Ceramides drive colorectal cancer metastasis through a revival stemness program [scRNA-seq]
Single-cell RNA-seq · mouse · 10 samples
- What it is
- Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
- Organism
- Mus musculus (mouse)
- Samples
- 10 samples: 10 samples, each with a distinct title — no replicated groupsGroups are read from the sample names, not from the paper. Check the list below.
- Data you can get
- A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.
Processed table
The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE285873_RAW.tar (it can run to tens of gigabytes).
Raw sequencing reads
The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.
What the files usually look like: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.
Also filed as BioProject PRJNA1206688 and SRA study SRP555351. Searching any of these in the dataset finder brings you back here.
Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.
How we read this: Read from 10 sample titles: 10 distinct titles with little repetition. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.
- GSM8711797 Colon tumor, HFD rep1, scRNAseq
- GSM8711798 Colon tumor, CD rep1, scRNAseq
- GSM8711799 Colon, HFD rep1, scRNAseq
- GSM8711800 Colon, CD rep1, scRNAseq
- GSM8711801 Colon tumor, CD rep2, scRNAseq
- GSM8711802 Colon tumor, HFD rep2, scRNAseq
- GSM8711803 Colon, CD rep2, scRNAseq
- GSM8711804 Colon, HFD rep2, scRNAseq
- GSM8711805 Colon tumor, HFD rep3, scRNAseq
- GSM8711806 Colon tumor, CD rep3, scRNAseq
- GSE218339 Gene expression profile at single cell level of murine colon organoids harbouring colorectal cancer-associated driver mutations [scRNA-seq] 9 samples shares colorectal, scrna-seq, drive
- GSE237954 Oncofetal reprogramming drives phenotypic plasticity in WNT-dependent colorectal cancer 122 samples shares colorectal, program, drive
- GSE290756 Lineage Plasticity Driven by GATA6 Loss Fuels Colorectal Cancer Metastasis 50 samples shares colorectal, metastasis, drive
- GSE217662 Decoding Trp53 regulatory program with knockout cell transcriptomic landscape analyses [scRNA-seq] 247 samples shares scrna-seq, program
- GSE231804 IL-1R signaling drives enteric glia-macrophage interactions in colorectal cancer 39 samples shares colorectal, drive
- GSE245315 Spatially restricted immune and microbiota-driven adaptation of the gut [scRNA-seq] 26 samples shares scrna-seq, drive
- GSE327176 Single-cell transcriptomic profiling unveils tumor-mediated reprogramming of neutrophils and their unique vulnerability that inhibits metastasis 25 samples shares metastasis, program
- GSE266900 Cxcr3 promotes protection from colorectal cancer liver metastasis by driving NK infiltration and plasticity 18 samples shares colorectal, metastasis
Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.