NovoSpaRc: flexible spatial reconstruction of single-cell gene expression with optimal transport

Single-cell RNA-sequencing (scRNA-seq) technologies have revolutionized modern biomedical sciences. A fundamental challenge is to incorporate spatial information to study tissue organization and spatial gene expression patterns. Here, we describe a detailed protocol for using novoSpaRc, a computational framework that probabilistically assigns cells to tissue locations. At the core of this framework lies a structural correspondence hypothesis, that cells in physical proximity share similar gene expression profiles. Given scRNA-seq data, novoSpaRc spatially reconstructs tissues based on this hypothesis, and optionally, by including a reference atlas of marker genes to improve reconstruction. We describe the novoSpaRc algorithm, and its implementation in an open-source Python package (https://pypi.org/ project/novosparc). NovoSpaRc maps a scRNA-seq dataset of 10,000 cells onto 1,000 locations in <5 min. We describe results obtained using novoSpaRc to reconstruct the mouse organ of Corti de novo based on the structural correspondence assumption and human osteosarcoma cultured cells based on marker gene information, and provide a step-by-step guide to Drosophila embryo reconstruction in the Procedure to demonstrate how these two strategies can be combined.

Automated summary

The single-cell RNA-sequencing (scRNA-seq) technologies have revolutionized modern biomedical sciences by incorporating spatial information to study tissue organization and spatial gene expression patterns. NovoSpaRc is a computational framework that probabilistically assigns cells to tissue locations based on a structural correspondence hypothesis, which spatially reconstructs tissues.