Parallel Accumulation-Serial Fragmentation (PASEF): Multiplying Sequencing Speed and Sensitivity by Synchronized Scans in a Trapped Ion Mobility Device

Recruitment of FBXO22 for targeted degradation of NSD2

Targeted protein degradation (TPD) is an emerging therapeutic strategy that would benefit from new chemical entities with which to recruit a wider variety of ubiquitin E3 ligases to target proteins for proteasomal degradation. Here we describe a TPD strategy involving the recruitment of FBXO22 to induce degradation of the histone methyltransferase and oncogene NSD2. UNC8732 facilitates FBXO22-mediated degradation of NSD2 in acute lymphoblastic leukemia cells harboring the NSD2 gain-of-function mutation p.E1099K, resulting in growth suppression, apoptosis and reversal of drug resistance. The primary amine of UNC8732 is metabolized to an aldehyde species, which engages C326 of FBXO22 to recruit the SCFFBXO22 Cullin complex. We further demonstrate that a previously reported alkyl amine-containing degrader targeting XIAP is similarly dependent on SCFFBXO22. Overall, we present a potent NSD2 degrader for the exploration of NSD2 disease phenotypes and a new FBXO22-recruitment strategy for TPD.

Hyphenation of Liquid Chromatography and Trapped Ion Mobility - Mass Spectrometry for Characterization of Isomeric Phosphatidylethanolamines with Focus on N-Acylated Species

In prior research, hydrophilic interaction liquid chromatography coupled to tandem mass spectrometry (HILIC-MS/MS) has demonstrated applicability for characterizing regioisomers in lipidomics studies, including phosphatidylglycerols (PG) and bis(monoacyl)glycerophosphates (BMP). However, there are other lipid regioisomers, such as phosphatidylethanolamines (PE) and lyso-N-acyl-PE (LNAPE), that have not been studied as extensively. Therefore, hyphenated mass spectrometric methods are needed to investigate PE and LNAPE regioisomers individually. The asymmetric structure of LNAPE favors isomeric species, which can result in coelution and chimeric MS/MS spectra. One way to address the challenge of chimeric MS/MS spectra is through mobility-resolved fragmentation using trapped ion mobility spectrometry (TIMS). Therefore, we developed a multidimensional HILIC-TIMS-MS/MS approach for the structural characterization of isomeric phosphatidylethanolamines in both negative and positive ionization modes. The study revealed the complementary fragmentation pattern and ion mobility behavior of LNAPE in both ionization modes, which was confirmed by a self-synthesized LNAPE standard. With this knowledge, a distinction of regioisomeric PE and LNAPE was achieved in human plasma samples. Furthermore, regioisomeric LNAPE species containing at least one unsaturated fatty acid were noted to exhibit a change in collision cross-section in positive ionization mode, leading to a lipid characterization with respect to fatty acyl positional level. Similar mobility behavior was also observed for the biological LNAPE precursor N-acyl-PE (NAPE). Application of this approach to plasma and cereal samples demonstrated its effectiveness in regioisomeric LNAPE and NAPE species' elucidation.

Liquid-phase separations coupled with ion mobility-mass spectrometry for next-generation biopharmaceutical analysis

INTRODUCTION

The pharmaceutical industry continues to expand its search for innovative biotherapeutics. The comprehensive characterization of such therapeutics requires many analytical techniques to fully evaluate critical quality attributes, making analysis a bottleneck in discovery and development timelines. While thorough characterization is crucial for ensuring the safety and efficacy of biotherapeutics, there is a need to further streamline analytical characterization and expedite the overall timeline from discovery to market.

AREAS COVERED

This review focuses on recent developments in liquid-phase separations coupled with ion mobility-mass spectrometry (IM-MS) for the development and characterization of biotherapeutics. We cover uses of IM-MS to improve the characterization of monoclonal antibodies, antibody-drug conjugates, host cell proteins, glycans, and nucleic acids. This discussion is based on an extensive literature search using Web of Science, Google Scholar, and SciFinder.

EXPERT OPINION

IM-MS has the potential to enhance the depth and efficiency of biotherapeutic characterization by providing additional insights into conformational changes, post-translational modifications, and impurity profiles. The rapid timescale of IM-MS positions it well to enhance the information content of existing assays through its facile integration with standard liquid-phase separation techniques that are commonly used for biopharmaceutical analysis.

Proteomics of the heart

Mass spectrometry-based proteomics is a sophisticated identification tool specializing in portraying protein dynamics at a molecular level. Proteomics provides biologists with a snapshot of context-dependent protein and proteoform expression, structural conformations, dynamic turnover, and protein-protein interactions. Cardiac proteomics can offer a broader and deeper understanding of the molecular mechanisms that underscore cardiovascular disease, and it is foundational to the development of future therapeutic interventions. This review encapsulates the evolution, current technologies, and future perspectives of proteomic-based mass spectrometry as it applies to the study of the heart. Key technological advancements have allowed researchers to study proteomes at a single-cell level and employ robot-assisted automation systems for enhanced sample preparation techniques, and the increase in fidelity of the mass spectrometers has allowed for the unambiguous identification of numerous dynamic posttranslational modifications. Animal models of cardiovascular disease, ranging from early animal experiments to current sophisticated models of heart failure with preserved ejection fraction, have provided the tools to study a challenging organ in the laboratory. Further technological development will pave the way for the implementation of proteomics even closer within the clinical setting, allowing not only scientists but also patients to benefit from an understanding of protein interplay as it relates to cardiac disease physiology.

Imputation of label-free quantitative mass spectrometry-based proteomics data using self-supervised deep learning

Imputation techniques provide means to replace missing measurements with a value and are used in almost all downstream analysis of mass spectrometry (MS) based proteomics data using label-free quantification (LFQ). Here we demonstrate how collaborative filtering, denoising autoencoders, and variational autoencoders can impute missing values in the context of LFQ at different levels. We applied our method, proteomics imputation modeling mass spectrometry (PIMMS), to an alcohol-related liver disease (ALD) cohort with blood plasma proteomics data available for 358 individuals. Removing 20 percent of the intensities we were able to recover 15 out of 17 significant abundant protein groups using PIMMS-VAE imputations. When analyzing the full dataset we identified 30 additional proteins (+13.2%) that were significantly differentially abundant across disease stages compared to no imputation and found that some of these were predictive of ALD progression in machine learning models. We, therefore, suggest the use of deep learning approaches for imputing missing values in MS-based proteomics on larger datasets and provide workflows for these.

Mobility-Assisted Pseudo-MS3 Sequencing of Protein Ions

The sequencing of intact proteins within a mass spectrometer has many benefits but is frequently limited by the fact that tandem mass spectrometry (MS/MS) techniques often generate poor sequence coverages when applied to protein ions. To overcome this limitation, exotic MS/MS techniques that rely on lasers and radical chemistry have been developed. These techniques generate high sequence coverages, but they require specialized instrumentation, create products through multiple dissociation mechanisms, and often require long acquisition times. Recently, we demonstrated that protein ions can be dissociated in a trapped ion mobility spectrometry (TIMS) device prior to mobility separation in a commercial timsTOF. All generated product ions were distributed throughout the mobility dimension, and this separation enabled deconvolution of complex tandem mass spectra and could enable facile pseudo-MS3 interrogation of generated product ions with the downstream quadrupole and collision cell. A second activation step improves sequence coverage because the most labile bonds have been depleted during the first dissociation and subsequent dissociation events are more evenly distributed throughout the product ion backbone. In this work, we explore the potential of this mobility-assisted pseudo-MS3 (MAP) method on a commercial timsTOF and timsTOF Pro 2. We demonstrate that while MAP only generates 92% of the sequence coverage of the most effective MS/MS technique, it accomplished this feat in 1.5 min and could be facilely integrated with liquid chromatographic separations.

