CD40 agonism in renal cell carcinoma enhances immune checkpoint activity through myeloid cells. Djureinovic et al.
Description
CD40 agonism was evaluated in combination with immune-checkpoint blockade in Renca. Single cell RNA-sequencing was performed on tumor and TILs isolated 24 hours after two treatments. Digital count matrices were analyzed to identify cell types and frequencies of the different cell populations in each treatment condition using R Studio and the Seurat package. Cell type assignments of clusters were performed using SingleR, analysis of expression patterns of canonical markers and by assessment of the top 5 conserved markers per cluster from the FindConservedMarkers function. Gene expression UMAP plots were generated using the FeaturePlot command. Cluster frequencies by experimental condition were normalized to the total number of cells per condition. The top differentially expressed genes comparing a single cluster to all other clusters were computed using the FindAllMarkers function and the data were scaled. T cell subsets were annotated using ProjecTILS and a reference mouse tumor-infiltrating T cell dataset.
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Digital count matrices were analyzed to identify cell types using Seurat package (version 4.0.5) in R Studio (version 4.1.2). To remove low quality cells, the following thresholds were applied: >500 nUMI,>250 genes,>0.8 log10GeneperUMI and <0.2 mitochondrial gene ratio and only genes expressed in 10 or more cells. Cell cycle scoring was performed using the CellCycleScoring command using mouse gene sets orthologous to gene sets previously described. Cell cycle factors were regressed out using the “sctransform” function, and the data were normalized and integrated on the 3,000 most variable features. Principal component (PC) scores from the first 40 PCs were used for clustering with the FindClusters command, and a resolution of 0.6. Uniform Manifold Approximation and Projection (UMAP) was used for dimensionality reduction. Cell type assignments of clusters were performed using SingleR, analysis of expression patterns of canonical markers and by assessment of the top 5 conserved markers per cluster from the FindConservedMarkers function. Gene expression UMAP plots were generated using the FeaturePlot command. Cluster frequencies by experimental condition were normalized to the total number of cells per condition. The top differentially expressed genes comparing a single cluster to all other clusters were computed using the FindAllMarkers function and the data were scaled. Gene set enrichment analysis was performed using EnrichR. T cell subsets were annotated using ProjecTILS and a reference mouse tumor-infiltrating T cell dataset.