Mass spectrometry (MS)-based proteomics methods can give unique insight into protein structure and interactions. Here, we have developed a novel top-down footprinting method, Methionine Oxidation Footprinting in Intact Proteins (MOFIP), to probe solvent accessibility in intact proteoforms. For MOFIP, natively folded protein lysates are incubated with and without hydrogen peroxide (H2O2) to evaluate solvent accessibility of methionine residues. Top-down proteomics analysis allows the characterization of the solvent accessibility of each methionine residue within intact proteins to obtain structural information. Here, intact E. coli lysate was used to evaluate the feasibility of complex biological sample analysis using the MOFIP platform. Overall, we profiled 137 methionine residues from 69 proteoforms that contained at least one methionine residue suitable for methionine footprinting. These methionine residues from intact proteoforms fell into three general categories: solvent-accessible (50%), solvent-inaccessible (16%), and partially solvent-accessible (34%). Overall, we have demonstrated that our novel MOFIP platform is capable of characterizing the solvent accessibility of individual methionine residues within intact proteoforms in complex biological samples, which is exceedingly challenging using bottom-up proteomics methods.

Mass spectrometry (MS) offers robust, label-free approaches for characterizing ligand-protein interactions through two main strategies: affinity-based and stability-based assays. Affinity-based methods, such as affinity-selection MS (AS-MS) and mass spectrometry binding assays (MSBA), detect ligand-target interactions by identifying bound ligands or measuring displacement of a reporter ligand. Stability-based techniques, including thermal proteome profiling (TPP) and limited proteolysis-MS (LiP-MS), infer interactions based on changes in a protein’s thermal or proteolytic stability. These MS-based workflows enable proteome-wide analyses with high specificity and throughput. However, their application to membrane proteins (MPs)—a major class of drug targets—has been limited by challenges such as structural complexity, low native expression, and poor compatibility with detergent-based MS protocols. Recent advances in membrane mimetic (MM) systems, including nanodiscs, Peptidiscs, and styrene-maleic acid (SMA) polymers, help address these barriers by maintaining native-like lipid environments and preserving functional MP conformations. These mimetics facilitate proteome-scale solubilization of MPs in forms compatible with MS screening. This review outlines key affinity- and stability-based MS approaches and examines their adaptation for MPs. It also highlights how combining MS techniques with MM systems is expanding the reach of high-resolution, functional analysis of MP–ligand interactions.

Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by cognitive decline and pathological protein aggregation. Comprehensive quantitative proteomics of brain tissues from AD patients is critical for pursuing a better understanding of the molecular mechanisms that drive AD progression. Here we present one of the first quantitative top-down proteomics (TDP) studies of postmortem cortex samples from AD patients and healthy controls to profile their proteoform differences by coupling CZE (liquid-phase ion mobility) to MS/MS. We identified 3,191 unique proteoforms and uncovered a drastic transformation in the proteoform profile in AD compared to healthy controls. Over 2,200 proteoforms were exclusively identified in either AD or healthy control samples, and 157 proteoforms identified in both AD and control samples showed statistically significant abundance differences between the two conditions. Gene Ontology and pathway analysis of the genes associated with those proteoforms revealed broad changes in biological processes in AD brains, e.g., telomere organization, substantia nigra development, amyloid fibril formation, microtubule cytoskeleton organization, progressive neurological disorders, long-term synaptic potentiation, and axogenesis. These biological processes are highly associated with the development of AD. Our study revealed a pool of potential novel proteoform biomarkers of AD in human brain samples for early diagnosis and therapy development.

Top-down proteomics (TDP) is a powerful approach for characterizing intact protein molecules and their diverse proteoforms. Despite recent advances, current TDP software tools often suffer from fragmented workflows, steep learning curves for non-experts, or limited interactive visualization capabilities. To address these challenges, we introduce TDEase, an integrated analytical framework designed to streamline and enhance TDP data interpretation, with a current focus in integration with the TopPIC suite package for targeted proteoform characterization. TDEase features a modular architecture comprising TDPipe, a multi-process data processing engine, and TDVis, an interactive web-based visualization module. TDPipe automates the execution of mainstream TDP analysis algorithms through a user-configurable pipeline, ensuring seamless and reproducible data processing. The TDVis module then transforms these results into dynamic, interactive dashboards, enabling multidimensional data exploration, including feature maps and PTM analysis. An alternative version, TDVisWeb, is also available for visualizing the results on an internet server or intranet workstation at institutional core facilities. We demonstrated the software capabilities in proteoform identification and comparative analysis using published histone datasets. TDEase is built with Python and open-source, allowing future improvements and incorporation of more data types as the TDP community develops new software. Source code is available at https://github.com/Elcherneske/TDEase.

