Tissue Reassembly with Generative AI

The spatial arrangement of cells plays a critical role in determining their functions and interactions within tissues. However, single-cell RNA sequencing dissociates cells from their native tissue context, resulting in a loss of spatial information. Here, we show that complex tissue structures can be reassembled from the gene expression profiles of dissociated cells. To achieve this, we developed LUNA, a generative AI model that reconstructs tissues conditioned solely on the gene expression of cells by learning spatial priors over existing spatially resolved datasets. We show that LUNA effectively reconstructs slices from the MERFISH whole mouse brain atlas with over 1.2 million cells. Applying LUNA to the mouse central nervous system scRNA-seq atlas, we show that LUNA is applicable for de novo generation of tissue structures. We envision that AI-driven tissue reassembly can help to overcome current technological limitations and advance our understanding of tissue organization and function.