Differentiation of pluripotent cells: a quantitative multiparametric approach

Background

This project will develop tools to extract meaningful biological information from the complex single-cell multi-parametric datasets that are emerging from new image analysis technologies. The student will use these tools to understand differentiation decisions of pluripotent cells

We already know the extrinsic signals that direct pluripotent cells into particular cell fates, but cells do not respond reliably to these signals for reasons we do not understand. We have accumulated evidence that a number of properties of cells can influence the way they respond to differentiation signals. These properties include the way cells are organised in 3D space (Blin et al., 2017) the way they stick to each other (Malaguti et al., 2013), and the way they interact with their immediate neighbours (Lowell et al., 2006; Zhou et al., 2013). Other likely influences include the shape or position of the cell, the activity of particular signalling pathways, and the expression of particular transcription factors. However do not understand which of these factors are most important, nor how they might act together to influence differentiation.

The Lowell lab (School of Biological Sciences) have developed quantitative image analysis tools for following differentiation at single cell resolution in embryos, organoid-type cultures, or conventional monolayer cultures (Wellcome Trust-funded project, unpublished). We can measure a large number of parameters in each individual cell within these tissues/cultures over time, including activity of signalling pathways, expression of transcription factors and the morphological and cell biological events that we believe are influencing differentiation. This type of analysis generates large multi-parametric datasets containing multiple measurements for thousands of cells, including information about cell-cell interactions over time. The challenge now is to find ways to explore these data and extract combinations of parameters that correlate with or predict particular differentiated cell fates.

The Robertson lab (School of Informatics) have expertise quantitative models of dynamic interactions. They have an ongoing collaboration with the Lowell lab to study the social and dynamic behaviors of stem cells based on quantitative imaging data from a simple 2D differentiation system (currently led by a second-year PhD student). The proposed PhD project extends this approach in order to explore how cell-cell interactions modulate differentiation within 3D tissues or organoid-type cultures.

This analysis will generate testable hypotheses about the combinations of influences that may help us to control differentiation more reliably. We will use our standard lab toolkit (genome engineering, biophysical manipulations, organoid- type differentiation of pluripotent cells, quantitative analysis of cell fates) in order to test these hypotheses.

Aims

1. Gather multi-parametric single-cell-resolution data from pluripotent mouse cells undergoing differentiation within monolayer cultures, organoid cultures, or embryos.
2. Organise these data into a graph database that can be manually interrogated in order to explore specific hypotheses on combinatorial control of cellular behavoir.
3. Use statistical analysis and modelling approaches to identify particular combinations of parameters or interactions that correlate with or predict particular cell behaviours, and use these data to generate new hypotheses on combinatorial control of cellular behavoir.
4. Test these hypotheses using genome engineering, biophysical manipulations, and other standard wet-lab approaches to manipulate the appropriate cellular properties.

Training outcomes

1. Wet lab skills: differentiation of pluripotent cells in monolayer and organoid cultures, embryology, genome engineering (routine techniques in Lowell lab).
2. Imaging skills: confocal and light-sheet microscopy, use of high-level quantitative image analysis software (already custom written and in use within Lowell lab)
3. Quantitative and computational tools: computational and statistical analysis of single-cell multi-parametric datasets, modelling.

This MRC programme is joint between the Universities of Edinburgh and Glasgow. You will be registered at the host institution of the primary supervisor detailed in your project selection.

All applications should be made via the University of Edinburgh, irrespective of project location:

http://www.ed.ac.uk/studying/postgraduate/degrees/index.php?r=site/view&id=919

Please note, you must apply to one of the projects and you are encouraged to contact the primary supervisor prior to making your application. Additional information on the application process if available from the link above.

For more information about Precision Medicine visit:

http://www.ed.ac.uk/usher/precision-medicine