henhaobio
Sign in

bioRxivBioengineeringPreprint

HaloTag display enables quantitative single-particle characterization and functionalization of engineered extracellular vesicles

R. E. Mitrut, D. M. Stranford, B. N. DiBiase, J. M. Chan, M. D. Bailey, M. Luo, C. S. Harper, T. J. Meade, M. Wang, J. N. Leonard

PDFDOI

Abstract

Extracellular vesicles (EVs) play key roles in diverse biological processes, transport biomolecules between cells, and have been engineered for therapeutic applications. A useful EV bioengineering strategy is to express engineered proteins on the EV surface to confer targeting, bioactivity, and other properties. Measuring how incorporation varies across a population of EVs is important for characterizing such materials and understanding their function, yet it remains challenging to quantitatively characterize the absolute number of engineered proteins incorporated at single-EV resolution. To address these needs, we developed a HaloTag-based characterization platform in which dyes or other synthetic species can be covalently and stoichiometrically attached to engineered proteins on the EV surface. To evaluate this system, we employed several orthogonal quantification methods, including flow cytometry and fluorescence microscopy, and found that HaloTag-mediated quantification is generally robust across EV analysis methods. We compared HaloTag-labeling to antibody-labeling of EVs using single vesicle flow cytometry, enabling us to measure the substantial degree to which antibody labeling can underestimate proteins present on an EV. Finally, we demonstrate the use of HaloTag to compare between protein designs for EV bioengineering. Overall, the HaloTag system is a useful EV characterization tool which complements and expands existing methods.

Reproducibility ledger

Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.

Sign in to file a reproducibility report.

Similarity Map
SelectedSame fieldCross-field
THISPham '25Gálisová '26Zheng '23Zheng '24Gupta '20Peruzzi '23
70% semantic matchMolecular Biology
M Pham, Jona Benjamin Krohn, Janis Meyer+8 more·bioRxiv·Nov 26, 2025