An engineered ovarian somatic organoid model exhibits age-dependent changes in cellular organization and function
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: 2 × T1 young D vs 2 × T1 old D vs 2 × T2 young D vs 2 × T2 old DGroups 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
- Self-Organizing Ovarian Somatic Organoids Preserve Cellular Heterogeneity and Reveal Cellular Contributions to Ovarian AgingAging cell · 2026
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 GSE273891_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 PRJNA1143554 and SRA study SRP523950. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 8 samples.
How we read this: 4 replicated groups read from 12 sample titles; they account for 8 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.
- GSM8438820 T1 young monolayer
- GSM8438821 T1 old monolayer
- GSM8438822 T1 young D1 T1 young D
- GSM8438823 T1 old D1 T1 old D
- GSM8438824 T2 young monolayer
- GSM8438825 T2 old monolayer
- GSM8438826 T2 young D1 T2 young D
- GSM8438827 T2 old D1 T2 old D
- GSM8438828 T1 young D6 T1 young D
- GSM8438829 T1 old D6 T1 old D
- GSM8438830 T2 young D6 T2 young D
- GSM8438831 T2 old D6 T2 old D
- GSE330865 Multiorgan transcriptomics and circulating extracellular vesicle profiling reveal age-dependent systemic vulnerability to isoflurane anesthesia and surgery 120 samples shares age-dependent, cellular
- GSE329635 Single-cell RNA sequencing of small intestinal organoid self-organization [scRNA-Seq] 10 samples shares organization, organoid
- GSE246770 Profiling of murine tumors initiated by transplantation of engineered prostate organoids 27 samples shares engineered, organoid
- GSE326100 scRNA-seq of mouse cochlea organoid cells 768 samples shares organoid
- GSE327470 Temporal Uncoupling of Radial Glial Progenitor Cell Lineage Progression in Self-organizing Organoid System 292 samples shares organoid
- GSE327468 Temporal Uncoupling of Radial Glial Progenitor Cell Lineage Progression in Self-organizing Organoid System – MADM-CloneSeq 287 samples shares organoid
- GSE218178 Adaptation to an acid microenvironment promotes pancreatic cancer organoid growth and drug resistance 183 samples shares organoid
- GSE218173 Adaptation to an acid microenvironment promotes pancreatic cancer organoid growth and drug resistance [acid] 150 samples shares organoid
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.