{"doi":"10.7554/elife.64653","title":"Unsupervised machine learning reveals key immune cell subsets in COVID-19, rhinovirus infection, and cancer therapy","abstract":"For an emerging disease like COVID-19, systems immunology tools may quickly identify and quantitatively characterize cells associated with disease progression or clinical response. With repeated sampling, immune monitoring creates a real-time portrait of the cells reacting to a novel virus before disease-specific knowledge and tools are established. However, single cell analysis tools can struggle to reveal rare cells that are under 0.1% of the population. Here, the machine learning workflow Tracking Responders EXpanding (T-REX) was created to identify changes in both rare and common cells across human immune monitoring settings. T-REX identified cells with highly similar phenotypes that localized to hotspots of significant change during rhinovirus and SARS-CoV-2 infections. Specialized MHCII tetramer reagents that mark rhinovirus-specific CD4+ cells were left out during analysis and then used to test whether T-REX identified biologically significant cells. T-REX identified rhinovirus-specific CD4+ T cells based on phenotypically homogeneous cells expanding by ≥95% following infection. T-REX successfully identified hotspots of virus-specific T cells by comparing infection (day 7) to either pre-infection (day 0) or post-infection (day 28) samples. Plotting the direction and degree of change for each individual donor provided a useful summary view and revealed patterns of immune system behavior across immune monitoring settings. For example, the magnitude and direction of change in some COVID-19 patients was comparable to blast crisis acute myeloid leukemia patients undergoing a complete response to chemotherapy. Other COVID-19 patients instead displayed an immune trajectory like that seen in rhinovirus infection or checkpoint inhibitor therapy for melanoma. The T-REX algorithm thus rapidly identifies and characterizes mechanistically significant cells and places emerging diseases into a systems immunology context for comparison to well-studied immune changes.","journal":"eLife","year":2021,"id":159685,"datarank":1.0352811107669384,"base_score":3.713572066704308,"endowment":3.713572066704308,"self_citation_contribution":0.5570358100056463,"citation_network_contribution":0.4782453007612922,"self_endowment_contribution":0.5570358100056463,"citer_contribution":0.4782453007612922,"corpus_percentile":null,"corpus_rank":null,"citation_count":40,"citer_count":24,"citers_with_citation_signal":18,"citers_with_endowment":18,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9498,"is_data_producer":true,"deposit_databanks":{"ClinicalTrials.gov":["NCT02796001"]},"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":671018,"name":"A Paul","orcid":"0000-0002-9318-3760","position":1,"is_corresponding":false},{"id":350268,"name":"Lyndsey M. Muehling","orcid":"0000-0003-3203-3264","position":2,"is_corresponding":false},{"id":226797,"name":"Joanne Lannigan","orcid":"0000-0002-3981-8681","position":3,"is_corresponding":false},{"id":281006,"name":"William W. Kwok","orcid":"0000-0003-4843-4599","position":4,"is_corresponding":false},{"id":350270,"name":"Ronald B. Turner","orcid":"0000-0002-7090-3219","position":5,"is_corresponding":false},{"id":350271,"name":"Judith A. Woodfolk","orcid":"0000-0002-8915-4334","position":6,"is_corresponding":false},{"id":480936,"name":"Jonathan M. Irish","orcid":"0000-0001-9428-8866","position":7,"is_corresponding":false},{"id":480933,"name":"Sierra Barone","orcid":"0000-0001-5944-750X","position":0,"is_corresponding":true}],"reference_count":50,"raw_metadata":null,"created_at":"2026-07-18T23:44:39.550981Z","pmid":"34350827","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":[]}