Abstract

Long-term and high-spatiotemporal-resolution 3D imaging of living cells remains an unmet challenge for super-resolution microscopy, owing to the noticeable phototoxicity and limited scanning speed. While emerging light-field microscopy can mitigate this issue through three-dimensionally capturing biological dynamics with merely single snapshot, it suffers from suboptimal resolution insufficient for resolving subcellular structures. Here we propose an Adaptive Learning PHysics-Aware Light-Field Microscopy (Alpha-LFM) with a physics-aware deep learning framework and adaptive-tuning strategies capable for highly-generalizable light-field reconstruction of diverse subcellular dynamics. Alpha-LFM delivers sub-diffraction-limit spatial resolution ([~]120 nm) while maintaining high temporal resolution and low phototoxicity. It enables rapid (at hundreds of volumes per second), long-term (up to 60 hours) 3D super-resolution imaging of diverse intracellular dynamics with exceptional details. Using Alpha-LFM approach, we finely resolve the lysosome-mitochondrial interactions, capture rapid motion of peroxisome and the endoplasmic reticulum, and reveal the variations in mitochondrial fission activity throughout two complete cell cycles.