RXRα Suppression Drives Hepatic Metabolic and Immune Dysfunction in Sepsis: RXRα KO in CLP/SHAM
RNA-seq (gene expression) · mouse · 12 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
- 12 samples: 3 × Sham, WT, vs 3 × CLP, WT, vs 3 × Sham, KO, vs 3 × CLP, KO,Groups 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.
Read the full summary ↓
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 GSE319852_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: 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 PRJNA1425311 and SRA study SRP677955. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 12 samples.
How we read this: 4 replicated groups read from 12 sample titles; they account for 12 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.
- GSM9527852 Sham, WT, replicate 1 Sham, WT,
- GSM9527853 Sham, WT, replicate 2 Sham, WT,
- GSM9527854 Sham, WT, replicate 3 Sham, WT,
- GSM9527855 CLP, WT, replicate 1 CLP, WT,
- GSM9527856 CLP, WT, replicate 2 CLP, WT,
- GSM9527857 CLP, WT, replicate 3 CLP, WT,
- GSM9527858 Sham, KO, replicate 1 Sham, KO,
- GSM9527859 Sham, KO, replicate 2 Sham, KO,
- GSM9527860 Sham, KO, replicate 3 Sham, KO,
- GSM9527861 CLP, KO, replicate 1 CLP, KO,
- GSM9527862 CLP, KO, replicate 2 CLP, KO,
- GSM9527863 CLP, KO, replicate 3 CLP, KO,
- GSE319754 RXRα Suppression Drives Hepatic Metabolic and Immune Dysfunction in Sepsis: Gene induction by BEX in Sham & CLP (8h) 12 samples shares sham, rxr, clp, suppression, dysfunction, hepatic, drives, sepsis
- GSE260635 A critical role for Hepatocyte Nuclear Factor 4 alpha in polymicrobial sepsis-associated metabolic reprogramming and death: effect of CLP/SHAM in Hnf4a WT and KO mice 16 samples shares sham, clp, sepsis
- GSE275526 snRNA sequencing on the kidney tissue of cecal ligation and puncture (CLP) and Sham mice. 6 samples shares sham, clp
- GSE317638 Effect of RXRα agonist bexarotene on gene expression in hepatocytes of hepatic RXRα knockout mice 12 samples shares rxr, hepatic
- GSE249835 Single-cell transcriptomics in the lungs of CLEC5A-deficient mice with CLP-induced polymicrobial sepsis 6 samples shares clp, sepsis
- GSE260577 A critical role for Hepatocyte Nuclear Factor 4 alpha in polymicrobial sepsis-associated metabolic reprogramming and death: H3K27ac and H3K4me3 after CLP sepsis model 18 samples shares clp, sepsis
- GSE226649 The immune landscape of the “deregulated host response” in sepsis: newly emerging MDSCs with high expression of CD47 leads to B cell suppression via App-CD74 signaling 64 samples shares suppression, sepsis
- GSE222996 The immune landscape of the “deregulated host response” in sepsis: newly emerging MDSCs with high expression of CD47 leads to B cell suppression via App-CD74 signaling [RNA-seq] 61 samples shares suppression, sepsis
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.