Zero-shot reconstruction of mutant spatial transcriptomes

Analysis of mutants is a core of biological/pathological research, and measuring their spatial gene expression can facilitate phenotypic understanding of their tissue disorganization. However, considering the existence of enormous numbers of mutants which are worth to be investigated, the cost- and technically-demanding nature of the experiments measuring spatial transcriptomes could be a bottleneck. Although spatial transcriptomes have been computationally predicted from single cell RNA sequencing (scRNA-seq) data by referring to teaching data of spatial gene expressions of some genes, this task is still challenging because the teaching data is not available in most of the mutants. Zero-shot learning shows potential for solving general prediction problems without teaching data in various machine learning tasks. Here, we provide the first zero-shot framework (ZENomix) to predict mutant spatial transcritptomes from mutant scRNA-seq data without teaching data (e.g. mutant spatial reference atlas). We validated ZENomix by demonstrating accurate prediction of spatial transcriptomes of Alzheimer’s disease-model mice and mutant zebrafish embryos losing Nodal signaling. Furthermore, we proposed a spatially-informed screening approach based on ZENomix’s prediction, which discovered new Nodal-downregulated genes in zebrafish. We expect ZENomix to reveal new phenotypic insights by utilizing massive mutant/disease scRNA-seq data that are and will be acquired.