Multimodal super-resolution nanoscopy for metabolic imaging in aging and disease

We have developed a nonlinear multimodal imaging system that combines Stimulated Raman Scattering (SRS), multiphoton fluorescence (MPF), and second harmonic generation (SHG). This integrated platform allows for the investigation of metabolic activities and the distribution of metabolites within the organelles of cells and tissues. Our system is enhanced by novel super-resolution image deconvolution algorithms, which can improve image resolution beyond the diffraction limit. These algorithms, along with correlation coefficient mapping, provide deeper insights into metabolic processes occurring within super-resolved nanoscale regions. Furthermore, we have implemented an advanced chemical image clustering algorithm for the precise identification of signals originating from specific subcellular organelles. This comprehensive methodology shows significant promises for enhancing disease detection and prognosis, evaluating therapeutic results, and furthering our knowledge in aging and biomedical science.