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The Rapid Evolution of Chronic Inflammation to Tolerance / Immune Suppression: Mapping Bidirectional Checkpoints

GSE263901 Mus musculus (mouse) RNA-seq (gene expression) 11 samples Submitted 2024/04/17 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 11 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Mus musculus (mouse)
Samples
11 samples: 3 × Control vs 3 × LPS_24H_Acute vs 3 × Chronic_7Day vs 2 × Chronic_11DayGroups 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.
Summary (written by the submitters)
Currently, there is truly a universal lack of knowledge as to exactly how chronic inflammation leads to the initiation of cancer. Likewise, there exists an equal deficiency in knowledge to define the phenomenon of immune “ tolerance”. Tolerance is, however, generally described as the diminished biological response to repeated or sustained antigen exposure associated with limitation to efficacy of toll-like receptor (TLR) synthetic lipid A lipoglycans, or microbial antigen molecular pattern (MAMP) based tumor immune therapies.
Read the full summary ↓
The current work was designed to gain greater understanding into these questions, by employing a model of sustained antigenic TLR4 activation with gram-negative cell wall (LPS) for up to 11 days in macrophages under highly controlled conditions.
This dataset
Open GSE263901 in finder → Find more mouse RNA-seq datasets →
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 GSE263901_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1099701 and SRA study SRP501548. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
3 × Control vs 3 × LPS_24H_Acute vs 3 × Chronic_7Day vs 2 × Chronic_11Day

Supports a between-group comparison across 11 samples.

How we read this: 4 replicated groups read from 11 sample titles; they account for 11 of them. 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.

Samples in this study
Datasets on a similar topic

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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.