in vitro construction of an embryonic caudal organizer
Single-cell RNA-seq · human · 8 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
- Homo sapiens (human)
- Samples
- 8 samples: 8 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
- BMP4 initiates and patterns ventral-caudal structures in zebrafish and human pluripotent stem cell aggregatesThe EMBO journal · 2026
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 GSE270989_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 PRJNA1129168 and SRA study SRP516621. 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 8 sample titles: 8 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.
- GSM8366750 Bmp4_explant_6h
- GSM8366751 Bmp4_explant_8h
- GSM8366752 Bmp4_explant_10h
- GSM8366753 Bmp4_explant_12h
- GSM8366754 Bmp4_explant_18h
- GSM8366755 Bmp4_explant_24h
- GSM8366756 BMP4_human_EB_5D
- GSM8366757 BMP4_human_EB_6D
No close match found for this topic, so these are simply the most-viewed datasets of the same kind.
- GSE81713 Whole organism lineage tracing by combinatorial and cumulative genome editing 136 samples
- GSE259251 ndufs2-/- zebrafish model Leigh syndrome with shortened lifespan, developmental anomalies, altered transcriptome and disrupted metabolism 30 samples
- GSE325556 Amniotic fluid cell-free RNA profiling reveals AGMAT as a biomarker for fetal renal abnormalities 28 samples
- GSE12039 Regulation of endothelial gene expression by miR-126 in human and zebrafish 18 samples
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.