| Experiment Id | GSE272932 | Name | Pregnancy Restricts an Age-Driven Accumulation of Hybrid Cells in the Mammary Gland [scRNA-Seq] |
| Experiment Type | RNA-Seq | Study Type | Baseline |
| Source | GEO | Curation Date | 2025-02-03 |
| description | Aging increases breast cancer risk while an early first pregnancy reduces a womans life-long risk. Several studies have explored the effect of either aging or pregnancy on mammary epithelial cells (MECs), but the combined effect of both remains unclear. Here, we interrogate the functional and transcriptomic changes at single cell resolution in the mammary gland of aged nulliparous and parous mice to discover that pregnancy normalizes age-related imbalances in lineage composition, while also inducing a permanently differentiated cell state. Importantly, we uncover a minority population of Il33-expressing hybrid cells with high cellular potency that accumulate in aged nulliparous mice but is significantly reduced in aged parous mice. Functionally, IL33 treatment of basal, but not luminal, epithelial cells from young mice phenocopies aged nulliparous MECs and promotes formation of organoids with Tp53 knockdown. Collectively, our study demonstrates that pregnancy blocks the age-associated loss of lineage integrity in the basal layer through a decrease in Il33+ hybrid cells, potentially contributing to pregnancy-induced breast cancer protection. We performed scRNA-seq from mammary glands of 18m NP and 18m P mice using 10x chromium scRNA-seq (n=3 mice/group) as per manufactures protocol using the 5 Gene expression workflow (1 GEM per condition, 3 mice pooled in equal proportions per condition) Libraries were sequenced on the Illumina NovoSeq 6000 Platform by Medgenome, Inc. Reads were mapped to the mouse genome (mm10). Additionally, we integrated an 18m NP data set from Tabula Muris Senis. Prior to preprocessing, there were 21,995 cells and 32,285 genes. For quality control, we used Scanpys scanpy.pp.calculate_qc_metrics, scanpy.pp.filter_genes, and scanpy.pp.filter_cells. We applied filters to eliminate (1) genes that are detected in less than 3 cells, (2) cells that have less than 200 genes, (3) cells with gene counts < 600 or > 8,000, (4) cells with total counts of UMIs per cell < 2,000 or > 12,000, and (5) cells with mitochondrial gene ratio > 1.5%. The mitochondrial gene ratio was defined as the percentage of UMIs mapped to mitochondrial genes compared to non-mitochondrial genes within each cell; cells with a high ratio are indicative of non-viable or apoptotic cells. Doublets were identified using Scrublet, which detected 588 cells as doublets; these cells were then filtered out. After filtering, the datasets were concatenated, the UMI counts for each cell were then normalized using a target sum of 1e4, and then log transformed using scanpy.pp.log1p, which computes X=log(X+1), where log denotes the natural logarithm. This quality control resulted in the detection of 10,001 cells and 13,892 genes. |
