GEO series

Single-cell transcriptome atlas of human embryos after gastrulation

GSE325829 Homo sapiens (human) Single-cell RNA-seq 124 samples Submitted 2026/03/26 Platform GPL29480

At a glance

Single-cell RNA-seq · human · 124 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism: Homo sapiens (human)
Samples: 124 samples: 15 conditions, mostly with about 6 replicates eachGroups 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: Spatiotemporal transcriptome atlas of human embryos after gastrulationNature · 2026

Summary (written by the submitters)

The comprehensive spatiotemporal atlas of gene expression during early human embryonic development is critical for insights into embryogenesis, organogenesis, and disease origins. Leveraging Stereo-seq technology, we generated spatial transcriptomic profiles across 77 sagittal sections of 13 whole human embryos ranging from Carnegie stage 12 to 23, integrated with single-nucleus RNA-seq to elucidate gene expression patterns within defined cellular contexts, revealing the cellular heterogeneity that drives organ-specific differentiation.

Read the full summary ↓

Our study has established a regulatory profile for the development of 50 organs and 198 substructures, and identified potential tissue-identity regulators. Notably, it uncovered previously uncharacterized gene functions in cardiac and brain development. The atlas not only substantiates and refines current understanding of human organ development but also highlights key organs susceptible to viral infections and genetic disorders. Furthermore, we characterized the allelic gene expression within specific organs at different developmental stages. This work presents a groundbreaking compilation of genome-wide gene expression profiles for each spatially defined cell population, which can be visualized as a spatial display of the embryonic transcriptional landscape. These results offer the most thorough delineation to data of the spatiotemporal transcriptomic dynamics of human organogenesis.

Published in

Spatiotemporal transcriptome atlas of human embryos after gastrulation

Pan J, Li Y, Lin Z et al. · Nature 2026 · PMID 42203883 · doi:10.1038/s41586-026-10545-0

This dataset

Open GSE325829 in finder → Find more human RNA-seq datasets →

Download · which file do I need?

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.

1 · Just have a look

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.

Series matrix ↓

2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE325829_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓

3 · Start from the raw reads

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.

Raw reads on ENA ↗

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 PRJNA1442040. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42203883) ↗

Study design

15 conditions, mostly with about 6 replicates each

CS23_E1S1, ×30 CS19_E1S1, ×10 CS23_E1S2, ×10 CS23_E1S3, ×10 CS20_E2S1, ×6 CS20_E2S2, ×6 CS20_E2S3, ×6 CS20_E2S4, ×6 +7 more

Supports a between-group comparison across 124 samples.

How we read this: 15 replicated groups read from 124 sample titles; they account for 124 of them. 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.

Samples in this study

GSM9614052 CS20_E2S1, run1 CS20_E2S1,
GSM9614053 CS20_E2S1, run2 CS20_E2S1,
GSM9614054 CS20_E2S1, run3 CS20_E2S1,
GSM9614055 CS20_E2S1, run4 CS20_E2S1,
GSM9614056 CS20_E2S1, run5 CS20_E2S1,
GSM9614057 CS20_E2S1, run6 CS20_E2S1,
GSM9614058 CS20_E2S2, run1 CS20_E2S2,
GSM9614059 CS20_E2S2, run2 CS20_E2S2,
GSM9614060 CS20_E2S2, run3 CS20_E2S2,
GSM9614061 CS20_E2S2, run4 CS20_E2S2,
GSM9614062 CS20_E2S2, run5 CS20_E2S2,
GSM9614063 CS20_E2S2, run6 CS20_E2S2,
GSM9614064 CS20_E2S3, run1 CS20_E2S3,
GSM9614065 CS20_E2S3, run2 CS20_E2S3,
GSM9614066 CS20_E2S3, run3 CS20_E2S3,
GSM9614067 CS20_E2S3, run4 CS20_E2S3,
GSM9614068 CS20_E2S3, run5 CS20_E2S3,
GSM9614069 CS20_E2S3, run6 CS20_E2S3,
GSM9614070 CS20_E2S4, run1 CS20_E2S4,
GSM9614071 CS20_E2S4, run2 CS20_E2S4,
GSM9614072 CS20_E2S4, run3 CS20_E2S4,
GSM9614073 CS20_E2S4, run4 CS20_E2S4,
GSM9614074 CS20_E2S4, run5 CS20_E2S4,
GSM9614075 CS20_E2S4, run6 CS20_E2S4,
GSM9614076 CS19_E1S1, run1 CS19_E1S1,
GSM9614077 CS19_E1S1, run2 CS19_E1S1,
GSM9614078 CS19_E1S1, run3 CS19_E1S1,
GSM9614079 CS19_E1S1, run4 CS19_E1S1,
GSM9614080 CS19_E1S1, run5 CS19_E1S1,
GSM9614081 CS19_E1S1, run6 CS19_E1S1,
GSM9614082 CS19_E1S1, run7 CS19_E1S1,
GSM9614083 CS19_E1S1, run8 CS19_E1S1,
GSM9614084 CS19_E1S1, run9 CS19_E1S1,
GSM9614085 CS19_E1S1, run10 CS19_E1S1,
GSM9614086 CS19_E1S2, run1 CS19_E1S2,
GSM9614087 CS19_E1S2, run2 CS19_E1S2,
GSM9614088 CS19_E1S2, run3 CS19_E1S2,
GSM9614089 CS19_E1S2, run4 CS19_E1S2,
GSM9614090 CS19_E1S2, run5 CS19_E1S2,
GSM9614091 CS19_E1S3, run1 CS19_E1S3,

+ 84 more — browse all 124 samples with per-sample file links →

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