The researchers will focus on pulmonary fibrosis and lung cancer, using HHG microscopy to track the tissue in 3D during culture, resulting in 4D data. They will develop analytical methods based on artificial intelligence (AI) to analyze the HHG images and use these to quantify the production and breakdown of connective tissue, the numbers of healthy and tumor cells over time, and the microstructure of the tissue, thus measuring the response to treatment. They will investigate and validate the predictive power of these characteristics with feedback from patients on the treatment they actually received from their clinicians.
The research group has already developed a system for culturing lung tissue from patients and microscopically imaging the cells and all parts of the tissue in 3D for five days. In this so-called precision-cut lung slice (PCLS) model, equipped with
an innovative higher harmonic generation (HHG) microscope, lung tissue can be exposed to various therapeutic agents, and the therapy response can be directly assessed. Currently, the model is still in the experimental phase, but in DOPREDICT, the model will be further optimized for clinical application.
As a PhD candidate, you will be part of the DoPredict project at Amsterdam UMC, PUFFIN lab, responsible for further optimizing and improving tissue culture, testing various (experimental) drugs, and analyzing the tissue after experiments using immunohistochemistry, Western blot, and qPCR. These analyses, the 'gold standard,' will be compared with the HHG images to validate them.
You will closely collaborate with a PhD student at the VU LaserLab in developing the testbed where multiple tissue slices/biopsies can be cultured and imaged simultaneously with the HHG microscope and general HHG imaging.
Additionally, you will conduct experiments testing various experimental compounds on their effect on fibrotic processes and tumor tissue in lung tissue, potentially leading to new insights for therapeutic interventions for these conditions.