Inference of emergent spatio-temporal processes from single-cell sequencing reveals feedback between de novo DNA methylation and chromatin condensates
Single-cell RNA-seq · mouse · 615 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
- 615 samples: 24 conditions, mostly with about 12 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
- Scaling and self-similarity in the formation of the embryonic epigenomeNature physics · 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 GSE166226_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 PRJNA699614 and SRA study SRP304188. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 288 samples.
How we read this: 24 replicated groups read from the first 400 of 615 sample titles; they account for 288 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.
- GSM5058948 D0_A01 D0_A
- GSM5058949 D0_A02 D0_A
- GSM5058950 D0_A03 D0_A
- GSM5058951 D0_A04 D0_A
- GSM5058952 D0_A05 D0_A
- GSM5058953 D0_A06 D0_A
- GSM5058954 D0_A07 D0_A
- GSM5058955 D0_A08 D0_A
- GSM5058956 D0_A09 D0_A
- GSM5058957 D0_A10 D0_A
- GSM5058958 D0_A11 D0_A
- GSM5058959 D0_A12 D0_A
- GSM5058960 D0_B01 D0_B
- GSM5058961 D0_B02 D0_B
- GSM5058962 D0_B03 D0_B
- GSM5058963 D0_B04 D0_B
- GSM5058964 D0_B05 D0_B
- GSM5058965 D0_B06 D0_B
- GSM5058966 D0_B07 D0_B
- GSM5058967 D0_B08 D0_B
- GSM5058968 D0_B09 D0_B
- GSM5058969 D0_B10 D0_B
- GSM5058970 D0_B11 D0_B
- GSM5058971 D0_B12 D0_B
- GSM5058972 D0_C01 D0_C
- GSM5058973 D0_C02 D0_C
- GSM5058974 D0_C03 D0_C
- GSM5058975 D0_C04 D0_C
- GSM5058976 D0_C05 D0_C
- GSM5058977 D0_C06 D0_C
- GSM5058978 D0_C07 D0_C
- GSM5058979 D0_C08 D0_C
- GSM5058980 D0_C09 D0_C
- GSM5058981 D0_C10 D0_C
- GSM5058982 D0_C11 D0_C
- GSM5058983 D0_C12 D0_C
- GSM5058984 D0_D01 D0_D
- GSM5058985 D0_D02 D0_D
- GSM5058986 D0_D03 D0_D
- GSM5058987 D0_D04 D0_D
+ 575 more — browse all 615 samples with per-sample file links →
- GSE166225 Inference of emergent spatio-temporal processes from single-cell sequencing reveals feedback between de novo DNA methylation and chromatin condensates [RNA-seq] 288 samples shares spatio-temporal, single-cell, methylation, condensates, inference, processes, chromatin, emergent
- GSE294772 Single-Cell Epigenomics Uncovers Heterochromatin Instability and Transcription Factor Dysfunction during Mouse Brain Aging [snATAC-Seq] [10X Multiome] 142 samples shares single-cell, chromatin
- GSE216371 A single-cell atlas of chromatin accessibility in mouse organogenesis 68 samples shares single-cell, chromatin
- GSE264193 H3K27 dimethylation dynamics reveal stepwise establishment of facultative heterochromatin in early mouse embryos [ChIP-seq] 57 samples shares methylation, chromatin
- GSE333887 Matched single-cell chromatin, transcriptome, and surface marker profiling captures in vivo epigenomic reprogramming during basal-to-luminal transition in the mammary gland [transplantations_OneCell_multiome] 14 samples shares single-cell, chromatin
- GSE283906 RXR-Mediated Remodeling of Transcriptional and Chromatin Landscapes in APP Mouse Brain: Insights from Integrated Single-Cell RNA and ATAC Profiling 10 samples shares single-cell, chromatin
- GSE192687 Single-cell RNAseq analysis of renal endothelial cells in murine diabetic nephropathy 5464 samples shares single-cell
- GSE215963 Subclonal evolution revealed by single-cell profiles of expressed mutations identifies adaptation mechanisms to immunotherapy in melanoma. 1497 samples shares single-cell
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.