Non-neural expression of SARS-CoV-2 entry genes in the olfactory epithelium suggests mechanisms underlying anosmia in COVID-19 patients

David Brann; Tatsuya Tsukahara; Caleb Weinreb; Darren W. Logan; Sandeep Robert Datta

Single-cell RNA-seq data from HBC-derived cells from Fletcher et al. and Gadye et al (27, 42) , labeled via Krt5-CreER driver mice, were downloaded from GEO at accession GSE99251 using the file "GSE95601_oeHBCdiff_Cufflinks_eSet_counts_table.txt.gz". Processing was performed as described above, including total counts normalization and filtering for highly variable genes using the SPRING gene filtering function "filter_genes" with parameters (75, 20, 10) . The resulting data were visualized in SPRING and a subset of cells were removed for quality control, including a cluster of cells with low total counts and another with predominantly reads from ERCC spike-in controls. Putative doublets were also identified using Scrublet and removed (6% of cells) (63) . The resulting data were visualized in SPRING and partitioned using Louvain clustering on the SPRING knearest-neighbor graph using the top 40 principal components. Cell type annotation was performed manually using the same set of markers genes listed above. Three clusters were removed for quality control, including one with low total counts and one with predominantly reads from ERCC spike-in controls (likely background), and one with high mitochondrial counts (likely stressed cells). For visualization, and clustering the remaining cells were projected to 15 dimensions using PCA and visualized with UMAP with parameters (n_neighbors=15, min_dist=0.4, alpha=0.5, maxiter=500). Clustering was performed using the Leiden algorithm (resolution=0.4) and cell types were manually annotated using known marker genes.