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Ensembles of genome-coverage single-cell histone modifications reveal epigenetic lineages during mouse preimplantation development

GSE235109 Mus musculus (mouse) Single-cell RNA-seq 47 samples Submitted 2024/12/21 Platform GPL24247Platform GPL13112
At a glance

Single-cell RNA-seq · mouse · 47 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
47 samples: 47 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
Genome-coverage single-cell histone modifications for embryo lineage tracingNature · 2025
Summary (written by the submitters)
We applied TACIT to profile seven histone modifications at single-cell resolution in mouse early embryos from zygotes to blastocysts. Profound epigenetic resetting during early embryo development from fertilization to blastocyst ensures zygotic genome activation and also begets progressive cellular heterogeneities1-6. Mapping single-cell epigenomic profiles of core histone modifications covering each individual cell onto the lineage tree (epigenetic lineages) is a fundamental goal in developmental biology.
Read the full summary ↓
Here, we develop a method, TACIT, achieving genome-coverage single-cell profiling of seven histone modifications in mouse early embryos from zygotes to blastocysts. TACIT yields up to half million fragments per cell with peaks equivalent of bulk assays. Integrating these single-cell histone modifications with scRNA-seq data7, we chart a cell-kinship informed epigenetic lineage landscape. Multimodal chromatin state annotations show that the ZGA onset at early 2-cell stage already primes heterogeneities in totipotency. Epigenetic lineages also allow for nominating totipotency gene regulatory networks including stage-specific transposable elements and putative transcription factors (TFs). In addition, machine learning models together with single-cell co-profiles of multiple histone modifications facilitate prediction of the earliest cell branching toward inner cell mass (ICM) and trophectoderm (TE) in latent multimodal space as well as feature regulatory elements and previously unknown lineage-specifying TFs. Together, our work reconstructs single-cell holistic epigenetic lineages, elucidating multimodal regulation of cellular heterogeneities and cell fate priming during mouse pre-implantation development.
Published in
Genome-coverage single-cell histone modifications for embryo lineage tracing
Liu M, Yue Y, Chen X et al. · Nature 2025 · PMID 40011786 · doi:10.1038/s41586-025-08656-1
This dataset
Open GSE235109 in finder → Find more mouse ChIP / ATAC / CUT&Tag 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 GSE235109_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 PRJNA984604 and SRA study SRP444396. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40011786) ↗
Study design
47 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 47 sample titles: 47 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.

Samples in this study

+ 7 more — browse all 47 samples with per-sample file links →

Datasets on a similar topic

Search all mouse ChIP / ATAC / CUT&Tag datasets in GEO →

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