Using co-localization priors and microenvironment statistics to reconstruct tissue organization from single-cell data

Computational reconstruction of tissue structure based on single-cell data has supported the inference of the emergence of structure along development, division of labor mechanisms across tissues, and variations in health and disease. However, while multiple computational methods have been proposed to approach this task over the past few years, it can still be very challenging for complex tissues, especially given a limited reference atlas. Here we show how information about tissue microenvironments statistics, such as cell type neighborhoods, or co-localization priors, can enhance tissue reconstruction in such cases. Specifically, we incorporate co-localization priors as a generalization to novoSpaRc, an optimal transport-based framework for tissue reconstruction based on single-cell data, which relies at its core on an interpolation between a structural correspondence assumption and a potential reference atlas. We demonstrate that incorporating cell type co-localization priors enhances the reconstruction of the mammalian organ of Corti and testicular spatial structure.