Introduction
Eosinophilic esophagitis (EoE) is a chronic, immune-mediated inflammatory disease of the esophagus, characterized by dense eosinophilic infiltration and progressive esophageal dysfunction. Its incidence and prevalence have risen markedly since the 1990s, making it one of the most common causes of chronic esophageal symptoms in children and adults. Clinically, patients present with dysphagia, esophageal food impaction, and reflux-like symptoms such as heartburn, which can substantially impair health-related quality of life and, if left untreated, lead to esophageal strictures and fibrosis. Diagnosis and follow-up of EoE currently rely on upper endoscopy with esophageal biopsies, an invasive, time-consuming, and often uncomfortable procedure. There is therefore a critical need for non-invasive, easily measurable biomarkers to improve patient care. Extracellular vesicles (EVs), nano-sized membrane-bound particles secreted by virtually all cell types into body fluids, carry molecular cargo that reflects the physiological or pathological state of their cells of origin, making them promising blood-based biomarker candidates across diverse conditions, including cancer, neurodegenerative disorders, and inflammatory diseases such as EoE.
Materials and methods
We established and validated a robust methodology for isolating EVs from blood plasma, combining size-exclusion chromatography with CaptoCore400 depletion resin to enhance particle purity by effectively removing contaminating plasma proteins and lipoproteins. Isolated EVs were characterized by nanoparticle tracking analysis for size distribution and concentration, tetraspanin profiling (CD9, CD63, CD81) to confirm EV identity, and mass spectrometry-based proteomics to define their protein cargo in depth. This protocol is being applied to a well-characterized clinical cohort comprising healthy controls, patients with EoE in remission, and patients with active disease.
Results
The optimized isolation workflow yields EV preparations of high purity, with reduced contamination by plasma proteins and lipoproteins compared to standard size-exclusion chromatography alone, as confirmed by nanoparticle tracking analysis and tetraspanin marker enrichment. Application of this protocol across the clinical cohort is expected to generate high-quality, reproducible EV profiles spanning the full disease spectrum, providing a solid basis for downstream proteomic comparison between healthy controls, patients in remission, and patients with active EoE.
Discussion
This optimized workflow will be used to identify candidate biomarkers in blood plasma for the minimally invasive diagnosis and monitoring of eosinophilic esophagitis, potentially reducing reliance on repeated endoscopic biopsies. Beyond EoE, we plan to integrate this dataset with data from other cohorts and diseases to build a machine learning-based predictive model capable of distinguishing disease-specific EV signatures. Ultimately, this work aims to translate EV biology into a practical clinical tool, supporting earlier diagnosis, more precise disease monitoring, and a less invasive path to personalized care for patients with EoE and, potentially, other chronic inflammatory conditions.