Tissue Reassembly with Generative AI

The spatial arrangement of cells is fundamental to their function, but single-cell RNA sequencing loses spatial context by dissociating cells from tissues. We present LUNA, a generative AI model that reassembles dissociated cells into tissue structures solely from gene expression by learning spatial priors from existing spatially resolved datasets. We apply and validate LUNA across multiple technologies including reconstructing the MERFISH whole mouse brain atlas with over 1.2 million cells, de novo reassembly of mouse central nervous system scRNA-seq atlas and inference of the spatial locations of nuclei lost during Slide-tags profiling. Furthermore, we show that LUNA generalizes to unseen cell types and to pathological samples in the Parkinson’s disease mouse model, identifying regions of change due to pathology. We envision that AI-driven tissue reassembly can help to overcome current technological limitations and advance our understanding of tissue organization and function.