Scalable workflow for characterization of cell-cell communication in COVID-19 patients

Abstract

COVID-19 patients display a wide range of disease severity, ranging from asymptomatic to critical symptoms with high mortality risk. Our ability to understand the interaction of SARS-CoV-2 infected cells within the lung, and of protective or dysfunctional immune responses to the virus, is critical to effectively treat these patients. Currently, our understanding of cell-cell interactions across different disease states, and how such interactions may drive pathogenic outcomes, is incomplete. Here, we developed a generalizable and scalable workflow for identifying cells that are differentially interacting across COVID-19 patients with distinct disease outcomes and use this to examine eight public single-cell RNA-seq datasets (six from peripheral blood mononuclear cells, one from bronchoalveolar lavage and one from nasopharyngeal), with a total of 211 individual samples. By characterizing the cell-cell interaction patterns across epithelial and immune cells in lung tissues for patients with varying disease severity, we illustrate diverse communication patterns across individuals, and discover heterogeneous communication patterns among moderate and severe patients. We further illustrate patterns derived from cell-cell interactions are potential signatures for discriminating between moderate and severe patients. Overall, this workflow can be generalized and scaled to combine multiple scRNA-seq datasets to uncover cell-cell interactions. Author summary Despite the availability of several studies of single-cell transcriptomics profiles from different geographic locations, our knowledge of cell-cell interactions across distinct disease states and how such interactions may drive pathogenic outcomes remains limited. Motivated by the need to gain insights into health and disease and to address challenges associated with the compilation and exploration of multiple large-scale data, we developed a generalizable and scalable workflow for identifying cells that are differentially interacting across COVID-19 patients with distinct disease outcomes. Our workflow shows how to characterize cellular communication patterns for patients with varying disease severity and thus aids in the understanding of disease progression. We show the scalability and interpretability of our approach by combining around half million of cells from eight COVID-19 scRNA-seq experiments to demonstrate that individuals have heterogeneous communication patterns. Such patterns are potential signatures to discriminate between moderate and severe patients. Overall, this workflow can be generalized and scaled to combine multiple scRNA-seq datasets to uncover cell-cell interactions in complex diseases.