RNA Polymerase III inhinition leads to changes in protein coding gene expression
Single-nucleus RNA-seq · human · 54 samples
- What it is
- Single-nucleus RNA-seqGene activity measured in individual nuclei (used when whole cells are hard to isolate, for example from frozen tissue). Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
- Organism
- Homo sapiens (human)
- Samples
- 54 samples: 18 conditions, mostly in triplicateGroups 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.
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 GSE333557_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: 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 PRJNA1471568 and SRA study SRP704413. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 53 samples.
How we read this: 18 replicated groups read from 54 sample titles; they account for 53 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.
- GSM9769137 Scramble_ASO_200nM_ 48h_1 Scramble_ASO_200nM_ 48h
- GSM9769138 Scramble_ASO_200nM_ 48h_2 Scramble_ASO_200nM_ 48h
- GSM9769139 control_siRNA_200nM_48_h_1 control_siRNA_200nM_48_h
- GSM9769140 control_siRNA_200nM_48_h_2 control_siRNA_200nM_48_h
- GSM9769141 control_siRNA_200nM_48_h_3 control_siRNA_200nM_48_h
- GSM9769142 POLR3G_siRNA_200nM_48_h_1 POLR3G_siRNA_200nM_48_h
- GSM9769143 POLR3G_siRNA_200nM_48_h_2 POLR3G_siRNA_200nM_48_h
- GSM9769144 POLR3G_siRNA_200nM_48_h_3 POLR3G_siRNA_200nM_48_h
- GSM9769145 control siRNA 25nM 48 h_1 control siRNA 25nM 48 h
- GSM9769146 control siRNA 25nM 48 h_2 control siRNA 25nM 48 h
- GSM9769147 control siRNA 25nM 48 h_3 control siRNA 25nM 48 h
- GSM9769148 siPOLR3GL_siE4_25nM 48 h_1 siPOLR3GL_siE4_25nM 48 h
- GSM9769149 siPOLR3GL_siE4_25nM 48 h_2 siPOLR3GL_siE4_25nM 48 h
- GSM9769150 siPOLR3GL_siE4_25nM 48 h_3 siPOLR3GL_siE4_25nM 48 h
- GSM9769151 siPOLR3GL_siE6_25nM 48 h_1 siPOLR3GL_siE6_25nM 48 h
- GSM9769152 siPOLR3GL_siE6_25nM 48 h_2 siPOLR3GL_siE6_25nM 48 h
- GSM9769153 siPOLR3GL_siE6_25nM 48 h_3 siPOLR3GL_siE6_25nM 48 h
- GSM9769154 siPOLR3GL_siE8_25nM 48 h_1 siPOLR3GL_siE8_25nM 48 h
- GSM9769155 siPOLR3GL_siE8_25nM 48 h_2 siPOLR3GL_siE8_25nM 48 h
- GSM9769156 siPOLR3GL_siE8_25nM 48 h_3 siPOLR3GL_siE8_25nM 48 h
- GSM9769157 POLR3G_ASO_200nM_ 48h_1 POLR3G_ASO_200nM_ 48h
- GSM9769158 POLR3G_ASO_200nM_ 48h_2 POLR3G_ASO_200nM_ 48h
- GSM9769159 POLR3G_ASO_200nM_ 48h_3 POLR3G_ASO_200nM_ 48h
- GSM9769160 Scramble_ASO_200nM_48h_3
- GSM9769161 control_eGFP_OE_48h_1 control_eGFP_OE_48h
- GSM9769162 control_eGFP_OE_48h_2 control_eGFP_OE_48h
- GSM9769163 control_eGFP_OE_48h_3 control_eGFP_OE_48h
- GSM9769164 MAF1_OE_48h_1 MAF1_OE_48h
- GSM9769165 MAF1_OE_48h_2 MAF1_OE_48h
- GSM9769166 MAF1_OE_48h_3 MAF1_OE_48h
- GSM9769167 CHX_10ug_per_ml_4.5h_1 CHX_10ug_per_ml_4.5h
- GSM9769168 CHX_10ug_per_ml_4.5h_2 CHX_10ug_per_ml_4.5h
- GSM9769169 CHX_10ug_per_ml_4.5h_3 CHX_10ug_per_ml_4.5h
- GSM9769170 CHX_10ug_per_ml_4.5h_ML625uM_4h_1 CHX_10ug_per_ml_4.5h_ML…
- GSM9769171 CHX_10ug_per_ml_4.5h_ML625uM_4h_2 CHX_10ug_per_ml_4.5h_ML…
- GSM9769172 CHX_10ug_per_ml_4.5h_ML625uM_4h_3 CHX_10ug_per_ml_4.5h_ML…
- GSM9769173 DMSO_1 DMSO
- GSM9769174 DMSO_2 DMSO
- GSM9769175 DMSO_3 DMSO
- GSM9769176 ML6_25uM_4h_1 ML6_25uM_4h
+ 14 more — browse all 54 samples with per-sample file links →
- GSE325942 Decoding a combinatorial RNA polymerase II CTD grammar that specifies protein-coding gene transcription and mRNA export 24 samples shares polymerase, protein, coding
- GSE310006 The NuRD chromatin remodeling complex regulates tRNA genes through direct interactions with RNA polymerase III 54 samples shares iii, polymerase
- GSE306294 An RNA polymerase III tissue and tumor atlas uncovers context-specific activities linked to 3D epigenome regulatory mechanisms 24 samples shares iii, polymerase
- GSE311392 The NuRD chromatin remodeling complex regulates tRNA genes through direct interactions with RNA polymerase III [Nuclei run-on (nascent RNA) profiling] 6 samples shares iii, polymerase
- GSE269317 Reduced WNT4 expression in normal skin fibroblasts leads to ‘Dupuytren-like’ changes in the transcriptome 16 samples shares changes, leads
- GSE284268 Post-transcriptional modifications on tRNA fragments confer functional changes to high-density lipoproteins in atherosclerosis 20 samples shares changes, protein
- GSE231993 Selective oxidative protection leads to tissue topological changes orchestrated by macrophage during ulcerative colitis 12 samples shares changes, leads
- GSE208747 Decoding spatial gene activity changes in multiple sclerosis lesion progression 218 samples shares changes, coding
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.