Best practices for the execution, analysis, and data storage of plant single-cell/nucleus transcriptomics.

Single-cell and single-nucleus RNA-sequencing technologies capture the expression of plant genes at an unprecedented resolution. Therefore, these technologies are gaining traction in plant molecular and developmental biology for elucidating the transcriptional changes across cell types in a specific tissue or organ, upon treatments, in response to biotic and abiotic stresses, or between genotypes. Despite the rapidly accelerating use of these technologies, collective and standardized experimental and analytical procedures to support the acquisition of high-quality data sets are still missing. In this commentary, we discuss common challenges associated with the use of single-cell transcriptomics in plants and propose general guidelines to improve reproducibility, quality, comparability, and interpretation and to make the data readily available to the community in this fast-developing field of research.

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

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.

A 3D gene expression atlas of the floral meristem based on spatial reconstruction of single nucleus RNA sequencing data.

Cellular heterogeneity in growth and differentiation results in organ patterning. Single-cell transcriptomics allows characterization of gene expression heterogeneity in developing organs at unprecedented resolution. However, the original physical location of the cell is lost during this methodology. To recover the original location of cells in the developing organ is essential to link gene activity with cellular identity and function in plants. Here, we propose a method to reconstruct genome-wide gene expression patterns of individual cells in a 3D flower meristem by combining single-nuclei RNA-seq with microcopy-based 3D spatial reconstruction. By this, gene expression differences among meristematic domains giving rise to different tissue and organ types can be determined. As a proof of principle, the method is used to trace the initiation of vascular identity within the floral meristem. Our work demonstrates the power of spatially reconstructed single cell transcriptome atlases to understand plant morphogenesis. The floral meristem 3D gene expression atlas can be accessed at http://threed-flower-meristem.herokuapp.com .