Data-independent acquisition: A milestone and prospect in clinical mass spectrometry-based proteomics

Data-independent acquisition (DIA) has revolutionized the field of mass spectrometry (MS)-based proteomics over the past few years. DIA stands out for its ability to systematically sample all peptides in a given mass-to-charge range, allowing an unbiased acquisition of proteomics data. This greatly mitigates the issue of missing values and significantly enhances quantitative accuracy, precision, and reproducibility compared to many traditional methods. This review focuses on the critical role of DIA analysis software tools, primarily focusing on their capabilities and the challenges they address in proteomic research. Advances in MS technology, such as trapped ion mobility spectrometry, or high field asymmetric waveform ion mobility spectrometry require sophisticated analysis software capable of handling the increased data complexity and exploiting the full potential of DIA. We identify and critically evaluate leading software tools in the DIA landscape, discussing their unique features, and the reliability of their quantitative and qualitative outputs. We present the biological and clinical relevance of DIA-MS and discuss crucial publications that paved the way for in-depth proteomic characterization in patient-derived specimens. Furthermore, we provide a perspective on emerging trends in clinical applications and present upcoming challenges including standardization and certification of MS-based acquisition strategies in molecular diagnostics. While we emphasize the need for continuous development of software tools to keep pace with evolving technologies, we advise researchers against uncritically accepting the results from DIA software tools. Each tool may have its own biases, and some may not be as sensitive or reliable as others. Our overarching recommendation for both researchers and clinicians is to employ multiple DIA analysis tools, utilizing orthogonal analysis approaches to enhance the robustness and reliability of their findings.

The Future of Proteomics is Up in the Air: Can Ion Mobility Replace Liquid Chromatography for High Throughput Proteomics?

The coevolution of liquid chromatography (LC) with mass spectrometry (MS) has shaped contemporary proteomics. LC hyphenated to MS now enables quantification of more than 10,000 proteins in a single injection, a number that likely represents most proteins in specific human cells or tissues. Separations by ion mobility spectrometry (IMS) have recently emerged to complement LC and further improve the depth of proteomics. Given the theoretical advantages in speed and robustness of IMS in comparison to LC, we envision that ongoing improvements to IMS paired with MS may eventually make LC obsolete, especially when combined with targeted or simplified analyses, such as rapid clinical proteomics analysis of defined biomarker panels. In this perspective, we describe the need for faster analysis that might drive this transition, the current state of direct infusion proteomics, and discuss some technical challenges that must be overcome to fully complete the transition to entirely gas phase proteomics.

Maximizing Glycoproteomics Results through an Integrated Parallel Accumulation Serial Fragmentation Workflow

Glycoproteins play important roles in numerous physiological processes and are often implicated in disease. Analysis of site-specific protein glycobiology through glycoproteomics has evolved rapidly in recent years thanks to hardware and software innovations. Particularly, the introduction of parallel accumulation serial fragmentation (PASEF) on hybrid trapped ion mobility time-of-flight mass spectrometry instruments combined deep proteome sequencing with separation of (near-)isobaric precursor ions or converging isotope envelopes through ion mobility separation. However, the reported use of PASEF in integrated glycoproteomics workflows to comprehensively capture the glycoproteome is still limited. To this end, we developed an integrated methodology using timsTOF Pro 2 to enhance N-glycopeptide identifications in complex mixtures. We systematically optimized the ion optics tuning, collision energies, mobility isolation width, and the use of dopant-enriched nitrogen gas (DEN). Thus, we obtained a marked increase in unique glycopeptide identification rates compared to standard proteomics settings, showcasing our results on a large set of glycopeptides. With short liquid chromatography gradients of 30 min, we increased the number of unique N-glycopeptide identifications in human plasma samples from around 100 identifications under standard proteomics conditions to up to 1500 with our optimized glycoproteomics approach, highlighting the need for tailored optimizations to obtain comprehensive data.

Four-dimensional lipidomics profiling in X-linked adrenoleukodystrophy using trapped ion mobility mass spectrometry

Lipids play pivotal roles in an extensive range of metabolic and physiological processes. In recent years, the convergence of trapped ion mobility spectrometry and MS has enabled 4D-lipidomics, a highly promising technology for comprehensive lipid analysis. 4D-lipidomics assesses lipid annotations across four distinct dimensions-retention time, collisional cross section, m/z (mass-to-charge ratio), and MS/MS spectra-providing a heightened level of confidence in lipid annotation. These advantages prove particularly valuable when investigating complex disorders involving lipid metabolism, such as adrenoleukodystrophy (ALD). ALD is characterized by the accumulation of very-long-chain fatty acids (VLCFAs) due to pathogenic variants in the ABCD1 gene. A comprehensive 4D-lipidomics strategy of ALD fibroblasts demonstrated significant elevations of various lipids from multiple classes. This indicates that the changes observed in ALD are not confined to a single lipid class and likely impacts a broad spectrum of lipid-mediated physiological processes. Our findings highlight the incorporation of mainly saturated and monounsaturated VLCFA variants into a range of lipid classes, encompassing phosphatidylcholines, triacylglycerols, and cholesterol esters. These include ultra-long-chain fatty acids with a length of up to thirty carbon atoms. Lipid species containing C26:0 and C26:1 were the most frequently detected VLCFA lipids in our study. Furthermore, we report a panel of 121 new candidate biomarkers in fibroblasts, exhibiting significant differentiation between controls and individuals with ALD. In summary, this study demonstrates the capabilities of a 4D-lipid profiling workflow in unraveling novel insights into the intricate lipid modifications associated with metabolic disorders like ALD.

Interplatform comparison between three ion mobility techniques for human plasma lipid collision cross sections

The implementation of ion mobility spectrometry (IMS) in liquid chromatography-high-resolution mass spectrometry (LC-HRMS) workflows has become a valuable tool for improving compound annotation in metabolomics analyses by increasing peak capacity and by adding a new molecular descriptor, the collision cross section (CCS). Although some studies reported high repeatability and reproducibility of CCS determination and only few studies reported good interplatform agreement for small molecules, standardized protocols are still missing due to the lack of reference CCS values and reference materials. We present a comparison of CCS values of approximatively one hundred lipid species either commercially available or extracted from human plasma. We used three different commercial ion mobility technologies from different laboratories, drift tube IMS (DTIMS), travelling wave IMS (TWIMS) and trapped IMS (TIMS), to evaluate both instrument repeatability and interlaboratory reproducibility. We showed that CCS discrepancies of 0.3% (average) could occur depending on the data processing software tools. Moreover, eleven CCS calibrants were evaluated yielding mean RSD below 2% for eight calibrants, ESI Low concentration tuning mix (Tune Mix) showing the lowest RSD (< 0.5%) in both ion modes. Tune Mix calibrated CCS from the three different IMS instruments proved to be well correlated and highly reproducible (R2 > 0.995 and mean RSD ≤ 1%). More than 90% of the lipid CCS had deviations of less than 1%, demonstrating high comparability between techniques, and the possibility to use the CCS as molecular descriptor. We highlighted the need of standardized procedures for calibration, data acquisition, and data processing. This work demonstrates that using harmonized analytical conditions are required for interplatform reproducibility for CCS determination of human plasma lipids.

Multi-tiered actions of Legionella effectors to modulate host Rab10 dynamics

Rab GTPases are representative targets of manipulation by intracellular bacterial pathogens for hijacking membrane trafficking. Legionella pneumophila recruits many Rab GTPases to its vacuole and exploits their activities. Here, we found that infection-associated regulation of Rab10 dynamics involves ubiquitin signaling cascades mediated by the SidE and SidC families of Legionella ubiquitin ligases. Phosphoribosyl-ubiquitination of Rab10 catalyzed by the SidE ligases is crucial for its recruitment to the bacterial vacuole. SdcB, the previously uncharacterized SidC-family effector, resides on the vacuole and contributes to retention of Rab10 at the late stages of infection. We further identified MavC as a negative regulator of SdcB. By the transglutaminase activity, MavC crosslinks ubiquitin to SdcB and suppresses its function, resulting in elimination of Rab10 from the vacuole. These results demonstrate that the orchestrated actions of many L. pneumophila effectors fine-tune the dynamics of Rab10 during infection.

