Single-cell transcriptomic based heterogeneity of adipose-derived regenerative cells is independent of gender and BMI
Single-cell RNA-seq · human · 17 samples
- What it is
- Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
- Organism
- Homo sapiens (human)
- Samples
- 17 samples: 17 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
- Single-cell transcriptomics of clinical grade adipose-derived regenerative cells reveals consistency between donors independent of gender and BMIStem cell research & therapy · 2025
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 GSE286321_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 PRJNA1208674 and SRA study SRP556395. 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 17 sample titles: 17 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.
- GSM8723590 ADRCs, Male no. 01, scRNAseq
- GSM8723591 ADRCs, Male no. 02, scRNAseq
- GSM8723592 ADRCs, Male no. 03, scRNAseq
- GSM8723593 ADRCs, Male no. 04, scRNAseq
- GSM8723594 ADRCs, Male no. 05, scRNAseq
- GSM8723595 ADRCs, Male no. 06, scRNAseq
- GSM8723596 ADRCs, Male no. 07, scRNAseq
- GSM8723597 ADRCs, Female no. 01, sample 1, scRNAseq
- GSM8723598 ADRCs, Female no. 01, sample 2, scRNAseq
- GSM8723599 ADRCs, Female no. 02, sample 1, scRNAseq
- GSM8723600 ADRCs, Female no. 02, sample 2, scRNAseq
- GSM8723601 ADRCs, Female no. 03, scRNAseq
- GSM8723602 ADRCs, Female no. 04, scRNAseq
- GSM8723603 ADRCs, Female no. 05, sample 1, scRNAseq
- GSM8723604 ADRCs, Female no. 05, sample 2, scRNAseq
- GSM8723605 ADRCs, Female no. 06, scRNAseq
- GSM8723606 ADRCs, Female no. 07, scRNAseq
- GSE243013 A single-cell atlas of immune heterogeneity in anti-PD1-treated non-small cell lung cancer 243 samples shares heterogeneity, single-cell
- GSE319709 Single-cell transcriptomics reveals etiology-specific T-cell heterogeneity in hepatocellular carcinoma and implicates regulatory 18 samples shares heterogeneity, single-cell
- GSE220774 Side- and disease-dependent changes in human aortic valve cell population and transcriptomic heterogeneity determined by single-cell RNA sequencing 15 samples shares heterogeneity, single-cell
- GSE298567 Single-Cell transcriptomics reveals biomarker heterogeneity linked to CDK4/6 Inhibitor resistance in breast cancer cell lines 14 samples shares heterogeneity, single-cell
- GSE280639 Multichrome encoding-based multiplexed, spatially resolved imaging reveals single-cell RNA epigenetic modifications heterogeneity 21 samples shares heterogeneity, single-cell
- GSE248497 Deciphering the heterogeneity of differentiating hPSC-derived corneal limbal stem cells through single-cell RNA-sequencing 19 samples shares heterogeneity, single-cell
- GSE290040 single-cell spatial analysis reveals molecular mechanisms underlying immune cell organization and cancer cell heterogeneity in HPV-associated oropharyngeal carcinoma 15 samples shares heterogeneity, single-cell
- GSE281736 Single-cell RNA Sequencing Reveals the Heterogeneity in Differentiation Trajectory and Tumor Microenvironment Leading to More Aggressive Phenotypes of Papillary Thyroid Cancer in Children and Young Adult Patients 12 samples shares heterogeneity, single-cell
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.