{"doi":"10.3390/v14050903","title":"HIV RGB: Automated Single-Cell Analysis of HIV-1 Rev-Dependent RNA Nuclear Export and Translation Using Image Processing in KNIME","abstract":"Single-cell imaging has emerged as a powerful means to study viral replication dynamics and identify sites of virus−host interactions. Multivariate aspects of viral replication cycles yield challenges inherent to handling large, complex imaging datasets. Herein, we describe the design and implementation of an automated, imaging-based strategy, “Human Immunodeficiency Virus Red-Green-Blue” (HIV RGB), for deriving comprehensive single-cell measurements of HIV-1 unspliced (US) RNA nuclear export, translation, and bulk changes to viral RNA and protein (HIV-1 Rev and Gag) subcellular distribution over time. Differentially tagged fluorescent viral RNA and protein species are recorded using multicolor long-term (>24 h) time-lapse video microscopy, followed by image processing using a new open-source computational imaging workflow dubbed “Nuclear Ring Segmentation Analysis and Tracking” (NR-SAT) based on ImageJ plugins that have been integrated into the Konstanz Information Miner (KNIME) analytics platform. We describe a typical HIV RGB experimental setup, detail the image acquisition and NR-SAT workflow accompanied by a step-by-step tutorial, and demonstrate a use case wherein we test the effects of perturbing subcellular localization of the Rev protein, which is essential for viral US RNA nuclear export, on the kinetics of HIV-1 late-stage gene regulation. Collectively, HIV RGB represents a powerful platform for single-cell studies of HIV-1 post-transcriptional RNA regulation. Moreover, we discuss how similar NR-SAT-based design principles and open-source tools might be readily adapted to study a broad range of dynamic viral or cellular processes.","journal":"Viruses","year":2022,"id":289952,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":4,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9565,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":972457,"name":"Ginger M. Pocock","orcid":null,"position":1,"is_corresponding":false},{"id":972458,"name":"Gabriel Einsdorf","orcid":null,"position":2,"is_corresponding":false},{"id":786859,"name":"Ryan T. Behrens","orcid":"0000-0001-5761-9056","position":3,"is_corresponding":false},{"id":426217,"name":"Ellen T. A. Dobson","orcid":"0000-0002-3986-7782","position":4,"is_corresponding":false},{"id":427324,"name":"Marcel Wiedenmann","orcid":null,"position":5,"is_corresponding":false},{"id":972459,"name":"Christian Birkhold","orcid":null,"position":6,"is_corresponding":false},{"id":456752,"name":"Paul Ahlquist","orcid":"0000-0003-4584-9318","position":7,"is_corresponding":false},{"id":72584,"name":"Kevin W. Eliceiri","orcid":"0000-0001-8678-670X","position":8,"is_corresponding":false},{"id":461939,"name":"Nathan M. Sherer","orcid":"0000-0001-9974-236X","position":9,"is_corresponding":false},{"id":461938,"name":"Edward L. Evans","orcid":"0000-0003-0278-4227","position":0,"is_corresponding":true}],"reference_count":73,"raw_metadata":null,"created_at":"2026-07-19T00:30:26.667578Z","pmid":"35632645","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}