Isolating Exosomes using a novel ExoTIC device from HIV-infected Patient Plasma and Saliva

CERSI Collaborators: Utkan Demirci, Stanford University; Timothy Henrich, UCSF
FDA Collaborators: David McGivern; Mohan Haleyurgirisetty; Krishnakumar Devadas
Project Start Date: September 17, 2019

Regulatory Science Challenge: 

HIV testing is important to identify infected individuals so that they can receive appropriate care, but many current tests can miss infections in people whose virus levels are very low or who are taking antiviral medicines. Our project worked to close this gap by developing a new way to capture tiny particles released by cells, called extracellular vesicles (EVs), from the blood of people living with HIV. These EVs carry genetic signals that show whether the virus is replicating at high or low levels. By identifying these signals, we generated new knowledge that could guide the development of more accurate HIV tests, helping improve care and reduce the risk of transmission.

Project Description and Aims/Goals:

Our goal was to analyze plasma-derived extracellular vesicles (EVs) to identify signatures that predict failure of antiviral therapy, distinguishing low viral load from high viral load. We did this by using a device called ExoTIC to capture EVs—tiny particles released by cells—from blood samples donated by people living with HIV. We collected plasma from 20 individuals, half with high viral loads and half with low viral loads. From these EVs, we extracted genetic material and used sequencing technology to identify patterns that could serve as signals of infection status. This approach allowed us to link specific changes in EV content to the amount of virus in the blood.

Outcomes/Impact:

We successfully isolated extracellular vesicles from clinical plasma samples (Stanford site) that we previously proved to have high or low viral load (UCSF site). We extracted RNA from the isolated samples and used RNA sequencing to identify and quantify the different RNAs in 20 samples. We analyzed the samples at Stanford’s Bioinformatics as a Service facility (BaaS) and we found that specific RNA molecules are differentially regulated between the low and the high viral load groups.