Yihong Xiao

Fluorescence lifetime imaging microscopy (FLIM) enables quantitative measurement of molecular environments, interactions and protein conformations. However, a large number of photons are required for accurate lifetime determination, restricting its practical applications in fast deep-tissue imaging. Here, we present event-based first-photon FLIM (EFLIM), a self-supervised denoising method that infers fluorescence lifetime at extremely low light. By representing each excitation event as a binary process instead of histogram accumulation, EFLIM reduces photon requirement by over two orders of magnitude compared to state-of-the-art algorithms, leading to an apparent mean lifetime measurement below one photon per pixel with strong robustness to intensity artifacts. To demonstrate EFLIM's applicability, we observed transient intracellular dynamics of ligand-dependent molecular states, captured putative vesicle-mediated contacts between different lymphocytes through multiplexed imaging in a single spectral channel and achieved rapid label-free visualization of tumor heterogeneity in human glioma tissue. These results illustrate EFLIM's strong potential in neuroscience, cell biology, immunology and pathology by probing dynamic molecular processes in vivo.

Intravital fluorescence microscopy is hampered by optical aberrations arising from heterogeneous distributions of the refractive index. Adaptive optics (AO) methods are either costly and slow, requiring additional hardware, or inaccurate due to lack of wavefront information in multiple angular directions. Here we present a latent-space-enhanced digital AO (LEAO) method that uses wave-optics priors embedded in high-dimensional spatial–angular data and semantically disentangles their representations in the latent space. LEAO achieves more than sixfold higher aberration estimation accuracy than the existing approach (coordinate-based neural representations for computational AO). It also exhibits strong robustness across different system configurations and imaging conditions, achieving almost an order of magnitude higher accuracy than iterative digital AO under extreme conditions such as a low signal-to-noise ratio of 3.4 dB. We experimentally demonstrate that LEAO improves diverse biological observations in vivo, such as large-scale tracking of T cells across an entire lymph node, multiregional neural recording in mouse cortex and long-term monitoring of neutrophil activation, extravasation and clearing through mouse intact skull after traumatic brain injury.