# Novel Separation Methods for exRNA Carriers: Extracellular Vesicles, Lipoprotein Particles, and Protein Aggregates

> **NIH NIH UH3** · JOHNS HOPKINS UNIVERSITY · 2021 · $549,132

## Abstract

ABSTRACT
Extracellular RNA (exRNA) is a particularly attractive molecular component of liquid biopsy because RNA
species can be specifically amplified. Of the three major classes of exRNA vehicle—extracellular vesicles
(EVs), lipoprotein particles (LPPs), and free ribonucleoproteins (RNPs)—EVs have so far received the most
attention. Within each class, there is also tremendous diversity by physical characteristics of size, density, and
surface charge. Indeed, to our knowledge, no study to date has profiled exRNA in multiple members of the
three carrier classes that have been isolated rigorously from the same samples. There is a strong need to
develop new strategies and controls to ensure that comparisons of exRNA carriers are not confounded by co-
isolation (different classes of carriers present in the same fraction) or contamination (detection of uncomplexed
and/or foreign RNAs introduced during sample collection and processing). To this end, we assemble a team of
experts on EVs, LPPs, and RNPs, along with experts in cutting-edge separation and characterization methods.
 In an initial UG3 phase, we will first (Aim 1) use a combination of state-of-the-field physical and biochemical
separation methods to separate a library of eight subtypes of EVs, LPPs, and RNPs from the same biological
samples and with the best achievable purity. “Gold standard” proteomic, lipidomic, glycomic, and RNomic
datasets will be generated. Carefully designed “process” controls will for the first time establish an across-the-
board baseline of contaminants and other artifacts that may complicate interpretation. In Aim 2, we will test
asymmetric field-flow fractionation (AF4) and affinity capture platforms including the ExoView platform and
sensitive electrochemical sensors as superior alternatives to the most commonly used legacy method,
differential centrifugation. We will seek gains in speed, resolution, and purity compared with legacy techniques.
If go/no-go criteria are met by the end of the second year (UG3), we will proceed to a UH3 phase. This phase
will include an Aim 3, validating results in multiple locations and with approximately 6 times the original sample
numbers to account for influence of sex and age. The AF4 method will be further developed with additional
modifications based on our engineering and analytical chemistry expertise, while ExoView technology will be
harnessed to screen antibodies and other affinity materials for rapid isolations and to detect abundant RNA
species directly in immobilized exRNA carriers. Finally, an Aim 4 will assess the biological factor of diet with
valuable samples from intervention studies, along with the possible desirability of collecting samples in RNase
inhibitors to preserve more fragile RNA species. Overall, we hypothesize that 1) AF4, on its own or with
methodologic modifications, as well as 2) novel affinity separation approaches will improve substantially on
ultracentrifuge-based methods in ease and purity and on ...

## Key facts

- **NIH application ID:** 10470432
- **Project number:** 4UH3CA241694-03
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Kenneth W Witwer
- **Activity code:** UH3 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $549,132
- **Award type:** 4N
- **Project period:** 2019-08-01 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10470432

## Citation

> US National Institutes of Health, RePORTER application 10470432, Novel Separation Methods for exRNA Carriers: Extracellular Vesicles, Lipoprotein Particles, and Protein Aggregates (4UH3CA241694-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10470432. Licensed CC0.

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