# Vesicle Epitope Transcript sequencing (VET-seq): Droplet-based Multiomic Profiling Platform for Single Vesicle Analysis

> **NIH NIH R61** · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · 2024 · $218,532

## Abstract

ABSTRACT
Extracellular vesicles (EVs) are bilayer membrane structures of diameters 30 – 1000 nm released into the blood
by cells throughout the body, at concentrations on the order of 1010 per ml. Their molecular content of proteins,
dsDNA oligomers, microRNAs (miRNAs), mRNAs, and other analytes, may play multiple functional roles via EV
trafficking, and may also provide a diagnostic report back on the disease site or tissue of origin. As such, EVs
can serve as potential sources of cancer biomarkers, perhaps even providing insights into the genetic and
functional characteristics of the tumor microenvironment. However, this potential remains largely untapped due
to technical challenges.
We propose to develop an ultra-high-throughput droplet-based multiomic profiling platform, Vesicle Epitope
Transcript sequencing (VET-seq), that can simultaneously resolve the surface proteome, internal proteins,
and broad RNA spectrum with single extracellular vesicle (sEV) resolution. In AIM 1, we manipulate EVs to
permit access to encapsulated EV cargoes and allow in situ modifications of EV molecules. This will yield
protocols for detecting a broad spectrum of EV molecules. Here, we also begin to generate and validate custom
probes for VET-seq. In AIM 2, we propose an EV indexing approach to add vesicle-specific barcodes to EV
molecules that enable the identification of their vesicle-source. This strategy will significantly increase the
throughput for droplet-based EV profiling by overcoming the need for limiting dilution. To address the unmet
need for a cost-effective multiomic sEV profiling method, in AIM 3, we integrate the in situ modification protocols
and the EV indexing protocol to form the VET-seq workflow. We will benchmark the VET-seq protocol on EVs
isolated from human cancer cell lines and drug resistance models. The EV detection limit of VET-seq will be
determined for cancer-cell derived EVs in a background of EVs isolated from human plasma and serum. High-
dimensional sEV profiling enabled by VET-seq will deepen our understanding of the molecular contents and
biological implications of EVs in the context of cancer. This will potentially shed light into the utility of EVs as
cancer biomarkers for early detection of cancer or recurrent cancer as well as for monitoring treatment response
to cancer therapeutics.
Our multidisciplinary team is comprised of scientists and clinicians with expertise in micro-nanotechnologies,
multiomics analysis, EV biology, computational biology, clinical oncology, and assay development.

## Key facts

- **NIH application ID:** 10843319
- **Project number:** 5R61CA278559-02
- **Recipient organization:** UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH
- **Principal Investigator:** Yue Lu
- **Activity code:** R61 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $218,532
- **Award type:** 5
- **Project period:** 2023-06-01 → 2026-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10843319

## Citation

> US National Institutes of Health, RePORTER application 10843319, Vesicle Epitope Transcript sequencing (VET-seq): Droplet-based Multiomic Profiling Platform for Single Vesicle Analysis (5R61CA278559-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10843319. Licensed CC0.

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