Proteolytic cleavage is an irreversible post translational modification (PTM), and dysregulation of protease activity is often a hallmark in disease. Aberrant proteolysis can alter protein abundance or function, disturbing cellular state and resulting in disease specific biomarkers or therapeutic targets. Positional proteomics facilitates global identification and precise quantification of position-specific peptides such as those located N- or C-terminal in the protein sequence. These techniques enable the study of both natural and protease generated protein termini, as well as associated PTMs. Despite its importance, proteolysis remains understudied due to experimental challenges and complex data processing. In this review, we outline key strategies for data analysis and processing in positional proteomics, emphasizing how identification, quantification, and interpretation of proteolytic cleavage sites differs from standard proteomics data analysis pipelines. We discuss differences in common approaches for terminomics-focused workflows, comparing N- vs. C-terminomics as well as different labeling strategies and acquisition methods. Additionally, we highlight considerations for proper normalization approaches, specifically the need to normalize cleavage abundances relative to protein and/or protease abundance, and we explain the importance of integrating structural data, solvent accessibility, and tissue expression profiles during data analysis to better evaluate the biological significance of experimental results.

To assess the potential for high resolution ion mobility (HRIM) as an alternative means of precursor isolation for mass spectrometry fragmentation analysis we performed a meta-analysis of predicted tryptic peptide features from the human proteome to measure the rate of chimeric spectrum generation relative to traditional quadrupole-based isolation. Results indicate that for proteomic mixtures, HRIM separation with a peak capacity of 100 produces chimeric spectra at a rate commensurate with a ~5 Th quadrupole isolation window, while providing the additional benefit of generating non-chimeric spectra for many isobaric and isomeric peptides unresolvable with a quadrupole filter. This behavior was verified experimentally using a HRIM-QTOF mass spectrometry system. The ability to combine HRIM and MS isolation resulted in >10x increase in precursor isolation specificity as compared to either of the techniques independently.

The brain’s extracellular matrix (ECM) is an intricate and dynamic network that plays essential roles in neurodevelopment, synaptic plasticity, and circuit stability. Despite its importance, the molecular composition and spatial organization of the developing brain ECM remain poorly characterized. In this commentary, we highlight the recent study by Vilicich et al., which employs a multi-modal approach, integrating single-cell transcriptomics, proteomics, and immunohistofluorescence, to construct a map of the ECM in the developing neocortex of humans and non-human primates. By curating a comprehensive list of extracellular proteins termed the ”Exomatrix” and analyzing their distribution across cortical layers and developmental stages, the authors reveal layer-specific and evolutionarily conserved ECM features. These findings not only expand our understanding of ECM’s role in shaping the brain during early development but also emphasize its potential involvement in the pathogenesis of neurodevelopmental disorders, urging further research into ECM biology as a frontier in neuroscience.

Extracellular vesicles (EVs) are critical mediators of intercellular communication and valuable sources of biomarkers for diagnostic and therapeutic applications. However, the complexity and heterogeneity of EVs present significant challenges for their proteomic characterization. Additional challenges in EV proteomics include low yield, technical variability, and the need for high-throughput approaches capable of multiplexed analysis. In this study, we implement a tandem mass tag (TMT)-based MS workflow for comprehensive, quantitative proteomic profiling of isolated EVs. Through comparison with label-free quantitation (LFQ), we highlight the potential pitfalls and limitations of methodological choices in EV proteomic analyses. Our study integrates common EV isolation protocols with robust TMT labeling and high-resolution MS, providing insights into practical EV analysis. Utilizing this approach, we profiled EVs isolated from human fibroblasts treated with ionizing radiation. The TMT workflow uncovered novel proteomic signatures reflective of EV cellular origins and functional roles compared to LFQ, revealing potential biomarkers and molecular targets. Our case study underscores the potential of TMT-based proteomics to overcome common barriers in EV proteomic research, facilitating new discoveries in EV biology and advancing their application in biomarker development and translational research.