A Workflow for Improved Analysis of Cross-Linking Mass Spectrometry Data Integrating Parallel Accumulation-Serial Fragmentation with MeroX and Skyline

Cross-linking mass spectrometry (XL-MS) has evolved into a pivotal technique for probing protein interactions. This study describes the implementation of Parallel Accumulation-Serial Fragmentation (PASEF) on timsTOF instruments, enhancing the detection and analysis of protein interactions by XL-MS. Addressing the challenges in XL-MS, such as the interpretation of complex spectra, low abundant cross-linked peptides, and a data acquisition bias, our current study integrates a peptide-centric approach for the analysis of XL-MS data and presents the foundation for integrating data-independent acquisition (DIA) in XL-MS with a vendor-neutral and open-source platform. A novel workflow is described for processing data-dependent acquisition (DDA) of PASEF-derived information. For this, software by Bruker Daltonics is used, enabling the conversion of these data into a format that is compatible with MeroX and Skyline software tools. Our approach significantly improves the identification of cross-linked products from complex mixtures, allowing the XL-MS community to overcome current analytical limitations.

Fragment ion intensity prediction improves the identification rate of non-tryptic peptides in timsTOF

Immunopeptidomics is crucial for immunotherapy and vaccine development. Because the generation of immunopeptides from their parent proteins does not adhere to clear-cut rules, rather than being able to use known digestion patterns, every possible protein subsequence within human leukocyte antigen (HLA) class-specific length restrictions needs to be considered during sequence database searching. This leads to an inflation of the search space and results in lower spectrum annotation rates. Peptide-spectrum match (PSM) rescoring is a powerful enhancement of standard searching that boosts the spectrum annotation performance. We analyze 302,105 unique synthesized non-tryptic peptides from the ProteomeTools project on a timsTOF-Pro to generate a ground-truth dataset containing 93,227 MS/MS spectra of 74,847 unique peptides, that is used to fine-tune the deep learning-based fragment ion intensity prediction model Prosit. We demonstrate up to 3-fold improvement in the identification of immunopeptides, as well as increased detection of immunopeptides from low input samples.

Neuropeptidomics of the American Lobster Homarus americanus

The American lobster, Homarus americanus, is not only of considerable economic importance but has also emerged as a premier model organism in neuroscience research. Neuropeptides, an important class of cell-to-cell signaling molecules, play crucial roles in a wide array of physiological and psychological processes. Leveraging the recently sequenced high-quality draft genome of the American lobster, our study sought to profile the neuropeptidome of this model organism. Employing advanced mass spectrometry techniques, we identified 24 neuropeptide precursors and 101 unique mature neuropeptides in Homarus americanus. Intriguingly, 67 of these neuropeptides were discovered for the first time. Our findings provide a comprehensive overview of the peptidomic attributes of the lobster's nervous system and highlight the tissue-specific distribution of these neuropeptides. Collectively, this research not only enriches our understanding of the neuronal complexities of the American lobster but also lays a foundation for future investigations into the functional roles that these peptides play in crustacean species. The mass spectrometry data have been deposited in the PRIDE repository with the identifier PXD047230.

The One Hour Human Proteome

We describe deep analysis of the human proteome in less than 1 h. We achieve this expedited proteome characterization by leveraging state-of-the-art sample preparation, chromatographic separations, and data analysis tools, and by using the new Orbitrap Astral mass spectrometer equipped with a quadrupole mass filter, a high-field Orbitrap mass analyzer, and an asymmetric track lossless (Astral) mass analyzer. The system offers high tandem mass spectrometry acquisition speed of 200 Hz and detects hundreds of peptide sequences per second within data-independent acquisition or data-dependent acquisition modes of operation. The fast-switching capabilities of the new quadrupole complement the sensitivity and fast ion scanning of the Astral analyzer to enable narrow-bin data-independent analysis methods. Over a 30-min active chromatographic method consuming a total analysis time of 56 min, the Q-Orbitrap-Astral hybrid MS collects an average of 4319 MS1 scans and 438,062 tandem mass spectrometry scans per run, producing 235,916 peptide sequences (1% false discovery rate). On average, each 30-min analysis achieved detection of 10,411 protein groups (1% false discovery rate). We conclude, with these results and alongside other recent reports, that the 1-h human proteome is within reach.

Critical steps in an environmental metaproteomics workflow

Environmental metaproteomics is a rapidly advancing field that provides insights into the structure, dynamics, and metabolic activity of microbial communities. As the field is still maturing, it lacks consistent workflows, making it challenging for non-expert researchers to navigate. This review aims to introduce the workflow of environmental metaproteomics. It outlines the standard practices for sample collection, processing, and analysis, and offers strategies to overcome the unique challenges presented by common environmental matrices such as soil, freshwater, marine environments, biofilms, sludge, and symbionts. The review also highlights the bottlenecks in data analysis that are specific to metaproteomics samples and provides suggestions for researchers to obtain high-quality datasets. It includes recent benchmarking studies and descriptions of software packages specifically built for metaproteomics analysis. The article is written without assuming the reader's familiarity with single-organism proteomic workflows, making it accessible to those new to proteomics or mass spectrometry in general. This primer for environmental metaproteomics aims to improve accessibility to this exciting technology and empower researchers to tackle challenging and ambitious research questions. While it is primarily a resource for those new to the field, it should also be useful for established researchers looking to streamline or troubleshoot their metaproteomics experiments.

A critical evaluation of ultrasensitive single-cell proteomics strategies

Success of mass spectrometry characterization of the proteome of single cells allows us to gain a greater understanding than afforded by transcriptomics alone but requires clear understanding of the tradeoffs between analytical throughput and precision. Recent advances in mass spectrometry acquisition techniques, including updated instrumentation and sample preparation, have improved the quality of peptide signals obtained from single cell data. However, much of the proteome remains uncharacterized, and higher throughput techniques often come at the expense of reduced sensitivity and coverage, which diminish the ability to measure proteoform heterogeneity, including splice variants and post-translational modifications, in single cell data analysis. Here, we assess the growing body of ultrasensitive single-cell approaches and their tradeoffs as researchers try to balance throughput and precision in their experiments.

Ion mobility in gas and liquid phases: How much orthogonality is obtained in capillary electrophoresis-ion mobility-mass spectrometry?

Ion mobility-mass spectrometry (IM-MS) is an ever-evolving tool to separate ions in the gas phase according to electrophoretic mobility with subsequent mass determination. CE is rarely coupled to IM-MS, possibly due to similar separation mechanisms based on electrophoretic mobility. Here, we investigate the orthogonality of CE and ion mobility (IM) by analyzing a complex peptide mixture (tryptic digest of HeLa proteins) with trapped ion mobility mass spectrometry (TIMS-MS). Using the nanoCEasy interface, excellent sensitivity was achieved by identifying thousands of peptides and achieving a peak capacity of 7500 (CE: 203-323 in a 150 cm long capillary, IM: 27-31). Plotting CE versus mass and CE versus (inverse) mobility, a clear grouping in curved striped patterns is observed according to the charge-to-size and mass-to-charge ratios. The peptide charge in the acidic background electrolyte can be estimated from the number of basic amino acids, with a few exceptions where neighboring effects reduce the positive charge. A surprisingly high orthogonality of CE and IM is observed, which is obviously caused by solvation effects leading to different charges and sizes in the liquid phase compared to the gas phase. A high orthogonality of CE and ion mobility is expected to be observed for other peptide samples as well as other substance classes, making CE-IM-MS a promising tool for various applications.

