Transcriptional regulation of microglia priming by IRF7, IRF9 and SMAD3 in accelerated aging mice [RNA-seq]
RNA-seq (gene expression) · mouse · 16 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
- 16 samples: 4 × Ercc_mic_6w_wt vs 4 × Ercc_mic_6w_ko vs 4 × Ercc_mic_12w_ko vs 3 × Ercc_mic_12w_wtGroups 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 GSE227606_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 PRJNA945900 and SRA study SRP427880. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 15 samples.
How we read this: 4 replicated groups read from 16 sample titles; they account for 15 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.
- GSM7103856 Ercc_mic_6w_wt_1 Ercc_mic_6w_wt
- GSM7103857 Ercc_mic_6w_wt_2 Ercc_mic_6w_wt
- GSM7103858 Ercc_mic_6w_wt_3 Ercc_mic_6w_wt
- GSM7103859 Ercc_mic_6w_wt_4 Ercc_mic_6w_wt
- GSM7103860 Ercc_mic_6w_ko_1 Ercc_mic_6w_ko
- GSM7103861 Ercc_mic_6w_ko_2 Ercc_mic_6w_ko
- GSM7103862 Ercc_mic_6w_ko_3 Ercc_mic_6w_ko
- GSM7103863 Ercc_mic_6w_ko_4 Ercc_mic_6w_ko
- GSM7103864 Ercc_mic_12w_wt_3 Ercc_mic_12w_wt
- GSM7103865 Ercc_mic_12w_wt_4 Ercc_mic_12w_wt
- GSM7103866 Ercc_mic_12w_wt_5_6
- GSM7103867 Ercc_mic_12w_wt_8 Ercc_mic_12w_wt
- GSM7103868 Ercc_mic_12w_ko_1 Ercc_mic_12w_ko
- GSM7103869 Ercc_mic_12w_ko_2 Ercc_mic_12w_ko
- GSM7103870 Ercc_mic_12w_ko_3 Ercc_mic_12w_ko
- GSM7103871 Ercc_mic_12w_ko_4 Ercc_mic_12w_ko
- GSE227607 Transcriptional regulation of microglia priming by IRF7, IRF9 and SMAD3 in accelerated aging mice 24 samples shares smad3, irf7, irf9, accelerated, microglia, priming, aging
- GSE227605 Transcriptional regulation of microglia priming by IRF7, IRF9 and SMAD3 in accelerated aging mice [ATAC-seq] 8 samples shares smad3, irf7, irf9, accelerated, microglia, priming, aging
- GSE241176 Fibroblast-specific TGF-b signaling mediates dysfunction, fibrosis and hypertrophy in a mouse model of obesity-associated type 2 diabetes [RNA-seq: Smad3 in dbdb] 12 samples shares smad3, rna-seq
- GSE317712 IRF7 links HK1-dependent histone lactylation to fibroblast activation and cardiac fibrosis [RNA-Seq] 8 samples shares irf7, rna-seq
- GSE303886 SETDB2 mitigates podocyte dysfunction in diabetic kidney disease through epigenetic silencing of SMAD3 [MPC5-RNA-Seq] 6 samples shares smad3, rna-seq
- GSE303885 SETDB2 mitigates podocyte dysfunction in diabetic kidney disease through epigenetic silencing of SMAD3 [Conditional knockout mice-Glomeruli-RNA-Seq] 6 samples shares smad3, rna-seq
- GSE303884 SETDB2 mitigates podocyte dysfunction in diabetic kidney disease through epigenetic silencing of SMAD3 [Glomeruli RNA-Seq] 6 samples shares smad3, rna-seq
- GSE303883 SETDB2 mitigates podocyte dysfunction in diabetic kidney disease through epigenetic silencing of SMAD3 [Kidney RNA-Seq] 6 samples shares smad3, rna-seq
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.