PMC:7039956 / 13618-17914 JSONTXT

{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/7039956","sourcedb":"PMC","sourceid":"7039956","source_url":"https://www.ncbi.nlm.nih.gov/pmc/7039956","text":"Materials and methods\n\nPublic datasets acquisition and analysis\nBulk RNA-seq data of para-carcinoma normal tissues which were taken as control tissues in the studies were downloaded from The Cancer Genome Atlas (TCGA; https://www.cancer.gov/tcga), and 695 para-carcinoma normal tissues distributed in different organs were obtained for this study, which included intestine (51 tissues), kidney (129 tissues), stomach (35 tissues), bile duct (9 tissues), liver (50 tissues), oral cavity (32 tissues), lung (110 tissues), thyroid (59 tissues), esophagus (11 tissues), bladder (19 tissues), breast (113 tissues), uterus (25 tissues), and prostate (52 tissues). The RNA-seq data were batch effects normalized and log2-transformed for the subsequent analysis. Violin plot was used to show the distribution of ACE2 expression among different organs, t test was performed to compare the ACE2 expression between two different groups shown in boxplot.\nBesides, Bulk RNA-seq data of normal tissues were downloaded from Functional Annotation of The Mammalian Genome Cap Analysis of Gene Expression (FANTOM5 CAGE) dataset25, as only two samples of this dataset which owns 60 samples in total located in the tongue, we just downloaded 14 organ types to validate the ACE2 expression in oral cavity with bar plot, including colon, ovary, breast, cerebellum, epididymis, esophagus, gallbladder, heart muscle, kidney, liver, lung, pancreas, prostate, and tongue.\nAll analyses were performed in R (R version 3.6.0) and significant level was set as 0.05.\n\nOral tissues of in-house cohort\nDue to our previous project about oral potential malignant disorders, four tissues of oral mucosa were obtained from patients after informed consent and ethical approval from West China Hospital of Stomatology, Sichuan University. These oral tissues had been sent for single cell RNA sequence. The four were taken from three patients with an average age of 50, which were all diagnosed as hyperkeratosis without dysplasia by pathologists, just showing an increase in cell number in the spinous layer and/or in the basal/parabasal cell layers without cellular atypia, and its genetic profiles would be much more closed to normal tissue than malignant tissue22–24. Two tissues were from independent lesions of one patient’s dorsum linguae. The other two tissues came from buccal and gingival sites.\n\nTissue dissociation from fresh biopsies\nFresh biopsy tissues were washed twice by D-PBS (Hyclone) and collected into prechilled MACS Tissue Storage Solution (Miltenyi). Single-cell suspensions were generated from biopsy tissues using Whole Skin Dissociation Kit human (Miltenyi) manufacturer guidelines and filtered by 70 μm MACS Smartstrainers (Miltenyi). Carryover red blood cells were lysed by Red Blood Cell Lysis Buffer (Abcam) and dead cells were removed by Easysep Dead Cell Removal (Annexin V) Kit (STEMCELL). Finally, cell pellets were re-suspended in D-PBS.\n\nSingle cell RNA-sequencing library preparation\nTo generate single-cell Gel Beads-in-Emulsion (GEMs), the 10× chromium platform was used to capture and barcode cells. Cells were partitioned into GEMs along with gel beads coated with oligonucleotides and cDNAs with both barcodes were amplified, and a library was constructed using the 10× Genomics Chromium Single Cell Kit (v3 chemistry) for each sample. The resulting libraries were sequenced on an Illumina NovaSeq 6000 System.\n\nSingle cell RNA-seq data preprocessing and analysis\nThe FASTQ files were analyzed with the Cell Ranger Software Suite (version 3.1; 10× Genomics). The Seurat (version 3.0) was applied to read the gene-barcode matrix of four tissues. To control quality, we removed cells with \u003c200 genes and 500 UMI counts, and as well as the cells with mitochondrial content higher than 5%. Besides, the genes detected in \u003c3 cells were filtered out. The “sctransform” wrapper in Seurat was applied to normalize the data and remove confounding sources of variation, the “IntegrateData” was used for integrated the Seurat objects from four tissues. The uniform manifold approximation and projection (UMAP) was used for dimensionality reduction and clustering the cells, cell types were assigned based on their canonical markers. 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