Assessing Senescence in ASC Product and Discovery of Potential Biomarkers
RNA-seq (gene expression) · human · 18 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
- Homo sapiens (human)
- Samples
- 18 samples: 18 samples, each with a distinct title — no replicated groupsGroups 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
- Senescence profiling and biomarker identification in cell product based on adipose tissue-derived mesenchymal stromal cellsStem cells translational medicine · 2026
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 GSE309666_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 PRJNA1336714 and SRA study SRP630027. Searching any of these in the dataset finder brings you back here.
Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.
How we read this: Read from 18 sample titles: 18 distinct titles with little repetition. 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.
- GSM9271434 adipose stromal cells, passage 1, donor D
- GSM9271435 adipose stromal cells, passage 10, donor D
- GSM9271436 adipose stromal cells, passage 3, donor D
- GSM9271437 adipose stromal cells, passage 6, donor D
- GSM9271438 adipose stromal cells, passage 1, donor B
- GSM9271439 adipose stromal cells, passage 3, donor B
- GSM9271440 adipose stromal cells, passage 7, donor B
- GSM9271441 adipose stromal cells, passage 8, donor B
- GSM9271442 adipose stromal cells, passage 1, donor E
- GSM9271443 adipose stromal cells, passage 3, donor E
- GSM9271444 adipose stromal cells, passage 6, donor E
- GSM9271445 adipose stromal cells, passage 10, donor E
- GSM9271446 adipose stromal cells, passage 1, donor C
- GSM9271447 adipose stromal cells, passage 3, donor C
- GSM9271448 adipose stromal cells, passage 9, donor C
- GSM9271449 adipose stromal cells, passage 1, donor A
- GSM9271450 adipose stromal cells, passage 3, donor A
- GSM9271451 adipose stromal cells, passage 7, donor A
- GSE308390 Identifying Potential Biomarkers Associated with Sebum Production, Pore Enlargement, and Comedones Using Transcriptomic Approaches 24 samples shares biomarkers, potential, product
- GSE320212 Identifying Senescence and Immune Biomarkers Predictive of Benefit to Combined CDK4/6 and PD-1 Blockade in Dedifferentiated Liposarcoma: Single-Cell RNA-seq of Serial Tumor Biopsies 23 samples shares senescence, biomarkers
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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.