VesicleVoyager: In vivo selection of surface displayed proteins that direct extracellular vesicles to tissue-specific targets

Abstract

The development of technologies for screening proteins that bind to specific tissues in vivo and facilitate delivery of large cargos remains challenging, with most approaches limited to cell culture systems that often yield clinically irrelevant hits. To overcome this limitation, we developed a novel molecular screening platform using an extracellular vesicle (EV) display library. EVs are natural molecular carriers capable of delivering diverse cargos, which can be engineered to enhance specificity and targeting through surface modifications. We constructed an EV-display library presenting monobody repertoires on EV surfaces, with genetic cargo inside the EVs corresponding to the displayed proteins. These libraries were screened for tissue specific delivery through serial passage in mice via sequential intravenous administration in and recovery of tissue-selected EVs and amplification of their encapsulated monobody genes at each passage. Our results demonstrated successful selection of tissue-specific targeting proteins, as revealed by fluorescence and bioluminescence imaging followed by DNA sequencing. To understand the stochastic relationship between displayed proteins and packaged genes, we developed a Markov chain model that quantified selection dynamics and predicted enrichment patterns despite the imperfect correlation between phenotype and genotype. This EV-based monobody screening approach, combined with mathematical modeling, is a significant advancement in targeted drug delivery by leveraging the natural capabilities of EVs with the selection of targeting proteins in a physiologically relevant environment.

Keywords: Extracellular vesicles; Markov chain modeling; in vivo screening; ligand display screening; nanodrugs; targeted delivery.

Figure 1.. Schematic illustration of monobody-EV library screening strategy.

Overview of EV-based monobody screening processes, involving monobody library pDNA generation, pDNA transfection to EV donor cells (HEK293T), EV isolation from the cell culture media, EV treatment or administration, DNA extraction from cell or organ, monobody amplification and re-cloning to generate enriched monobody library pool. This image was created with BioRender.com.

Figure 2.. EV characterization showing successful EV isolation, pDNA loading, and surface display of monobody proteins.

(A) Size distribution, (B) Peak size distribution of multiple samples, (C) Amount of pDNA loading in eEV, (D) immunoblot analysis, (E) Representative image of eEV before screening and (F) population analysis, and (G-H) after five rounds of screening, respectively. (I) Number of monobody molecules expressed on the EV surface.

Figure 3.. Enrichment of target sequences on in vitro EV-Monobody library screening (A)

(A) Validation of fold change of RDG and E626 in A431 cells by qPCR. (B) The ratio of E626 (positive control) to RDG (negative control) sequences quantified for each of five passages across six separate campaigns. (C) Enrichment growth rate of each variant calculated using: Final count = initial count × Exp (enrichment growth rate × time). (D) Phylogenetic tree showing relatedness between positive control (E626), negative control (RDG), and novel lead monobody variants EG64, EG100, EG130, EG142, and EG165.

Figure 4.. Evaluation of selected clones using bioluminescence imaging (BLI).

(A) A431 and MCF7 cells were treated with monobody-displayed EVs co-labeled with NanoLuc. Total photon flux (p/s) from EVs bound to cells was quantified using IVIS. Values represent mean ± SD (n=3). Two-way ANOVA was used to assess time course effects. Significance against 0 min is expressed as: * p ≤ 0.05, ** p ≤ 0.01, *** p ≤ 0.001, and **** p ≤ 0.0001. NP: non-peptide, non-binder negative control. (B) A431 and MCF-7 cells were co-cultured and treated with Monobody-mCherry co-labeled EVs for 10 min. Binding was assessed by confocal laser scanning imaging of EVs (red), anti-EGFR antibody (green), and DAPI nuclear staining (blue). Scale bar: 25 μm.

Figure 5.. Five rounds of in vivo screening created a library with high pancreatic accumulation.

EVs were co-labeled with NanoLuc and each Library DNA (P0, Pan-P5), high binder candidate P1316 or non-binder RDG. Equal numbers of particles were introduced into mice by IV injection, and ex vivo imaging was performed after 1 hour of circulation. (A) Ex vivo images of each organ, comparison of percentage of detected BLI from injected EVs, (B) ex vivo image of pancreases, accumulation degree of pancreatic library relative to control (original library). (C) Biodistribution of P1316-EVs co-labeled with ThermoLuc-CD63. (D) Ex vivo image of each organ, comparison of percentage of detected BLI from injected EVs. Unpaired t-test was used to evaluate the enrichment in each organ. Significance is expressed as follows: * p ≤ 0.05, ** p ≤ 0.01.

Figure 6.. Markov chain model of EV-displayed monobody selection process.

(A) Schematic representation of the Markov chain model, showing the seven states and transition probabilities between states. Each state represents a key stage in the selection process, with arrows indicating possible transitions. (B) Simulated population distribution across states after each selection round, showing the progressive enrichment of the library and cumulative loss through multiple stages. (C) Enrichment of E626 monobody frequency over five rounds of selection from an initial 1% to 69%, demonstrating the effectiveness of the selection process despite imperfect genotype-phenotype linkage. (D) Comparison of organ distribution profiles between the initial library (P0) and pancreas-enriched library (Pan-P5), showing the shift in targeting specificity following selection. Error bars represent standard deviation from three independent simulations. (E) Stochastic relationship between displayed monobodies and packaged DNA. Each EV displays approximately 20 monobody molecules but contains an average of only 1.5 copies of plasmid DNA, resulting in a ~7.5% probability of correct linkage between a displayed monobody and its encoding DNA. Despite this imperfect correlation, the iterative selection process successfully enriches target-binding sequences. Error bars represent standard deviation from three independent simulations.