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

The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/2020.03.25.009084 doi: bioRxiv preprint Human scSeq data from Durante et al. (44) was downloaded from the GEO at accession GSE139522. 10X Genomics mtx files were filtered to remove any cells with fewer than 500 total counts. Additional preprocessing 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 (90, 3, 10) . The resulting data were visualized in SPRING and partitioned using Louvain clustering on the SPRING k-nearest-neighbor graph. Four clusters were removed for quality control, including two with low total counts (likely background) and two with high mitochondrial counts (likely stressed or dying cells). Putative doublets were also identified using Scrublet and removed (7% of cells). The remaining cells were projected to 40 dimensions using PCA. PCA-batch-correction was performed using Patient 4 as a reference, as previously described (68) . The filtered data were then re-partitioned using Louvain clustering on the SPRING graph and each cluster was annotated using known marker genes, as described in (44) . For example, immature and mature OSNs were identified via their expression of GNG8 and GNG13, respectively. HBCs were identified via the expression of KRT5 and TP63 and olfactory HBCs were distinguished from respiratory HBCs via the expression of CXCL14 and MEG3. Identification of SUS cells (CYP2A13, CYP2J2), Bowman's gland (SOX9, GPX3), and MV ionocytes-like cells (ASCL3, CFTR, FOXI1) was also performed using known marker genes. For visualization, the top 40 principal components were reduced to two dimensions using UMAP with parameters (n_neighbors=15, min_dist=0.4).