Integrated Single-cell Multiomic Analysis of HIV Latency Reversal Reveals Novel Regulators of Viral Reactivation

• Integrated scRNA-seq and scATAC-seq profiling of ~125,000 latently infected CD4+ T cells reveals transcriptomic and epigenomic changes upon latency reversal. • Machine learning models achieve 75-79% accuracy in predicting viral reactivation from single-cell multiomic data. • FOXP1 and GATA3 are identified and validated as novel regulators of HIV transcription. • The study demonstrates the power of multimodal single-cell analysis to uncover host factors controlling HIV latency.