Trajectory inference of breast differentiation from single-cell RNA sequencing data

DANIELA SENRA; NARA GUISONI; LUIS DIAMBRA

Virtual meeting

Conferencia; 2nd WBDS-LA (2nd Women in Bioinformatics & Data Science LA); 2021

Women in Bioinformatics & Data Science LA

The human breast is an organ composed mainly of glandular, adipose and connective tissue. The basic structure of the mammary gland consists of lobular units that produce breast milk interconnected by an intricate system of ducts. The vast majority of breast tumours arise from the epithelial cells lining the terminal ductal lobular units. For this reason, the characterization of the healthy mammary epithelium is an important aspect to comprehend the origins of breast cancer. In this sense, it is of particular interest to understand the differentiation pathway of the mammary epithelium. Many trajectory inference techniques using scRNA-seq data have been developed in recent years, most of which require the selection of an origin to infer the path of differentiation, which is usually set by previously known stemnessmarkers. In this work we propose a Protein-Protein Interaction Network (PPIN) approach to identify the stem-like cells for later trajectory inference. We implement the method to calculate the activity of the PPIN for each cellin R. As the goal is to find the stem cells, the activity of the protein network associated to cellular differentiation process is a parameter that indicates the differentiation activity. In this way, the cells with the highest differentiation activity are determined and selected as the root for the trajectory.