scRNA-seq analysis

scRNA-seq analysis

Analyses of the scRNA-seq datasets including filtering, normalization and clustering were conducted using Seurat 3.1 (42) (for more information https://satijalab.org/seurat/vignettes.html).  Human lung data from Han et al. (14), was downloaded from https://figshare.com/articles/HCL_DGE_Data/7235471, in the form of batch-corrected digital gene expression matrices and cell annotation csv files. Cell annotation included cell types, tissue of origin and age. Gene expressions were log-normalized with a scale factor of 10 000 using the NormalizeData function using Seurat 3.1. Next, data was scaled using the ScaleData function and the number of UMI and the percentage of mitochondrial gene content were regressed out as described by the authors (18). The first 40 principal components were considered for the UMAP (Uniform Manifold Approximation and Projection). Human olfactory neuroepithelium raw data from Durante et al. (19), was downloaded from Gene Expression Omnibus under accession code GSE139522. Data was quality controlled, pre-processed, filtered, normalized, scaled and clustered as described in detail by the authors (19). Data from 4 patients were integrated using the Seurat 3 standard integration (42) using the parameters described by Durante et al. (19). Since cell type annotations were not provided publicly, we have used the cell type specific markers indicated by the authors to identify cell type clusters. Organotypic human bronchial epithelial cells (HBECs) datasets from Ravindra et al. (13) were kindly provided by the authors upon request. Differentially expressed genes table from Ravindra et al. (13), was obtained from the GitHub page maintained by the authors (https://github.com/vandijklab/HBEC_SARS-CoV-2_scRNA-seq). Datasets from different time points were already integrated and batch-corrected as previously described (13). Bronchoalveolar lavage fluid (BALF) data was acquired from GSE145926 and only the scRNA-seq data was used (14). Datasets from 12 samples were quality controlled, filtered and integrated using the Seurat 3 standard integration workflow as described by the authors. Differentially expressed genes were identified comparing SARS-CoV-2 positive and negative cells with the FindMarkers function in Seurat. Cell annotation of infection status, cell type, etc. was provided by the authors on their GitHub page (https://github.com/zhangzlab/covid_balf). For all datasets except the DEG table from Navindra et al. (13), DotPlot and DimPlot functions within Seurat were used for visualization purposes.

References and Notes are numbered according to the original manuscript:

Cantuti-Castelvetri, L., et al., (2020) Neuropilin-1 facilitates SARS-CoV-2 cell entry and provides a possible pathway into the central nervous system. Science eabd298

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