Rapid development of FRET sensors optimized for FLIM readouts of mechanobiological sensing and cellular response in imaging cancer

Objectives

With only very few exceptions, FRET sensors have been tediously optimized to display an optimal change in ratio of intensities, which typically takes several years of trial-and-error mutations and rigorous testing. To be able to employ those sensors with our quantitative FLIM technology, we thus aim to develop an approach to convert good ratiosensors to good FLIM sensors without engaging in new and tedious characterization.  Unpublished work (straight-forward biophysical experiments using FLIM and FCCS experimentation by NKI partner) has yielded important clues as to the cause of this paradox. Based on these insights, NKI have developed a simple (undisclosed) strategy to convert (the majority of the) well-characterized  ratio-FRET sensors into good FLIM sensors.

  1. Prepare and characterize a small set of showcase converted sensors in the lab of K. Jalink. Y1.

  2. Extend the set of FLIM-optimized sensors using a selection of 5-10 ratiosensors rationalised from literature.

  3. Collaborate with others in showcasing the efficacy of new FLIM sensors in screening experiments and/or in cell biological experiments. 

  4. implementation and multiplexing of FLIM-based sensors to probe membrane tension, mechanoresponsive protein activation and biochemical signals in cells within 3D scaffolds of tuned stiffness and e) cross correlate this with phenotypic endpoints and

  5. define metabolic changes associated with these force transmission events for further investigation as mechano-sensitive drivers of tumour progression. We will use 3D cultures and organoids from breast and head & neck cancers to explore these parameters. These models will enable in situ monitoring of how manipulation of mediators of biomechanical signalling affect cell phenotypes and drug-induced cell toxicity. The DC will first establish a model of cancer in a microwell chamber adapted to the FLIM imaging platform (in collaboration with DC8) and team up with DC1 in the multiplexed data modelling using a Machine Learning approach towards classification of drug induced responses. 

Host Academic Institution: Ghent University