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Implications for postpartum reprograming of SEMA7A and SOX9 in mammary cells and breast cancer

GSE319717 Mus musculus (mouse) Single-cell RNA-seq 24 samples Submitted 2026/05/20 Platform GPL34328
At a glance

Single-cell RNA-seq · mouse · 24 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
Mus musculus (mouse)
Samples
24 samples: 8 conditions, mostly in triplicateGroups 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)
Postpartum mammary gland involution is a coordinated process of cell death and remodeling that returns the tissue to a near pre-pregnant state following lactation and weaning. In models of postpartum breast cancer, defined as breast cancer diagnosed in women under age 45 and within 10 years of recent childbirth, involution induces durable phenotypes in breast tumor cells that promote progression and are associated with increased risk for therapeutic resistance, metastasis, and death in patients.
Read the full summary ↓
SRY-Box Transcription Factor 9 (SOX9), a known regulator of mammary stem and progenitor cells, also promotes resistance to therapy and metastasis in breast cancers. Yet the contribution of SOX9 to the involution process is not well understood. We utilized single-cell RNA sequencing of mouse mammary glands during involution to delineate Sox9-expressing cell populations during lactation and involution. We found that Sox9 mRNA is primarily expressed in luminal progenitor cells that are largely absent during lactation and present during early involution. We also reveal that Sox9 is involved in a shift in cell state from lactational to non-lactational and is expressed in the surviving cells during involution. Prior work revealed that Semaphorin-7a (SEMA7A) also promotes cancer stem cell and pro-survival phenotypes in luminal progenitor cells during involution, and we observe a population of luminal progenitor cells that co-expresses Sox9 and Sema7a during involution. Mechanistically, we demonstrate that knockdown of Sox9 in cultured mammary epithelial cells results in increased SEMA7A expression, mesenchymal phenotypes, and loss of lactogenic differentiation capacity, identifying a potential regulatory axis where SOX9 balances SEMA7A expression in normal mammary epithelium and that disruption of this balance results in a dedifferentiated state that resembles mesenchymal cells. We validated a spatial relationship between SOX9 and SEMA7A proteins in a unique set of breast tissue samples from healthy human donors to show co-expression during early involution. In breast cancer datasets, we observe elevated expression of SOX9 and SEMA7A in triple-negative breast cancers, as well as in the mesenchymal subtype of triple-negative breast cancers, suggesting disruption of this regulatory axis in breast cancer. Finally, we observe that co-expression increases metastatic risk in both estrogen receptor-negative and -positive breast cancers. Collectively, these findings define a novel SOX9–SEMA7A relationship in healthy mammary tissues and illustrate how studies of normal progenitor cell phenotypes can delineate cellular mechanisms that contribute to breast tumor progression.
This dataset
Open GSE319717 in finder → Find more mouse 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 GSE319717_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 PRJNA1424993 and SRA study SRP677789. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
8 conditions, mostly in triplicate
HC11_Inv4_shCtrl_Biol ×3 HC11_Inv4_shSOX9KD1_Biol ×3 HC11_Inv4_EV_Biol ×3 HC11_Inv4_S7OE_Biol ×3 EpH4_Inv4_shCtrl_Biol ×3 EpH4_Inv4_shSOX9KD1_Biol ×3 EpH4_Inv4_EV_Biol ×3 EpH4_Inv4_S7OE_Biol ×3

Supports a between-group comparison across 24 samples.

How we read this: 8 replicated groups read from 24 sample titles; they account for 24 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.

Samples in this study
Datasets on a similar topic

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