Proteomics最新文献

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.

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 53 identified studies, approximately half lacked publicly available raw data, leaving four eligible studies containing sequencing data from a total of 11 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.

Diverse extracellular vesicles (EVs) are present in all body fluids; however, knowledge of large EVs (lEVs) remains limited. Molecular EV profiles vary depending on EV size and the physiological circulatory system, even within the same patient. In this study, we aimed to characterize the proteomic profile of IEVs in ovarian cancer patients and identify lEV-protein biomarkers. We collected tissue, serum, and ascites from patients with high-grade serous ovarian cancer and concurrently separated small EVs (sEVs) and lEVs through sequential multistep centrifugation. Proteomic analysis of tissues and EVs revealed distinct EV profiles in serum and ascites, identifying 11 lEV-specific proteins in serum and 14 in ascites that were absent in sEV. Of these, seven serum-specific and 10 ascites-specific proteins were further analyzed using transcriptomic databases, revealing candidate diagnostic and prognostic lEV-protein biomarkers. Our findings underscore the importance of size-based EV separation, as particle size influences biosynthetic mechanisms, in identifying lEV-specific proteins with potential diagnostic and prognostic values. SUMMARY: This study underscores the importance of distinguishing extracellular vesicle (EV) subtypes and considering body fluid specificity in biomarker discovery. By isolating EVs based on size and stepwise separation and analyzing their proteomic profiles in ovarian cancer, we identified potential large EV (lEV)-specific biomarkers that reflect disease pathology. These findings provide a foundation for lEV-protein-based liquid biopsy approaches that could enhance the accuracy of early detection and patient stratification. Further validation in clinical settings may pave the way for more precise and personalized ovarian cancer diagnostics.

Seminal extracellular vesicles (SEVs) carry a diverse array of bioactive molecules, including proteins, lipids, and nucleic acids, which influence sperm function and have potential to modulate the female reproductive tract immune response after intromission. However, the full spectrum of SEV cargo involved in these processes remains incompletely defined. Here, we employed label-free quantitative high-resolution mass spectrometry to characterize the human SEV proteome, identifying 5079 associated proteins. These proteins were shown to likely originate from multiple regions of the male reproductive tract, notably the seminal vesicles and prostate, providing evidence for heterogeneous tissue origins of SEVs. Bioinformatic analysis revealed enrichment in sperm- and immune-related functions, as well as functions linked to protein translation. Notably, we identified several proteins with established roles in sperm physiology and immune signaling that had not previously been linked with SEV function. These included; Adenylate kinase isoenzyme (AK)2/9, and Calcium-binding tyrosine-phosphorylation regulated protein (CABYR), implicated in sperm motility, and immune regulators such as the Toll-like receptor 4 ligand, High mobility group protein B1 (HMGB1), and the Nuclear factor kappa-light-chain-enhancer of activated B cells (NFκB) inhibitor epsilon (NFκBIE). Altogether, these findings expand the known SEV proteome and highlight proteins that may influence both male and female reproductive capacity.

Extracellular vesicles (EVs) are membrane-bound vesicles secreted by various cell types into the extracellular space and play a role in intercellular communication. Their molecular cargo varies depending on the cell of origin and its functional state. As a result, EVs serve as representatives of their parent cells and reservoirs of disease biomarkers. Their presence in diverse bodily fluids has fueled interest in their potential for biomarker discovery and signaling research. Advances in mass spectrometry, high-throughput sequencing, and bioinformatics have expanded the molecular characterization of EVs, while emerging tools, including artificial intelligence (AI), image-based systems biology, and curated EV repositories, are driving exploration of disease-associated molecular signatures. Omics technologies generate extensive, multidimensional datasets that can be analyzed using bioinformatics techniques in conjunction with traditional statistical methods. Systems-based approaches, such as network analysis, computer modeling, and AI, are particularly effective for interpreting these complex datasets. However, their application in EV studies requires a solid understanding of EV-specific biological principles and analytical tools to ensure accuracy. By leveraging these analytical strategies, systems biology aims to unravel the intricate organization of biological processes, providing insights into how EVs interact within cells and organisms, and how they can be utilized to advance disease diagnostics, monitor disease progression, and develop novel therapeutic strategies. This review aims to elucidate the state-of-the-art in EV research, integrating multiomics, modeling, and disease-specific insights. EV-specific data repositories and the future of EVs in systems biology will also be highlighted.

