Spatial transcriptomics has transformed the mapping of gene expression within intact tissues, yet current sequencing-based platforms are limited by coarse spot-level resolution and sparse sampling that leaves large interspot regions unmeasured. Here we introduce PanoSpace, a computational framework that integrates low-resolution spatial transcriptomics with high-resolution histology and matched single-cell RNA sequencing to reconstruct a continuous, single-cell-level map across entire tissue sections. Originally developed for tumors, PanoSpace accurately reconstructs cellular locations, cell identities and gene expression profiles, enabling detailed characterization of intracell-type heterogeneity and spatially organized cell-cell interactions. Application to breast and prostate cancers reveals complex cellular architectures and tumor microenvironment dynamics mediated by cancer-associated fibroblasts. Thanks to its modular design, PanoSpace can be seamlessly adapted to noncancerous tissues, as demonstrated by precise spatial reconstruction in mouse brain. Together, these results demonstrate that PanoSpace enables comprehensive spatial transcriptomic analysis and facilitates biological discovery.

In multicellular systems, cell fate determination emerges from the integration of intracellular signaling and intercellular communication. Spatial transcriptomics (ST) provides opportunities to elucidate these regulatory processes, yet inferring the spatiotemporal dynamics of cell state transitions (CSTs) governed by cell-cell communication (CCC) remains a challenge. Here we introduce CCCvelo to reconstruct CCC-driven CST dynamics by jointly optimizing a dynamic CCC signaling network and a latent CST clock. CCCvelo formulates a unified multiscale nonlinear kinetic model that integrates intercellular ligand-receptor signaling gradients with intracellular transcription-factor activation cascades to capture gene expression dynamics encoding CSTs. Moreover, we devise PINN-CELL, a physics-informed neural-network-based coevolution learning algorithm, which simultaneously optimizes model parameters and pseudotemporal ordering. Application of CCCvelo to high-resolution ST datasets, including mouse cortex, embryonic trunk development and human prostate cancer datasets, demonstrates its ability to successfully recover known morphogenetic trajectories while uncovering dynamic CCC signaling rewiring that orchestrates CST progression.

Prediction models that generate neuronal spikes from upstream neural activities offer a promising way to re-establish neural functional connectivity. Traditional methods train these models by supervised learning, which requires downstream recordings as ground truth. However, functional downstream activity cannot be recorded when neurological disorders exist. Here we introduce a reinforcement learning (RL)-based point process framework to generate spike trains that directly maximize behavior-level rewards, thus bypassing downstream recordings. This yields a generative spike model that directly transforms upstream activity into spike patterns modulated to desired behavior. We show that these RL-based generative models produce movement-modulated spike patterns akin to downstream recordings from healthy subjects, providing a biomimetic spike encoding framework. This RL framework outperforms existing methods and demonstrates a strong adaptation capability across different decoder settings, highlighting its potential for neural prostheses in restoring transregional communication with biomimetic cortical stimulation.

Here we introduce a framework for molecular structure optimization using a denoising model on a physics-informed Riemannian manifold (R-DM). Unlike conventional approaches operating in Euclidean space, our method leverages a Riemannian metric that better aligns with molecular energy change, enabling more robust modeling of potential energy surfaces. By incorporating internal coordinates reflective of energetic properties, R-DM achieves chemical accuracy with an energy error below 1 kcal mol-1. Comparative evaluations on QM9, QM7-X and GEOM datasets demonstrate improvements in both structural and energetic accuracy, surpassing conventional Euclidean-based denoising models. This approach highlights the potential of physics-informed coordinates for tackling complex molecular optimization problems, with implications for tasks in computational chemistry and materials science.

Accelerated materials discovery is critical for addressing global challenges. However, developing new laboratory workflows relies heavily on real-world experimental trials, and this can hinder scalability because of the need for numerous physical make-and-test iterations. Here we present MATTERIX, a multiscale, graphics processing unit-accelerated robotic simulation framework designed to create high-fidelity digital twins of chemistry laboratories, thus accelerating workflow development. This multiscale digital twin simulates robotic physical manipulation, powder and liquid dynamics, device functionalities, heat transfer and basic chemical reaction kinetics. This is enabled by integrating realistic physics simulation and photorealistic rendering with a modular graphics processing unit-accelerated semantics engine, which models logical states and continuous behaviors to simulate chemistry workflows across different levels of abstraction. MATTERIX streamlines the creation of digital twin environments through open-source asset libraries and interfaces, while enabling flexible workflow design via hierarchical plan definition and a modular skill library that incorporates learning-based methods. Our approach demonstrates sim-to-real transfer in robotic chemistry setups, reducing reliance on costly real-world experiments and enabling the testing of hypothetical automated workflows in silico.

First-principles electronic structure calculations have profoundly advanced research in physics, chemistry and materials science, yet their further development remains constrained by the accuracy-efficiency dilemma. Here we highlight recent breakthroughs in deep-learning methodologies that address this challenge, including the deep-learning quantum Monte Carlo method for the accurate study of correlated electrons and deep-learning density functional theory for efficient large-scale material simulations. These advances extend the reach of first-principles calculations to unprecedented scales and complexity, enhancing the impact of quantum mechanics in scientific discovery.