Imaging ONE WORLD - "Biological Discovery from Large-Scale Imaging Data"
1 March 2021
This week will feature Heba Sailem from Institute of Biomedical Engineering, University of Oxford
Scientific Organisers: Stefanie Reichelt, Alex Sossick, Nick Barry, Alessandro Esposito and Kirti Prakash
The meeting will begin at 13:00GMT.
As part of the 'Imaging ONE WORLD' series, the focus of these lectures is on microscopy and image analysis methods and how to apply these to your research. Almost all aspects of imaging such as sample preparation, labelling strategies, experimental workflows, ‘how-to’ image and analyse, as well as facilitating collaborations and inspiring new scientific ideas will be covered. Speakers will be available for questions and answers. The organisers, CRUK CI core facility staff, Gurdon Institute, MRC-LMB, MRC Cancer Unit and NPL will be able to continue the discussion and provide advice on your imaging projects.
Dr Heba Sailem
University of Oxford
Dr Sailem research is focused on understanding the interplay between genetic and phenotypic components underlying changes in tissue architecture. To achieve that she develops statistical and machine learning methodologies for analysing large biomedical datasets with a focus on cellular imaging and single cell data. In 2017, She has been awarded a Sir Henry Wellcome Research Fellowship to develop a knowledge-driven machine learning framework for characterising gene functions in different cell types. These methods revealed a potential role for olfactory receptors in epithelial colorectal cell organisation. She did her PhD at the Institute of Cancer Research in London under the supervision of Prof Chris Bakal. While at the ICR she developed methods for integrating phenotypic data with gene expression, modelling of the relationship between cell signalling and its context, and modelling the dynamics of cell morphogenesis. In these studies, she discovered new links between cell shape and breast cancer progression. She is also interested in data visualisation as an important tool for science communication. She devised PhenoPlot, one of the first tools that are specifically designed for visualising phenotypic data. This method facilitates the interpretation of high dimensional data by generating pictorial representations of cells based on hundreds to thousands of measurements
Multicellular organisation requires the coordination of multiple signalling pathways that regulate cell shape as well as cell-cell and cell-microenvironment interactions. Such coordination is often lost in cancer, resulting in changes in tissue architecture, uncontrolled growth, and metastasis. High throughput imaging provides a powerful approach for studying these processes. However, analysing and translating image data to biological insights and discoveries pose many challenges. I will discuss the development of several machine learning and computer vision methodologies for automated identification of genetic programmes underlying tissue organisation. In particular, how we can use prior knowledge for automated phenotypic discovery based on data of millions of cells toward inference of gene functions in genetic screens. I will also present our recent work on developing intuitive visualisation methods to facilitate the interpretation of imaging data.