High-resolution, noninvasive single-cell lineage tracing in mice and humans based on DNA methylation epimutations
Single-cell RNA-seq · human · 697 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
- 697 samples: 44 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
- High-resolution, noninvasive single-cell lineage tracing in mice and humans based on DNA methylation epimutationsNature methods · 2025
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 GSE262580_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 PRJNA1092584 and SRA study SRP491733. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 400 samples.
How we read this: 44 replicated groups read from the first 400 of 697 sample titles; they account for 400 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.
- GSM8111887 scBSseq_293T_P10_A1 scBSseq_293T_P10_A
- GSM8111888 scBSseq_293T_P10_A2 scBSseq_293T_P10_A
- GSM8111889 scBSseq_293T_P10_A3 scBSseq_293T_P10_A
- GSM8111890 scBSseq_293T_P10_A4 scBSseq_293T_P10_A
- GSM8111891 scBSseq_293T_P10_A5 scBSseq_293T_P10_A
- GSM8111892 scBSseq_293T_P10_A6 scBSseq_293T_P10_A
- GSM8111893 scBSseq_293T_P10_B1 scBSseq_293T_P10_B
- GSM8111894 scBSseq_293T_P10_B2 scBSseq_293T_P10_B
- GSM8111895 scBSseq_293T_P10_B3 scBSseq_293T_P10_B
- GSM8111896 scBSseq_293T_P10_B4 scBSseq_293T_P10_B
- GSM8111897 scBSseq_293T_P10_B6 scBSseq_293T_P10_B
- GSM8111898 scBSseq_293T_P8_A1 scBSseq_293T_P8_A
- GSM8111899 scBSseq_293T_P8_A2 scBSseq_293T_P8_A
- GSM8111900 scBSseq_293T_P8_A3 scBSseq_293T_P8_A
- GSM8111901 scBSseq_293T_P8_A4 scBSseq_293T_P8_A
- GSM8111902 scBSseq_293T_P8_A5 scBSseq_293T_P8_A
- GSM8111903 scBSseq_293T_P8_A6 scBSseq_293T_P8_A
- GSM8111904 scBSseq_293T_P8_B1 scBSseq_293T_P8_B
- GSM8111905 scBSseq_293T_P8_B2 scBSseq_293T_P8_B
- GSM8111906 scBSseq_293T_P8_B3 scBSseq_293T_P8_B
- GSM8111907 scBSseq_293T_P8_B4 scBSseq_293T_P8_B
- GSM8111908 scBSseq_293T_P8_B5 scBSseq_293T_P8_B
- GSM8111909 scBSseq_293T_P8_B6 scBSseq_293T_P8_B
- GSM8111910 scBSseq_293T_P8_C1 scBSseq_293T_P8_C
- GSM8111911 scBSseq_293T_P8_C2 scBSseq_293T_P8_C
- GSM8111912 scBSseq_293T_P8_C3 scBSseq_293T_P8_C
- GSM8111913 scBSseq_293T_P8_C4 scBSseq_293T_P8_C
- GSM8111914 scBSseq_293T_P8_C5 scBSseq_293T_P8_C
- GSM8111915 scBSseq_293T_P8_C6 scBSseq_293T_P8_C
- GSM8111916 scBSseq_293T_P8_D1 scBSseq_293T_P8_D
- GSM8111917 scBSseq_293T_P8_D3 scBSseq_293T_P8_D
- GSM8111918 scBSseq_293T_P8_D4 scBSseq_293T_P8_D
- GSM8111919 scBSseq_293T_P8_D5 scBSseq_293T_P8_D
- GSM8111920 scBSseq_293T_P8_D6 scBSseq_293T_P8_D
- GSM8111921 scBSseq_293T_P8_E1 scBSseq_293T_P8_E
- GSM8111922 scBSseq_293T_P8_E2 scBSseq_293T_P8_E
- GSM8111923 scBSseq_293T_P8_E3 scBSseq_293T_P8_E
- GSM8111924 scBSseq_293T_P8_E4 scBSseq_293T_P8_E
- GSM8111925 scBSseq_293T_P8_E5 scBSseq_293T_P8_E
- GSM8111926 scBSseq_293T_P8_E6 scBSseq_293T_P8_E
+ 657 more — browse all 697 samples with per-sample file links →
- GSE198204 Endothelial plasticity in atrial fibrosis by integrating single-cell sequencing and genetic lineage tracing 8 samples shares single-cell, lineage, tracing
- GSE65360 Single-cell chromatin accessibility data using scATAC-seq 1632 samples shares single-cell
- GSE150672 Highly Efficient, Massively-Parallel Single-Cell RNA-Seq Reveals Cellular States and Molecular Features of Human Skin Pathology 438 samples shares single-cell
- GSE246613 Single-cell and spatial profiling identify three response trajectories to pembrolizumab and radiation therapy in triple negative breast cancer 266 samples shares single-cell
- GSE80006 Single-cell Hi-C reveals unique chromatin reorganization at oocyte-to-zygote transition 246 samples shares single-cell
- GSE305889 Trimodal single-cell profiling of transcriptome, epigenome and 3D genome in complex tissues with scHiCAR 177 samples shares single-cell
- GSE38495 Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells 121 samples shares single-cell
- GSE92872 Pooled CRISPR screening with single-cell transcriptome read-out 100 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.