ArticleFrontiers in immunology2023
Machine learning of flow cytometry data reveals the delayed innate immune responses correlate with the severity of COVID-19.
Article in Frontiers in immunology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 6 citations in OpenAlex.
- Cytotoxic CD4Signal transduction and targeted therapy · 2026Review
- A Modular Mathematical Model of the Immune Response for Investigating the Pathogenesis of Infectious Diseases.Viruses · 2025Article
- Predicting viral proteins that evade the innate immune system: a machine learning-based immunoinformatics tool.BMC bioinformatics · 2024Article
- Identifying phenotype-associated subpopulations through LP_SGL.Briefings in bioinformatics · 2023Article
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: The COVID-19 pandemic has posed a major burden on healthcare and economic systems across the globe for over 3 years. Even though vaccines are available, the pathogenesis is still unclear. Multiple studies have indicated heterogeneity of immune responses to SARS-CoV-2, and potentially distinct patient immune types that might be related to disease features. However, those conclusions are mainly inferred by comparing the differences of pathological features between moderate and severe patients, some immunological features may be subjectively overlooked. Methods: In this study, the relevance scores(RS), reflecting which features play a more critical role in the decision-making process, between immunological features and the COVID-19 severity are objectively calculated through neural network, where the input features include the immune cell counts and the activation marker concentrations of particular cell, and these quantified characteristic data are robustly generated by processing flow cytometry data sets containing the peripheral blood information of COVID-19 patients through PhenoGraph algorithm. Results: Specifically, the RS between immune cell counts and COVID-19 severity with time indicated that the innate immune responses in severe patients are delayed at the early stage, and the continuous decrease of classical monocytes in peripherial blood is significantly associated with the severity of disease. The RS between activation marker concentrations and COVID-19 severity suggested that the down-regulation of IFN-γ in classical monocytes, Treg, CD8 T cells, and the not down-regulation of IL_17a in classical monocytes, Tregs are highly correlated with the occurrence of severe disease. Finally, a concise dynamic model of immune responses in COVID-19 patients was generalized. Discussion: These results suggest that the delayed innate immune responses in the early stage, and the abnormal expression of IL-17a and IFN-γ in classical monocytes, Tregs, and CD8 T cells are primarily responsible for the severity of COVID-19.
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