Extracellular vesicles (EVs) are membrane-bound particles secreted by cells, playing crucial roles in intercellular communication. The composition of EVs can undergo changes in response to stress and disease conditions, making them excellent biomarker candidates. However, extracting protein information from EVs can be challenging due to their low abundance in complex biofluids and copurification with contaminant proteins and particles. Techniques to enrich EVs have their strengths and limitations, without one being able to purify EVs to complete homogeneity. This can lead to compromised recovery rates and increased complexity, making data interpretation difficult. In this viewpoint article, we explore the concept that better characterization of EV composition, followed by quantification of EV proteins in complex samples, might be a more viable route for biomarker development. Mass spectrometers can provide reproducible deep coverage of the EV proteome, despite sample impurities. This paradigm shift presents opportunities to integrate advanced bioinformatics tools to refine the EV proteome landscape, identify novel biomarkers, and streamline validation processes in biomarker development. By focusing on leveraging technology rather than achieving absolute purity, this approach can transform current practices and open opportunities for robust biomarker discovery. Herein, we highlight not only such opportunities but also challenges to implement this concept. SUMMARY: Extracellular vesicles (EVs) have enormous potential as biomarkers of diseases, as they can carry signatures of the cells they are derived from and the pathogenesis process. Biofluids, such as blood plasma, are highly complex and contain many components with physicochemical properties similar to those of EVs, making it challenging to obtain pure EV fractions. Challenges in obtaining pure preparations represent a main hurdle for studying EVs, and their components are potential biomarkers. This article explores the concept of studying EV proteins within complex samples, discussing opportunities and needs to move this field forward.
Extracellular vesicles (EVs) provide non-invasive information on cellular health and disease. Yet, with the small size of EVs and more than 200 cell types contributing EVs to the extracellular fluids, it is challenging to determine whether changes in EV-associated lipids, RNAs, and proteins occur because of differences in expression or cell type-specific EV abundances. This limits our use of EV-based biomarkers and our understanding of how EVs contribute to health and diseases. In recent decades, next-generation RNA sequencing methods have fueled the development of transcriptome deconvolution methods to determine cell type proportions in tissue RNA samples. These methods can also estimate cell type-specific EV abundances using the EV's RNA "fingerprint"; however, differences between cell and EV RNA composition can significantly bias the estimates. Based on a recent benchmarking study of transcriptome deconvolution methods, we will review technical and biological factors that drive the most accurate deconvolution, focusing on mRNA sequencing data from EVs. Moreover, we will describe biological factors that can affect the interpretation of the deconvolution methods of cell type-specific EV abundance estimates in acute and chronic conditions and give a perspective on how deconvolution can be used to monitor physiological and disease processes in the human body.
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 capillary zone electrophoresis (CZE, liquid-phase ion mobility) to tandem mass spectrometry (MS/MS). We identified 3191 unique proteoforms and uncovered a drastic transformation in the proteoform profile in AD compared to healthy controls. Over 2200 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, for example, 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. SUMMARY: Alzheimer's disease (AD) is a chronic neurodegenerative disease, destroying brain cells and causing thinking ability and memory to decline over time. Proteins (e.g., amyloid and tau) play key roles in the development of AD. Global and accurate protein measurement of human brains of AD patients and healthy controls will shed new light on the molecular mechanisms driving AD progression and discover new biomarkers for AD diagnosis and therapeutic development. Here, we performed the first CZE-MS/MS-based quantitative top-down proteomics (TDP) of a small cohort of AD human brain samples and healthy controls (5 AD and 5 control) to determine the differentially quantified proteoforms between the two health conditions. Over 3000 proteoforms were identified, and only about 700 proteoforms were detected in both conditions, indicating drastically different proteoform profiles between the two conditions. The differentially quantified proteoforms (e.g., tau, neurogranin, and calmodulin-1 proteoforms) are associated with biological processes relevant to AD development, for example, amyloid fibril formation, microtubule disruption, synaptic transmission, and axogenesis. The results offer a deep view of the proteoform transformation in the AD human brain compared to the healthy control, providing potential proteoform biomarkers for AD diagnosis and proteoform targets for therapeutic development.
