A versatile phenotyping platform combining three-photon microscopy, optimized tissue clearing, and deep-learning...

Imaging the internal structure of plant organs at the subcellular level is essential for understanding key processes such as photosynthesis, growth, and stress responses. Yet conventional optical microscopy struggles to visualize deep tissues due to intense light scattering and spectral crosstalk from autofluorescent components-most prominently chlorophyll. To overcome these barriers, we present an integrated platform that combines deep-penetrating three-photon microscopy (3PM), tissue clearing, and a custom deep-learning network (SE-UNet) for intelligent signal separation. First, our Sucrose Vacuum-Infused Refractive-index Optimization (Su-VIRO) method drastically reduces scattering. Combined with 3PM, it overcomes practical scattering barriers, enabling high-resolution volumetric imaging of chloroplasts throughout entire Arabidopsis leaves (>230 μm). This revealed a gradient in chloroplast size and distribution from the palisade to the spongy mesophyll. Second, to resolve crosstalk without additional hardware, we developed SE-UNet. Trained on a small labeled dataset, it computationally disentangles overlapping signals from single-wavelength acquisitions, enabling simultaneous four-channel visualization of chloroplasts, cell walls, mitochondria, reactive oxygen species, and nuclei within the same leaf volume. Spatial analysis observed a non-random organelle organization consistent with functional interactions, demonstrating the platform's capacity for spatial organelle analysis. We further demonstrate applicability of our platform in diverse leaf types by dual-channel imaging of chloroplasts and the plasma membrane in rice. Our work establishes a versatile, deep-tissue, multi-parameter phenotyping platform for plant cell biology, providing a versatile platform to support future genotype-to-phenotype studies.