Fluorescence time-lapse microscopy with automatic cell detection

Fluorescence microscopy is an indispensable tool in several scientific research fields, such as biology, medicine, and physics. This study presents a low-cost Raspberry Pi-based epifluorescence microscopy system optimized for capturing both fast and slow cellular processes, enabling detailed observation of cellular dynamics and molecular interactions. The system integrates advanced hardware and open-source software to facilitate the acquisition of high-fidelity technical images leveraging on the Picamera2 API for RAW data numerical acquisition. A novel approach, based on Numpy Python, for automatic peak identification in fluorescence images is implemented, allowing for efficient detection of regions of interest in cellular cultures. Additionally, the system enables precise extraction of the temporal evolution of calcium signals in in-vitro cultured astrocytes using a fluorophore. By combining affordability with advanced imaging and analysis capabilities, this system offers a robust solution for real-time cellular biophysical research, accessible to a wider range of laboratories.