A novel high throughput microwell outgrowth assay for HIV infected cells

AbstractAlthough antiretroviral therapy (ART) is highly effective at suppressing HIV replication, a viral reservoir persists that can reseed infection if ART is interrupted. Curing HIV will require elimination or functional containment of this reservoir, but the size of the HIV reservoir is highly variable between individuals. To evaluate the overall size of the HIV reservoir, several assays have been developed, including PCR based assays for viral DNA, the Intact Proviral DNA Assay (IPDA), and the Quantitative Viral Outgrowth Assay (QVOA). QVOA is the gold standard assay for measuring inducible replication competent proviruses, but this assay is technically challenging and time consuming. To provide a more rapid and less laborious tool for quantifying cells infected with replication competent HIV, we developed the Microwell Outgrowth Assay (MOA), in which HIV infected CD4 T cells are cocultured with an HIV-detecting reporter cell line in a polydimethylsiloxane (PDMS)/polystyrene array of nanoliter sized wells (rafts). Transmission of HIV from infected cells to the reporter cell line induces fluorescent reporter protein expression that is detected by automated scanning across the array. We show that this assay can detect HIV infected cells with a high degree of sensitivity and precision. Using this approach, we were able to detect HIV infected cells from ART-naïve people with HIV (PWH) and from PWH on ART. Furthermore, we demonstrate that infected cells can be recovered from individual rafts and used to analyze the diversity of viral sequences. This assay may be a useful tool for quantifying and characterizing infected cells from PWH.Author summaryMeasuring the size of the HIV reservoir in people with HIV (PWH) will be important for determining the impact of HIV cure strategies. However, measuring this reservoir is challenging. We report a new method for quantifying HIV infected cells that involves culturing cells from PWH in an array of microwells with a cell line that detects HIV infection. We show that this approach can detect rare HIV infected cells and derive detailed virus sequence information for each infected cell.