{"doi":"10.1016/j.clim.2023.109634","title":"Unique molecular signatures sustained in circulating monocytes and regulatory T cells in convalescent COVID-19 patients","abstract":"Over two years into the COVID-19 pandemic, the human immune response to SARS-CoV-2 during the active disease phase has been extensively studied. However, the long-term impact after recovery, which is critical to advance our understanding SARS-CoV-2 and COVID-19-associated long-term complications, remains largely unknown. Herein, we characterized single-cell profiles of circulating immune cells in the peripheral blood of 100 patients, including convalescent COVID-19 and sero-negative controls. Flow cytometry analyses revealed reduced frequencies of both short-lived monocytes and long-lived regulatory T (Treg) cells within the patients who have recovered from severe COVID-19. sc-RNA seq analysis identifies seven heterogeneous clusters of monocytes and nine Treg clusters featuring distinct molecular signatures in association with COVID-19 severity. Asymptomatic patients contain the most abundant clusters of monocytes and Tregs expressing high CD74 or IFN-responsive genes. In contrast, the patients recovered from a severe disease have shown two dominant inflammatory monocyte clusters featuring S100 family genes: one monocyte cluster of S100A8 & A9 coupled with high HLA-I and another cluster of S100A4 & A6 with high HLA-II genes, a specific non-classical monocyte cluster with distinct IFITM family genes, as well as a unique TGF-β high Treg Cluster. The outpatients and seronegative controls share most of the monocyte and Treg clusters patterns with high expression of HLA genes. Surprisingly, while presumably short-lived monocytes appear to have sustained alterations over 4 months, the decreased frequencies of long-lived Tregs (high HLA-DRA and S100A6) in the outpatients restore over the tested convalescent time (≥ 4 months). Collectively, our study identifies sustained and dynamically altered monocytes and Treg clusters with distinct molecular signatures after recovery, associated with COVID-19 severity.","journal":"Clinical Immunology","year":2023,"id":323957,"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":35,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9481,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1039787,"name":"Sam E Weinberg","orcid":null,"position":1,"is_corresponding":false},{"id":553689,"name":"Suchitra Swaminathan","orcid":"0000-0002-8292-122X","position":2,"is_corresponding":false},{"id":646490,"name":"Shuvam Chaudhuri","orcid":"0000-0002-4934-8556","position":3,"is_corresponding":false},{"id":227877,"name":"Hannah F. Almubarak","orcid":"0000-0001-5895-9968","position":4,"is_corresponding":false},{"id":633713,"name":"Matthew J. Schipma","orcid":null,"position":5,"is_corresponding":false},{"id":554595,"name":"Chengsheng Mao","orcid":"0000-0002-1515-9626","position":6,"is_corresponding":false},{"id":1003620,"name":"Xinkun Wang","orcid":"0000-0002-6044-5090","position":7,"is_corresponding":false},{"id":620524,"name":"Lamiaa El-Shennawy","orcid":"0000-0001-5705-9220","position":8,"is_corresponding":false},{"id":297921,"name":"Nurmaa K. Dashzeveg","orcid":"0000-0002-6702-5187","position":9,"is_corresponding":false},{"id":1039337,"name":"Juncheng Wei","orcid":"0000-0002-1858-6121","position":10,"is_corresponding":false},{"id":845070,"name":"Paul J. Mehl","orcid":null,"position":11,"is_corresponding":false},{"id":1039788,"name":"Laura J. Shihadah","orcid":null,"position":12,"is_corresponding":false},{"id":1036120,"name":"Ching Man Wai","orcid":"0000-0002-2913-5721","position":13,"is_corresponding":false},{"id":845072,"name":"Carolina Ostiguin","orcid":null,"position":14,"is_corresponding":false},{"id":465401,"name":"Yuzhi Jia","orcid":"0000-0002-6534-0036","position":15,"is_corresponding":false},{"id":1039789,"name":"Paolo D'Amico","orcid":null,"position":16,"is_corresponding":false},{"id":1039790,"name":"Neale R. Wang","orcid":null,"position":17,"is_corresponding":false},{"id":58247,"name":"Yuan Luo","orcid":"0000-0003-0195-7456","position":18,"is_corresponding":false},{"id":554009,"name":"Alexis R. Demonbreun","orcid":"0000-0001-6823-125X","position":19,"is_corresponding":false},{"id":283132,"name":"Michael G. Ison","orcid":"0000-0003-3347-9671","position":20,"is_corresponding":false},{"id":297924,"name":"Huiping Liu","orcid":"0000-0003-4822-7995","position":21,"is_corresponding":false},{"id":240965,"name":"Deyu Fang","orcid":"0000-0002-4211-2751","position":22,"is_corresponding":false},{"id":297919,"name":"Andrew D. Hoffmann","orcid":"0000-0002-5479-944X","position":0,"is_corresponding":true}],"reference_count":58,"raw_metadata":null,"created_at":"2026-07-19T01:08:06.496579Z","pmid":"37150240","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":[]}