Cellular heterogeneity is frequently found in solid tumours and is thought to contribute to resistance to cancer therapy. Thanks to new technologies that enable detailed characterization of gene expression of individual cells, we are now poised to understand at a molecular level how heterogeneity affects clinical outcomes. Single cell transcriptome data provide a molecular, gene expression-based definition of a cell state. However, we currently lack a comprehensive understanding of how to connect cell state (defined here as a specific gene expression profile) and cellular phenotype. For example, to accurately stage the cell cycle progression of individual cells, researchers have correlated single cell gene expression profiles to large scale analyses of cell cycle-regulated gene expression studies1. As transcriptomic and proteomic data from clinical samples become increasingly available, it will be equally important to have functional data linking gene expression and protein abundance profiles to cellular phenotype, such as senescence, cell cycle stage, etc.
One of the hallmarks of cancer is aberrant cell cycle control leading to pathological cell proliferation. Transitions between different phases of the cell cycle are controlled by key biochemical switches that ensure that cell cycle progression is unidirectional. These critical biochemical switches are frequently exploited in clinical therapies to treat cancer by preventing cell cycle progression. Emerging data from mass spectrometry-based proteomics2 and timelapse microscopy3 suggest the ‘classic’ cell cycle interphase stages of the cell cycle (G1, S, G2) can be further divided by molecular differences into subphases that have divergent cellular phenotypes in response to stress. For example, early versus late G2 cells differ in how they respond to exogenous stresses, such as DNA damage-induced senescence4. The broader aim of the PhD project is to explore new cell cycle transitions during G2 and to define the molecular markers that underpin the divergent cell fate trajectories. We anticipate meta analysis of the large datasets generated from this project with emerging clinical proteomic datasets (TCGA, Cancer Moonshot Project, etc.) will enable deeper understanding of the cellular phenotype from clinical data.
1. A proteomic characterization of early versus late G2 cells
Early and late G2 cells will be separated using intracellular immunostaining of appropriate markers (such as Cyclin B1 and phospho-PLK1 substrates) followed by Fluorescence Activated Cell Sorting (FACS). Cells will then be processed for mass spectrometry-based quantitative analysis of the proteome and phosphoproteome using tandem mass tag (TMT) quantitation and the current generation Orbitrap mass spectrometry technology (Orbitrap Fusion Lumos).
2. Large-scale identification of Polo-like kinase 1 (Plk1) G2-phase substrates
Kinetic in vitro assays will be performed using recombinant Plk1 and ATM. Kinase assays will be performed in G2-phase arrested cells. Substrate phosphorylation dynamics will be measured by mass spectrometry-based phosphoproteomics using the workflow described in Aim 1. The resulting data will be used to define Plk1- and ATM- substrate relationships.
3. Mechanisms underlying DNA-damage induced senescence in G2
Time-lapse imaging and quantitative microscopy will be performed to study how DNA-damage induced senescence is forced at different stages within G2 phase. In particular, Plk1 and ATM activity will be followed in single live cells by FRET-based probes. After fixation, the cells will be stained for immunofluorescence, with a particular focus on proteins and phosphorylations identified in aim 1 and 2. The functional relevance of kinase activities will be tested by addition of selective inhibitors and the use of knock-out cell lines.
The student will be trained in ‘wet lab’ biochemistry and cell biology techniques, including immunocytochemistry, flow cytometry, immunoblotting, and cell culture. The student will learn how to perform mass spectrometry-based proteomics and phosphoproteomics, how to analyse the resulting datasets, and how to correlate these data with publicly available clinical datasets containing patient outcomes. Bioinformatic data analysis will be performed with the assistance and training of the core bioinformatics facility at the Wellcome Centre for Cell Biology. Molecular profiles will be compared with phenotypic data from timelapse and immunofluorescence microscopy. The combined expertise at Edinburgh University and Karolinska Institutet provide for excellent training in these areas.
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 should 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 https://www.ed.ac.uk/usher/precision-medicine/project-opportunities
1. Scialddone et al. Computational assignment of cell-cycle stage from single-cell transcriptome data. Methods 2015, 85, 54-61.
2. Ly et al. Proteomic analysis of cell cycle progression in asynchronous cultures, including mitotic subphases, using PRIMMUS. eLife 2017, 6, e27574.
3. Akopyan et al. Assessing kinetics from fixed cells reveals activation of the mitotic entry network at the S/G2 transition. Mol. Cell. 2014, 53, 843-853.
4. Jaiswal et al. ATM/Wip1 activities at chromatin control Plk1 re-activation to determine G2 checkpoint duration. EMBO J. 2017, 36, 2161-2176.