Computational Resolution Enhancement of Mitochondria monitoring in Multiple Organs using Intravital Two-Photon...

Background and Objective: Understanding living cell mechanisms and enabling intracellular monitoring requires advanced and often costly imaging technologies. Conventional fluorescence microscopy is widely used but suffers from resolution limitations, making it challenging to capture fine subcellular structures like mitochondria. While super-resolution microscopy may overcome these constraints, it introduces tradeoffs, including its limitation in intravital imaging fields and complexity in analyzing multiple cells simultaneously. To address these challenges, we developed a computational approach that enhances resolution and signal clarity without the need for specialized hardware, applicable for intravital imaging studies. Methods: We utilized Dendra2 transgenic mice to observe mitochondria in hepatocytes under normal physiological condition and in response to alcohol-induced liver stress. To achieve high-resolution in vivo imaging, we employed high-magnification objectives, ensuring precise visualization of subcellular structures. We implemented a trained self-supervised denoising model to suppress background noise and improve signal intensity, ensuring a clearer visualization of the mitochondria in hepatic cells. Additionally, enhanced super-resolution radial fluctuations (eSRRF) analysis was applied to image sequences to achieve subcellular resolution. Various numerical modifications and parameter optimizations were performed to refine the technique. The methodology was validated through computational analysis of different imaging conditions to assess its robustness and effectiveness. Results: Our approach successfully enhanced image resolution to the subcellular level, enabling the visualization of discrete mitochondrial structures and monitoring intracellular events in vivo. The denoising model effectively reduced background interference while preserving essential biological signals. Furthermore, the application of eSRRF significantly improved the clarity of cellular components, even when the original images exhibited poor lateral resolution, allowing for improved interpretation of intracellular structures. Conclusions: The proposed computational technique provides a cost-effective and accessible solution for achieving super-resolution live imaging without the need for high-end microscopy equipment. By reducing noise and enhancing resolution, this approach facilitates detailed intracellular analysis, suitable for live animal studies.