Spatial transcriptomics measures gene expression while preserving where each cell sits in the tissue. The inverse, what a given transcriptomic state looks like, is less explored but equally useful: it lets researchers visualize the morphological consequence of a molecular state and interpret a spatial assay's molecular calls against real tissue structure. GeneFlow, the current state of the art on the Xenium gene expression-to-image benchmark, trains a model from scratch, incurring a high cost on every new target dataset it is applied to. We instead propose GESTALT, which reformulates gene expression-to-image synthesis as predicting a pathology foundation model's image embedding. A lightweight gene-token transformer predicts this embedding from multi-cell gene expression, and a frozen or LoRA-adapted diffusion decoder (PixCell) renders it as an image. Because this visual knowledge is reused rather than relearned, GESTALT trains in a fraction of the time. In both multi-cell and single-cell settings, our method outperforms the SoTA on perceptual metrics across all three Xenium datasets. Beyond improved image quality, GESTALT natively synthesizes at 1024 x 1024, and is evaluated in three generalization settings: zero-shot transfer across gene panels and datasets, held-out patients and disease states, and transfer across studies and tissue sources. Coupled with a statistical model of spatial expression, GESTALT also provides, to our knowledge, the first paired synthesis of cell-resolved transcriptomic fields and tissue images with explicit control over cell type, density, and composition. Code is available at https://github.com/trongnhandev1011/Gestalt.

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