AAV-based gene therapy ameliorated CNS-specific GPI defect in mouse models

Thirty genes are involved in the biosynthesis and modification of glycosylphosphatidylinositol (GPI)-anchored proteins, and defects in these genes cause inherited GPI deficiency (IGD). PIGA is X-linked and involved in the first step of GPI biosynthesis, and only males are affected by variations in this gene. The main symptoms of IGD are neurological abnormalities, such as developmental delay and seizures. There is no effective treatment at present. We crossed Nestin-Cre mice with Piga-floxed mice to generate CNS-specific Piga knockout (KO) mice. Hemizygous KO male mice died by P10 with severely defective growth. Heterozygous Piga KO female mice are mosaic for Piga expression and showed severe defects in growth and myelination and died by P25. Using these mouse models, we evaluated the effect of gene replacement therapy with adeno-associated virus (AAV). It expressed efficacy within 6 days, and the survival of male mice was extended to up to 3 weeks, whereas 40% of female mice survived for approximately 1 year and the growth defect was improved. However, liver cancer developed in all three treated female mice at 1 year of age, which was probably caused by the AAV vector bearing a strong CAG promoter.

Parallel accumulation-serial fragmentation method for in-depth proteomic analysis of bronchoalveolar lavage fluid collected from patients with nonsmall cell lung cancer

PURPOSE

Advances in mass spectrometry-based quantitative proteomic analysis have successfully demonstrated the in-depth detection of protein biomarkers in bronchoalveolar lavage fluid (BALF) from patients with lung cancers. Recently, ion mobility technology was incorporated into the mass spectrometers escalating the sensitivity and throughput. Utilizing these advantages, herein, we employed the parallel accumulation-serial fragmentation (PASEF) implanted in a timsTOF Pro mass spectrometer to examine the alteration of BALF proteomes in patients with nonsmall cell lung cancers (NSCLCs).

EXPERIMENTAL DESIGN

BALF proteins were processed from patients with NSCLC and analyzed in a timsTOF Pro mass spectrometer with the PASEF method using a peptide input of 100 ng. Label-free mass spectrometry data were analyzed in the FragPipe platform.

RESULTS

We quantitated over 1400 proteins from a single injection of 100 ng of peptides per sample with a median of ∼2000 proteins. We were able to find a few potential biomarker proteins upregulated in NSCLC.

CONCLUSIONS AND CLINICAL RELEVANCE

The alterations of the BALF proteome landscape vary among patients with NSCLC as previously observed in patients with small-cell lung cancers. The PASEF method has significantly enhanced the sensitivity and throughput, demonstrating its effectiveness in clinical research and application.

Untargeted 4D-metabolomics using Trapped Ion Mobility combined with LC-HRMS in extra virgin olive oil adulteration study with lower-quality olive oils

Metabolomics is widely established in the field of food authenticity to address demanding issues, such as adulteration cases. Trapped ion mobility spectrometry (TIMS) coupled to liquid chromatography (LC) and high-resolution mass spectrometry (HRMS) provides an additional analytical dimension, introducing mobility-enhanced metabolomics in four dimensions (4D). In the present work, the potential of LC-TIMS-HRMS as a reliable analytical platform for authenticity studies is being explored, applied in extra virgin olive oil (EVOO) adulteration study. An integrated untargeted 4D-metabolomics approach is being implemented to investigate adulteration, with refined olive oils (ROOs) and olive pomace oils (OPOs) set as adulterants. Robust prediction models are built, successfully discriminating authentic EVOOs from adulterated ones and highlighting markers in each group. Noteworthy outcomes are retrieved regarding TIMS added value in LC-HRMS workflows, resulting in a significant increase of metabolic coverage, while, thanks to platform's enhanced sensitivity, detection of adulteration is being achieved down to 1%.

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.

Comprehensive phytochemical profiles and antioxidant activity of Korean local cultivars of red chili pepper (Capsicum annuum L.)

Red chili pepper (Capsicum annuum L.), which belongs to the Solanaceae family, contains a variety of phytochemicals with health-promoting properties including capsaicinoids, phenolics and fatty acids. Red chili pepper is one of the most consumed vegetables in Korea and occupies the largest cultivated area among spices. In this study, the ethanolic extracts from two Korean local cultivars, namely Subicho and Eumseong, were analyzed using a hybrid trapped ion mobility Q-TOF mass spectrometer equipped with a UPLC system, and their phytochemical profiles were then compared with those of a common phytophthora disease-resistant cultivar called Dokbulwang, which is extensively used for red chili pepper powder in public spaces across Korea. Utilizing high-resolution ion-mobility Q-TOF MS analysis, 458 and 192 compounds were identified from the three different red chili peppers in positive and negative ion modes, respectively, by matching with a reference spectral library. Principal component analysis revealed clear distinctions among the three cultivars, allowing us to identify key phytochemical components responsible for discriminating the local cultivars from the public cultivar. Furthermore, the assessment of total flavonoid, phenolic, and antioxidant activity in the red pepper extracts, highlighted their diverse molecular and chemical profiles. Despite the higher total flavonoid and phenolic content values observed in the public cultivar, the radical scavenging rate was higher in the local cultivars, particularly in Subicho. This suggest the presence of stronger antioxidant compounds in the local cultivar, indicating their potential health benefits due to their rich content of bioactive compounds. Notably, the local cultivars exhibited significantly higher proportions of organic compounds (more than four times) and terpenoids (more than two times) compared to the public cultivar. Specifically, higher levels of five major capsaicinoid compounds were found in the local cultivars when compared to the public cultivar. The observed disparities in phytochemical composition and antioxidant activities indicate the molecular diversity present among these cultivars. Further exploration of the bioactive compounds in these local cultivars could prove invaluable for the development of native crops, potentially leading to the discovery of novel sources of bioactive molecules for various applications in health and agriculture.

Uncovering Distinct Peptide Charging Behaviors in Electrospray Ionization Mass Spectrometry Using a Large-Scale Dataset

Electrospray ionization is a powerful and prevalent technique used to ionize analytes in mass spectrometry. The distribution of charges that an analyte receives (charge state distribution, CSD) is an important consideration for interpreting mass spectra. However, due to an incomplete understanding of the ionization mechanism, the analyte properties that influence CSDs are not fully understood. Here, we employ a machine learning-based approach and analyze CSDs of hundreds of thousands of peptides. Interestingly, half of the peptides exhibit charges that differ from what one would naively expect (the number of basic sites). We find that these peptides can be classified into two regimes (undercharging and overcharging) and that these two regimes display markedly different charging characteristics. Notably, peptides in the overcharging regime show minimal dependence on basic site count, and more generally, the two regimes exhibit distinct sequence determinants. These findings highlight the rich ionization behavior of peptides and the potential of CSDs for enhancing peptide identification.

Design of experiments approach for systematic optimization of a single-shot diaPASEF plasma proteomics workflow applicable for high-throughput

PURPOSE

Plasma is an abundant source of protein biomarkers. Mass spectrometry (MS) is an effective means to measure a large number of proteins in a single run. The recent development of data-independent acquisition with parallel accumulation and serial fragmentation (diaPASEF) on a trapped ion mobility spectrometer (TIMS) affords deep proteomic coverage with short liquid chromatography gradients. In this work, we utilized a process optimization approach, design of experiments (DoE), to maximize precursor identification for a plasma proteomic diaPASEF workflow.

EXPERIMENTAL DESIGN

A partial factorial design was used to screen 11 sample preparation factors and six diaPASEF MS acquisition factors. Selected factors were optimized using the response surface method.

