Single-cell morphological data reveals signaling network architecture

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. 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. Thesis Supervisor: Bonnie Berger Title: Professor of Applied Mathematics Chapter 1: Introduction Metastasis, the migration of cancer cells away from the primary site of tumorigenesis and the subsequent invasion of secondary tissues, is the cause of the vast majority of patient deaths due to cancer. In order for metastatic cells to spread throughout the body they must dramatically rearrange their cell shape and morphology in complex and dynamic fashions. As such, genes which encode for signaling proteins that regulate cell shape in normal cells are often targets of multiple mutations in cancer, and especially in highly metastatic forms of the disease. In order to study these key signaling proteins in locomotion and metastasis, we develop and validate statistical methods to extract information from high-throughput morphological data from genetic screens. More specifically, we develop techniques to identify genetic components of cellular morphological variability, utilize morphological data to identify genetic interactions and perform signaling pathway inference, and integrate morphological and transcriptional data to study determination of cell morphology. Background and Significance Understanding metastasis through systems-level analysis of signaling networks Cancer is soon to become the leading cause of death in the United States as mortality rates for the disease have remain largely unchanged for the past 50 years, while death-rates for cardiac, cerebrovascular, and infectious diseases have markedly declined [1]. As nutrients and space become limiting at the primary site of tumorigenesis, a small population of cancer cells become metastatic, meaning these cells acquire the ability to migrate from the tumor mass and invade other tissues. The conversion of a locally-growing tumor to one that gains the ability to metastasize is a critical and damaging point in cancer progression, as 90% of cancer patient deaths are due to the effects of tumor cells that have founded colonies in tissues distant from the primary site [2]. Metastasis is a biologically complex process that still remains poorly understood, but is known to involve dramatic changes in the morphology of tumor cells. In all eukaryotic cells, Rho family guanosine triphosphatases (GTPases) such as Rho, Rac, and Cdc42, are the master regulators of cell morphology as these proteins dynamically integrate a vast spectrum of upstream signals and directly control the actin and microtubule cytoskeletons, cell-cell or cell-matrix adhesion, vesicular trafficking, and cell polarity [3]. For example, cell migration is due to the coordinated actions of Rho and Rac GTPases on actin organization. In order to generate the driving force required for motility, Rac-type GTPases at the leading edge of cells promote actin polymerization and the formation of protrusive lamellipodia, while at the trailing edge of cells Rho-type GTPases control the actomyosin machinery in order to stimulate contraction [4]. During migration Rac and Rho also act to regulate the formation of integrin based adhesions at the leading edge, while coupling cell contraction at the rear of the cell to adhesion disassembly [ 4]. Defects in any of these processes lead to an inability of cells to migrate in an efficient manner. Dysregulated Rho signaling has been widely implicated in the metastasis of many tumor types, especially breast tumors [5].