Montell+Project

= = = Dynamics of In-Toto Organogenesis = = = Dani Cai, Wei NDai, Sebastian Streichan & Denise Montell

= Biological problem = Deciphering mechanisms of organ formation is greatly facilitated by multi-scale In-Toto live imaging, ranging from the tissue over individual cells to their organelles. The Drosophila ovary is a prime example, where advanced genetics combined with new culture conditions that support ex-vivo development in conjunction with live imaging, revealed previously unimagined dynamic phenomena. High accessibility and transparency render ovaries very well suited to live imaging and therefore allow for precise quantitative studies on the basic repertoire of morphogenesis. Egg chamber rotation and oscillating cellular contractions where shown to be essential for normal egg development and proper egg shape. Reduced complexity compared to other tissues increases tractability of theoretical approaches to these phenomena. In addition, the power of drosophila genetics enables employment of novel physical manipulations and measurements necessary to challenge theoretical predictions. In this practical, we will combine unique genetic tools, including an in vivo tension sensor and photo-activatable Rac, together with multiple imaging modalities and laser ablations to measure and manipulate mechanical forces during the development of the future egg. Together with quantitative analysis of molecular processes as well as mechanical properties of cells, we aim to develop an understanding of the contribution of tissue mechanics to the final shape of the egg. = Microscopy = - Zeiss LSM 780, MuVi Spim, Andor Spinning Disc, live imaging at sub cellular resolution, multi color.

= = = Data Analysis = 3 dimensional data stacks will be pre-process in order to simplify the analysis. Where possible we will exploit the fact that tissues such as the follicle epithelium form a monolayer, and project the spheroid on the plain, turning the 3D+time into a much more tractable 2D+time data analysis problem. Random forest machine learning algorithms will be used to segment images, allowing highly accurate identification of virtually any marked organelle based on input provided by the user. We will focus on Nuclei, Membrane and Myosin in order to identify cell position in space and determine such aspects as the shape of cells and the distribution of myosin. In-Toto single cell tracking will provide time traces of these quantities for each cell, allowing measurement of correlation of derived quantities, such as cell area or velocity over time and across cells. = = = Physical Aspects = While it is recognized that the observed oscillatory cell contractions and myosin localization oscillations are necessary for final egg shape, much less is understood about their spatial coordination and the interplay between the cytoskeletal dynamics of adjacent cells and the resultant cell shape changes. We will take an observation driven approach to test if taking into account ovary specific aspects and our current understanding of tissue mechanics is sufficient to explain the observed oscillations. For isolated cells, we expect myosin bursts to give rise to cell contractions, thus a simple relation between myosin and area frequency. Coupled cells face constraints, since mechanical feedback of direct neighbors for instance can frustrate anti-phase relations. On the other hand, aligned actin fibers, specific to the ovary could enhance coupling across one direction, possibly lifting such frustrations. To develop a better understanding of the interaction processes (myosin regulating area, mechanical feedback into myosin frequency…), we will characterize the frequency of myosin oscillations across the tissue and test for phase relations among cells. Possible outcomes range from static phase relations to adaption of myosin phase to mechanical constraints. Starting with coupled oscillators, we will formulate physical models of gradually increasing complexity, which we will evaluate by comparison to the acquired data. = = = Skills that students acquire = Fly handling, Ovary dissection and mounting, confocal and single plane illumination live imaging, image segmentation, single cell tracking,