Understanding cell state transitions could support therapeutic developments

Researchers from the Institute of Genetics and Cancer collaborated on a study describing a novel approach to mapping cell states, modelling cell state transitions, and predicting targeted interventions to alter cell fate decisions.

Our body is made up of trillions of cells, and at any given time, each cell is in a particular configuration called the cell state. It is composed of certain molecules and expresses certain genes. Its genome, proteins, lipids and other building blocks are arranged chemically and spatially in a certain way.

Cells change their state over time. They are in a constant state of flux as they perform their functions and respond to external stimuli. Certain undesirable cell states can lead to diseases such as neurodegenerative disorders or cancer. Even within a tumour, cells exist in different states, often resulting in varying degrees of aggressiveness and sensitivity to therapeutics.

It is estimated that a simple cell can contain up to 42 million individual protein molecules belonging to more than 10,000 different types of proteins. Many proteins can also undergo processing that alters their properties through proteolytic cleavage and/or the addition of a modifying group, such as phosphoryl, acetyl, glycosyl, methyl, or others, to one or more amino acids. These changes, called posttranslational modifications, further increase the biochemical complexity of the cellular environment and make it very difficult to analyse. The situation is similarly complex for other important building blocks of the cell, such as nucleic acids and lipids. This complexity is probably the biggest challenge for biomedical researchers trying to understand how our cells and bodies work.

Urgent need to develop mathematical models and computer algorithms for the analysis of huge amounts of data 

Nevertheless, over the years, scientists have developed a variety of sophisticated technologies (often referred to as "omics" technologies, e.g., genomics, transcriptomics, lipidomics, proteomics, metabolomics, etc.) that enable the collaborative characterization and quantification of pools of biological molecules. These technologies offer unprecedented insights into cell structure, function, and dynamics. However, they generate huge amounts of data that are almost impossible for humans to analyse and interpret without the help of computers. Therefore, there is an urgent need to develop mathematical models and computer algorithms that could help us analyse the huge amounts of data. This should help us better understand various diseases and develop more effective treatments for them. 

To this end, researchers from University College Dublin, Yale University School of Medicine, and the University of Edinburgh have teamed up to develop approaches that integrate "omics" cell signalling and cell phenotype data to map cell states, model transitions between them, and predict targeted interventions to alter cell fate decisions. Reverse Phase Protein Arrays (RPPA) data for the project were generated by Edinburgh Cancer Research scientists Kenneth Macleod and Neil Carragher in the Host and Tumour Profiling Unit (HTPU) at the Institute of Genetics and Cancer. RPPA technology enables the quantification of hundreds of proteins or protein changes in thousands of samples.

Results published in the journal Nature

The results of this collaborative work were published in the journal Nature in an article titled "Control of cell state transitions." The researchers, led by Boris Kholodenko of University College Dublin, presented a cell state transition assessment and regulation (cSTAR) approach that uses "omics" data as input, classifies cell states, and develops a workflow that converts the input data into mechanistic models that identify a central signalling network that controls cell fate transitions by influencing the cellular biochemical environment. cSTAR can utilise and integrate various "omics" data. This universality and scalability distinguishes cSTAR from other currently available approaches that are more specialised in terms of input data. It provides a cell-specific, mechanistic approach to describing, understanding, and targeting cell fate decisions. As such, it has numerous applications in biology that extend well beyond the experimental models used in this study.

Importantly, Professor Carragher and his team plan to further develop and use this methodology to identify and prioritise drug combinations for the treatment of brain tumours. The study is being conducted as part of a project funded by Cancer Research UK and the Brain Tumour Charity, which focuses on glioblastomas. The project, titled "Systems approach to therapeutic combinations for glioblastoma," involves researchers from the University of Edinburgh, the University of Oxford and the Massachusetts Institute of Technology.

We are very excited about the fact that cSTAR approach is able to identify precision interventions for controlling cell fate decisions as demonstrated in SH-SY5Y human neuroblastoma cells. We hope that this methodology will prove very useful in our ongoing studies aiming to find drugs and drug combinations that could be effective in treatment of glioblastoma, one of the most aggressive types of brain cancer.

Kenneth Macleod and Neil Carragher University of Edinburgh

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