A pan-family screen of Nuclear Receptors in immunocytes reveals ligand-dependent inflammasome control [RNA-seq 1]
RNA-seq (gene expression) · mouse · 52 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
- 52 samples: 21 conditions, mostly in duplicateGroups 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.
- Paper
- A pan-family screen of nuclear receptors in immunocytes reveals ligand-dependent inflammasome controlImmunity · 2024
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 GSE254406_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 PRJNA1070322 and SRA study SRP486663. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 48 samples.
How we read this: 21 replicated groups read from 52 sample titles; they account for 48 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.
- GSM8041628 LPM_BFP_CTRL_1 LPM_BFP_CTRL
- GSM8041629 LPM_BFP_CTRL_2 LPM_BFP_CTRL
- GSM8041630 LPM_BFP_CTRL_3 LPM_BFP_CTRL
- GSM8041631 LPM_BFP_CTRL_4 LPM_BFP_CTRL
- GSM8041632 LPM_BFP_CTRL_5 LPM_BFP_CTRL
- GSM8041633 LPM_GFP_CTRL_6 LPM_GFP_CTRL
- GSM8041634 LPM_GFP_CTRL_7 LPM_GFP_CTRL
- GSM8041635 LPM_GFP_CTRL_8 LPM_GFP_CTRL
- GSM8041636 LPM_GFP_CTRL_9 LPM_GFP_CTRL
- GSM8041637 LPM_RFP_CTRL_10 LPM_RFP_CTRL
- GSM8041638 LPM_RFP_CTRL_11 LPM_RFP_CTRL
- GSM8041639 LPM_RFP_CTRL_12 LPM_RFP_CTRL
- GSM8041640 LPM_RFP_Esr1_Mut_1 LPM_RFP_Esr1_Mut
- GSM8041641 LPM_RFP_Esr1_Mut_2 LPM_RFP_Esr1_Mut
- GSM8041642 LPM_GFP_Esrra_Mut_1 LPM_GFP_Esrra_Mut
- GSM8041643 LPM_GFP_Esrra_Mut_2 LPM_GFP_Esrra_Mut
- GSM8041644 LPM_GFP_Nr1d1_Mut_1
- GSM8041645 LPM_RFP_Nr1d1_Mut_2
- GSM8041646 LPM_GFP_Nr1d2_Mut_1
- GSM8041647 LPM_RFP_Nr1d2_Mut_2
- GSM8041648 LPM_BFP_Nr1h2_Mut_1 LPM_BFP_Nr1h2_Mut
- GSM8041649 LPM_BFP_Nr1h2_Mut_2 LPM_BFP_Nr1h2_Mut
- GSM8041650 LPM_GFP_Nr1h3_Mut_1 LPM_GFP_Nr1h3_Mut
- GSM8041651 LPM_GFP_Nr1h3_Mut_2 LPM_GFP_Nr1h3_Mut
- GSM8041652 LPM_RFP_Nr2c1_Mut_1 LPM_RFP_Nr2c1_Mut
- GSM8041653 LPM_RFP_Nr2c1_Mut_2 LPM_RFP_Nr2c1_Mut
- GSM8041654 LPM_GFP_Nr2c2_Mut_1 LPM_GFP_Nr2c2_Mut
- GSM8041655 LPM_GFP_Nr2c2_Mut_2 LPM_GFP_Nr2c2_Mut
- GSM8041656 LPM_RFP_Nr2f6_Mut_1 LPM_RFP_Nr2f6_Mut
- GSM8041657 LPM_RFP_Nr2f6_Mut_2 LPM_RFP_Nr2f6_Mut
- GSM8041658 LPM_RFP_Nr3c1_Mut_1 LPM_RFP_Nr3c1_Mut
- GSM8041659 LPM_RFP_Nr3c1_Mut_2 LPM_RFP_Nr3c1_Mut
- GSM8041660 LPM_BFP_Nr3c2_Mut_1 LPM_BFP_Nr3c2_Mut
- GSM8041661 LPM_BFP_Nr3c2_Mut_2 LPM_BFP_Nr3c2_Mut
- GSM8041662 LPM_BFP_Nr4a1_Mut_1 LPM_BFP_Nr4a1_Mut
- GSM8041663 LPM_BFP_Nr4a1_Mut_2 LPM_BFP_Nr4a1_Mut
- GSM8041664 LPM_RFP_Rara_Mut_1 LPM_RFP_Rara_Mut
- GSM8041665 LPM_RFP_Rara_Mut_2 LPM_RFP_Rara_Mut
- GSM8041666 LPM_BFP_Rarb_Mut_1 LPM_BFP_Rarb_Mut
- GSM8041667 LPM_BFP_Rarb_Mut_2 LPM_BFP_Rarb_Mut
+ 12 more — browse all 52 samples with per-sample file links →
- GSE273340 A pan-family screen of Nuclear Receptors in immunocytes reveals ligand-dependent inflammasome control [RNA-seq 2] 12 samples shares ligand-dependent, inflammasome, immunocytes, pan-family, receptors, nuclear, control, rna-seq
- GSE254572 A pan-family screen of Nuclear Receptors in immunocytes reveals ligand-dependent inflammasome control [scRNA-seq] 2 samples shares ligand-dependent, inflammasome, immunocytes, pan-family, receptors, nuclear, control, rna-seq
- GSE254573 A pan-family screen of Nuclear Receptors in immunocytes reveals ligand-dependent inflammasome control 70 samples shares ligand-dependent, inflammasome, immunocytes, pan-family, receptors, nuclear, control
- GSE254407 A pan-family screen of Nuclear Receptors in immunocytes reveals ligand-dependent inflammasome control [ChIPmentation] 4 samples shares ligand-dependent, inflammasome, immunocytes, pan-family, receptors, nuclear, control
- GSE274312 A cis-regulatory element controls HDAC9 expression to fine-tune inflammasome-dependent chronic inflammation 24 samples shares inflammasome, control
- GSE342046 Inflammasome Hyperactivation Disrupts Construction of the Gas Exchange Surface through the NLRP3-IL-18-TNFa Axis [RNA-seq] 17 samples shares inflammasome, rna-seq
- GSE207875 Nuclear Receptor Coactivator 6 is a Critical Regulator of NLRP3 Inflammasome Activation and Gouty Arthritis 8 samples shares inflammasome, nuclear
- GSE278604 Neutrophil KLF2 regulates inflammasome-dependent neonatal mortality from endotoxemia 28 samples shares inflammasome
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.