Multimodal Deep-Tissue Optical Imaging: From Label-Free Contrast to Lipid Metabolism
Yiqiang Wang1, Fangrui Lin2, Ziyi Luo1, Xiangcong Xu1, Ruofei Wu1, Chenshuang Zhang1, Zhenlong Huang1, Junle Qu1,3
1Medical Photonics Innovation Institute, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen, China
2Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China 000000
3Ioffe Institute, Russian Academy of Sciences, 26, Polytekhnicheskaya, St. Petersburg 194021, Russia
Abstract
Biological tissues demand optical imaging that simultaneously provides deep penetration, multidimensional molecular contrast, and quantitative functional readouts — capabilities no single modality can deliver. In this talk, I will present our optical imaging systems and their applications in deep-tissue and lipid biology. On the instrumentation side, we have developed two complementary systems. The first is a point-scanning confocal multimodal system that integrates coherent anti-Stokes Raman scattering (CARS), two-photon excitation fluorescence (TPEF), second-harmonic generation (SHG), and analog mean-delay (AMD)-based fast fluorescence lifetime imaging (FLIM), enabling simultaneous acquisition of structural, metabolic, and molecular information with imaging depth exceeding 800 μm and NIR-II extension beyond 1500 μm. The second is a wide-field streak-FLIM system that delivers quantitative, high-speed fluorescence lifetime imaging at up to 200 Hz for capturing rapid dynamic functional signals. On the application side, we apply these systems to three problems in lipid biology: longitudinal tracking of cortical lipid dynamics in Alzheimer's disease, lipid metabolic remodeling around activated microglia, and glucose-dependent lipid droplet regulation. Combined with deep-learning-based de-scattering, our approach visualizes deep-brain neurovascular structures with a 6.7-fold signal-to-background improvement. Together, these capabilities form a label-free, quantitative, deep-tissue imaging framework for interrogating lipid metabolism in health and disease.
J.Qu is grateful to the Russian Science Foundation (RSF) for financial support (Project No. 26-75-31002).
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Speaker
Junle Qu
Shenzhen University
P. R. China
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