Three-dimensional spatial transcriptomics at isotropic resolution enabled by artificial intelligence

isoST is a generative model designed to reconstruct 3D spatial transcriptomic profiles with isotropic resolutions from sparsely sampled serial sections. Accurately mapping isotropic-resolution 3D spatial transcriptomes is a major challenge in biology. Current technologies cannot directly achieve full 3D profiling, so tissues are typically sectioned into serial 2D slices for individual profiling. We present <b>isoST</b>, a framework to reconstruct continuous, isotropic-resolution 3D transcriptomic landscapes from sparsely sampled serial sections. Assuming gene expression varies smoothly in 3D space, isoST models expression dynamics along tissue depth using stochastic differential equations (SDEs), producing a continuous 3D field that enables high-fidelity reconstruction from limited slices.