Single-cell transcriptomics preparation, sequencing, and analysis
Wild-type Single-cell suspensions were manually counted using a haemocytometer and concentration adjusted to a minimum of 300cells/μL. Cells were loaded according to standard protocol of the Chromium single-cell 3ʹ kit to capture around 5000 cells per chip position. Briefly, a single-cell suspension in PBS 0.04% BSA was mixed with RT-PCR master mix and loaded together with Single Cell 3ʹ Gel Beads and Partitioning Oil into a Single Cell 3ʹ Chip (10 × Genomics) according to the manufacturer’s instructions. RNA transcripts from single cells were uniquely barcoded and reverse transcribed. Samples were run on individual lanes of the Illumina HiSeq 2500.Fastqs for each sample were generated and were demultiplexed using bcl2fastq software. The quality of the fastq files were checked using fastqc software. The analysis was carried out using Cellranger version 7.0.1, a proprietary software from 10 × genomics. Cellranger count module was run to analyse each sample fastq files using Mouse (mm10) reference (generated from GENCODE vM23/Ensembl 98) downloaded from 10 × website. A web summary file along with other intermediate files were generated for each sample. Cellranger aggregate module was used to aggregate all the 4 samples together using the h5 files from count output. A sequencing depth normalization is applied to all the samples during aggregate step to reduce any artifact from various sequencing depths of the samples by cellranger. The aggregated cloupe file was loaded into Loupe Browser version 6 for downstream analysis and visualizations.The dimensionality reduction of the cells was carried out using PCA. A total of 9 clusters were generated using GRAPH based clustering method, which uses sparse nearest-neighbor graph and Louvain Modularity Optimization algorithms. UMAP and t-SNE were used to visualize the data in loupe browser.A negative binomial test was carried out by cellranger to identify the genes which are expressed specifically to each cluster. The clusters were annotated with respect to the expressed markers. Reclustering was performed for the annotated Epithelial, Mesenchymal, and Immune cell clusters and quality control performed by filtering the cells with Mitochondrial counts > 10% and cells with unique features per barcode > 3000.Local distinguishing differential expression test were carried out between the groups using loupe browser. Top 100 differentially expressed genes with adjusted p-value < 0.05 by Wilcoxon Rank Sum test featuring in more than 1 count per cell across the cluster set were used for further analysis.Gene ontology (GO) analysis was performed on the selected top 100 differentially expressed genes using ShinyGO (v0.77), a R-shiny webserver. Top enriched GO Biological Process, Cellular Components and Molecular Function terms were generated from the genelist against Ensemble mouse gene sets. GO terms with an FDR < 0.05 were reported along with the dot plots for top 10 non redundant GO terms.