GEO series

Single nuclei chromatin accessibility and transcriptomic map of breast tissues of women of diverse genetic ancestry [10X single-cell multiome]

GSE244585 Homo sapiens (human) Single-nucleus RNA-seq 32 samples Submitted 2024/04/19 Platform GPL24676

At a glance

Single-nucleus RNA-seq · human · 32 samples

What it is: Single-nucleus RNA-seqGene activity measured in individual nuclei (used when whole cells are hard to isolate, for example from frozen tissue).
Organism: Homo sapiens (human)
Samples: 32 samples: 32 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.

Summary (written by the submitters)

Single nuclei analysis is allowing robust classification of cell types in an organ that helps to establish relationships between cell-type specific gene expression and chromatin accessibility status of gene regulatory regions. Using breast tissues of 92 healthy donors of various genetic ancestry, we have developed a comprehensive chromatin accessibility and gene expression atlas of human breast tissues.

Read the full summary ↓

Integrated analysis revealed 10 distinct cell types in the healthy breast, which included three major epithelial cell subtypes (luminal-hormone sensing, luminal adaptive secretory precursor, and basal-myoepithelial cells), two endothelial subtypes, two adipocyte subtypes, fibroblasts, T-cells, and macrophages. By integrating gene expression signatures derived from epithelial cell subtypes with spatial transcriptomics, we identify specific gene expression differences between lobular and ductal epithelial cells and age-associated changes in epithelial cell gene expression patterns and signaling networks. Among various cell types, luminal adaptive secretory cells and fibroblasts showed genetic ancestry dependent variability as a subpopulation of luminal adaptive secretory cells with alveolar progenitor (AP) cell state were enriched in Indigenous American (IA) ancestry and fibroblast populations were distinct in African ancestry. ESR1 expression pattern was distinctly different in tissues from IA compared to the rest, with a high level of ESR1 expression extending to AP cells and crosstalk between growth factors and Estrogen Receptor signaling is evident in these AP cells. In general, cell subtype-specific gene expression did not correlate with chromatin accessibility differences, suggesting that transcriptional regulation independent of chromatin accessibility governs cell type-specific gene expression in the breast.

This dataset

Open GSE244585 in finder → Find more human 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 GSE244585_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 PRJNA1023624 and SRA study SRP464412. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

32 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 32 sample titles: 32 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

GSM7821192 Ashkenazi_NormalBreastSample_multiome_Pool1, scATAC-seq
GSM7821193 KTB_pool-3-Ashkenazi_jew, scATAC-seq
GSM7821194 KTB_pool-4-Ashkenazi_jew, scATAC-seq
GSM7821195 KTBpool5_Ashkenazi_jew, scATAC-seq
GSM7821196 KTBpool6_Ashkenazi_jew, scATAC-seq
GSM7821197 Pool7_BRCA2Positive, scATAC-seq
GSM7821198 Pool8_BRCA1Positive, scATAC-seq
GSM7821199 KTBpool9-White American, scATAC-seq
GSM7821200 KTBpool11-White American, scATAC-seq
GSM7821201 KTBpool12-White American, scATAC-seq
GSM7821202 KTBpool_13-Indigenous American, scATAC-seq
GSM7821203 KTBpool_14-Indigenous American, scATAC-seq
GSM7821204 KTBpool15_Asian, scATAC-seq
GSM7821205 KTBpool17_Asian, scATAC-seq
GSM7821206 C23_KTBpool-23_Hispanic, scATAC-seq
GSM7821207 C25_KTBpool-25_Hispanic, scATAC-seq
GSM7821208 Ashkenazi_NormalBreastSample_multiome_Pool1, scRNA-seq
GSM7821209 KTB_pool-3-Ashkenazi_jew, scRNA-seq
GSM7821210 KTB_pool-4-Ashkenazi_jew, scRNA-seq
GSM7821211 KTBpool5_Ashkenazi_jew, scRNA-seq
GSM7821212 KTBpool6_Ashkenazi_jew, scRNA-seq
GSM7821213 Pool7_BRCA2Positive, scRNA-seq
GSM7821214 Pool8_BRCA1Positive, scRNA-seq
GSM7821215 KTBpool9-White American, scRNA-seq
GSM7821216 KTBpool11-White American, scRNA-seq
GSM7821217 KTBpool12-White American, scRNA-seq
GSM7821218 KTBpool_13-Indigenous American, scRNA-seq
GSM7821219 KTBpool_14-Indigenous American, scRNA-seq
GSM7821220 KTBpool15_Asian, scRNA-seq
GSM7821221 KTBpool17_Asian, scRNA-seq
GSM7821222 C23_KTBpool-23_Hispanic, scRNA-seq
GSM7821223 C25_KTBpool-25_Hispanic, scRNA-seq

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Search all human ChIP / ATAC / CUT&Tag 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.