SOAT1 knockout reshapes tumor immune microenvironment with enhanced CD8+ T cell infiltration and effector function [scRNA-seq]

To investigate the mechanism, we performed single-cell RNA sequencing (scRNA-seq) to profile the in vivo transcriptome and immune landscape (Figure 4F). Tumors from PBS- and anti-PD1-treated mice were purified using Ficoll and subjected to scRNA-seq, recovering 9,431 and 9,970 cells from SOAT1-KO tumors, and 10,880 and 8,580 cells from vector tumors, respectively. Overall design: For single-cell RNA sequencing (scRNA-seq) library preparation, the DNBelab C Series High-throughput Single-Cell RNA Library Prep Kit (MGI, Cat# 940-000519-00) was used. In brief, cells at a concentration of 1,000 cells/ µL were loaded into the cell reservoir of a microfluidic chip. Barcoded beads and droplet-generation oil were sequentially added to their respective reservoirs. Encapsulated droplets were generated and collected using the DNBelab C4 /DNBelab TaiM4 system. Beads capturing the mRNA were then recovered for reverse transcription (RT). After RT, complementary DNA (cDNA) was amplified via PCR, purified, and quantified using a Qubit dsDNA kit (Thermo Fisher). Libraries of 3'-end transcripts were subsequently constructed through cDNA fragmentation, size selection, end repair and A-tailing, adapter ligation, indexing PCR, and library cyclization, according to the manufacturer 's protocol. The sequencing libraries were purified and quantified using the Qubit ssDNA kit (Thermo Fisher) and the Qsep100 system (Bioptic). scRNA-seq was performed on the DNBelab C4/DNBelab TaiM4 system. The DNBelab C4 /DNBelab TaiM4 Series Single-Cell Library Prep Set (MGI) was used for sequencing. DNBs were loaded into patterned nanoarrays and sequenced on the DNBSEQ-T7 sequencer with pair-end sequencing. The sequencing reads contained a 30-bp read 1 (including a 10-bp cell barcode 1, a 10-bp cell barcode 2 and a 10-bp unique molecular identifiers (UMI)), a 100-bp read 2 for gene sequences, and a 10-bp barcode read for sample indexing. Sequencing data were processed using the open-source DNBelab C Series scRNA analysis software pipeline (https://github.com/MGI-tech-bioinformatics/DNBelab_C_Series_scRNA-analysis-software). Sample de-multiplexing, barcode processing, and single-cell 3' UMI counting were performed with default parameters. Processed reads were aligned to the GRCh38 genome reference using STAR (2.7.2b). Valid cells were automatically identified based on UMI distribution using the “barcodeRanks” function of the DropletUtils tool to remove background beads and low-UMI-count beads. Finally, we used PISA tool to calculate gene expression for each cell and create a gene-by-cell matrix for each library.