RESULTS

Three important sample preparation factors and the two important MS acquisition factors were identified in the screening experiments and were selected for separate optimization experiments. The optimal parameters were compared to our standard plasma proteomics workflows using either a 1-h or overnight trypsin digestion. The optimized method outperformed the 1-h digestion, and it was similar in performance to the overnight digestion, however, the optimized method could be completed in a day.

CONCLUSION AND CLINICAL RELEVANCE

We have used DoE to report an optimized plasma proteomics workflow for diaPASEF, however, established methods are already highly optimized, and resources may be better spent on running samples than comprehensive optimization.

Proteomics-based mass spectrometry profiling of SARS-CoV-2 infection from human nasopharyngeal samples

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of the on-going global pandemic of coronavirus disease 2019 (COVID-19) that continues to pose a significant threat to public health worldwide. SARS-CoV-2 encodes four structural proteins namely membrane, nucleocapsid, spike, and envelope proteins that play essential roles in viral entry, fusion, and attachment to the host cell. Extensively glycosylated spike protein efficiently binds to the host angiotensin-converting enzyme 2 initiating viral entry and pathogenesis. Reverse transcriptase polymerase chain reaction on nasopharyngeal swab is the preferred method of sample collection and viral detection because it is a rapid, specific, and high-throughput technique. Alternate strategies such as proteomics and glycoproteomics-based mass spectrometry enable a more detailed and holistic view of the viral proteins and host-pathogen interactions and help in detection of potential disease markers. In this review, we highlight the use of mass spectrometry methods to profile the SARS-CoV-2 proteome from clinical nasopharyngeal swab samples. We also highlight the necessity for a comprehensive glycoproteomics mapping of SARS-CoV-2 from biological complex matrices to identify potential COVID-19 markers.

Optimizing the PROTREC network-based missing protein prediction algorithm

This article summarizes the PROTREC method and investigates the impact that the different hyper-parameters have on the task of missing protein prediction using PROTREC. We evaluate missing protein recovery rates using different PROTREC score selection approaches (MAX, MIN, MEDIAN, and MEAN), different PROTREC score thresholds, as well as different complex size thresholds. In addition, we included two additional cancer datasets in our analysis and introduced a new validation method to check both the robustness of the PROTREC method as well as the correctness of our analysis. Our analysis showed that the missing protein recovery rate can be improved by adopting PROTREC score selection operations of MIN, MEDIAN, and MEAN instead of the default MAX. However, this may come at a cost of reduced numbers of proteins predicted and validated. The users should therefore choose their hyper-parameters carefully to find a balance in the accuracy-quantity trade-off. We also explored the possibility of combining PROTREC with a p-value-based method (FCS) and demonstrated that PROTREC is able to perform well independently without any help from a p-value-based method. Furthermore, we conducted a downstream enrichment analysis to understand the biological pathways and protein networks within the cancerous tissues using the recovered proteins. Missing protein recovery rate using PROTREC can be improved by selecting a different PROTREC score selection method. Different PROTREC score selection methods and other hyper-parameters such as PROTREC score threshold and complex size threshold introduce accuracy-quantity trade-off. PROTREC is able to perform well independently of any filtering using a p-value-based method. Verification of the PROTREC method on additional cancer datasets. Downstream Enrichment Analysis to understand the biological pathways and protein networks in cancerous tissues.

State-of-the-art mass spectrometry imaging applications in biomedical research

Mass spectrometry imaging has advanced from a niche technique to a widely applied spatial biology tool operating at the forefront of numerous fields, most notably making a significant impact in biomedical pharmacological research. The growth of the field has gone hand in hand with an increase in publications and usage of the technique by new laboratories, and consequently this has led to a shift from general MSI reviews to topic-specific reviews. Given this development, we see the need to recapitulate the strengths of MSI by providing a more holistic overview of state-of-the-art MSI studies to provide the new generation of researchers with an up-to-date reference framework. Here we review scientific advances for the six largest biomedical fields of MSI application (oncology, pharmacology, neurology, cardiovascular diseases, endocrinology, and rheumatology). These publications thereby give examples for at least one of the following categories: they provide novel mechanistic insights, use an exceptionally large cohort size, establish a workflow that has the potential to become a high-impact methodology, or are highly cited in their field. We finally have a look into new emerging fields and trends in MSI (immunology, microbiology, infectious diseases, and aging), as applied MSI is continuously broadening as a result of technological breakthroughs.

MultiPro: DDA-PASEF and diaPASEF acquired cell line proteomic datasets with deliberate batch effects

Mass spectrometry-based proteomics plays a critical role in current biological and clinical research. Technical issues like data integration, missing value imputation, batch effect correction and the exploration of inter-connections amongst these technical issues, can produce errors but are not well studied. Although proteomic technologies have improved significantly in recent years, this alone cannot resolve these issues. What is needed are better algorithms and data processing knowledge. But to obtain these, we need appropriate proteomics datasets for exploration, investigation, and benchmarking. To meet this need, we developed MultiPro (Multi-purpose Proteome Resource), a resource comprising four comprehensive large-scale proteomics datasets with deliberate batch effects using the latest parallel accumulation-serial fragmentation in both Data-Dependent Acquisition (DDA) and Data Independent Acquisition (DIA) modes. Each dataset contains a balanced two-class design based on well-characterized and widely studied cell lines (A549 vs K562 or HCC1806 vs HS578T) with 48 or 36 biological and technical replicates altogether, allowing for investigation of a multitude of technical issues. These datasets allow for investigation of inter-connections between class and batch factors, or to develop approaches to compare and integrate data from DDA and DIA platforms.

A novel role of TGFBI in macrophage polarization and macrophage-induced pancreatic cancer growth and therapeutic resistance

Tumor-associated macrophages (TAMs), as a major and essential component of tumor microenvironment (TME), play a critical role in orchestrating pancreatic cancer (PaC) tumorigenesis from initiation to angiogenesis, growth, and systemic dissemination, as well as immunosuppression and resistance to chemotherapy and immunotherapy; however, the critical intrinsic factors responsible for TAMs reprograming and function remain to be identified. By performing single-cell RNA sequencing, transforming growth factor-beta-induced protein (TGFBI) was identified as TAM-producing factor in murine PaC tumors. TAMs express TGFBI in human PaC and TGFBI expression is positively related with human PaC growth. By inducing TGFBI loss-of-function in macrophage (MΦs) in vitro with siRNA and in vivo with Cre-Lox strategy in our developed TGFBI-floxed mice, we demonstrated disruption of TGFBI not only inhibited MΦ polarization to M2 phenotype and MΦ-mediated stimulation on PaC growth, but also significantly improved anti-tumor immunity, sensitizing PaC to chemotherapy in association with regulation of fibronectin 1, Cxcl10, and Ccl5. Our studies suggest that targeting TGFBI in MΦ can develop an effective therapeutic intervention for highly lethal PaC.

A region-resolved proteomic map of the human brain enabled by high-throughput proteomics

Substantial efforts are underway to deepen our understanding of human brain morphology, structure, and function using high-resolution imaging as well as high-content molecular profiling technologies. The current work adds to these approaches by providing a comprehensive and quantitative protein expression map of 13 anatomically distinct brain regions covering more than 11,000 proteins. This was enabled by the optimization, characterization, and implementation of a high-sensitivity and high-throughput microflow liquid chromatography timsTOF tandem mass spectrometry system (LC-MS/MS) capable of analyzing more than 2,000 consecutive samples prepared from formalin-fixed paraffin embedded (FFPE) material. Analysis of this proteomic resource highlighted brain region-enriched protein expression patterns and functional protein classes, protein localization differences between brain regions and individual markers for specific areas. To facilitate access to and ease further mining of the data by the scientific community, all data can be explored online in a purpose-built R Shiny app (https://brain-region-atlas.proteomics.ls.tum.de).

