Advanced cell culture platforms: methods for drug testing with microfluidics and microstructured devices

Advanced cell cultures are developing rapidly in biomedical research. Nowadays, various approaches and technologies are being used, however, these culturing systems present limitations from increasing complexity, requiring high costs, and not easily customization. We present two versatile and cost-effective methods for developing culturing systems that integrate 3D cell culture and microfluidic platforms. Firstly, for drug screening applications, many high-quality cell spheres of homogeneous size and shape are required. Conventional approaches usually have a dearth of control over the size and geometry of cell spheres and require sample collection and manipulation. To overcome this difficulty, in this study, hundreds of spheroids of several cell lines were generated using multi-well plates that housed our microdevices. Tumor spheroids grow at a uniform rate (in scaffolded or scaffold-free environments) and can be harvested at will. Microscopy imaging are done in real time during or after the culture. After in situ immunostaining, fluorescence imaging can be conducted while keeping the spatial distribution of spheroids in the microwells. Drug effects were successfully observed through viability, growth, and morphologic investigations. Also, we fabricated a microfluidic device suitable for directed and selective cell culture treatments. The microfluidic device was used to reproduce and confirm in vitro investigations carried out using normal culture methods, using a microglia cell line. The device layout and the syringe pump system, entirely designed in our lab, successfully allowed culture growth and medium flow regulation. Solution flows can be finely controlled, allowing treatments and immunofluorescence in one single chamber selectively. To conclude, we propose the development of two culturing platforms (microstructured well devices and in-flow microfluidic chip), which are the result of separate scientific investigations but have the primary goal of performing treatments in a reproducible manner. Our devices shall improve future studies on drug exposure testing, representing adjustable and versatile cell culture systems.

Deep learning and spatial transcriptomics to investigate thyroid tumors

There are many diseases that affect the thyroid gland, and among them are carcinoma. Thyroid cancer is the most common endocrine neoplasm and the second most frequent cancer in the 0-49 age group. This thesis deals with two studies I conducted during my PhD. The first concerns the development of a Deep Learning model to be able to assist the pathologist in screening of thyroid cytology smears. This tool created in collaboration with Prof. Diciotti, affiliated with the DEI-UNIBO "Guglielmo Marconi" Department of Electrical Energy and Information Engineering, has an important clinical implication in that it allows patients to be stratified between those who should undergo surgery and those who should not. The second concerns the application of spatial transcriptomics on well-differentiated thyroid carcinomas to better understand their invasion mechanisms and thus to better comprehend which genes may be involved in the proliferation of these tumors. This project specifically was made possible through a fruitful collaboration with the Gustave Roussy Institute in Paris. Studying thyroid carcinoma deeply is essential to improve patient care, increase survival rates, and enhance the overall understanding of this prevalent cancer. It can lead to more effective prevention, early detection, and treatment strategies that benefit both patients and the healthcare system.