Abstract GS1-02: Towards a human breast cell atlas

Tk Seth,S Bai,M Hu, E Sei, A Wood, J Wiley,H Chen,A Contreras,M Teshome,B Lim, Ne Navin

CANCER RESEARCH(2019)

引用 0|浏览57
暂无评分
摘要
The human breast tissue consists of lobules connected to a complex network of ducts that are evolutionarily designed to produce and transport milk to nourish offspring. Histopathology has identified 10 major cell types based on morphological features but have provided limited information on cell states - the transcriptional programs of cell types that reflect different biological functions. In this study, we have generated an unbiased 9cell atlas9 of the normal human breast to define the cell types and cell states using single cell RNA sequencing methods. We performed 39 microdroplet based single cell RNA sequencing of 31,442 stromal cells from 11 women with pathologically normal breast tissues that were collected from mastectomies. Unbiased expression analysis identified three major cell types: epithelial cells (luminal and basal), fibroblasts and endothelial cells, in addition to several minor cell types: macrophages, T-cells, natural killer cells, pericytes and smooth muscle cells. Analysis of cell states of these cell types revealed different transcriptional programs in luminal epithelial cells (hormone receptor positive and secretory), basal epithelial cells (myoepithelial or basement-like), endothelial cells (lymphatic or vascular), macrophages (M1 or M2) and fibroblasts (three subgroups) and provided insight into progenitors of each cell types. These data provide a valuable reference for the research community and will provide new insights into how normal cell types are transformed in the tumor microenvironment to promote or inhibit the progression of breast cancer. Citation Format: Seth TK, Bai S, Hu M, Sei E, Wood A, Wiley J, Chen H, Contreras A, Teshome M, Lim B, Navin NE. Towards a human breast cell atlas [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr GS1-02.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要