Systems Biology

Extracellular vesicles (EVs) are nanoscale particles secreted by all cells and present in all biological fluids, where they carry molecular cargo reflective of health and disease states. Their diagnostic potential is often obscured by the high abundance of non-EV proteins and lipoproteins (e.g., albumin, apolipoproteins) that complicate proteomic analysis of primary biofluids, such as ascites fluid. Conventional isolation strategies face a persistent trade-off between EV purity and yield. To overcome this, a magnetic bead-based protocol (Mag-Net) to enrich EVs according to electrochemical surface charge using strong anion-exchange chemistry (SAX) was adapted for proteomics. Our workflow is specifically adapted to ascites fluid from human or murine sources. This approach effectively separates EVs from high-abundance proteins and lipoproteins, enabling proteomic profiling from as little as 2 μL of ascites fluid. Demonstrated in both murine and human ovarian cancer models, Mag-Net offers a reproducible, scalable, and automation-ready solution for EV isolation from various biofluids.

The cellular secretome is a rich source of biomarkers and extracellular signaling molecules, but proteomic profiling remains challenging, especially when processing culture volumes greater than 5 mL. Low protein abundance, high serum contamination, and sample loss during preparation limit reproducibility and sensitivity in mass spectrometry–based workflows. Here, we present an optimized and scalable protocol that integrates (i) 50 kDa molecular weight cutoff ultrafiltration, (ii) spin column depletion of abundant serum proteins, and (iii) acetone/TCA precipitation for protein recovery. This workflow enables balanced recovery of both low- and high-molecular-weight proteins while reducing background from serum albumin, thereby improving sensitivity, reproducibility, and dynamic range for LC–MS/MS analysis. Validated in human mesenchymal stromal cell cultures, the protocol is broadly applicable across diverse cell types and experimental designs, making it well-suited for biomarker discovery and extracellular proteomics.

Glioblastoma (GBM) is the most aggressive brain tumor, and different efforts have been employed in the search for new drugs and therapeutic protocols for GBM. A label-free, mass spectrometry–based quantitative proteomics has been developed to identify and characterize proteins that are differentially expressed in GBM to gain a better understanding of the interactions and functions that lead to the pathological state focusing on the extracellular matrix (ECM). The main challenge in GBM research has been to identify novel molecular therapeutic targets and accurate diagnostic/prognostic biomarkers. To better investigate the GBM secretome upon in vitro treatment with histone deacetylase inhibitor (iHDAC), we employed a high-throughput label-free methodology of protein identification and quantification based on mass spectrometry followed by in silico studies. Our analysis revealed significant changes in the ECM protein profile, particularly those associated with the angiogenic matrisome. Proteins such as decorin, ADAM10, ADAM12, and ADAM15 were differentially regulated upon in silico analysis. In contrast, key angiogenesis markers such as VEGF and ECM proteins like fibronectin and integrins did not display significant changes. These results suggest that iHDAC inhibitors may modulate or suppress tumor behavior growth by targeting ECM proteins’ secretion rather than directly inhibiting angiogenesis.

Proteomics analysis is crucial for understanding the molecular mechanisms underlying muscle adaptations to different types of exercise, such as concentric and eccentric training. Traditional methods like two-dimensional gel electrophoresis and standard mass spectrometry have been used to analyze muscle protein content and modifications. This protocol details the preparation of muscle samples for proteomics analysis using ultra-high-performance liquid chromatography (UHPLC). It includes steps for muscle biopsy collection, protein extraction, digestion, and UHPLC-based analysis. The UHPLC method offers high-resolution separation of complex protein mixtures, providing more detailed and accurate proteomic profiles compared to conventional techniques. This protocol significantly enhances sensitivity, reproducibility, and efficiency, making it ideal for comprehensive muscle proteomics studies.

The extracellular matrix (ECM) is a complex network of proteins that provides structural support and biochemical cues to cells within tissues. Characterizing ECM composition is critical for understanding this tissue component’s roles in development, homeostasis, and disease processes. This protocol describes an integrated pipeline for profiling both cellular and ECM proteins across varied tissue types using mass spectrometry–based proteomics. The workflow covers stepwise extraction of cellular and extracellular proteins, enzymatic digestion into peptides, peptide cleanup, mass spectrometry analysis, and bioinformatic data processing. The key advantages include unbiased coverage of cellular, ECM-associated, and core-ECM proteins, including the fraction of ECM that cannot be solubilized using strong chaotropic agents such as urea or guanidine hydrochloride. Additionally, the method has been optimized for reproducible ECM enrichment and quantification across diverse tissue samples. This protocol enables systematic mapping of the ECM at a proteome-wide scale.