Figure 1. Sample-to-sample variability in transcriptional cell state in the premenopausal human breast.

(A) Single-cell transcriptional analysis links biological variables with person-to-person heterogeneity in transcriptional cell state. scRNA-seq workflow: Reduction mammoplasty samples were processed to epithelial-enriched tissue fragments, then to single cells, followed by MULTI-seq sample barcoding, library preparation using the 10X Chromium system, and sequencing.

(B) The major epithelial and stromal cell types in the breast were identified and visualized by UMAP dimensionality reduction and unsupervised clustering of twenty-eight samples reduction mammoplasty samples (GSE198732, Table S1).

(C) Density plots (arbitrary units, linear scale) highlighting the transcriptional cell state of hormone-responsive (HR+) luminal cells and basal/myoepthelial cells from each sample.

(D) Overview of conceptual approach: We hypothesized that hormone receptor activation would represent a major source of transcriptional variability in our dataset, and that hormone receptor activation in hormone-responsive (HR+) luminal cells would correlate with transcriptional changes in other cell types—representing the downstream paracrine response. Based on differences in hormone levels due to menstrual cycling (depicted, left) or hormonal contraceptive use, we predicted that gene expression programs representing ER/PR signaling in HR+ luminal cells and the downstream signaling response in other cell types would co-vary across samples (right).

(E) Using individual pairwise correlations between cell activities, DECIPHER-seq builds a tissue-level map of the cell-cell interactions present in the healthy human breast and identifies modules of transcriptional states that co-occur across the same sets of samples. In downstream analyses, we exclude modules driven by non-cell-type specific responses to shared signals, and uncover modules enriched for putative direct cell-cell signaling interactions. We define activity programs using gene set enrichment analysis, identify common pathways enriched across activity programs in a module, and uncover potential sources of biological variation by testing association with annotated metadata features.