Transcriptome profiling of starved Actr3 KO mIMCD3 cells
Single-cell RNA-seq · mouse · 12 samples
- What it is
- Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
- Organism
- Mus musculus (mouse)
- Samples
- 12 samples: 3 × Actr3 Ctrl.-1 vs 3 × Actr3 KO-1 vs 3 × Actr3-Ift88 dKO vs 3 × Actr3 KO-1 CBGroups 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
- Arp2/3-dependent regulation of ciliogenesis governs adaptive distal tubular epithelial cell states in kidney diseaseScience advances · 2025
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 GSE287677_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 PRJNA1214241 and SRA study SRP559137. 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.
- GSM8749727 Actr3 Ctrl.-1 repl.-1 Actr3 Ctrl.-1
- GSM8749728 Actr3 Ctrl.-1 repl.-2 Actr3 Ctrl.-1
- GSM8749729 Actr3 Ctrl.-1 repl.-3 Actr3 Ctrl.-1
- GSM8749730 Actr3 KO-1 repl.-1 Actr3 KO-1
- GSM8749731 Actr3 KO-1 repl.-2 Actr3 KO-1
- GSM8749732 Actr3 KO-1 repl.-3 Actr3 KO-1
- GSM8749733 Actr3-Ift88 dKO repl.-1 Actr3-Ift88 dKO
- GSM8749734 Actr3-Ift88 dKO repl.-2 Actr3-Ift88 dKO
- GSM8749735 Actr3-Ift88 dKO repl.-3 Actr3-Ift88 dKO
- GSM8749736 Actr3 KO-1 CB repl.-1 Actr3 KO-1 CB
- GSM8749737 Actr3 KO-1 CB repl.-2 Actr3 KO-1 CB
- GSM8749738 Actr3 KO-1 CB repl.-3 Actr3 KO-1 CB
- GSE287610 Transcriptome profiling of Actr3 KO mIMCD3 cells 8 samples shares mimcd3, actr3
- GSE287609 Transcriptome profiling of kidney medulla of Actr3 KO mice 4 samples shares actr3
- GSE56671 Expression data from MEFs wt cells starved as compared to MEFs wt cells grown in Normal Medium 6 samples shares starved
- GSE56672 Expression data from PPP3R1 cell line starved as compared to PPP3R1 cell line grown in Normal Medium 6 samples shares starved
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.