Seeing in the Dark: Intelligent Fourier Light Field Imaging for Bioluminescence Microscopy

Abstract

Bioluminescence microscopy offers a uniquely noninvasive window into cellular dynamics, but its use has traditionally been limited by the intrinsically low brightness of luciferases. Their poor photon budget requires long exposure times, preventing faithful visualization of rapid physiological processes, especially in three dimensions. To overcome this barrier, we developed a Fourier light field microscope coupled with deep-learning-based reconstruction that achieves subsecond volumetric bioluminescence imaging, enabling direct recovery of 3D volumes from photon-limited measurements while suppressing reconstruction noise. This approach substantially mitigates the speed–resolution trade-off of conventional light field methods and bypasses the need for slow classical deconvolution priors on the depth-dependent point-spread function. We evaluate its performance by imaging 3D calcium activity in muscles and neurons of freely moving Caenorhabditis elegans and by quantifying cell dynamics within stem-cell-derived spheroids using fluorescenti acquired with fluorescence and bioluminescence contrast. Together, these results establish our framework as a practical tool for dynamic, volumetric studies of living systems.