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High-Fidelity, Efficient Human Post-Implantation Embryo Models Generated by STAT3-Induced Cell Plasticity [scRNA-Seq]

GSE270343 Homo sapiens (human) Single-cell RNA-seq 196 samples Submitted 2025/09/27 Platform GPL30209Platform GPL24676
At a glance

Single-cell RNA-seq · human · 196 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
196 samples: 96 × scRNA-seq (Smartseq2) of 120h SAC SUSD2H - cell vs 96 × scRNA-seq (Smartseq2) of 120h SAC SUSD2L - cellGroups 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.
Summary (written by the submitters)
Human embryo models hold great promise as surrogates for studying development and as platforms for medicine. However, existing models lack efficiency and fidelity in replicating post-implantation stages. Here we asked if STAT3 could reprogram pluripotent stem cells (PSCs) into all blastocyst fates and generate embryo models. Using a medium for enhanced STAT3 activity (SAM), PSCs rapidly reprogram into intermediates that specify all blastocyst fates: hypoblast, trophectoderm and naïve-pluripotent epiblast, within 60 hours.
Read the full summary ↓
Strikingly, following dissociation of SAM-treated PSCs on 60 or 120 hours and subsequent 3D assembly, structures resembling post-implantation embryos develop within 4 days, with up to 52.41±8.92% efficiency. These exhibited a bilaminar disc-like structure, including epiblast and hypoblast discs, yolk-sac and amniotic cavities, mesenchyme tissue, chorionic cavity, and trophoblast. The STAT3-mediated embryo models (stEMs) also demonstrate unparalleled molecular alignment with the Carnegie Stage 6-7 embryo reference. Thus, stEMs offer state-of-the-art models for advancing our understanding of post-implantation human embryogenesis.
This dataset
Open GSE270343 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 GSE270343_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 PRJNA1126232 and SRA study SRP515046. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
96 × scRNA-seq (Smartseq2) of 120h SAC SUSD2H - cell vs 96 × scRNA-seq (Smartseq2) of 120h SAC SUSD2L - cell

Supports a between-group comparison across 192 samples.

How we read this: 2 replicated groups read from 196 sample titles; they account for 192 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

+ 156 more — browse all 196 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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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.