A Hands-On Guide to Generate Spatial Gene Expression Profiles by Integrating scRNA-seq and 3D-Reconstructed Microscope-Based Plant Structures

Abstract

Transcriptome profiles of individual cells in the plant are strongly dependent on their relative position. Cell differentiation is associated with tissue-specific transcriptomic changes. For that reason, it is important to study gene expression changes in a spatial context, and therefore to link those to potential morphological changes over developmental time. Even though great experimental advances have been made in recording spatial gene expression profiles, those attempts are limited in the plant field. New computational approaches attempt to solve this problem by integrating spatial expression profiles of few marker genes with single-cell/single-nuclei RNA-seq (scRNA-seq) methodologies. In this chapter, we provide a practical guide on how to predict gene expression patterns in a 3D plant structure by combining scRNA-seq data and 3D microscope-based reconstructed expression profiles of a small set of reference genes. We also show how to visualize these results.

Keywords: Arabidopsis thaliana; Computational biology; Flower meristem; Spatial transcriptomics; scRNA-seq.