scRNA-seq assessment of the impact of anti-IL6R and anti-OSM in a model of fibrotic lung disease during resolution (day 24) [NGS4659]
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 GSE263042_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 PRJNA1095508 and SRA study SRP499387. 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.
- GSM8184403 CD45_PBS_GEX [NGS4659]
- GSM8184404 CD31_EPCAM_PBS_GEX [NGS4659]
- GSM8184405 TripleNegative_PBS_GEX [NGS4659]
- GSM8184406 CD45_BLM_Control_GEX [NGS4659]
- GSM8184407 CD31_EPCAM_BLM_Control_GEX [NGS4659]
- GSM8184408 TripleNegative_BLM_Control_GEX [NGS4659]
- GSM8184409 CD45_BLM_aOSM_GEX [NGS4659]
- GSM8184410 CD31_EPCAM_BLM_aOSM_GEX [NGS4659]
- GSM8184411 TripleNegative_BLM_aOSM_GEX [NGS4659]
- GSM8184412 CD45_BLM_aIL6R_GEX [NGS4659]
- GSM8184413 CD31_EPCAM_BLM_aIL6R_GEX [NGS4659]
- GSM8184414 TripleNegative_BLM_aIL6R_GEX [NGS4659]
- GSM8184415 CD45_BLM_aOSM_aIL6R_GEX [NGS4659]
- GSM8184416 CD31_EPCAM_BLM_aOSM_aIL6R_GEX [NGS4659]
- GSM8184417 TripleNegative_BLM_aOSM_aIL6R_GEX [NGS4659]
- GSM8184418 CD45_PBS_HTO [NGS4659]
- GSM8184419 CD31_EPCAM_PBS_HTO [NGS4659]
- GSM8184420 TripleNegative_PBS_HTO [NGS4659]
- GSM8184421 CD45_BLM_Control_HTO [NGS4659]
- GSM8184422 CD31_EPCAM_BLM_Control_HTO [NGS4659]
- GSM8184423 TripleNegative_BLM_Control_HTO [NGS4659]
- GSM8184424 CD45_BLM_aOSM_HTO [NGS4659]
- GSM8184425 CD31_EPCAM_BLM_aOSM_HTO [NGS4659]
- GSM8184426 TripleNegative_BLM_aOSM_HTO [NGS4659]
- GSM8184427 CD45_BLM_aIL6R_HTO [NGS4659]
- GSM8184428 CD31_EPCAM_BLM_aIL6R_HTO [NGS4659]
- GSM8184429 TripleNegative_BLM_aIL6R_HTO [NGS4659]
- GSM8184430 CD45_BLM_aOSM_aIL6R_HTO [NGS4659]
- GSM8184431 CD31_EPCAM_BLM_aOSM_aIL6R_HTO [NGS4659]
- GSM8184432 TripleNegative_BLM_aOSM_aIL6R_HTO [NGS4659]
- GSE263041 scRNA-seq assessment of the impact of anti-IL6R and anti-OSM in a model of fibrotic lung disease during fibrosis (day 14) [NGS4272] 30 samples shares anti-il6r, assessment, scrna-seq, anti-osm, fibrotic, impact
- 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 resolution, scrna-seq
- GSE205592 Obesity-instructed TREM2-high macrophages identified by comparative analysis of diabetic mouse and human kidney at single-cell resolution [scRNA-seq: DKD_mouse] 41 samples shares resolution, scrna-seq
- GSE205593 Obesity-instructed TREM2-high macrophages identified by comparative analysis of diabetic mouse and human kidney at single-cell resolution [scRNA-seq: Baseline_mouse] 24 samples shares resolution, scrna-seq
- GSE81682 The transcriptional landscape of mouse blood stem/progenitor cell transitions at single cell resolution 3840 samples shares resolution
- GSE103334 Temporal tracking of microglia activation in neurodegeneration at single-cell resolution 2208 samples shares resolution
- GSE266977 Spatiotemporal and genetic cell lineage tracing of endodermal organogenesis at single-cell resolution 983 samples shares resolution
- GSE277789 Spatiotemporal and genetic cell lineage tracing of endodermal organogenesis at single-cell resolution [Smart-seq3-RNA-seq-add] 830 samples shares resolution
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.