We report a dual-contrast method of simultaneously measuring and visualizing the volumetric structural information in live biological samples in three-dimensional(3D) space. By introducing a direct way of deriving the...We report a dual-contrast method of simultaneously measuring and visualizing the volumetric structural information in live biological samples in three-dimensional(3D) space. By introducing a direct way of deriving the 3D scattering potential of the object from the synthesized angular spectra, we obtain the quantitative subcellular morphology in refractive indices(RIs) side-by-side with its fluorescence signals. The additional contrast in RI complements the fluorescent signal, providing additional information of the targeted zones. The simultaneous dual-contrast 3D mechanism unveiled interesting information inaccessible with previous methods, as we demonstrated in the human immune cell(T cell) experiment. Further validation has been demonstrated using a Monte Carlo model.展开更多
基金Australian Research Council(ARC)(DE120102352)National Natural Science Foundation of China(NSFC)(61427819)+2 种基金Shenzhen Science and Technology Innovation Commission(KQCS2015032416183980)Government of Guangdong Province(00201505)Schweizerischer Nationalfonds zur Forderung der Wissenschaftlichen Forschung(SNF)(149652)
文摘We report a dual-contrast method of simultaneously measuring and visualizing the volumetric structural information in live biological samples in three-dimensional(3D) space. By introducing a direct way of deriving the 3D scattering potential of the object from the synthesized angular spectra, we obtain the quantitative subcellular morphology in refractive indices(RIs) side-by-side with its fluorescence signals. The additional contrast in RI complements the fluorescent signal, providing additional information of the targeted zones. The simultaneous dual-contrast 3D mechanism unveiled interesting information inaccessible with previous methods, as we demonstrated in the human immune cell(T cell) experiment. Further validation has been demonstrated using a Monte Carlo model.
