# Magnetically directed single cell transcriptome analysis in HIV latency

> **NIH NIH R01** · DUKE UNIVERSITY · 2020 · $554,169

## Abstract

   
DESCRIPTION (provided by applicant): This project leverages new collaborations between biomedical and engineering investigators to develop new methods for the sorting and isolation of immune cells, where the precise trajectory of individual cells are controlled and sorted on a chip analogous to the way electrons are controlled inside computer circuits. This set of tools allows for fundamentally new methods to study phenotype, genotype, and the morphology of single or pairs of single cells, over extended periods of time with precision that is unparalleled by existing techniques, such as flow cytometry and flow cell sorting. The overall stated aims of this research are to develop the engineering platform and demonstrate a biological application for a lab-on-a-chip device that can analyze thousands of single cells over long durations, exposure to multiple stimuli, and enable the extraction of individual, high-value, cells for furthe immunological analyses (RT-PCR, clonal expansion, etc.). We aim to 1) design and fabricate a lab-on-chip assay optimized for biological applications, 2) optimize the assay for biological applications, and 3) use this novel assay to study early cellular events in the disruption of HIV latency.

## Key facts

- **NIH application ID:** 9991743
- **Project number:** 5R01AI150465-05
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** DAVID MARTIN MURDOCH
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $554,169
- **Award type:** 5
- **Project period:** 2016-09-20 → 2023-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9991743

## Citation

> US National Institutes of Health, RePORTER application 9991743, Magnetically directed single cell transcriptome analysis in HIV latency (5R01AI150465-05). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9991743. Licensed CC0.

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