eSPRESSO: a spatial self-organizing-map clustering method for single-cell transcriptomes of various tissue structures using graph-based networks

Abstract

Animal cells are spatially organized as tissues and cellular gene expression data contain such information that governs body structure and morphogenesis during developmental process. Although several computational tissue reconstruction methods using transcriptomic data have been proposed, those methods are insufficient with regard to arranging cells in their correct positions in tissues or organs unless spatial information is explicitly provided. Here, we propose eSPRESSO, a powerful in silico three-dimensional (3D) tissue reconstruction method using stochastic self-organizing map (stochastic-SOM) clustering, together with optimization of gene set by Markov chain Monte Carlo (MCMC) framework, to estimate the spatial domain structure of cells in any topology of tissues or organs from only their transcriptome profiles. We confirmed the performance of eSPRESSO by mouse embryo, embryonic heart, adult cortical layers, and human pancreas organoid with high reproducibility (success rate = 72.5-100%), while discovering morphologically important spatial discriminator genes (SDGs). Furthermore, we applied eSPRESSO to analysis of human adult heart diseases by virtual gene knockouts, and revealed candidate mechanisms of deformation of heart structure. The eSPRESSO may provide novel methods to analyze the mechanisms of 3D structure formation and morphology-based disease mechanisms.