Statistical analysis

The statistical significance of harvest cell count and harvest to total cell count ratios between different sample groups were determined by t-test using Graphpad software.

Dimensional reduction, clustering, and analysis of single-cell RNA sequencing data was performed using the R package Seurat version 3.2.2. Cells with expression of fewer than 200 or more than 5000 genes were filtered out of the analysis. The data was normalized using "LogNormalize" method of Seurat with a scaling factor of 10000 and variable genes were identified using the Seurat "FindVariableFeatures" method. Principal component analysis (PCA) for dimensional reduction was performed using Seurat functions based on the variable genes previously identified. Cell clustering and tSNE visualization were performed using the "FindClusters" and "RunTSNE" functions, respectively. To identify how the expression of the genes of interest changed across the different clusters, a dotplot was created using Seurat function "DotPlot". The size of the dot in the plot corresponds to the percentage of cells expressing the gene in each cluster and the color represents the average expression level.