Applications for single-cell and spatial transcriptomics in plant research

Abstract

Cells of multicellular plants possess inherent heterogeneity. Recent progress in single-cell RNA sequencing (scRNA-seq) allows researchers to classify, characterize, and distinguish individual cells at the transcriptome level, enabling the identification of rare cell populations with functional importance. However, scRNA-seq obscures spatial information about cells. Spatial transcriptomics approaches have substantially improved our capacity to detect the spatial distribution of RNA transcripts throughout tissues, yet it remains challenging to characterize whole-transcriptome-level data for single cells spatially. In this review, we offer a concise overview of the scRNA-seq and spatial transcriptomics experimental and computational procedures and the computational strategies required to integrate scRNA-seq data with spatial transcriptomics. We demonstrate their impact on plant fundamental cell biology, discuss their advantages and current challenges, and provide an outlook on the future.