scRNA-seq assessment of the impact of anti-IL6R and anti-OSM in a model of fibrotic lung disease during fibrosis (day 14) [NGS4272]
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.
- Organism
- Mus musculus (mouse)
- Samples
- 30 samples: 30 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 GSE263041_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 PRJNA1095510 and SRA study SRP499389. 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 30 sample titles: 30 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.
- GSM8184373 CD45_BLM_Control_GEX [NGS4272]
- GSM8184374 CD31_EPCAM_BLM_Control_GEX [NGS4272]
- GSM8184375 TripleNegative_BLM_Control_GEX [NGS4272]
- GSM8184376 CD45_BLM_aOSM_GEX [NGS4272]
- GSM8184377 CD31_EPCAM_BLM_aOSM_GEX [NGS4272]
- GSM8184378 TripleNegative_BLM_aOSM_GEX [NGS4272]
- GSM8184379 CD45_BLM_aIL6R_GEX [NGS4272]
- GSM8184380 CD31_EPCAM_BLM_aIL6R_GEX [NGS4272]
- GSM8184381 TripleNegative_BLM_aIL6R_GEX [NGS4272]
- GSM8184382 CD45_BLM_aOSM_aIL6R_GEX [NGS4272]
- GSM8184383 CD31_EPCAM_BLM_aOSM_aIL6R_GEX [NGS4272]
- GSM8184384 TripleNegative_BLM_aOSM_aIL6R_GEX [NGS4272]
- GSM8184385 CD45_PBS_GEX [NGS4272]
- GSM8184386 CD31_EPCAM_PBS_GEX [NGS4272]
- GSM8184387 TripleNegative_PBS_GEX [NGS4272]
- GSM8184388 CD45_BLM_Control_HTO [NGS4272]
- GSM8184389 CD31_EPCAM_BLM_Control_HTO [NGS4272]
- GSM8184390 TripleNegative_BLM_Control_HTO [NGS4272]
- GSM8184391 CD45_BLM_aOSM_HTO [NGS4272]
- GSM8184392 CD31_EPCAM_BLM_aOSM_HTO [NGS4272]
- GSM8184393 TripleNegative_BLM_aOSM_HTO [NGS4272]
- GSM8184394 CD45_BLM_aIL6R_HTO [NGS4272]
- GSM8184395 CD31_EPCAM_BLM_aIL6R_HTO [NGS4272]
- GSM8184396 TripleNegative_BLM_aIL6R_HTO [NGS4272]
- GSM8184397 CD45_BLM_aOSM_aIL6R_HTO [NGS4272]
- GSM8184398 CD31_EPCAM_BLM_aOSM_aIL6R_HTO [NGS4272]
- GSM8184399 TripleNegative_BLM_aOSM_aIL6R_HTO [NGS4272]
- GSM8184400 CD45_PBS_HTO [NGS4272]
- GSM8184401 CD31_EPCAM_PBS_HTO [NGS4272]
- GSM8184402 TripleNegative_PBS_HTO [NGS4272]
- GSE274973 Mouse liver assembloids model periportal architecture and biliary fibrosis [scRNA-seq] 13 samples shares scrna-seq, fibrosis
- GSE261783 Identification of epigenetic regulators of fibrotic transformation in cardiac fibroblasts through bulk and single-cell CRISPR screens [scRNA-seq] 10 samples shares scrna-seq, fibrotic
- GSE241770 ACSS2 mediates an epigenetic pathway to regulate maternal Beta-cell adaptation and progression of gestational diabetes (scRNA-Seq) 7899 samples shares scrna-seq
- GSE264105 Plate-based scRNA-seq of plasma-positive microglia and plasma-negative microglia in young mice 1111 samples shares scrna-seq
- GSE271100 Stem cells tightly regulate dead cell clearance to maintain tissue fitness [scRNA-seq] 1037 samples shares scrna-seq
- GSE326100 scRNA-seq of mouse cochlea organoid cells 768 samples shares scrna-seq
- GSE265909 yHSC and oHSC scRNAseq with oHSC AThi ATlo Index Sorting. Assessing the transcriptional changes associated with autophagy engagement in old hematopoietic stem cells at single cell resolution. [scRNA-Seq] 563 samples shares scrna-seq
- GSE217662 Decoding Trp53 regulatory program with knockout cell transcriptomic landscape analyses [scRNA-seq] 247 samples shares scrna-seq
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.