Analysis of cellular heterogeneity in immune microenvironment of Primary central nervous system lymphoma by single cell sequencing

Human PCNSL tumor samples were obtained surgically from clinical patients confirmed by pathology from our hospital. After sample collection processing and suspensions, we performed scRNA-seq following the manufacturer's protocol. Briefly, the cells were washed with PBS and resuspended in 500 µl PBS. scRNA-seq libraries were prepared using a Chromium Single cell 3' Reagent kit, version 2. Amplified cDNA and final libraries were evaluated using a High Sensitivity DNA Kit (Agilent Technologies). Sequencing was performed on NovaSeq 6000 (Illumina) at a depth of approximately 400M reads. Overall design: The Cell Ranger software pipeline (version 4.0, http://support.10Xgenomics.com/single-cell-gene-expression/software/overview/welcome) provided by 10x Genomics was used to demultiplex cellular barcodes. Unique molecular identifier (UMI) counts were obtained by mapping reads to the human reference genome (GRCH38 3.1.0) genome and align transcriptomes using the STAR aligner and down-sample reads as required to generate normalized aggregate data across samples. In the end, a matrix of gene counts by cells was produced. We processed the UMI count matrix using the Seurat R package (version 4.0.2), resulting in 34,851 cells with 36,601 genes for PCNSL samples. We first removed the likely multiplet captures which is a major concern in microdroplet-base experiments through doubletfinder. We filtered cells at the cell and gene levels to obtain the reliability results of PCNSL scRNA-seq data, respectively. We removed the low qualify cells with the following criteria: (i) The number of expressed genes was <200 or >2,000; (ii) The number of total counts was > 20,000. (iii) The percentage of mitochondrial counts > 10%. We only kept the genes detected in at least 20 cells. After applying these quality control criteria, we obtained 20,307 cells with 12,229 genes in total, which were used for downstream analysis. Similar to normal CNS scRNA-seq data, we used the following criteria: (i) The number of expressed genes was <100 or >3,000; (ii) The number of total counts was > 20,000. (iii) The percentage of mitochondrial counts > 10%. We only kept the genes detected in at least 20 cells. After applying these quality control criteria, we obtained 20,015 cells with 16,608 genes from 21,255 cells with 33,694 genes in total, which were used for downstream analysis.