Pre-processing for precise super-resolution multimodal image analysis

Abstract

We have developed a novel methodology to capture images of various biomolecules at a resolution surpassing the traditional diffraction limit of optical microscopy. By harnessing a multimodal imaging platform that combines stimulated Raman scattering (SRS), multiphoton fluorescence (MPF), and second harmonic generation (SHG), together with sophisticated image deconvolution algorithms, we have successfully generated super-resolution images that reveal the details of biomolecular metabolism. These images enable us to explore the intricate associations between metabolic activities and the spatial distribution of metabolites within breast cancer tissues. To enhance the accuracy of this measurement technique, in this study, we designed a pre-processing workflow that incorporates both denoising and drift correction processes. Our cutting-edge, nonlinear multimodal imaging approach, when applied in a super-resolution context with new workflow, holds significant promise for advancing early detection of breast cancer, prognostication, evaluation of therapeutic outcomes, and deepening our mechanistic understanding of diseases.