The social and structural architecture of the yeast protein interactome

Cellular functions are mediated by protein-protein interactions, and mapping the interactome provides fundamental insights into biological systems. Affinity purification coupled to mass spectrometry is an ideal tool for such mapping, but it has been difficult to identify low copy number complexes, membrane complexes and complexes that are disrupted by protein tagging. As a result, our current knowledge of the interactome is far from complete, and assessing the reliability of reported interactions is challenging. Here we develop a sensitive high-throughput method using highly reproducible affinity enrichment coupled to mass spectrometry combined with a quantitative two-dimensional analysis strategy to comprehensively map the interactome of Saccharomyces cerevisiae. Thousand-fold reduced volumes in 96-well format enabled replicate analysis of the endogenous GFP-tagged library covering the entire expressed yeast proteome1. The 4,159 pull-downs generated a highly structured network of 3,927 proteins connected by 31,004 interactions, doubling the number of proteins and tripling the number of reliable interactions compared with existing interactome maps2. This includes very-low-abundance epigenetic complexes, organellar membrane complexes and non-taggable complexes inferred by abundance correlation. This nearly saturated interactome reveals that the vast majority of yeast proteins are highly connected, with an average of 16 interactors. Similar to social networks between humans, the average shortest distance between proteins is 4.2 interactions. AlphaFold-Multimer provided novel insights into the functional roles of previously uncharacterized proteins in complexes. Our web portal ( www.yeast-interactome.org ) enables extensive exploration of the interactome dataset.

On-tissue dataset-dependent MALDI-TIMS-MS2 bioimaging

Trapped ion mobility spectrometry (TIMS) adds an additional separation dimension to mass spectrometry (MS) imaging, however, the lack of fragmentation spectra (MS2) impedes confident compound annotation in spatial metabolomics. Here, we describe spatial ion mobility-scheduled exhaustive fragmentation (SIMSEF), a dataset-dependent acquisition strategy that augments TIMS-MS imaging datasets with MS2 spectra. The fragmentation experiments are systematically distributed across the sample and scheduled for multiple collision energies per precursor ion. Extendable data processing and evaluation workflows are implemented into the open source software MZmine. The workflow and annotation capabilities are demonstrated on rat brain tissue thin sections, measured by matrix-assisted laser desorption/ionisation (MALDI)-TIMS-MS, where SIMSEF enables on-tissue compound annotation through spectral library matching and rule-based lipid annotation within MZmine and maps the (un)known chemical space by molecular networking. The SIMSEF algorithm and data analysis pipelines are open source and modular to provide a community resource.

TOFIMS mass spectrometry-based immunopeptidomics refines tumor antigen identification

T cell recognition of human leukocyte antigen (HLA)-presented tumor-associated peptides is central for cancer immune surveillance. Mass spectrometry (MS)-based immunopeptidomics represents the only unbiased method for the direct identification and characterization of naturally presented tumor-associated peptides, a key prerequisite for the development of T cell-based immunotherapies. This study reports on the implementation of ion mobility separation-based time-of-flight (TOFIMS) MS for next-generation immunopeptidomics, enabling high-speed and sensitive detection of HLA-presented peptides. Applying TOFIMS-based immunopeptidomics, a novel extensive benignTOFIMS dataset was generated from 94 primary benign samples of solid tissue and hematological origin, which enabled the expansion of benign reference immunopeptidome databases with > 150,000 HLA-presented peptides, the refinement of previously described tumor antigens, as well as the identification of frequently presented self antigens and not yet described tumor antigens comprising low abundant mutation-derived neoepitopes that might serve as targets for future cancer immunotherapy development.

Mass spectrometry in the discovery of peptides involved in intercellular communication: From targeted to untargeted peptidomics approaches

Endogenous peptide hormones represent an essential class of biomolecules, which regulate cell-cell communications in diverse physiological processes of organisms. Mass spectrometry (MS) has been developed to be a powerful technology for identifying and quantifying peptides in a highly efficient manner. However, it is difficult to directly identify these peptide hormones due to their diverse characteristics, dynamic regulations, low abundance, and existence in a complicated biological matrix. Here, we summarize and discuss the roles of targeted and untargeted MS in discovering peptide hormones using bioassay-guided purification, bioinformatics screening, or the peptidomics-based approach. Although the peptidomics approach is expected to discover novel peptide hormones unbiasedly, only a limited number of successful cases have been reported. The critical challenges and corresponding measures for peptidomics from the steps of sample preparation, peptide extraction, and separation to the MS data acquisition and analysis are also discussed. We also identify emerging technologies and methods that can be integrated into the discovery platform toward the comprehensive study of endogenous peptide hormones.

Peptide collision cross sections of 22 post-translational modifications

Recent advances have rekindled the interest in ion mobility as an additional dimension of separation in mass spectrometry (MS)-based proteomics. Ion mobility separates ions according to their size and shape in the gas phase. Here, we set out to investigate the effect of 22 different post-translational modifications (PTMs) on the collision cross section (CCS) of peptides. In total, we analyzed ~4300 pairs of matching modified and unmodified peptide ion species by trapped ion mobility spectrometry (TIMS). Linear alignment based on spike-in reference peptides resulted in highly reproducible CCS values with a median coefficient of variation of 0.26%. On a global level, we observed a redistribution in the m/z vs. ion mobility space for modified peptides upon changes in their charge state. Pairwise comparison between modified and unmodified peptides of the same charge state revealed median shifts in CCS between -1.4% (arginine citrullination) and +4.5% (O-GlcNAcylation). In general, increasing modified peptide masses were correlated with higher CCS values, in particular within homologous PTM series. However, investigating the ion populations in more detail, we found that the change in CCS can vary substantially for a given PTM and is partially correlated with the gas phase structure of its unmodified counterpart. In conclusion, our study shows PTM- and sequence-specific effects on the cross section of peptides, which could be further leveraged for proteome-wide PTM analysis.

Advances in data-independent acquisition mass spectrometry towards comprehensive digital proteome landscape

The data-independent acquisition mass spectrometry (DIA-MS) has rapidly evolved as a powerful alternative for highly reproducible proteome profiling with a unique strength of generating permanent digital maps for retrospective analysis of biological systems. Recent advancements in data analysis software tools for the complex DIA-MS/MS spectra coupled to fast MS scanning speed and high mass accuracy have greatly expanded the sensitivity and coverage of DIA-based proteomics profiling. Here, we review the evolution of the DIA-MS techniques, from earlier proof-of-principle of parallel fragmentation of all-ions or ions in selected m/z range, the sequential window acquisition of all theoretical mass spectra (SWATH-MS) to latest innovations, recent development in computation algorithms for data informatics, and auxiliary tools and advanced instrumentation to enhance the performance of DIA-MS. We further summarize recent applications of DIA-MS and experimentally-derived as well as in silico spectra library resources for large-scale profiling to facilitate biomarker discovery and drug development in human diseases with emphasis on the proteomic profiling coverage. Toward next-generation DIA-MS for clinical proteomics, we outline the challenges in processing multi-dimensional DIA data set and large-scale clinical proteomics, and continuing need in higher profiling coverage and sensitivity.

Integration of data-independent acquisition (DIA) with co-fractionation mass spectrometry (CF-MS) to enhance interactome mapping capabilities

Proteomics technologies are continually advancing, providing opportunities to develop stronger and more robust protein interaction networks (PINs). In part, this is due to the ever-growing number of high-throughput proteomics methods that are available. This review discusses how data-independent acquisition (DIA) and co-fractionation mass spectrometry (CF-MS) can be integrated to enhance interactome mapping abilities. Furthermore, integrating these two techniques can improve data quality and network generation through extended protein coverage, less missing data, and reduced noise. CF-DIA-MS shows promise in expanding our knowledge of interactomes, notably for non-model organisms (NMOs). CF-MS is a valuable technique on its own, but upon the integration of DIA, the potential to develop robust PINs increases, offering a unique approach for researchers to gain an in-depth understanding into the dynamics of numerous biological processes.