Chemical proteomics probes serve as critical tools for investigating small molecule--protein interactions within complex biological systems. Traditionally, they are categorized into covalent probes and photoaffinity probes. These probes have found extensive applications in bioactive molecule target identification, targeted ligand screening, enzyme activity monitoring, protein post-translational modification studies, and cross-omics integration. They facilitate drug target discovery, targeted ligand screening, dynamic evaluation of enzyme activities in disease contexts, protein modification mapping, and bridging proteomics with other omics platforms. Despite their significant utility, challenges remain in probe design optimization, reduction of non-specific interactions, and expansion of targetable proteomic landscapes. Future research efforts are expected to focus on the development of novel probes, the integration of chemical proteomics with structural biology and artificial intelligence, and the advancement of clinical applications. These innovations will deepen our understanding of protein functions and support the advancement of precision medicine. In this review, we summarize the classification and fundamental principles of chemical proteomics probes and provide an in-depth discussion of their diverse applications.

We systematically reviewed published studies to assess reproducibility in miRNA expression profiles from extracellular vesicles (EVs) isolated from mouse serum. Our search specifically targeted mouse studies employing precipitation methods for EV isolation from blood and small RNA sequencing of EV-miRNAs in control groups. Out of 48 identified studies, approximately half lacked publicly available raw data, leaving six eligible studies containing sequencing data from a total of 14 mice. miRNA expression counts were standardized using z-scores for comparability. Within individual studies, miRNA profiles showed reasonable consistency; however, significant variability was observed across different studies. Principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), and Spearman correlation consistently demonstrated study-specific clustering rather than biological similarity. Methodological discrepancies in EV isolation, RNA extraction protocols, and unreported confounders such as platelet contamination or blood-handling procedures likely contributed to this variability. Our findings emphasize substantial reproducibility challenges in EV-miRNA research across murine studies, highlighting an urgent need for standardized methodologies and transparent reporting to improve reliability of miRNA biomarker discovery and facilitate meta-analytic integration in preclinical research.

Top-down proteomics (TDP) is increasingly being applied in proteoform-resolved biomedical and clinical research. However, the complexity of TDP data demands flexible visualization tools integrated with analysis workflows to streamline interpretation and validation. Existing tools lack adaptability and interactivity, often requiring researchers to invest considerable resources on additional manual processing and analysis to generate publication-ready results and figures. This added layer of manual intervention impacts reproducibility, posing a significant challenge to consistent scientific outcomes. FLASHApp addresses these challenges by offering a web-based, platform-independent application for TDP data analysis and visualization. It integrates key tools like FLASHDeconv, featuring automated processing, interactive publication-ready visualizations, and direct team collaboration via shareable URLs. FLASHApp is open-source software as part of OpenMS and available at https://www.openms.org/FLASHApp/.

Extracellular vesicles (EVs) and particles (EPs) are diverse micro- and nanoparticles that circulate in bodily fluids and can attach to, or be deposited onto, the extracellular matrix (ECM) and other surfaces. To date, the nomenclature and classification of matrix-bound or matrix-associated EVs and EPs (MEVPs) has been unclear, largely due to an apparent bias in the field towards EVs found in fluids and a lack of consensus guidelines. Recently, there has been a growing appreciation for several subtypes of MEVPs and their roles in applications ranging from wound healing to metastasis. However, progress in these fields has largely been achieved in silos, with minimal consideration for overlap or complementary function between different MEVPs. In this article, we briefly describe this growing field with a focus on heterogeneity and the lack of consensus, then discuss challenges and opportunities in improving MEVP isolation and characterization. Importantly, proteomic analyses of these unique MEVPs will be crucial in promoting rigor, reproducibility, and understanding in this exciting new field.

Advances in methodologies and technologies over the previous decade has led to a depth of analysis of a cell’s biomolecules that is unprecedented, with entire genomes able to be sequenced in hours and up to 10,000 transcripts or ORF products (proteins) able to be quantifed from a single cell. Methods for analysing individual omes are now optimised, reliable and robust but are often performed in isolation with other biomolecules considered contaminants. However, there is a growing body of systems biology studies that aim to study multiple omes from the same sample. This review details the current state of the “multi-omics” field, trying to define what the field is, the methodologies employed and the challenges facing researchers in this field. It also critically evaluates whether these approaches are “fit-for-purpose” and how the field needs to evolve to enhance our understanding of how biomolecules from distinct omes interact with one another to alter cellular phenotype in response to change.

