MIMYR: Generative modeling of missing tissue in spatial transcriptomics

Abstract

Spatial transcriptomics enables the study of how gene expression is organized across tissues, revealing how cells interact within their native microenvironments in health and disease. However, tissue damage during sectioning and the allocation of intermediate slices to other assays often result in regions or entire planes missing from the data, limiting downstream analysis. Here, we introduce MO_SCPLOWIMYRC_SCPLOW, a generative framework for reconstructing realistic spatial transcriptomics data in unmeasured tissue regions. MO_SCPLOWIMYRC_SCPLOW addresses this challenge through three coupled components: predicting cell locations via guided diffusion, assigning cell types through supervised classification, and generating gene expression profiles with a transformer conditioned on spatial and cellular context. MO_SCPLOWIMYRC_SCPLOW accurately reconstructs held-out regions in mouse brain data and generalizes across experimental conditions, including variations in gene panels and slicing orientations. After finetuning on limited Alzheimers disease data, MO_SCPLOWIMYRC_SCPLOW captures disease-associated transcriptional changes in unmeasured brain regions. By enabling high-fidelity spatial imputation from limited training data, MO_SCPLOWIMYRC_SCPLOW extends the utility of spatial transcriptomics, allowing researchers to recover unmeasured tissue states and deepen investigations into tissue spatial organization and dynamics.