Understanding cell state transitions might aid therapeutic developments

Edinburgh researchers contributed to a study describing a novel approach for mapping cell states, modelling cell state transitions and predicting targeted interventions to convert cell fate decisions: September 2022

[scald=154108:sdl_editor_representation {"alt":"cSTAR methodology used for separation of proliferation and differentiation signalling patterns in neuroblastoma cells.","caption":"cSTAR methodology used for separation of proliferation and differentiation signalling patterns in neuroblastoma cells. For details see Rukhlenko et al. Nature 609, 975–985, 2022. "}]

Our bodies consist of trillions of cells and, at any given point in time, every cell exists in a certain configuration known as cell state. It is comprised of certain molecules and it is expressing certain genes. Its genome, proteins, lipids and other building blocks are chemically and spatially configured in a specific way.

Cells change their state over time. They are in a constant flux as they perform their functions and respond to external stimuli. Certain undesired cell states might lead to diseases such as neurodegenerative disorders or cancer. Even within a single tumour there are cells in different states, which often results in various degrees of aggressiveness and different sensitivity to therapeutics.

It is estimated that a simple cell can contain as many as 42 million individual protein molecules belonging to more than 10,000 different kinds of proteins. Many proteins can also undergo processing events that change their properties by proteolytic cleavage and/or adding a modifying group, such as phosphoryl, acetyl, glycosyl, methyl, or other, to one or more amino acids. These changes, collectively known as post-translational modifications, further increase biochemical complexity of cellular environment making it very difficult to analyse. The situation is similarly complex for other major building blocks of the cell like nucleic acids and lipids. This complexity likely represents the biggest challenge biomedical researchers face in trying to understand how our cells and bodies function.

Nonetheless, over the years, scientists have developed multiple sophisticated technologies (often described as “omics” technologies, e.g. genomics, transcriptomics, lipidomics, proteomics, metabolomics etc.), that enable collective characterisation and quantification of pools of biological molecules. These technologies provide unprecedented insights into the structure, function, and dynamics of the cell. However, they generate huge amounts of data that are almost impossible for humans to analyse and interpret without the help of computers. Consequently, there is an urgent need to develop mathematical models and computer algorithms that could help us to explore the vast amount of data generated. This should help us better understand multiple 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, teamed together to develop approaches integrating “omics”-type cell signalling and cell phenotype data for mapping cell states, modelling transitions between them and predicting targeted interventions to convert cell fate decisions. The 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 quantification of hundreds of proteins or protein modifications in thousands of samples.

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 from University College Dublin, presented cell state transition assessment and regulation (cSTAR) approach that uses “omics” data as input, classifies cell states, and develops a workflow that transforms the input data into mechanistic models that identify a core signalling network, which controls cell fate transitions by influencing cellular biochemical environment. cSTAR can utilise and integrate diverse “omics” data. This universality and scalability distinguishes cSTAR from other currently available approaches that are more specialised in terms of input data. It offers a cell-specific, mechanistic approach to describe, understand and purposefully manipulate cell fate decisions. As such it has numerous applications across biology that go far beyond experimental models used in the study.

Importantly, Professor Carragher and his team are keen to further develop and use this methodology to help identify and prioritise drug combinations for brain cancer treatment. It will be done as part of a glioblastoma focused project funded by Cancer Research UK and the Brain Tumour Charity. The project, titled “Systems approach to therapeutic combinations for glioblastoma”, brings together researchers from University of Edinburgh, University of Oxford and 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

Understanding cell state transitions might aid therapeutic developments

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