Recognizing SARS-CoV-2 infection of nasopharyngeal tissue at the single-cell level by machine learning method

SARS-CoV-2 has posed serious global health challenges not only because of the high degree of virus transmissibility but also due to its severe effects on the respiratory system, such as inducing changes in multiple organs through the ACE2 receptor. This virus makes changes to gene expression at the single-cell level and thus to cellular functions and immune responses in a variety of cell types. Previous studies have not been able to resolve these mechanisms fully, and so our study tries to bridge knowledge gaps about the cellular responses under conditions of infection. We performed single-cell RNA-sequencing of nasopharyngeal swabs from COVID-19 patients and healthy controls. We assembled a dataset of 32,588 cells for 58 subjects for analysis. The data were sorted into eight cell types: ciliated, basal, deuterosomal, goblet, myeloid, secretory, squamous, and T cells. Using machine learning, including nine feature ranking algorithms and two classification algorithms, we classified the infection status of single cells and analyzed gene expression to pinpoint critical markers of SARS-CoV-2 infection. Our findings show distinct gene expression profiles between infected and uninfected cells across diverse cell types, with key indicators such as FKBP4, IFITM1, SLC35E1, CD200R1, MT-ATP6, KRT13, RBM15, and FTH1 illuminating unique immune responses and potential pathways for viral spread and immune evasion. The machine learning methods effectively differentiated between infected and non-infected cells, shedding light on the cellular heterogeneity of SARS-CoV-2 infection. The findings will improve our knowledge of the cellular dynamics of SARS-CoV-2.