Ha My K, Postovskaya Anna, Kuznetsova Maria, Meysman Pieter, Van Deuren Vincent, Van Ierssel Sabrina, De Reu Hans, Schippers Jolien, Peeters Karin, Besbassi Hajar, Heyndrickx Leo, Willems Betty, Mariën Joachim, Bartholomeus Esther, Vercauteren Koen, Beutels Philippe, Van Damme Pierre, Lion Eva, Vlieghe Erika, Laukens Kris, Coenen Samuel, Naesens Reinout, Ariën Kevin K, Ogunjimi Benson
Center for Health Economics Research and Modelling Infectious Diseases (CHERMID), Vaccine and Infectious Disease Institute, University of Antwerp, Wilrijk, Belgium.
Antwerp Center for Translational Immunology and Virology (ACTIV), Vaccine and Infectious Disease Institute, University of Antwerp, Wilrijk, Belgium.
Sci Adv. 2025 May 16;11(20):eadt2926. doi: 10.1126/sciadv.adt2926.
To complement serology as a tool in public health interventions, we introduced the "celluloepidemiology" paradigm where we leveraged pathogen-specific T cell responses at a population level to advance our epidemiological understanding of infectious diseases, using SARS-CoV-2 as a model. Applying flow cytometry and machine learning on data from more than 500 individuals, we showed that the number of T cells with positive expression of functional markers not only could distinguish patients who recovered from COVID-19 from controls and pre-COVID donors but also identify previously unrecognized asymptomatic patients from mild, moderate, and severe recovered patients. The celluloepidemiology approach was uniquely capable to differentiate health care worker groups with different SARS-CoV-2 exposures from each other. T cell receptor (TCR) profiling strengthened our analysis by revealing that SARS-CoV-2-specific TCRs were more abundant in patients than in controls. We believe that adding data on T cell reactivity will complement serology and augment the value of infection morbidity modeling for populations.
为补充血清学作为公共卫生干预工具的不足，我们引入了“细胞流行病学”范式，即以严重急性呼吸综合征冠状病毒2（SARS-CoV-2）为模型，在人群层面利用病原体特异性T细胞反应来深化我们对传染病的流行病学认识。通过对500多名个体的数据应用流式细胞术和机器学习，我们发现功能性标志物呈阳性表达的T细胞数量不仅可以区分新冠肺炎康复患者与对照组及新冠疫情前的捐赠者，还能从轻症、中症和重症康复患者中识别出此前未被发现的无症状患者。细胞流行病学方法具有独特的能力，能够区分不同SARS-CoV-2暴露程度的医护人员群体。T细胞受体（TCR）分析通过揭示SARS-CoV-2特异性TCR在患者中比在对照组中更丰富，加强了我们的分析。我们认为，增加T细胞反应性数据将补充血清学，并提高人群感染发病率建模的价值。