Prostate cancer (PCa) is a leading male malignancy worldwide, with metabolic reprogramming being a critical hallmark of its progression. Extracellular vesicles (EVs) derived from tissues directly reflect the tumor microenvironment, offering unique insights into cancer pathophysiology that are unattainable through cell line or biofluid-derived EVs. However, the functional roles of tissue-derived EVs in PCa metabolism remain poorly understood. Leveraging our expertise in murine PCa model establishment and EV isolation from prostate tissue, this study aimed to characterize functional differences between PCa and normal prostate tissue via proteomic analysis of tissue-derived sEVs. We orthotopically implanted luciferase-labeled PCa cells into nude mice to establish an in situ PCa model, confirmed tumor formation via in vivo imaging, and harvested tissues after 4 weeks. sEVs were isolated using ultracentrifugation combined with an iodixanol density cushion and characterized by transmission electron microscopy, nanoparticle tracking analysis, and protein marker profiling. Proteomic analysis identified 28 upregulated and 24 downregulated proteins in PCa-derived sEVs compared to normal controls. Subcellular localization revealed enrichment in the cytoplasm, while pathway analysis highlighted significant involvement in metabolic processes, particularly glycolysis, amino acid biogenesis, carbon metabolism, and pyruvate metabolism. Our study establishes a robust method for isolating prostate tissue sEVs and provides the first evidence that PCa tissue-derived sEVs exhibit profound metabolic pathway alterations. These findings enhance our understanding of PCa progression mechanisms and may facilitate the development of novel diagnostic biomarkers and therapeutic strategies targeting metabolic dysregulation in PCa. SUMMARY: In this study, we created a method to isolate prostate tissue small EVs, based on our knowledge of the murine prostate cancer model building. Our data suggested that prostate tissue small EVs proteins significantly changed in many metabolism pathways, such as Glycolysis, Biogenesis of amino acids, Carbon metabolism and Pyruvate metabolism. In this study, we are the first to report prostate tissue-derived EVs proteins enriched in alterations of cancer metabolism. These differential proteins in PCa tissue EVs reflect metabolic changes in PCa and may provide insights into the development of early diagnostic biomarkers or novel therapeutic strategies.

Small extracellular vesicles (sEV) play a pivotal role in intercellular communication and hold immense therapeutic potential. In this study, we aimed to characterize sEV derived from bone marrow mesenchymal stromal cells (BM-MSCs) and Wharton's jelly mesenchymal stromal cells (WJ-MSCs), cultured under normoxic and hypoxic conditions for elucidating their functional inclinations. Using high-throughput miRNA sequencing, we identified distinct miRNA profiles across cell types and oxygenation states, revealing unique signatures associated with hypoxia-driven cellular adaptations. Concurrently, mass spectrometry-based proteomic analysis of sEV provided a comprehensive catalog of proteins, highlighting the molecular cargo influenced by both cell source and environmental conditions. Comparative analyses showed overlapping and unique miRNA-protein networks between BM-MSC and WJ-MSC sEV, shedding light on their differential regulatory roles. Conclusively, hypoxia was found to enhance the enrichment of specific miRNAs and proteins implicated in angiogenesis, immunomodulation, and tissue regeneration. These findings underscore the influence of the cellular microenvironment on sEV composition and provide insights into their potential application in regenerative medicine and therapeutic development. SUMMARY: This study provides a comprehensive comparative analysis of sEV derived from BM and WJ-MSCs, highlighting how both cell source and oxygenation state influence their molecular composition and functional potential. By integrating high-throughput miRNA sequencing with proteomic profiling, we demonstrate that hypoxic preconditioning induces distinct shifts in sEV cargo, enriching their regenerative and immunomodulatory profiles. The findings suggest that the therapeutic efficacy of MSC-sEV can be significantly modulated by microenvironmental conditions, particularly oxygen availability. Bioinformatic analyses reveal unique molecular interaction networks and clustering patterns reflective of each MSC source, supporting the idea that different MSC-sEV may be suited to specific therapeutic contexts. The work advances the understanding of MSC-sEV heterogeneity and lays the groundwork for the rational design of sEV-based therapies. By identifying key environmental and cellular determinants of sEV composition, the study offers valuable insights for optimizing their use in regenerative medicine, with potential applications in targeting inflammation, ischemia, and tissue repair. Ultimately, the findings contribute to the development of more effective, tailored, and cell-free therapeutic strategies.

Extracellular vesicles (EVs) are heterogeneous and play important roles in intercellular communication, contributing to physiological and pathological processes. Since few markers currently exist to differentiate subtypes of EVs, this study aimed to determine proteomic and lipidomic differences among four EV subpopulations. Large and small EVs (L-EVs and S-EVs) were isolated from human mast cells (HMC-1) and monocytes (THP-1) by differential ultracentrifugation and then further separated by density cushions into two different densities [low-density (LD) and high-density (HD)]. L-EVs were pelleted at 16,500 × g, and S-EVs were pelleted at 118,000 × g. LD EVs were collected at 1.079-1.146 g/mL, while HD EVs were collected at 1.146-1.185 g/mL. The morphology, size and yield of EVs were determined by TEM and western blot. The proteome and lipidome of the EV subpopulations were determined with mass spectrometry. A total of 5364 proteins were quantified, and L-EVs LD were enriched in mitochondrial proteins such as TIMM/TOMM and MICOS proteins, while L-EVs HD were enriched in cytoskeleton- and cytokinesis-associated proteins, such as KIF proteins. S-EVs LD were enriched in tetraspanins, ADAM10 and ESCRT machinery proteins, while S-EVs HD were enriched in proteins commonly viewed as contaminants, such as histones, complement factors and collagen. Proteins involved in membrane trafficking between the plasma membrane and organelles, such as adaptor protein complexes, the conserved oligomeric Golgi complex, the trafficking protein particle complex, sortin-nexins, TBC1 domain proteins and coatomer subunits, were expressed at similar levels across all EV subtypes. Furthermore, 107 lipids were quantified, and phosphatidylethanolamine (PE) was less abundant in L-EVs LD as compared to the other EV subtypes, while ceramides were enriched in L-EVs as compared to S-EVs.This study demonstrates that there is a core proteome and lipidome that is similar across all four EV subtypes, but importantly, it also shows that a portion of the proteome and lipidome differs in EV subpopulations separated based on size and density. We suggest that these could be important markers in future EV studies and that they may reflect a different biogenesis and EV function.