Extracellular Vesicles (EVs) are membranous vesicles produced by all cells under physiological and pathological conditions. In hematological malignancies, tumor-derived EVs might reprogram the bone marrow environment, suppress antileukemic immunity, mediate drug resistance and interfere with immunotherapies. EVs collected from the serum of leukemic samples might correlate with disease stage, drug-/immunological resistance, or might correlate with antileukemic immunity/immune response. Special EV surface protein patterns in serum have the potential as noninvasive biomarker candidates to distinguish several disease-related patterns ex vivo or in vivo.
Researchers at the University Hospital of Munich isolated EVs from the serum of acute myeloid leukemia (AML), acute lymphoid leukemia (ALL), chronic lymphoid leukemia (CLL) patients, and healthy volunteers. EVs were characterized by transmission electron microscopy and fluorescence nanoparticle tracking analysis, and EV surface protein profiles were analyzed by multiplex bead-based flow cytometry to identify tumor- or immune system-related EVs of AML, ALL, CLL, and healthy samples. Aiming to provide proof-of-concept evidence and methodology for the potential role of serum-derived EVs as biomarkers in leukemic versus healthy samples in this study, the researchers hope to pave the way for future detection of promising biomarkers for imminent disease progression and the identification of potential targets to be used in a therapeutic strategy.
Establishment of a robust workflow to directly quantify
EV surface protein expression in serum samples by MBFCM
a Gating strategy applied to identify capture bead populations. b Examples for signals detected when using different seruminput volumes for the MBFCM assay. Control indicates procedural control without EVs but stained with pan-tetraspanin detection antibodies, volumes given indicate seruminput volumes. Allsamples were diluted in MACSPlex buffer to a final volume of 60 µL during the initial capture step. c Example data showing detected background-subtracted signal intensities for each marker at different seruminput volumes.