Bottom-up proteomics relies on efficient and repeatable sample preparation for accurate protein identification and precise quantification. This study evaluates the performance of adapted SPEED (Sample Preparation by Easy Extraction and Digestion) protocols, a simplified, detergent-free approach tailored for various biological matrices, including lysis-resistant samples. Protein extraction and denaturation steps were refined for 12 biological matrices enabling standardized, high-throughput, and scalable proteomics analysis on 96-well plates. For tissue samples requiring downstream applications like Western blotting, we used a low-detergent RIPA buffer, ensuring robust protein extraction and epitope integrity. Notably, the protocols demonstrate remarkable down-scalability, enabling robust proteomics measurements from as few as 3,000 cells per sample and even down to 300 cells per injection. Key advancements include a 30-minute nLC-MS/MS run, significantly enhancing throughput, and leveraging the power of DIA-PASEF using thoroughly optimized DIA-windows to enhance proteome coverage. These adaptations streamline workflows, enabling proteomics analyses in matrices with challenging physical and biochemical properties. This study underscores the importance of early-stage optimization and feasibility testing in proteomics pipelines to inform study design and sample selection. By showcasing robust, scalable adaptations of the SPEED protocol, we provide a foundation for reproducible, high-throughput proteomic studies across diverse biological contexts.

Colorectal cancer (CRC) development is closely associated with the accumulation of both genetic and epigenetic alterations. Many efforts have been made to investigate the role of epigenetic modifications in CRC metastasis. In this work, we present the first quantitative top-down proteomics study focusing on histone proteoforms between metastatic (SW620) and nonmetastatic (SW480) CRC cells to reveal potentially critical histone proteoforms in CRC metastasis. We isolated histone proteins from CRC cells, fractionated them by sodium dodecyl-sulfate (SDS)-polyacrylamide gel electrophoresis (PAGE), and analyzed them by capillary zone electrophoresis (CZE)-tandem mass spectrometry (MS/MS). A total of 230 histone proteoforms were quantified in SW480 and SW620 cell lines, among which 34 proteoforms were significantly altered in abundance in the metastatic cells, indicating a significant transformation of histone proteoforms during metastasis. We observed a significant increase in abundance of all nine differentially expressed histone H4 proteoforms in metastatic SW620 cells compared to SW480 cells, while differentially expressed proteoforms of other histone proteins display diversified expression patterns. Additionally, two histone H2A proteoforms with a combination of N-terminal acetylation and phosphorylation were upregulated in the metastatic CRC cells. These differentially expressed histone proteoforms could be novel proteoform biomarkers of CRC metastasis.

Implementing proteomics-based solutions has emerged as a potentially highly powerful and transformative approach in enhancing patient management by streamlining clinical decision-making and accelerating drug development. Specific changes in proteins are generally responsible for onset and progression of disease. This understanding serves as a foundation for identifying biomarkers, discovering drug targets, and developing novel therapeutics. Despite substantial progress, several challenges and gaps persist. These include among others technical limitations, regulatory hurdles, legal, ethics and data protection issues. Addressing these obstacles necessitates a multifaceted approach involving critical evaluation, cross-disciplinary collaboration, and the inclusion of diverse stakeholder perspectives. Such a comprehensive strategy appears essential for advancing proteomics from research to implementation, ultimately benefiting patients. To this end, the PROTEOMICS journal is launching a special section dedicated to clinical proteomics. This initiative aims to highlight the critical aspects of (prote)omics-guided biomarker and drug development, address existing challenges, and propose potential solutions for implementation. By fostering collaboration among scientists, clinicians, patients, regulators, insurance companies, pharmaceutical companies, and funding agencies, we hope to spark discussions that pave the way for adopting proteomics-based approaches to improve patient outcomes.

Highly laborious plating of cultures onto solid media is inevitable in conventional yeast two-hybrid systems. We have successfully developed Liquid Y2H-Seq, a method which replaces solid media with liquid media for culturing and that displaces visual inspection of colonies with sequencing. Soybean (Glycine max) is a typical photoperiod-sensitive crop, meaning that specific duration light regime has a huge impact on soybean flowering and production. We obtained a lot of putatively interactions for soybean flowering genes. Finally, these reports introduce a labor-reducing and time-saving method for identifying protein-protein interactions, based on simple modifications to a ubiquitous protocol in life science research.

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that leads to dementia. Many cases are diagnosed annually and there is no currently available cure. Understanding the underlying disease biology of AD through the study of molecular networks, particularly by mapping clinical variants to tissue-specific interactomes and regulatory macromolecular assemblies, offers a promising avenue to elucidate altered disease pathways. This, in turn, could provide valuable insights for drug discovery. In this study, we leverage our manually curated AD-specific dataset from the IMEx consortium, which provides detailed interaction data, including the relationship between interacting partners, detection in specific host tissues and cell lines, and the impact of variants on interaction outcomes. By integrating these data with information on protein complex composition taken from the Complex Portal, we have identified relevant macromolecular assemblies enriched in AD networks. Further pathway enrichment analysis is conducted using Reactome, enabling a comprehensive exploration of disease mechanisms and potential therapeutic targets.