Super-resolution spectral chemical imaging to study metabolic correlation in subcellular organelle

Investigating metabolic reactions within cells is crucial for unraveling the numerous biological functions. Established imaging modalities, including MRI, PET, Fluorescence, and Mass Spectrometry, present various drawbacks. To address these issues, we have developed a nonlinear multimodal imaging system. This system combines stimulated Raman scattering, multiphoton fluorescence, and second harmonic generation. It was designed to probe the spatial distribution of metabolic activities within cells and tissues by measuring multiple molecular signals. To support the analysis scheme, we have also pioneered cutting-edge algorithms, notably the Adam-based Pointillism Deconvolution (A-PoD) and Correlation Coefficient Mapping (CoCoMap). They allow us to analyze correlations between super-resolution images of nanoscale Regions of Interest. Additionally, our research has introduced a novel clustering algorithm known as Multi-SRS reference matching (Multi-SRM). This algorithm is particularly tailored to isolate signals exclusively from specific subcellular organelles. The application of this innovation offers significant potential to study aging and disease related metabolic changes.