 proteins were identified and quantified in four samples, including an average of 70 DMEs. Technical and biological variabilities were distinguishable, with technical variability accounting for about 10% of total variability. The biological variation between patients was clearly identified, with samples showing a range of expression profiles for cytochrome P450 and uridine 5'-diphosphoglucuronosyltransferase enzymes. Our results showed excellent agreement with previous data from targeted methods. The label-free method, however, allowed a fuller characterization of the in vitro system, showing, for the first time, that HLMs are significantly heterogeneous. Further, the traditional units of measurement of DMEs (pmol mg^-1 HLM protein) are shown to introduce error arising from variability in unrelated, highly abundant proteins. Simulations of this variability suggest that up to 1.7-fold variation in apparent CYP3A4 abundance is artifactual, as are background positive correlations of up to 0.2 (Spearman correlation coefficient) between the abundances of DMEs. We suggest that protein concentrations used in pharmacokinetic predictions and scaling to in vivo clinical situations (physiologically based pharmacokinetics and in vitro-in vivo extrapolation) should be referenced instead to tissue mass.

Characterizing the lipid and metabolite changes associated with placental function and pregnancy complications using ion mobility spectrometry-mass spectrometry and mass spectrometry imaging

Successful pregnancy is dependent upon discrete biological events, which include embryo implantation, decidualization, and placentation. Problems associated with each of these events can cause infertility or conditions such as preeclampsia. A greater understanding of the molecular changes associated with these complex processes is necessary to aid in identifying treatments for each condition. Previous nuclear magnetic resonance spectroscopy and mass spectrometry studies have been used to identify metabolites and lipids associated with pregnancy-related complications. However, due to limitations associated with conventional implementations of both techniques, novel technology developments are needed to more fully understand the initiation and development of pregnancy related problems at the molecular level. In this perspective, we describe current analytical techniques for metabolomic and lipidomic characterization of pregnancy complications and discuss the potential for new technologies such as ion mobility spectrometry-mass spectrometry and mass spectrometry imaging to contribute to a better understanding of the molecular changes that affect the placenta and pregnancy outcomes.