GEO series

Deep transcriptional profiling of the two cancer stem cell populations present in the SW480 cell line as presented in "Colorectal Cancer Stem Cell Subtypes Orchestrate Distinct Tumor Microenvironments" [bulk RNA-seq]

GSE254891 Homo sapiens (human) Single-cell RNA-seq 10 samples Submitted 2024/04/25 Platform GPL20301

At a glance

Single-cell RNA-seq · human · 10 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism: Homo sapiens (human)
Topic: Colorectal cancerThis series is part of our curated briefing on it.
Samples: 10 samples
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.

Summary (written by the submitters)

Tumor heterogeneity derives from diverse populations of cancer cells that contribute unique properties to the microenvironment and tumor progression. While multiple classification systems have been developed to define tumor subtypes in colorectal cancer (CRC), the lack of single cell resolution has prohibited a better understanding of how these distinct cancer cell subtypes influence the overall tumor phenotype.

Read the full summary ↓

Here were report that two cancer stem cell subtypes that are oncogenic versions of the intestinal Crypt Base Columnar (CBC) and Regenerative Stem Cell (RSC) populations, are commonly co-present in human CRC. We develop subtype-specific xenograft models utilizing a heterogeneous patient cell line (SW480) that contains CBCs and RSCs, and we define their tumor microenvironments at high resolution via single cell RNA sequencing. RSCs create differentiated, inflammatory, slow growing tumors. CBCs create proliferative, immune-suppressive, invasive tumors. With this enhanced resolution, we unify current CRC patient classification schema with TME phenotypes and organization.

This dataset

Open GSE254891 in finder → Find more human RNA-seq datasets → Colorectal cancer landscape →

Download · which file do I need?

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.

1 · Just have a look

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.

Series matrix ↓

2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE254891_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓

3 · Start from the raw reads

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.

Raw reads on ENA ↗

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 PRJNA1072239 and SRA study SRP487873. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Samples in this study

The sample list for this study is not cached yet. Press Load samples and it will be fetched from NCBI.

+ 10 more — browse all 10 samples with per-sample file links →

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GSE283752 Differential unfolded protein response regulation in KRAS silencing sensitive versus innately resistant colorectal cancer cells [SW480 and SW837] 12 samples shares sw480, colorectal
GSE216329 Single cell RNA sequencing reveals distinct T cell populations in immune-related adverse events of checkpoint inhibitors 48 samples shares populations, distinct
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Search all human RNA-seq datasets in GEO →

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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.