The quantification and interrogation of how the cells organise and interact with their environment is fundamental for advancing developmental biology, cancer research, pharmaceutics and medical diagnostics.
Our lab has recently published quantitative image analysis tools capable of measuring the collective organisation and the history of cellular interactions within crowded cellular environments such as 3D stem cell cultures, tumours or whole mammalian embryos (Blin et al., 2019). The technology generates rich multi-dimensional datasets that expert scientists can use for hypothesis-driven data exploration and analysis.
However, a robust method that can extract meaningful information from such datasets in an unbiased and automated fashion is missing. One of the reasons is that image analysis frameworks able to process complex multicellular systems are only emerging, thus the need for a novel data integration method is only now being realised.
The other reason is the unfamiliar structure of the data: In such datasets, data points are feature vectors that summarise individual cell state at each time point. What differs from conventional time series is that data points also form nodes within multiple graph layers: cellular lineage trees are reconstructed and data points are connected to form binary trees with typed relationships (identity/mother/daughter/sister) forming branches that can be grouped into diverse categories. In addition, direct cell neighbours are identified for each individual cell to form a ‘cellular social network’ that changes over time.
We decide to call such a dataset CeLINet for Cell Lineages Interaction Network.
Importantly both experimental and synthetic CeLINets are readily available in our lab (Blin et al., 2019; Wang et al., 2018). Others have also published annotated datasets and made these publicly available (McDole et al., 2018). With the advances in imaging techniques such datasets will multiply rapidly and we anticipate that a data integration framework that will enable biologist and medical scientists to detect and visualise hidden patterns within CeLINets will quickly grow.
We are convinced that CeLINets will become transformative in our ability to 1) comprehend developing multicellular systems 2) detect subtle phenotypes in comparative analyses and 3) increase the robustness of our quantitative image analysis tools. Yet, while research for statistical inference on (non-connected) lineage trees is just emerging (Hicks et al., 2019), a theoretical framework for complete CeLINets analysis remains to be established.
The aims of this PhD project are to build on recent theoretical work in order to:
1) Define a formal mathematical description of CeLINets;
2) Build a new data integration method for data pattern recognition and enhanced data visualisation;
3) Test the ability of the new method to classify phenotypes and make predictions using in vitro models of development available in the lab as well as other datasets that are readily publicly available;
4) Test the new method for data imputation in order to restore ‘broken’ lineage trees due to missing frames in real world datasets and improve current cell tracking methods in dealing with such a situation.
One of the deliverables of this PhD will be a widely applicable open source computational framework for the analysis and exploration of CeLINets. Individual modules of this framework will be unit tested with synthetic data generated from agent based models with known features readily available in the lab.
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. For those applying to a University of Glasgow project, your application along with any supporting documents will be shared with University of Glasgow. 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 must contact the primary supervisor prior to making your application. Additional information on the application process is available from the link above.
For more information about Precision Medicine visit: http://www.ed.ac.uk/usher/precision-medicine
1. Blin, G., Sadurska, D., Migueles, R.P., Chen, N., Watson, J.A., and Lowell, S. (2019). Nessys: A new set of tools for the automated detection of nuclei within intact tissues and dense 3D cultures. PLOS Biology 17, e3000388.
2. Hicks, D.G., Speed, T.P., Yassin, M., and Russell, S.M. (2019). Maps of variability in cell lineage trees. PLOS Computational Biology 15, e1006745.
3. McDole, K., Guignard, L., Amat, F., Berger, A., Malandain, G., Royer, L.A., Turaga, S.C., Branson, K., and Keller, P.J. (2018). In Toto Imaging and Reconstruction of Post-Implantation Mouse Development at the Single-Cell Level. Cell 175, 859-876.e33.
4. Wang M., Tsanas A., Blin G., Robertson D. : Investigating motility and pattern formation in pluripotent stem cells through agent-based modeling, 19th IEEE International Conference on BioInformatics and BioEngineering, Athens, Greece, 28-30 October 2019 (in press)