Increasing taxonomic and functional characterization of host-microbiome interactions by DIA-PASEF metaproteomics

Introduction

Metaproteomics is a rapidly advancing field that offers unique insights into the taxonomic composition and the functional activity of microbial communities, and their effects on host physiology. Classically, data-dependent acquisition (DDA) mass spectrometry (MS) has been applied for peptide identification and quantification in metaproteomics. However, DDA-MS exhibits well-known limitations in terms of depth, sensitivity, and reproducibility. Consequently, methodological improvements are required to better characterize the protein landscape of microbiomes and their interactions with the host.

Methods

We present an optimized proteomic workflow that utilizes the information captured by Parallel Accumulation-Serial Fragmentation (PASEF) MS for comprehensive metaproteomic studies in complex fecal samples of mice.

Results and discussion

We show that implementing PASEF using a DDA acquisition scheme (DDA-PASEF) increased peptide quantification up to 5 times and reached higher accuracy and reproducibility compared to previously published classical DDA and data-independent acquisition (DIA) methods. Furthermore, we demonstrate that the combination of DIA, PASEF, and neuronal-network-based data analysis, was superior to DDA-PASEF in all mentioned parameters. Importantly, DIA-PASEF expanded the dynamic range towards low-abundant proteins and it doubled the quantification of proteins with unknown or uncharacterized functions. Compared to previous classical DDA metaproteomic studies, DIA-PASEF resulted in the quantification of up to 4 times more taxonomic units using 16 times less injected peptides and 4 times shorter chromatography gradients. Moreover, 131 additional functional pathways distributed across more and even uniquely identified taxa were profiled as revealed by a peptide-centric taxonomic-functional analysis. We tested our workflow on a validated preclinical mouse model of neuropathic pain to assess longitudinal changes in host-gut microbiome interactions associated with pain - an unexplored topic for metaproteomics. We uncovered the significant enrichment of two bacterial classes upon pain, and, in addition, the upregulation of metabolic activities previously linked to chronic pain as well as various hitherto unknown ones. Furthermore, our data revealed pain-associated dynamics of proteome complexes implicated in the crosstalk between the host immune system and the gut microbiome. In conclusion, the DIA-PASEF metaproteomic workflow presented here provides a stepping stone towards a deeper understanding of microbial ecosystems across the breadth of biomedical and biotechnological fields.

Mitochondrial dysfunction promotes microbial composition that negatively impacts on ulcerative colitis development and progression

Recent evidence demonstrates potential links between mitochondrial dysfunction and inflammatory bowel diseases (IBD). In addition, bidirectional interactions between the intestinal microbiota and host mitochondria may modulate intestinal inflammation. We observed previously that mice deficient in the mitochondrial protein MCJ (Methylation-controlled J protein) exhibit increased susceptibility to DSS colitis. However, it is unclear whether this phenotype is primarily driven by MCJ-/- associated gut microbiota dysbiosis or by direct effects of MCJ-deficiency. Here, we demonstrate that fecal microbiota transplantation (FMT) from MCJ-deficient into germ-free mice was sufficient to confer increased susceptibility to colitis. Therefore, an FMT experiment by cohousing was designed to alter MCJ-deficient microbiota. The phenotype resulting from complex I deficiency was reverted by FMT. In addition, we determined the protein expression pathways impacted by MCJ deficiency, providing insight into the pathophysiology of IBD. Further, we used magnetic activated cell sorting (MACS) and 16S rRNA gene sequencing to characterize taxa-specific coating of the intestinal microbiota with Immunoglobulin A (IgA-SEQ) in MCJ-deficient mice. We show that high IgA coating of fecal bacteria observed in MCJ-deficient mice play a potential role in disease progression. This study allowed us to identify potential microbial signatures in feces associated with complex I deficiency and disease progression. This research highlights the importance of finding microbial biomarkers, which might serve as predictors, permitting the stratification of ulcerative colitis (UC) patients into distinct clinical entities of the UC spectrum.

Immunoaffinity LC-MS/MS Quantification of the Sepsis Biomarker Procalcitonin Using Magnetic- and Polystyrene-Bead Immobilized Polyclonal Antibodies

Procalcitonin (PCT) is a biomarker for bacterial sepsis, and accurate quantification of PCT is critical for sepsis diagnosis and treatment. Immunological PCT quantification methods are routinely used in clinical laboratories, yet there is a need for harmonization of PCT quantification protocols. An orthogonal method to clinical immunological assays, such as LC-MS/MS, is required. In this study, a highly sensitive and robust immunoaffinity LC-MRM quantitative method for detecting procalcitonin in human serum has been developed. An initial comparison of immunocapture of PCT with a polyclonal anti-PCT antibody immobilized on polystyrene nanoparticles (Latex) and magnetic beads demonstrated superior performance with magnetic beads. Three tryptic PCT peptides from the N- and C-terminal regions of PCT were selected for LC-MS/MS quantification. For PCT quantification, an LLOQ of 0.25 ng/mL of PCT in human serum was achieved using a sample volume of 1 mL. The method's trueness and precision consistently lie within the 15% margin. The parallel measurement of three PCT peptides may allow future differentiation of intact PCT vs other PCT forms originating from potential degradation, processing, or polymorphisms. An established and validated LC-MRM-based quantification of PCT will be relevant as an orthogonal method for harmonization and standardization of clinical assays for PCT.

Top-Down Protein Analysis by Tandem-Trapped Ion Mobility Spectrometry/Mass Spectrometry (Tandem-TIMS/MS) Coupled with Ultraviolet Photodissociation (UVPD) and Parallel Accumulation/Serial Fragmentation (PASEF) MS/MS Analysis

"Top-down" proteomics analyzes intact proteins and identifies proteoforms by their intact mass as well as the observed fragmentation pattern in tandem mass spectrometry (MS/MS) experiments. Recently, hybrid ion mobility spectrometry-mass spectrometry (IM/MS) methods have gained traction for top-down experiments, either by allowing top-down analysis of individual isomers or alternatively by improving signal/noise and dynamic range for fragment ion assignment. We recently described the construction of a tandem-trapped ion mobility spectrometer/mass spectrometer (tandem-TIMS/MS) coupled with an ultraviolet (UV) laser and demonstrated a proof-of-principle for top-down analysis by UV photodissociation (UVPD) at 2-3 mbar. The present work builds on this with an exploration of a top-down method that couples tandem-TIMS/MS with UVPD and parallel-accumulation serial fragmentation (PASEF) MS/MS analysis. We first survey types and structures of UVPD-specific fragment ions generated in the 2-3 mbar pressure regime of our instrument. Notably, we observe UVPD-induced fragment ions with multiple conformations that differ from those produced in the absence of UV irradiation. Subsequently, we discuss how MS/MS spectra of top-down fragment ions lend themselves ideally for probability-based scoring methods developed in the bottom-up proteomics field and how the ability to record automated PASEF-MS/MS spectra resolves ambiguities in the assignment of top-down fragment ions. Finally, we describe the coupling of tandem-TIMS/MS workflows with UVPD and PASEF-MS/MS analysis for native top-down protein analysis.

