Proteomics最新文献

Proteomics has demonstrated that each protein consists of various proteoforms that provide an additional layer of biological data next to gene-, transcript-, or protein expression levels. As a result, proteome analyses are increasingly being complemented with proteoform maps. Identification and quantification of the type, site, and dynamics of a proteoform modification contribute to a better understanding of human biology. Testing the hypothesis that proteoform-resolved data can provide novel tools in precision medicine requires robust and high-throughput proteoform measurements. The prime candidate for this purpose is top-down proteomics (TDP), commonly performed with mass spectrometry. In this Special Issue: Top-Down Proteomics, Fornelli et al. report a comparative analysis of various SDS-removal methods that are needed for proteoform mapping in TDP experiments. Their work provides technical guidance on an important aspect of the TDP sample preparation process.

Extracellular vesicles (EVs) are heterogeneous lipid membrane-bound nanoparticles that carry diverse biomolecular cargos, including proteins, nucleic acids, lipids, and metabolites. Serving as fundamental mediators of intercellular communication in both physiological and pathological contexts, EVs have become a central focus in basic and translational research. Their participation in diverse biological processes-from immune regulation and tissue homeostasis to cancer progression and neurodegenerative diseases-highlights their dual functional roles. However, the intrinsic heterogeneity of EVs poses significant challenges for accurate characterization using conventional ensemble-averaging methods. Nano-flow cytometry (nFCM) represents a transformative technological advancement for single-particle EV analysis, offering high sensitivity, throughput, and multiparametric detection capabilities. This review first examines recent progress in nFCM instrumentation that improves sensitivity and resolution. We then summarize key applications of nFCM in uncovering previously inaccessible biophysical and biochemical characteristics of EVs at the single-particle level. Finally, we discuss current limitations hindering broader implementation and outline future directions to advance the field.

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.

Microglia are abundantly distributed throughout the central nervous system (CNS) to play critical roles in neural development and homeostasis, and act as immune sentinels to constantly monitor their surrounding neural environment. Given their high reactivity to brain insults, we hypothesised that the cerebral microenvironment altered by ischaemic stroke would significantly impact microglial morphology and function in a spatially dependent manner. To investigate this, we examined regional gene expression changes associated with microglial activation and neuroinflammation, microglial morphology using 3D image reconstruction and unbiased proteomics at 24 h after transient middle cerebral artery occlusion (tMCAO). We found the microenvironment within the ischaemic infarct core has a distinct proinflammatory profile versus that of the sham-operated controls. Moreover, stroke induces region-specific changes to microglia morphology with those closer to the infarct displaying a more ameboid shape and less complex dendritic processes. Additionally, we identified 108 differentially expressed proteins in microglia that were isolated from the ipsilateral ischaemic hemisphere compared to those isolated from the contralateral hemisphere. These differentially expressed proteins are predicted to influence signalling pathways that mediate TNFα superfamily cytokine production, chemokine activities and leukocyte chemotaxis and migration. These findings support microglia as critical regulators of the inflammatory signalling after stroke.

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 neo-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 differ from standard proteomics data analysis pipelines. We discuss differences in common approaches for terminomics-focused workflows, comparing N- versus 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 protease abundance. 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.

Omics analyses are crucial for understanding molecular mechanisms in biological research. The vast quantity of detected biomolecules presents a significant challenge in identifying potential biomarkers. Traditional methods rely on labor-intensive literature mining to extract meaningful insights from long lists of regulated candidates of biomolecules. To address this, we developed OmixLitMiner 2 (OLM2) to improve the efficiency of omics data interpretation, speed up the validation of results and accelerate further evaluation based on the selection of marker candidates for subsequent experiments. The updated tool utilizes UniProt for synonym and protein name retrieval and employs the PubMed database as well as PubTator 3.0 for searching titles or abstracts of available biomedical literature. It allows for advanced keyword-based searches and provides classification of proteins or genes with respect to their representation in the literature in relation to scientific questions. OLM2 offers improved functionality over the previous version and comes with a user-friendly Google Colab interface. In comparison to the previous version, OLM2 improves the retrieval of relevant publications and the classification of biomolecules. We use a case study of spatially resolved proteomic data from the mouse brain cortex to demonstrate that the tool significantly reduces the time compared to manual searches and enhances the interpretability of molecular analysis.