Effects of β-catenin deficiency on adipose tissue physiology
Single-cell RNA-seq · mouse · 30 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
- 30 samples: 6 conditions, mostly with about 6 replicates eachGroups 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.
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 GSE294513_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 PRJNA1250432 and SRA study SRP578397. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 30 samples.
How we read this: 6 replicated groups read from 30 sample titles; they account for 30 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.
- GSM8908260 Male_Ads_Ctnnb1flfl, rep 1 Male_Ads_Ctnnb1flfl,
- GSM8908261 Male_Ads_Ctnnb1flfl, rep 2 Male_Ads_Ctnnb1flfl,
- GSM8908262 Male_Ads_Ctnnb1flfl, rep 3 Male_Ads_Ctnnb1flfl,
- GSM8908263 Male_Ads_Ctnnb1flfl, rep 4 Male_Ads_Ctnnb1flfl,
- GSM8908264 Male_Ads_Ctnnb1flfl, rep 5 Male_Ads_Ctnnb1flfl,
- GSM8908265 Male_Ads_Ctnnb1flfl, rep 6 Male_Ads_Ctnnb1flfl,
- GSM8908266 Male_Ads_Ctnnb1KO, rep 1 Male_Ads_Ctnnb1KO,
- GSM8908267 Male_Ads_Ctnnb1KO, rep 2 Male_Ads_Ctnnb1KO,
- GSM8908268 Male_Ads_Ctnnb1KO, rep 3 Male_Ads_Ctnnb1KO,
- GSM8908269 Male_Ads_Ctnnb1KO, rep 4 Male_Ads_Ctnnb1KO,
- GSM8908270 Male_Ads_Ctnnb1KO, rep 5 Male_Ads_Ctnnb1KO,
- GSM8908271 Male_SVC_Ctnnb1flfl, rep 1 Male_SVC_Ctnnb1flfl,
- GSM8908272 Male_SVC_Ctnnb1flfl, rep 2 Male_SVC_Ctnnb1flfl,
- GSM8908273 Male_SVC_Ctnnb1flfl, rep 3 Male_SVC_Ctnnb1flfl,
- GSM8908274 Male_SVC_Ctnnb1KO, rep 1 Male_SVC_Ctnnb1KO,
- GSM8908275 Male_SVC_Ctnnb1KO, rep 2 Male_SVC_Ctnnb1KO,
- GSM8908276 Male_SVC_Ctnnb1KO, rep 3 Male_SVC_Ctnnb1KO,
- GSM8908277 Male_SVC_Ctnnb1KO, rep 4 Male_SVC_Ctnnb1KO,
- GSM8908278 Female_Ads_Ctnnb1flfl, rep 1 Female_Ads_Ctnnb1flfl,
- GSM8908279 Female_Ads_Ctnnb1flfl, rep 2 Female_Ads_Ctnnb1flfl,
- GSM8908280 Female_Ads_Ctnnb1flfl, rep 3 Female_Ads_Ctnnb1flfl,
- GSM8908281 Female_Ads_Ctnnb1flfl, rep 4 Female_Ads_Ctnnb1flfl,
- GSM8908282 Female_Ads_Ctnnb1flfl, rep 5 Female_Ads_Ctnnb1flfl,
- GSM8908283 Female_Ads_Ctnnb1flfl, rep 6 Female_Ads_Ctnnb1flfl,
- GSM8908284 Female_Ads_Ctnnb1KO, rep 1 Female_Ads_Ctnnb1KO,
- GSM8908285 Female_Ads_Ctnnb1KO, rep 2 Female_Ads_Ctnnb1KO,
- GSM8908286 Female_Ads_Ctnnb1KO, rep 3 Female_Ads_Ctnnb1KO,
- GSM8908287 Female_Ads_Ctnnb1KO, rep 4 Female_Ads_Ctnnb1KO,
- GSM8908288 Female_Ads_Ctnnb1KO, rep 5 Female_Ads_Ctnnb1KO,
- GSM8908289 Female_Ads_Ctnnb1KO, rep 6 Female_Ads_Ctnnb1KO,
- GSE267727 Transcriptome profiling of non-islet metabolic tissues in ~500 DO mouse liver, adipose, heart, and skeletal muscle tissue from mice fed high-fat, high-sugar diet [heart] 482 samples shares adipose
- GSE266549 Transcriptome profiling of non-islet metabolic tissues in ~500 DO mouse liver, adipose, and skeletal muscle tissue from mice fed high-fat, high-sugar diet. [Adipose] 468 samples shares adipose
- GSE310044 Dietary sulfur amino acid restriction elicits a cold-like transcriptional response in inguinal but not epididymal white adipose tissue of male mice 110 samples shares adipose
- GSE278003 Single cell RNA sequencing of CD45+ immune cells from tumor and tumor-adjacent mammary adipose tissue in a postmenopausal breast cancer model of lean, obese, and weight loss Trem2+/+ and Trem2-/- mice 92 samples shares adipose
- GSE253735 Loss of skeletal muscle Bmal1 +/- Exercise Training [Mouse liver, heart, inguinal white adipose and lung tissues 64 samples shares adipose
- GSE289295 Heme Biosynthesis Regulates BCAA Catabolism and Thermogenesis in Brown Adipose Tissue 60 samples shares adipose
- GSE262523 Diet-Induced Obesity Disrupts Sexually Dimorphic Gene Expression in Liver and Adipose Tissue in Mice 48 samples shares adipose
- GSE194224 Perivascular adipose tissue plasticity in response to angiotensin II 40 samples shares adipose
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.