Back

Extracellular Vesicles from Multiple Sclerosis White Matter Exhibit Synaptic, Mitochondrial, Complement, and Aging-related Pathway Dysregulation

Jank, L.; Satheesh Kumar, M. K.; Ryu, T.; Thapa, R.; Gololobova, O.; Niepokny, T.; Calabresi, P. A.; Witwer, K. W.; Dutta, R.; Na, C. H.; Bhargava, P.

2025-06-06 neuroscience
10.1101/2025.06.02.657509 bioRxiv
Show abstract

Extracellular vesicles (EVs) are increasingly recognized as mediators of central nervous system (CNS) function and pathologies, including multiple sclerosis (MS). While plasma-derived EVs have been explored as biomarkers in MS, little is known about EVs in CNS tissue. Here, we characterize EVs from postmortem white matter (WM) of MS and control brains. EVs were separated by differential centrifugation followed by size exclusion chromatography and characterized using nanoflow cytometry, single-particle reflectance imaging sensing (SP-IRIS), and transmission electron microscopy. EV size, yield, and morphology did not differ significantly between MS and control samples. Proteomic analyses revealed downregulation of synaptic and mitochondrial proteins and upregulation of complement and inflammatory proteins and pathways in MS WM EVs. This suggests that EVs reflect ongoing synaptic pathology, metabolic dysfunction, and CNS-compartmentalized inflammation and that they may actively contribute to these pathological processes. Deconvolution analyses suggests a shift in EV cellular origin, with an increased astrocytic and decreased neuronal EV contributions in MS. Several proteomic changes we observed in CNS-derived EVs have also been reported in circulating EVs of people with MS, establishing this CNS tissue EV study as a valuable resource for identifying biomarker candidates for brain-derived plasma EV studies. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=136 SRC="FIGDIR/small/657509v1_ufig1.gif" ALT="Figure 1"> View larger version (48K): org.highwire.dtl.DTLVardef@1f88da5org.highwire.dtl.DTLVardef@11b7ee9org.highwire.dtl.DTLVardef@291d9forg.highwire.dtl.DTLVardef@157cb73_HPS_FORMAT_FIGEXP M_FIG C_FIG

Matching journals

The top 13 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.