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 \textsc{TopoScape}, a topology-anchored framework for transcriptome-to-histology generation under a \emph{Layout-Before-Pixels} principle. Instead of synthesizing pixels directly from transcriptomic embeddings, \textsc{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, \textsc{TopoScape} allows molecular states to deterministically shape both the macroscopic tissue architecture and the microscopic generative process. Across five TCGA benchmarks, \textsc{TopoScape} consistently outperforms state-of-the-art methods in generative fidelity, biological realism, and downstream predictive utility. Code and pretrained models will be released.