Improved methods to identify therapeutically relevant tumor neoantigens and their cognate T cells would aid the development of precision medicines for cancer. Here, we developed Slide-GoTags, a droplet-based single-nucleus spatial transcriptomics approach that characterizes neoantigen-specific immunity by integrating targeted transcript genotyping and T cell receptor (TCR) sequencing with single-nucleus RNA sequencing from the same slice of frozen tissue. Application of Slide-GoTags to mouse and human tumors revealed colocalization of clonally expanded, neoantigen-specific T cells with tumor cells expressing their cognate neoantigen. We also identified distinct spatial immune landscapes shaped by anti-PD1 or anti-CTLA4 blockade in mouse colorectal tumors. Across human tumor types, Slide-GoTags detected TCR-neoantigen interactions through spatial proximity and identified an enrichment of interferon-driven immunogenicity niches in immunologically 'hot' tumors compared to 'cold' tumors. These niches harbored three T cell clonotypes that colocalized with genotyped neoantigens, highlighting a spatially organized antitumor immune response. Collectively, Slide-GoTags establishes a framework for in situ mapping of T cell-tumor interactions directly from individual tissue.

Dissolved organic matter (DOM) is a central component of Earth's carbon cycle and one of the planet's most chemically diverse pools, yet the molecular structures of its constituents remain largely unresolved. This limitation has hindered our ability to link DOM composition to microbial processes and ecosystem function. Here we present ENVnet, a global molecular repository built from tandem mass spectrometry data collected across 13 terrestrial and aquatic environment types, including 419 newly generated samples that expand publicly available DOM metabolomics data and cover previously underrepresented environments. By computationally deconvolving chimeric mass spectra, a longstanding challenge in environmental metabolomics, we recover high-quality fragmentation data for >22,000 distinct molecular features (defined by a specific precursor mass and fragmentation pattern). Using ENVnet, we uncover conserved and environment-specific molecular patterns in DOM composition and underlying biogeochemical processes. We also use molecular features encoded in ENVnet to train predictive models of DOM persistence, allowing molecular-level assessment of microbial turnover in independent systems.

Simultaneously capturing subcellular dynamics across thousands of cells in mammals remains challenging because of trade-offs among spatial resolution, field of view, imaging speed and sample viability, particularly under high-NA (numerical aperture) conditions. Here we design a high-NA mesoscale objective lens and present a confocal volumetric mesoscope with an NA of 1.05, RUSH3D-HR, achieving ~390-nm resolution across a volume of 2.7 × 2.0 × 0.04 mm[3] at five volumes per second with low phototoxicity. By integrating our previously developed confocal scanning light-field framework with a parallelized deep-learning pipeline, RUSH3D-HR increases experimental efficiency by orders of magnitude. Long-term imaging at 70 billion voxels per second visualizes immune responses such as long-distance collective cell migrations during wound healing and large-scale migrasome generation and neutrophil-macrophage interactions during acute liver failure. Hour-long monitoring over 6,000 neutrophils reveals transient emergent swarming behaviors in mouse spleen during lipopolysaccharide-induced inflammation, which is suppressed with electroacupuncture at ST36, providing direct evidence for the immunomodulatory mechanism of peripheral electrical stimulation.

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.

Imaging reporter genes are essential tools in cancer research for monitoring tumor evolution, cell dissemination, gene activation and treatment response. However, germline reporter models typically rely on single imaging modalities operating over limited spatial scales. To address these limitations, we developed an inducible triple-reporter mouse model (Rosa26[LSL-NRL]) that integrates three reporters for complementary imaging modalities, fluorescence, bioluminescence and positron emission tomography (PET), along with inducible Cre-lox functionality for precise spatiotemporal control of reporter expression in genetically engineered mouse models. Using a multiscale, multimodal approach, we visualize deep tissue oncogenesis in models of hepatocellular carcinoma and lung adenocarcinoma and, guided by whole-body imaging with bioluminescence and [[18]F]tetrafluoroborate PET/magnetic resonance imaging (MRI), resolve cell-cell interactions within tumor microenvironments using in situ microscopy. This triple-reporter system enables multiscale investigation of biological processes within whole animals, facilitating sensitive, tissue-specific, in vivo cell tracking from whole-body to cellular resolution.