Phenotyping cellular motion

Thesis

In the development of multicellular organisms, tissue development and homeostasis require coordinated cellular motion. For example, in conditions such as wound healing, immune and epithelial cells need to proliferate and migrate. Deregulation of key signalling pathways in pathological conditions causes alterations in cellular motion properties that are critical for disease development and progression, in cancer it leads to invasion and metastasis. Consequently there is strong interest in identifying factors, including drugs that affect the motion and interactions of cells in disease using experimental models suitable for high-content screening. There are two main modes of cell migration; individual and collective migration. Currently analysis tools for robust, sensitive and comprehensive motion characterisation in varying experimental conditions for large extended timelapse acquisitions that jointly considers both modes are limited.

We have developed a systematic motion analysis framework, Motion Sensing Superpixels (MOSES) to quantitatively capture cellular motion in timelapse microscopy videos suitable for high-content screening. MOSES builds upon established computer vision approaches to deliver a minimal parameter, robust algorithm that can i) extract reliable phenomena-relevant motion metrics, ii) discover spatiotemporal salient motion patterns and iii) facilitate unbiased analysis with little prior knowledge through unique motion ‘signatures’. The framework was validated by application to numerous datasets including YouTube videos, zebrafish immunosurveillance and Drosophila embryo development. We demonstrate two extended applications; the analysis of interactions between two epithelial populations in 2D culture using cell lines of the squamous and columnar epithelia from human normal esophagus, Barrett’s esophagus and esophageal adenocarcinoma and the automatic monitoring of 3D organoid culture growth captured through label-free phase contrast microscopy. MOSES found unique boundary formation between squamous and columnar cells and could measure subtle changes in boundary formation due to external stimuli. MOSES automatically segments the motion and shape of multiple organoids even if present in the same field of view. Automated analysis of intestinal organoid branching following treatment agrees with independent RNA-seq results.

Authors: Zhou, F

• Type of award: DPhil
• Level of award: Doctoral
• Awarding institution: University of Oxford
• Language: English
• Keywords: collective motion; superpixels; temporal dynamic graphs
• Subjects: Computational biology; Machine learning