Transcriptomic profiling of genetically engineered mouse tumors using SOCRATES
This dataset contains single-cell RNA sequencing data from murine colorectal tumors engineered using the SOCRATES platform, a CRISPR-based multiplex genome engineering system. Tumors were initiated in genetically engineered mouse models via lentiviral delivery of pooled sgRNAs targeting key driver genes in colorectal cancer, including Apc, Kras, Trp53, Smad4, and Pten.  Following tumor formation, tissues were dissociated and processed using the 10x Genomics Chromium platform. Cells were multiplexed using TotalSeq-B hashing antibodies, and both gene expression and hashtag libraries were generated using the Chromium Next GEM Single Cell 3' Reagent Kit v3.1. Sequencing was performed on a NovaSeq6000 platform.  Fastq files, raw counts (gene-by-cell matrix), and associated cell metadata (including sample identifiers, genotypes, classification categories, and run information) are provided. The individual reads of the respective tumours need to be demultiplexed and assigned to the individual samples. In the raw counts file and the cell metadata file, the individual samples are already demultiplexed and the cells can be assigned with the metadata file.  This dataset enables the exploration of genotype-dependent transcriptional states, and microenvironmental composition at single-cell resolution.
This dataset contains single-cell RNA sequencing data from murine colorectal tumors engineered using the SOCRATES platform, a CRISPR-based multiplex genome engineering system. Tumors were initiated in genetically engineered mouse models via lentiviral delivery of pooled sgRNAs targeting key driver genes in colorectal cancer, including Apc, Kras, Trp53, Smad4, and Pten.  Following tumor formation, tissues were dissociated and processed using the 10x Genomics Chromium platform. Cells were multiplexed using TotalSeq-B hashing antibodies, and both gene expression and hashtag libraries were generated using the Chromium Next GEM Single Cell 3' Reagent Kit v3.1. Sequencing was performed on a NovaSeq6000 platform.  Fastq files, raw counts (gene-by-cell matrix), and associated cell metadata (including sample identifiers, genotypes, classification categories, and run information) are provided. The individual reads of the respective tumours need to be demultiplexed and assigned to the individual samples. In the raw counts file and the cell metadata file, the individual samples are already demultiplexed and the cells can be assigned with the metadata file.  This dataset enables the exploration of genotype-dependent transcriptional states, and microenvironmental composition at single-cell resolution.
European Bioinformatics Institute