{"doi":"10.3389/fimmu.2021.738073","title":"Immune Response in Severe and Non-Severe Coronavirus Disease 2019 (COVID-19) Infection: A Mechanistic Landscape","abstract":"The mechanisms underlying the immune remodeling and severity response in coronavirus disease 2019 (COVID-19) are yet to be fully elucidated. Our comprehensive integrative analyses of single-cell RNA sequencing (scRNAseq) data from four published studies, in patients with mild/moderate and severe infections, indicate a robust expansion and mobilization of the innate immune response and highlight mechanisms by which low-density neutrophils and megakaryocytes play a crucial role in the cross talk between lymphoid and myeloid lineages. We also document a marked reduction of several lymphoid cell types, particularly natural killer cells, mucosal-associated invariant T (MAIT) cells, and gamma-delta T (γδT) cells, and a robust expansion and extensive heterogeneity within plasmablasts, especially in severe COVID-19 patients. We confirm the changes in cellular abundances for certain immune cell types within a new patient cohort. While the cellular heterogeneity in COVID-19 extends across cells in both lineages, we consistently observe certain subsets respond more potently to interferon type I (IFN-I) and display increased cellular abundances across the spectrum of severity, as compared with healthy subjects. However, we identify these expanded subsets to have a more muted response to IFN-I within severe disease compared to non-severe disease. Our analyses further highlight an increased aggregation potential of the myeloid subsets, particularly monocytes, in COVID-19. Finally, we provide detailed mechanistic insights into the interaction between lymphoid and myeloid lineages, which contributes to the multisystemic phenotype of COVID-19, distinguishing severe from non-severe responses.","journal":"Frontiers in Immunology","year":2021,"id":162381,"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":48,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9244,"is_data_producer":false,"deposit_databanks":null,"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":679599,"name":"Priya Nayak","orcid":"0000-0001-6919-4286","position":1,"is_corresponding":false},{"id":501137,"name":"Chethan Ashokkumar","orcid":"0000-0001-6508-3407","position":2,"is_corresponding":false},{"id":679600,"name":"Sohail Rao","orcid":"0000-0001-5027-9992","position":3,"is_corresponding":false},{"id":680733,"name":"Jose Luis Almeda","orcid":null,"position":4,"is_corresponding":false},{"id":679601,"name":"Monica M. Betancourt-Garcia","orcid":"0000-0002-7028-5035","position":5,"is_corresponding":false},{"id":501144,"name":"Rakesh Sindhi","orcid":"0000-0001-8525-6694","position":6,"is_corresponding":false},{"id":108044,"name":"Shankar Subramaniam","orcid":"0000-0002-8059-4659","position":7,"is_corresponding":false},{"id":277294,"name":"Kavitha Mukund","orcid":"0000-0002-3570-2315","position":0,"is_corresponding":true}],"reference_count":109,"raw_metadata":null,"created_at":"2026-07-18T23:45:05.641178Z","pmid":"34721400","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":[]}