Divergent aging of nulliparous and parous mammary glands reveals IL33+ hybrid epithelial cells
Aging increases breast cancer risk while an early first pregnancy reduces a woman's life-long risk. While several studies have explored the effect of either aging or pregnancy on mammary stem/progenitor cells, 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 differentiated cell state. Importantly, we uncover a minority population of Il33-expressing epithelial cells that express both luminal and basal markers (i.e. hybrid), which accumulate in aged nulliparous mice but are significantly reduced in aged parous mice. Functionally, IL33 treatment of mammary epithelial cells from young mice phenocopies aged nulliparous epithelial cells, induces proliferation and promotes formation of organoids with Trp53 knockdown. Collectively, our study demonstrates that pregnancy blocks the age-associated imbalances in lineage integrity in the basal layer, including a decrease in Il33+ hybrid cells, that could potentially contribute to pregnancy-induced breast cancer protection. Overall design: 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 manufacture's 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. For quality control, we used Scanpy's '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, 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.