GEO series

Single-cell transcriptomic survey of adenomatous polyposis patients with germline APC mutations

GSE109308 Homo sapiens (human) Single-cell RNA-seq 97 samples Submitted 2019/10/25 Platform GPL20301

At a glance

Single-cell RNA-seq · human · 97 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism: Homo sapiens (human)
Topic: Colorectal cancerThis series is part of our curated briefing on it.
Samples: 97 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)

Patients with germline APC mutations are recognized by hundreds of adenoma polyps in colon, which will give rise to adenocarcinoma inevitably. Over 700 germline APC mutations have been reported to be the leading cause of adenomatous polyposis. However, the underlying mechanism of APC mutation triggered colonic cancer remains mysterious.

Read the full summary ↓

Here, using a modified STRT-seq protocol, we analyzed over 4000 single cells from the four matching adenomatous polyposis, adenocarcinoma, adjacent normal colon tissue and one lymphatic metastasis from four adenomatous polyposis patients with APC mutations. We identified the main cell types existed in human intestine such as B cells, mast cells, T cells, macrophage cells, endothelial cells, stromal cells and epithelial cells. And the proportional changes of various cell types during the development of adenoma were showed. The transcriptomic similarities and differences between adenoma, adenocarcinoma and adjacent normal tissue were comprehensively investigated. For better understanding the tumor progression process, we also take advantage of other genomic signatures, such as SNV and CNV. We found that the gene expression signatures of adenoma and adenocarcinoma were largely similar when compared with adjacent normal tissue. But the mutation accumulation was indeed observed during the progression from adenoma to adenocarcinoma. To summarized, our work will help us accurately investigate the pathogenic mechanism and heterogeneity of APC inactive mutation triggered colonic cancer, which will also help us better understand the non-familial colorectal cancers

This dataset

Open GSE109308 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 GSE109308_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 PRJNA430398 and SRA study SRP131079. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 31744909) ↗

Samples in this study

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+ 97 more — browse all 97 samples with per-sample file links →

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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.