Matched single-cell chromatin, transcriptome, and surface marker profiling captures in vivo epigenomic reprogramming during basal-to-luminal transition in the mammary gland
Single-cell RNA-seq · mouse · 37 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
- 37 samples: 8 conditions, mostly in duplicateGroups 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.
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 GSE290486_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 PRJNA1228144 and SRA study SRP566279. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 30 samples.
How we read this: 8 replicated groups read from 37 sample titles; they account for 30 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.
- GSM8815094 mm468_scChIP_h3k27me3
- GSM8815095 mm468_scCUT&Tag_10x_h3k27me3
- GSM8815098 frozen_pdx_OneCell_CUT&Tag_h3k4me1_rep1 frozen_pdx_OneCell_CUT&…
- GSM8815099 frozen_pdx_OneCell_CUT&Tag_h3k4me1_rep2 frozen_pdx_OneCell_CUT&…
- GSM8815468 zygotes_OneCell_multiome_h3k27me3_batch1 zygotes_OneCell_multiom…
- GSM8815469 zygotes_OneCell_multiome_h3k27me3_batch2 zygotes_OneCell_multiom…
- GSM8815470 zygotes_OneCell_multiome_rna
- GSM8815471 zygotes_h3k27me3_bulk_CUT&RUN_r1 zygotes_h3k27me3_bulk_C…
- GSM8815472 zygotes_h3k27me3_bulk_CUT&RUN_r2 zygotes_h3k27me3_bulk_C…
- GSM8815664 mm468_scChIC_h3k27me3
- GSM9774265 mm468_onecell_multiome_h3k27me3
- GSM9774266 mm468_onecell_multiome_rna
- GSM9776539 transplantations_chromatin_p1 transplantations_chroma…
- GSM9776540 transplantations_chromatin_p2 transplantations_chroma…
- GSM9776541 transplantations_chromatin_p3 transplantations_chroma…
- GSM9776542 transplantations_chromatin_p4 transplantations_chroma…
- GSM9776543 transplantations_chromatin_p5 transplantations_chroma…
- GSM9776544 transplantations_chromatin_p7 transplantations_chroma…
- GSM9776545 transplantations_chromatin_p8 transplantations_chroma…
- GSM9776546 transplantations_chromatin_p9 transplantations_chroma…
- GSM9776547 transplantations_chromatin_p10 transplantations_chroma…
- GSM9776548 transplantations_chromatin_p11 transplantations_chroma…
- GSM9776549 transplantations_chromatin_p12 transplantations_chroma…
- GSM9776550 transplantations_chromatin_p13 transplantations_chroma…
- GSM9776551 transplantations_chromatin_p14 transplantations_chroma…
- GSM9776552 transplantations_rna
- GSM9777372 zygotes_onecell_multiome_h3k4me1_batch1 zygotes_onecell_multiom…
- GSM9777373 zygotes_onecell_multiome_h3k4me1_batch2 zygotes_onecell_multiom…
- GSM9777374 zygotes_onecell_multiome_rna_for_h3k4me1_batch1 zygotes_onecell_multiom…
- GSM9777375 zygotes_onecell_multiome_rna_for_h3k4me1_batch2 zygotes_onecell_multiom…
- GSM9789445 mammary_gland_chromatin_mouse1 mammary_gland_chromatin…
- GSM9789446 mammary_gland_chromatin_mouse2 mammary_gland_chromatin…
- GSM9789447 mammary_gland_chromatin_mouse3 mammary_gland_chromatin…
- GSM9789448 mammary_gland_chromatin_mouse45 mammary_gland_chromatin…
- GSM9789449 mammary_gland_rna_mouse1 mammary_gland_rna_mouse
- GSM9789450 mammary_gland_rna_mouse23 mammary_gland_rna_mouse
- GSM9789451 mammary_gland_rna_mouse45 mammary_gland_rna_mouse
- GSE261788 AMAR-seq: automated multimodal sequencing of DNA methylation, chromatin accessibility, and RNA expression with single-cell resolution 83 samples shares single-cell, chromatin
- GSE249799 scNanoSeq-CUT&Tag: a long-read single-cell CUT&Tag sequencing method for efficient chromatin modification profiling within individual cells 46 samples shares single-cell, chromatin
- GSE246859 ChromaFold predicts the 3D contact map from single-cell chromatin accessibility 3 samples shares single-cell, chromatin
- GSE253161 Scalable ultra-high-throughput single-cell chromatin and RNA sequencing reveals gene regulatory dynamics linking macrophage polarization to autoimmune disease [NIH-3T3_K562_RNA_without_blocking] 1 samples shares single-cell, chromatin
- GSE202126 Single-cell Total-RNA Profiling Unveils Regulatory Hubs of Transcription Factors 1089 samples shares single-cell
- GSE325735 Transcriptomic reprogramming screen identifies SRSF1 as a regulator of aging 839 samples shares reprogramming
- GSE156337 Onco-fetal reprogramming of endothelial cells drives immunosuppressive macrophages in Hepatocellular Carcinoma 186 samples shares reprogramming
- GSE272618 Single-Molecule Barcoding Technology for Single-Cell Genomics 140 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.