was used to accurately quantify the relative abundances of six distinct immune cell types according to the ImmGen immune cell genes signature .After brain harvest, GFP+ tumors were dissected under a Leica MZFL II stereomicroscope. Tumor pieces were chopped and incubated for 5 min at 37 °C in an HBSS-papain-based lysis buffer containing DNAse and L-Cystein . Papain digestion was inhibited by ovomucoid .
Cutadapt 1.18 was used to trim nextera adapters in 3’ on reads, then a quality control of sequences was done with FastQC. Cellular and UMIs barcodes were extracted with the ddSeeker tool with default parameters. The following steps were done with Drop-seq tool . Trimming of 5’ adapter sequences and of polyA tails was performed. Unaligned BAM was transformed to fastq with the Picard tool, prior to alignment with STAR on mm10 reference genome.
The Cell Ranger Single-cell Software suite was used to process the data. First, a custom reference genome was created with the mkref function to include 3’LTR and 3MR sequences into the mm10 reference genome. Count function was used on each GEM well that was demultiplexed byto generate gene-cell matrices. Then, filtered_feature_bc_matrix output was loaded into the Seurat Bioconductor package to filter the datasets and identify cell types using R .
All samples were merged together for downstream analysis. As no batch effects were observed among the four samples, no integration step was performed. Gene expression matrix was normalized using the negative binomial regression method implemented in the Seuratfunction, via the selection of the top 3000 variable genes and regressed out the mitochondrion expression percentage. The final dataset was composed of 20,293 genes and 26,237 cells.
To cluster cells, we computed a principal components analysis on scaled variable genes, as determined above, using Seurat’sfunction, and visualized it by computing a Uniform Manifold Approximation and Projection using Seurat’s-nearest neighbor graph on the top 30 PCs, using Seurat’sfunction with varying resolution values. We chose a final value of 0.5 for the resolution parameter at this stage of clustering.
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