Layout Before Pixels: Topology-Anchored Transcriptome-to-Histology Generation

Transcriptome-conditioned whole-slide image (WSI) synthesis aims to decode the complex mapping from molecular states to histologic phenotypes. Existing generative paradigms predominantly rely on a direct black-box transcriptome-to-image mapping, tasking a single generator with the simultaneous interpretation of transcriptome signals, inference of multicellular spatial organization, and rendering of fine-grained textures. This formulation, however, is structurally under-constrained; the underlying tissue architecture, the critical substrate linking gene expression to morphology, remains latent and only weakly supervised by pixel-level objectives. We present TopoScape, a topology-anchored framework for transcriptome-to-histology generation under a Layout-Before-Pixels principle. Instead of synthesizing pixels directly from transcriptomic embeddings, TopoScape first resolves molecular states into explicit, multi-class cellular topologies via an RNA-guided Topological Prior Generator, reinforced by persistence-based topological regularization. These resolved topologies are further reparameterized into a suite of topology-derived controls, including structure-aware initialization, continuous density fields, and distance representations, which guide a hierarchical, frequency-decoupled flow-matching trajectory. By introducing multicellular organization as an explicit structural mediator, TopoScape allows molecular states to deterministically shape both the macroscopic tissue architecture and the microscopic generative process. Across five TCGA benchmarks, TopoScape consistently outperforms state-of-the-art methods in generative fidelity, biological realism, and downstream predictive utility. Code and pretrained models will be released.