Comparative, single-cell analysis of COVID-19 and other respiratory diseases

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PROJECT SUMMARY COVID-19 disease, caused by the novel coronavirus SARS-CoV-2, is a global health threat due to its rapid spread, morbidity, and mortality. It is estimated that at least 25% of severe COVID-19 disease survivors experience persistent respiratory complications. This underscores the need to better understand how the cell types, regenerative lineages, and cell-cell communication are altered following SARS-CoV-2 infection and what regulatory mechanisms and interactions in affected tissue may underlie Long-COVID, the long-term medical complications in COVID-19 long-haulers. Recent advances in high-throughput single cell RNA-sequencing (scRNA-seq) have enabled comprehensive characterization of the cellular census of the lung, which has led to a remarkable number of novel findings in new cell types and cell states at homeostasis and in disease. However, our understanding of the cell type relationships and their higher order tissue organization is still lacking in the context of COVID-19. Analyses by our lab and the Human Cell Atlas (HCA) lung network using a compendium of tissues from healthy individuals revealed that a distinct subset of epithelial cells in the nasal passages, airways, alveoli, and gut co-express ACE2 and TMPRSS2, consistent with organ-specific disease presentations of COVID-19. More recently, over one million cells of scRNA-seq data from greater than one hundred COVID-19 patients became publicly available, which provides an unprecedented resolution and statistical power to study the variation in the lung cellular microenviroment between individuals with different presentation of COVID-19. Such an endeavor presents several computational challenges due to the high- dimensionality of the data and because analysis tools of cellular communication in scRNA-seq data and integration with coordinated multicellular gene expression programs are still needed. We propose to integrate high-dimensional scRNA-seq data and correct for batch effects between laboratories in order to build a uniformly annotated single-cell atlas of more than one hundred COVID-19 patients. Such an atlas would enable us to characterize the similarities and differences in cellular composition and cell-cell interactions and assess the effects of clinical correlates--such as gender, age, medications, and smoking history--on tissue organization. Comparisons between the lung microenvironment across our large, integrated cohort of control and COVID-19 patients will uncover commonly dysregulated cell types and cell-cell interactions that can inform on shared pathways to target therapeutically. Moreover, comparison of the dysregulated cell types, regenerative lineages, and cell-cell interactions to those observed in other lung diseases will provide important insight on how to repurpose existing drugs and recommended treatments for COVID-19 long haulers with respiratory complications.