Sudoku: Understanding Spatial Organization of Tissue by Learning to Generate

Tissue function emerges from the spatial organization of cells across multiple scales. Local interactions shape the state of each cell, and their collective effects give rise to multicellular structures. Spatial transcriptomics (ST) now profiles millions of cells at known coordinates, making it possible to learn this organization from data. Most existing pre-training methods, however, focus on masked reconstruction at fixed masking rates to learn representations. Others generate cells autoregressively along an order that tissue does not have. Here we present Sudoku, a spatial foundation model that learns tissue organization through spatial discrete denoising. Sudoku represents cell states with a hierarchical vocabulary, and learns the distribution of hidden cell states given spatial context across a full masking range. This conditional formulation captures dependencies from local cellular interactions to coherent multicellular configurations without imposing a predefined order, and naturally unifies representation and generation as two inference regimes under full and partial observation. Pre-trained on a rigorously quality-controlled corpus of 49 million cells from 12 organs across human and mouse, Sudoku outperforms existing pre-trained models and specialized methods on niche retrieval, multi-scale clustering, spatial generation and immune-recruitment prediction, taking a step toward a virtual tissue model built from ST data.