Long-term observation of subcellular dynamics in living organisms is limited by background fluorescence originating from tissue scattering or dense labeling. Existing confocal approaches face an inevitable tradeoff among parallelization, resolution and phototoxicity. Here we present confocal scanning light-field microscopy (csLFM), which integrates axially elongated line-confocal illumination with the rolling shutter in scanning light-field microscopy (sLFM). csLFM enables high-fidelity, high-speed, three-dimensional (3D) imaging at near-diffraction-limit resolution with both optical sectioning and low phototoxicity. By simultaneous 3D excitation and detection, the excitation intensity can be reduced below 1 mW mm^{− 2}, with 15-fold higher signal-to-background ratio over sLFM. We imaged subcellular dynamics over 25,000 timeframes in optically challenging environments in different species, such as migrasome delivery in mouse spleen, retractosome generation in mouse liver and 3D voltage imaging in Drosophila. Moreover, csLFM facilitates high-fidelity, large-scale neural recording with reduced crosstalk, leading to high orientation selectivity to visual stimuli, similar to two-photon microscopy, which aids understanding of neural coding mechanisms.

Photon noise is one of the fundamental limits in fluorescence imaging. Despite broad applications, existing self-supervised denoising methods rely on either temporal redundancy or spatial redundancy, leading to degradation in either temporal resolution or spatial resolution, especially for 3D imaging. Here, we propose light field denoising (LF-denoising), a self-supervised transformer framework leveraging spatial-angular redundancy based on the high-dimensional light field measurements to achieve high-fidelity denoising without relying on temporal information and avoiding associated artefacts in spatial domain. We demonstrate the advantage of LF-denoising over previous methods in highly dynamic 3D imaging with both simulations and experimental data across different species. Combined with state-of-the-art light field microscopy variants, we achieve long-term high-speed high-resolution 3D intravital imaging on diverse animals including zebrafish, Drosophila and mice, with ultra-low excitation power of 10 μW/mm². Specifically, we show that LF-denoising well preserved the temporal causality with superior denoising performance, which is critical for quantitative biology analysis in immunology and neuroscience.

Despite the vital role of neuromodulators and neurotransmitters in the neural system, their spatiotemporal correlation with neuronal activities across multiple brain regions remain unclear. Here, we employed two-photon synthetic aperture microscopy (2pSAM) and neurochemical indicators to simultaneously record calcium and acetylcholine (ACh)/5-HT dynamics across multiple regions of the Drosophila brain over 2 h. Presenting 3 different odors across multiple trials, our analyses revealed signal-specific differences in responsiveness, functional connectivity, and odor classification accuracy across the brain. We further constructed low-dimensional manifolds to characterize the global odor-related dynamics. Incorporating both calcium and ACh signals enhanced odor classification accuracy in the global low-dimensional manifold and in specific brain regions where their functional connectivity network features exhibited complementary patterns. Moreover, ACh dynamics demonstrated relatively stable temporal characteristics compared to calcium and 5-HT. These results suggest the potential contribution of ACh to consistent odor representations and illustrate the utility of multi-signal imaging in studying neural computation.

Quantitative analysis of large-scale cellular behaviors plays an increasingly crucial role in understanding mechanisms of diverse physiopathological processes, but achieving cell tracking with both high performance and efficiency in practical applications remains a challenge. Here we introduce CELLECT, a contrastive embedding learning method for large-scale efficient cell tracking, and demonstrate it on the Caenorhabditis elegans dataset in the Cell Tracking Challenge. By contrastive learning of latent embeddings of diverse cellular structures, a CELLECT model pretrained on a single public dataset can be effectively applied across different imaging modalities and species with broad generalization. Using advanced two-photon imaging, CELLECT enables real-time 3D tracking of large-scale B cells with frequent divisions during germinal center formation in a mouse lymph node, quantitative identification of cell–bacterium interactions in the mouse spleen and high-fidelity extraction of neural signals during strong nonrigid motions. We believe that these results demonstrate broad applications of CELLECT in immunology, pathology and neuroscience.

Despite the vital role of neuromodulation in the neural system, the specific spatiotemporal dynamics of neuromodulators and their interactions with neuronal activities in vivo are still unclear, hampering our understanding of their information representation and functional contributions systemically. To address this problem, we employed two-photon synthetic aperture microscopy (2pSAM) to record long-term neuronal and neuromodulatory olfactory responses across the Drosophila brain at high speed. Our results revealed distinct response properties, global information propagation, functional connectivity, and odor identity representation among neuronal, cholinergic, and serotoninergic dynamics across multiple brain regions. We discovered the compensation between neuronal activity and cholinergic dynamics, both in the odor identity representation across the brain and the functional connectivity network structures of specific brain regions. Moreover, employing low-dimensional manifold and functional connectivity network analyses, we characterized the stable representation of cholinergic dynamics over a long term. Collectively, our unbiased and comprehensive investigation unveiled the prominent involvement of acetylcholine (ACh) in shaping olfactory representation across the brain, underscoring the inadequacy of solely considering neuronal activities when examining information representation of the brain.