# Uncovering exRNA and protein determinants of secreted vesicle heterogeneity by flow cytometric purification of vesicle subsets from cells and plasma

> **NIH NIH UH3** · VANDERBILT UNIVERSITY MEDICAL CENTER · 2021 · $1,049,684

## Abstract

PROJECT SUMMARY
All cells secrete small portions of their protein and RNA contents as lipid vesicles called extracellular vesicles
(EVs). In various diseases, normal EV cargos change as disease initiate and progress, altering what proteins
and RNAs are packaged into them. Small EVs, called exosomes, and larger EVs called microvesicles carry
many of these disease-associated cargos and we have shown that both protein and RNA exosomal and
microvesicle constituents change with cancer progression; such EVs can end up in the biofluids of the body
including blood, cerebral spinal fluid, urine and saliva providing a non invasive and readily available source of
biomarkers. Recently it has been shown that EVs released from cells are highly heterogeneous in nature and
that only small fractions are disease associated. Furthermore many extracellular constituents that were thought
to be associated with vesicles are not, either arising from non-vesicular or other lipoprotein complexes of
exRNAs and proteins. To advance the field beyond incremental science, we require a superior understanding
of the relationship between molecular heterogeneity (cargo composition) and physical heterogeneity for the
various types of vesicles secreted by cells and tissues. To this we developed, Fluorescence-Activated Vesicle
Sorting (FAVS), as a means to analyze and purify small and large EVs, on a per vesicle basis, from various
biofluids. FAVS is generally accessible since it uses a flow sorter available at many research institutions, so it
is an ideal method to be applied by this consortium. In this proposal we will demonstrate the capability of FAVS
to purify small EVs derived from colorectal cancer (CRC) and Glioblastoma Multiforme (GBM) models,
including cell line, PDX, mouse plasma and patient plasma sources of EVs. Both cancers are significant health
risks. GBMs are a common, yet incurable, malignant brain tumor (over 12,000 new cases predicted in 2018)
and CRC is the third leading cause of cancer deaths in the US. In the first Aim of this proposal we will optimize
the FAVS pipeline by: validating preprocessing steps that separate EVs based on their physical heterogeneity
(size and density), before performing FAVS; testing new candidate reagents for use with FAVS that more
clearly delineates EV subgroups; and uncovering new RNA and protein markers of EV heterogeneity. Because
such cancers are often associated with increased expression and activation of Epidermal Growth Factor
Receptor (EGFR) we will use EGFR-targeted antibodies, along with other EV cargo binding antibodies, to
purify EV subsets from these cancers. We will use EGFR antibodies to analyze CRC and GBM associated EVs
as we have done previously, using antibodies that bind total and active EGFR. The second Aim of the grant is
to uncover tissue specific markers of EV production by using a cell specific EV-tagging methodology in mouse
genetic models. We will also use orthotopically implanted GBM and CRC PDX xenograf...

## Key facts

- **NIH application ID:** 10470428
- **Project number:** 4UH3CA241685-03
- **Recipient organization:** VANDERBILT UNIVERSITY MEDICAL CENTER
- **Principal Investigator:** Alain Charest
- **Activity code:** UH3 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $1,049,684
- **Award type:** 4N
- **Project period:** 2019-07-15 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10470428

## Citation

> US National Institutes of Health, RePORTER application 10470428, Uncovering exRNA and protein determinants of secreted vesicle heterogeneity by flow cytometric purification of vesicle subsets from cells and plasma (4UH3CA241685-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10470428. Licensed CC0.

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