Single-nucleus analysis allows robust cell-type classification and helps to establish relationships between chromatin accessibility and cell-type-specific gene expression. Here, using samples from 92 women of several genetic ancestries, we developed a comprehensive chromatin accessibility and gene expression atlas of the breast tissue. Integrated analysis revealed ten distinct cell types, including three major epithelial subtypes (luminal hormone sensing, luminal adaptive secretory precursor (LASP) and basal-myoepithelial), two endothelial and adipocyte subtypes, fibroblasts, T cells, and macrophages. In addition to the known cell identity genes FOXA1 (luminal hormone sensing), EHF and ELF5 (LASP), TP63 and KRT14 (basal-myoepithelial), epithelial subtypes displayed several uncharacterized markers and inferred gene regulatory networks. By integrating breast epithelial cell gene expression signatures with spatial transcriptomics, we identified gene expression and signaling differences between lobular and ductal epithelial cells and age-associated changes in signaling networks. LASP cells and fibroblasts showed genetic ancestry-dependent variability. An estrogen receptor-positive subpopulation of LASP cells with alveolar progenitor cell state was enriched in women of Indigenous American ancestry. Fibroblasts from breast tissues of women of African and European ancestry clustered differently, with accompanying gene expression differences. Collectively, these data provide a vital resource for further exploring genetic ancestry-dependent variability in healthy breast biology.

单核分析可实现稳健的细胞类型分类，并有助于建立染色质可及性和细胞类型特异性基因表达之间的关系。在这里，使用来自 92 名具有多种遗传血统的女性的样本，我们开发了乳腺组织的综合染色质可及性和基因表达图谱。综合分析揭示了 10 种不同的细胞类型，包括三种主要的上皮亚型 （管腔激素感应、管腔适应性分泌前体 （LASP） 和基底肌上皮）、两种内皮和脂肪细胞亚型、成纤维细胞、T 细胞和巨噬细胞。除了已知的细胞身份基因 FOXA1 （管腔激素感应）、EHF 和 ELF5 （LASP）、TP63 和 KRT14 （基底肌上皮） 外，上皮亚型还显示出几个未表征的标志物和推断的基因调控网络。通过将乳腺上皮细胞基因表达特征与空间转录组学相结合，我们确定了小叶和导管上皮细胞之间的基因表达和信号传导差异以及信号网络中与年龄相关的变化。LASP 细胞和成纤维细胞显示出遗传祖先依赖性变异。具有肺泡祖细胞状态的 LASP 细胞的雌激素受体阳性亚群在美洲原住民血统的女性中富集。来自非洲和欧洲血统女性乳腺组织的成纤维细胞以不同的方式聚集，并伴有基因表达差异。总的来说，这些数据为进一步探索健康乳腺生物学中的遗传祖先依赖性变异性提供了重要资源。