4D-diaXLMS: Proteome-wide Four-Dimensional Data-Independent Acquisition Workflow for Cross-Linking Mass Spectrometry

Cross-linking mass spectrometry (XL-MS) is a powerful tool for examining protein structures and interactions. Nevertheless, analysis of low-abundance cross-linked peptides is often limited in the data-dependent acquisition (DDA) mode due to its semistochastic nature. To address this issue, we introduced a workflow called 4D-diaXLMS, representing the first-ever application of four-dimensional data-independent acquisition for proteome-wide cross-linking analysis. Cross-linking studies of the HeLa cell proteome were evaluated using the classical cross-linker disuccinimidyl suberate as an example. Compared with the DDA analysis, 4D-diaXLMS exhibited marked improvement in the identification coverage of cross-linked peptides, with a total increase of 36% in single-shot analysis across all 16 SCX fractions. This advantage was further amplified when reducing the fraction number to 8 and 4, resulting in 125 and 149% improvements, respectively. Using 4D-diaXLMS, up to 83% of the cross-linked peptides were repeatedly identified in three replicates, more than twice the 38% in the DDA mode. Furthermore, 4D-diaXLMS showed good performance in the quantitative analysis of yeast cross-linked peptides even in a 15-fold excess amount of HeLa cell matrix, with a low coefficient of variation and high quantitative accuracies in all concentrations. Overall, 4D-diaXLMS was proven to have high coverage, good reproducibility, and accurate quantification for in-depth XL-MS analysis in complex samples, demonstrating its immense potential for advances in the field.

Ionmob: a Python package for prediction of peptide collisional cross-section values

MOTIVATION

Including ion mobility separation (IMS) into mass spectrometry proteomics experiments is useful to improve coverage and throughput. Many IMS devices enable linking experimentally derived mobility of an ion to its collisional cross-section (CCS), a highly reproducible physicochemical property dependent on the ion's mass, charge and conformation in the gas phase. Thus, known peptide ion mobilities can be used to tailor acquisition methods or to refine database search results. The large space of potential peptide sequences, driven also by posttranslational modifications of amino acids, motivates an in silico predictor for peptide CCS. Recent studies explored the general performance of varying machine-learning techniques, however, the workflow engineering part was of secondary importance. For the sake of applicability, such a tool should be generic, data driven, and offer the possibility to be easily adapted to individual workflows for experimental design and data processing.

RESULTS

We created ionmob, a Python-based framework for data preparation, training, and prediction of collisional cross-section values of peptides. It is easily customizable and includes a set of pretrained, ready-to-use models and preprocessing routines for training and inference. Using a set of ≈21 000 unique phosphorylated peptides and ≈17 000 MHC ligand sequences and charge state pairs, we expand upon the space of peptides that can be integrated into CCS prediction. Lastly, we investigate the applicability of in silico predicted CCS to increase confidence in identified peptides by applying methods of re-scoring and demonstrate that predicted CCS values complement existing predictors for that task.

AVAILABILITY AND IMPLEMENTATION

The Python package is available at github: https://github.com/theGreatHerrLebert/ionmob.

Exploiting ion-mobility mass spectrometry for unraveling proteome complexity

Ion mobility spectrometry-mass spectrometry (IMS-MS) is experiencing rapid growth in proteomic studies, driven by its enhancements in dynamic range and throughput, increasing the quantitation precision, and the depth of proteome coverage. The core principle of ion mobility spectrometry is to separate ions in an inert gas under the influence of an electric field based on differences in drift time. This minireview provides an introduction to IMS operation modes and a description of advantages and limitations is presented. Moreover, the principles of trapped IMS-MS (TIMS-MS), including parallel accumulation-serial fragmentation are discussed. Finally, emerging applications linked to TIMS focusing on sample throughput (in clinical proteomics) and sensitivity (single-cell proteomics) are reviewed, and the possibilities of intact protein analysis are discussed.

An unconventional TOG domain is required for CLASP localization

Cytoplasmic linker-associated proteins (CLASPs) form a conserved family of microtubule-associated proteins (MAPs) that maintain microtubules in a growing state by promoting rescue while suppressing catastrophe.1 CLASP function involves an ordered array of tumor overexpressed gene (TOG) domains and binding to multiple protein partners via a conserved C-terminal domain (CTD).2,3 In migrating cells, CLASPs concentrate at the cortex near focal adhesions as part of cortical microtubule stabilization complexes (CMSCs), via binding of their CTD to the focal adhesion protein PHLDB2/LL5β.4,5 Cortical CLASPs also stabilize a subset of microtubules, which stimulate focal adhesion turnover and generate a polarized microtubule network toward the leading edge of migrating cells. CLASPs are also recruited to the trans-Golgi network (TGN) via an interaction between their CTD and the Golgin protein GCC185.6 This allows microtubule growth toward the leading edge of migrating cells, which is required for Golgi organization, polarized intracellular transport, and cell motility.7 In dividing cells, CLASPs are essential at kinetochores for efficient chromosome segregation and anaphase spindle integrity.8,9 Both CENP-E and ASTRIN bind and target CLASPs to kinetochores,10,11 although the CLASP domain required for this interaction is not known. Despite its high evolutionary conservation, the CTD remains structurally uncharacterized. Here, we find that the CTD can be structurally modeled as a TOG domain. We identify a surface-exposed and conserved arginine residue essential for CLASP CTD interaction with partner proteins. Together, our results provide a structural mechanism by which the CLASP CTD directs diverse sub-cellular localizations throughout the cell cycle.

Identification of Potential Bioactive Peptides in Sheep Milk Kefir through Peptidomic Analysis at Different Fermentation Times

Sheep farming is an important socioeconomic activity in most Mediterranean countries, particularly Spain, where it contributes added value to rural areas. Sheep milk is used in Spain mainly for making cheese, but it can be used also for making other dairy products, such as the lactic-alcoholic fermentation product known as kefir. Dairy products have health benefits because, among other reasons, they contain molecules with biological activity. In this work, we performed a proteomics strategy to identify the peptidome, i.e., the set of peptides contained in sheep milk kefir fermented for four different periods of time, aiming to understand changes in the pattern of digestion of milk proteins, as well as to identify potential bioactive peptides. In total, we identified 1942 peptides coming from 11 different proteins, and found that the unique peptides differed qualitatively among samples and their numbers increased along the fermentation time. These changes were supported by the increase in ethanol, lactic acid, and D-galactose concentrations, as well as proteolytic activity, as the fermentation progressed. By searching in databases, we found that 78 of the identified peptides, all belonging to caseins, had potential biological activity. Of these, 62 were not previously found in any milk kefir from other animal species. This is the first peptidomic study of sheep milk kefir comprising time-course comparison.

Right in two: capabilities of ion mobility spectrometry for untargeted metabolomics

This mini review focuses on the opportunities provided by current and emerging separation techniques for mass spectrometry metabolomics. The purpose of separation technologies in metabolomics is primarily to reduce complexity of the heterogeneous systems studied, and to provide concentration enrichment by increasing sensitivity towards the quantification of low abundance metabolites. For this reason, a wide variety of separation systems, from column chemistries to solvent compositions and multidimensional separations, have been applied in the field. Multidimensional separations are a common method in both proteomics applications and gas chromatography mass spectrometry, allowing orthogonal separations to further reduce analytical complexity and expand peak capacity. These applications contribute to exponential increases in run times concomitant with first dimension fractionation followed by second dimension separations. Multidimensional liquid chromatography to increase peak capacity in metabolomics, when compared to the potential of running additional samples or replicates and increasing statistical confidence, mean that uptake of these methods has been minimal. In contrast, in the last 15 years there have been significant advances in the resolution and sensitivity of ion mobility spectrometry, to the point where high-resolution separation of analytes based on their collision cross section approaches chromatographic separation, with minimal loss in sensitivity. Additionally, ion mobility separations can be performed on a chromatographic timescale with little reduction in instrument duty cycle. In this review, we compare ion mobility separation to liquid chromatographic separation, highlight the history of the use of ion mobility separations in metabolomics, outline the current state-of-the-art in the field, and discuss the future outlook of the technology. "Where there is one, you're bound to divide it. Right in two", James Maynard Keenan.