Correlative cytometry to measure biophysical properties of cells in health and disease

Our cells can alter their properties as a result of diseases or as response to environmental changes to be able to adapt and fulfil their function.

Although numerous studies aimed at finding changes in protein expression that are associated with these changes, there is a major gap in our understanding of how collective biophysical properties of the cells alter during these processes.

To have a thorough mechanistic perception of cellular physiology as well as disease, it is essential to fill this gap and have a clear and quantitative picture of biophysical remodelling of the cells during key cellular processes and in diseases.In this project, we aim to define the biophysical properties of cells during key biological processes and in diseases, by developing and applying fast, cheap, easy-to-use high-throughput methodologies that allow measuring multiple biophysical properties of millions of cells simultaneously.

We will combine this experimental pipeline with machine learning approaches to obtain a biophysical mapping of cellular states to be used for future diagnosis and treatment.

The outcome of this proposal will transform our understanding of cellular physiology in homeostasis and disease by adding a new biophysical perspective.

This will enable us to use physical parameters to predict diseases rather than focusing only on descriptive disease markers and eventually pave the way for better and more efficient therapies based on novel physical insight.