Single-cell morphological data reveals signaling network architecture

Abstract

Metastasis, the migration of cancer cells from the primary site of tumorigenesis and the subsequent invasion of secondary tissues, causes the vast majority of cancer deaths. To spread, metastatic cells dramatically rearrange their shape in complex, dynamic fashions. Genes encoding signaling proteins that regulate cell shape in normal cells are often mutated in cancer, especially in highly metastatic disease. To study these key signaling proteins in locomotion and metastasis, we develop and validate statistical methods to extract information from highthroughput morphological data from genetic screens. Our contributions fall into three major categories. 1) To define and apply robust statistical measures to identify genes regulating morphological variability. We develop and thoroughly test methods for measuring morphological variability of single-cells populations, and apply these metrics to genetic screens in yeast and fly. We further apply these techniques to subsets of genes involved in cellular processes to study genetic contributions to variability in these processes. We propose new roles for genes as suppressors or enhancers of morphological noise. We validate our findings on the basis of known gene function and network architecture. 2) To perform inference of protein signaling relationships by utilizing high-throughput morphological data. We apply machine-learning techniques to systematically identify genetic interactions between proteins on the basis of image-based data from double-knockout screens.

(cont.) Next, we focus on RhoGTPases and RhoGTPase Activating Proteins (RhoGAPs) in Drosophila., where by using basic knowledge of network architecture we apply our techniques to detect signaling relationships. 3) To integrate expression data with high-throughput morphological data to study the mechanisms for determination of cell morphology. We utilize morphological and microarray data from fly screens. By comparing expression data between control treatment conditions and treatment conditions displaying morphological phenotypes (e.g. high population variability), we identify genes and pathways correlated with this class distinction, thereby validating our previous studies and providing further insight into the determination of morphology. A key challenge in systems biology is to analyze emerging high-throughput image-based data to understand how cellular phenotypes are genetically encoded. Our work makes significant contributions to the literature on high-throughput morphological study and describes a path for future investigation.