Honey bees (Apis mellifera) are vital pollinators in fruit-producing agroecosystems like highbush blueberry (HBB) and cranberry (CRA). However, their health is threatened by multiple interacting stressors, including pesticides, pathogens, and nutritional changes. We tested the hypothesis that distinct agricultural ecosystems-with different combinations of agrochemical exposure, pathogen loads, and floral resources-elicit ecosystem-specific, tissue-level molecular responses in honey bees. We conducted an integrated multi-omics analysis using RNA-sequencing (RNA-seq), proteomics, and gut microbiome profiling across three key tissue types (head, abdomen, and gut) of honey bees collected from two agroecosystems over two field seasons. Quantification was performed for pesticide residues, pathogen loads (Nosema spp., Varroa destructor, and multiple viruses), and gut microbiota. Weighted gene co-expression network analysis (WGCNA) revealed tissue-specific protein modules with ecosystem-associated patterns, which differed from RNA co-expression networks. Microbiome composition also varied, with key genera like Gilliamella, Snodgrassella, and Bartonella correlating with metabolic modules. These findings underscore the complex, environment-dependent impacts of agroecosystem conditions on bee health. Our study provides a system-level understanding of how combined pesticide, pathogen, and parasitic stressors, mediated by diet and microbiome, shape molecular phenotypes in honey bees-informing strategies for pollinator protection in managed landscapes. SUMMARY: This study provides a comprehensive multi-omics analysis of honey bees foraging in blueberry and cranberry agroecosystems, offering novel insights into the molecular mechanisms underlying pollinator health in managed crop environments. By integrating transcriptomic, proteomic, and microbiome profiling across key tissues-head, abdomen, and gut-we reveal how environmental stressors, including pesticide exposure, pathogen infections, and parasitic infestations (e.g., Varroa destructor), differentially impact bee physiology and microbiome composition. Our findings highlight tissue-specific responses to these stressors, with distinct metabolic pathway alterations observed in each tissue. Proteomic and transcriptomic analyses uncovered dysregulated pathways linked to oxidative phosphorylation and protein synthesis, while microbiome analysis revealed crop-dependent shifts in gut bacterial communities, suggesting potential roles in pesticide detoxification and immune modulation. Notably, we identified key molecular biomarkers associated with stress adaptation, which may serve as early indicators of colony health deterioration. This research underscores the need for a system-level approach to understanding pollinator stress in agricultural landscapes. By elucidating the interactions between diet, pesticide residues, pathogen loads, and molecular stress responses, our study provides a foundation for targete
Mass spectrometry (MS)-based proteomics focuses on identifying and quantifying peptides and proteins in biological samples. Processing of MS-derived raw data, including deconvolution, alignment, and peptide-protein prediction, has been achieved through various software platforms. However, the downstream analysis, including quality control, visualizations, and interpretation of proteomics results, remains cumbersome due to the lack of integrated tools to facilitate the analyses. To address this challenge, we developed QuickProt, a series of Python-based Google Colab notebooks for analyzing data-independent acquisition (DIA) and parallel reaction monitoring (PRM) proteomics datasets. These pipelines are designed so that users with no coding expertise can utilize the tool. Furthermore, as open-source code, QuickProt notebooks can be customized and incorporated into existing workflows. As proof of concept, we applied QuickProt to analyze in-house DIA and stable isotope dilution (SID)-PRM MS proteomics datasets from a time-course study of human erythropoiesis. The analysis resulted in annotated tables and publication-ready figures revealing a dynamic rearrangement of the proteome during erythroid differentiation, with the abundance of proteins linked to gene regulation, metabolic, and chromatin remodeling pathways increasing early in erythropoiesis. Altogether, these tools aim to automate and streamline DIA and PRM-MS proteomics data analysis, making it more efficient and less time-consuming.
Innate immune signaling relies heavily on phosphorylation cascades to mount effective immune responses. Although traditional innate immune signaling cascades following TLR4 stimulation have been investigated through a temporally quantitative phosphoproteomic lens, far fewer studies have applied these methods to distinct signaling following the inflammasome trigger leading to IL-1β release. Here, we conducted time-resolved phosphoproteomic profiling to investigate kinase signaling downstream of the inflammasome trigger nigericin. We found that nigericin induces rapid and potent alterations in the phosphorylation landscape where immune-related signaling, mitogen-activated protein kinases (MAPKs), and PKC signaling are prevalent. We also found significant evidence of phospho-modified metabolic cascades, suggesting that phosphosignaling plays a role in previously described immunometabolic regulation. These signaling events preceded robust phosphorylation of DNA damage and chromatin reorganization proteins before pyroptotic rupture. Lastly, by performing temporal clustering of phospho-dynamics, we revealed novel ontology-level shifts in phosphosignaling cascades following nigericin treatment that highlight abrupt changes in cellular behavior during early and late intracellular inflammatory events. SUMMARY: Protein phosphorylation is critical to convey innate immune signaling information to specific effector arms of the cellular immune response. This study focuses on characterizing phosphoproteomic alterations stemming from the inflammasome trigger nigericin. By gaining a deeper understanding of global kinase phosphodynamics in response to inflammasome activation, we aim to identify novel pharmacological targets to treat chronic inflammatory diseases driven by inflammasome-dependent IL-1β release.
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 on 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/Computational-TDMS/TDEase.
The human gut microbiome is a diverse community of microorganisms residing in the gastrointestinal tract. The storage condition of fecal samples may impact the taxonomic and protein compositions of microbiomes in these samples. Here, we performed a mass spectrometry-based metaproteomic study to assess the impact of storage media on human gut microbiome in fecal samples. We evaluated FDA-authorized OMNIgene·GUT (OG), phosphate-buffered saline (PBS), and RNALater (RNAL) buffers and identified 38,185 microbial peptides corresponding to 7348 microbial proteins, which matched 16 phyla, 20 classes, 50 orders, 104 families, 332 genera, and 453 species. We found a high similarity among the fecal microbiomes preserved in OG, PBS, and RNAL in terms of the identification of proteins, taxa, and functional annotations. Both alpha and beta diversity suggested the high similarity among samples stored in the three media. Nonetheless, we also found some notable differences among buffers regarding the abundances of a few taxon groups. A partial human proteome (over 400 proteins) was identified in the fecal samples, with most of these proteins associated with the membrane and extracellular regions. The findings indicate the similarity among microbiomes in the fecal samples stored in OG, PBS, and RNAL regarding proteome profile, taxa, and functional capacity.
This study thoroughly analyzed and compared the metaproteomes of fecal samples preserved at −80°C in PBS, RNALater, and OMNIgene·GUT Dx buffers, offering novel insights into the effectiveness of these buffers in maintaining the stability and composition of the human gut microbiome.
We found a high similarity in the identification and quantification of proteins, taxa, and functional annotations across the three buffers, with notable quantitative differences highlighting subtle yet important variations in preservation efficacy.
The unique datasets and findings could offer valuable revelations into the impact of fecal sample preservation on translational and clinical analyses of the human gut microbiome.
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