Single cell analysis of immune cells from synovial tissues during the progression of osteoarthritis and obesity
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.
- Organism
- Mus musculus (mouse)
- Samples
- 12 samples: 12 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.
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 GSE279621_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 PRJNA1173659 and SRA study SRP538941. 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 12 sample titles: 12 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.
- GSM8577119 Control4Left
- GSM8577120 Control4Right
- GSM8577121 Control5Left
- GSM8577122 Control5Right
- GSM8577123 Control6Left
- GSM8577124 Control6Right
- GSM8577125 HFD4Left
- GSM8577126 HFD4Right
- GSM8577127 HFD5Left
- GSM8577128 HFD5Right
- GSM8577129 HFD6Left
- GSM8577130 HFD6Right
- GSE310028 Dissecting Semaglutide-dependent transcriptome response in obesity and osteoarthritis across tissues at single-cell resolution 28 samples shares osteoarthritis, obesity
- GSE294999 Obesity-driven inflammation enables triple-negative breast cancer progression through immunosuppression [scRNA-Seq] 16 samples shares progression, obesity
- GSE327205 RNA-seq analysis of dorsal root ganglia (DRG) from MIA-induced osteoarthritis mice with acupuncture or no treatment: Focus on neuro-immune crosstalk genes 8 samples shares osteoarthritis
- GSE263963 The temporal progression of lung immune remodeling during breast cancer metastasis 38 samples shares progression
- GSE241770 ACSS2 mediates an epigenetic pathway to regulate maternal Beta-cell adaptation and progression of gestational diabetes (scRNA-Seq) 7899 samples shares progression
- GSE159911 “Bench-to-Bedside and Back” analysis of NAFLD model reveals common transcriptomic shifts between mouse and human during disease progression 152 samples shares progression
- GSE327470 Temporal Uncoupling of Radial Glial Progenitor Cell Lineage Progression in Self-organizing Organoid System 292 samples shares progression
- GSE327468 Temporal Uncoupling of Radial Glial Progenitor Cell Lineage Progression in Self-organizing Organoid System – MADM-CloneSeq 287 samples shares progression
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.