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Genomic characterization of normal and aberrant human milk production [bulkRNA-seq]

GSE275626 Homo sapiens (human) Single-cell RNA-seq 22 samples Submitted 2024/11/30 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 22 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism
Homo sapiens (human)
Samples
22 samples: 22 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)
Breastfeeding is vital for reducing morbidity and mortality, yet exclusive breastfeeding rates are low, with insufficient milk supply being a major weaning factor whose molecular causes remain largely unknown. In this study, we collected fresh milk samples from 30 lactating individuals, classified as low, normal, or high milk producers at multiple postpartum stages, and conducted extensive genomic and microbiome analysis.
Read the full summary ↓
Using bulk RNA sequencing on human milk fat globules (MFG), milk cells, and breast tissue, we found that MFG-derived RNA closely resembles RNA from milk luminal cells. Furthermore, bulk and single-cell RNA-seq revealed changes in the transcriptome and cellular content linked to milk production. We identified specific genes and cell-type proportions differing in low and high milk production. Infant microbiome diversity was affected by feeding type, but not by maternal milk supply. This study provides a comprehensive human milk transcriptomic catalog, identifies genes associated with milk production, and highlights MFG as a useful biomarker for milk transcriptome analysis.
This dataset
Open GSE275626 in finder → Find more human RNA-seq 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 GSE275626_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 PRJNA1152268 and SRA study SRP528531. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
22 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 22 sample titles: 22 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
Datasets on a similar topic

Search all human RNA-seq 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.