EVd3x: a source-attributed multi-omic platform for mapping extracellular vesicle cargo evidence

Abstract

Extracellular vesicle (EV) profiling can identify hundreds to thousands of RNAs and proteins, but converting those lists into a coherent biological interpretation remains slow because the relevant evidence is scattered across cargo repositories, publications, pathway databases, cell atlases, and interaction resources. We introduce EVd3x.com, a source-linked bioinformatics platform that brings these evidence layers into one continuous workflow for EV research. EVd3x harmonizes more than 22 million records and interactions from 28 public resources into 17 analysis tables, including 234,090 reported EV-cargo records linked to 3,350 publications, 2.6 million miRNA-target relationships, 404,758 pathway memberships, more than 501,000 disease associations, 1.6 million cell-context records, 25,779 ligand-receptor pairs, and 13.7 million protein-interaction records. Each query generates a fixed, exportable evidence packet that follows the same molecules from reported EV detection through disease, pathway, cellular, regulatory, and interaction analyses. Starting with the complex disease query [early-onset Alzheimers disease with behavioral disturbance], EVd3x arrived at a focused, PSEN1-centered evidence packet and organized thousands of linked records into a source-traceable interpretation. The workflow recovered expected Alzheimer biology while keeping reported EV detection separate from broader contextual evidence, thereby exposing evidence gaps and context-dependent relationships. EVd3x provides EV researchers with a practical route from a cargo list, or from a disease, pathway, or cell-context question to a prioritized set of molecules, relationships, evidence gaps, and unresolved questions that researchers can use to formulate hypotheses and design subsequent experimental studies.