Spatial mapping reveals erythroid niches in mice during development and stress
RNA-seq (gene expression) · mouse · 14 samples
- What it is
- RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
- Organism
- Mus musculus (mouse)
- Samples
- 14 samples: 14 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
- Spatial transcriptomic analyses highlight distinct erythroid niches in mice and humansNature genetics · 2026
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 GSE271077_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.
Also filed as BioProject PRJNA1129566 and SRA study SRP516866. 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 14 sample titles: 14 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.
- GSM8368271 E14.5 fetal liver
- GSM8368272 Mouse P8 bone marrow
- GSM8368273 Adult spleen
- GSM8407228 Mouse 89 weeks old bone marrow
- GSM8927829 E13.5 fetal liver
- GSM8927830 Adult bone marrow_EDTA
- GSM9745604 WT2 E14.5 fetal liver
- GSM9745605 WT4 E14.5 fetal liver
- GSM9745606 C1qa KO2 E14.5 fetal liver
- GSM9745607 C1qa KO4 E14.5 fetal liver
- GSM9745608 WT1 spleen
- GSM9745609 WT2 spleen
- GSM9745610 C1qa KO1 spleen
- GSM9745611 C1qa KO2 spleen
- GSE249405 Spatial mapping of RNA turnover kinetics in the mouse brain 38 samples shares spatial, mapping
- GSE249403 Spatial mapping of RNA turnover kinetics in the mouse brain [Spatial NT-seq] 20 samples shares spatial, mapping
- GSE249398 Spatial mapping of RNA turnover kinetics in the mouse brain [scNT-seq2] 18 samples shares spatial, mapping
- GSE267904 Mapping spatially resolved transcriptomes in human and mouse pulmonary fibrosis 24 samples shares spatial, mapping
- GSE263333 Spatial-Mux-seq: Multiplexed spatial mapping of chromatin features, transcriptome, and proteins at tissue scale 12 samples shares spatial, mapping
- GSE233983 Spatial transcriptomics of the aging mouse brain reveals origins of inflammation in the white matter 44 samples shares spatial
- GSE334490 Mapping the unicellular transcriptome of the ascending thoracic aorta to changes in mechanosensing and mechanoadaptation during aging 16 samples shares mapping
- GSE255371 Characterization of cuprizone mouse model at single-cell and spatial transcriptomics level 86 samples shares spatial
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.