Cella: 3D data visualization for plant single-cell transcriptomics in Blender.

Abstract

Recent advancements in single-cell transcriptomics have facilitated the possibility of acquiring vast amounts of data at single-cell resolution. This development has provided a broader and more comprehensive understanding of complex biological processes. The growing datasets require a visualization tool that transforms complex data into an intuitive representation. To address this challenge, we have utilized an open-source 3D software Blender to design Cella, a cell atlas visualization tool, which transforms data into 3D heatmaps that can be rendered into image libraries. Our tool is designed to support especially research on plant development. To validate our method, we have created a 3D model representing the Arabidopsis thaliana root meristem and mapped an existing single-cell RNA-seq dataset into the 3D model. This provided a user-friendly visual representation of the expression profiles of 21,489 genes from two perspectives (42,978 images). This approach is not limited to single-cell RNA-seq data of the Arabidopsis root meristem. We provide detailed step-by-step instructions to generate 3D models and a script that can be customized to project data onto different tissues. Our tool provides a proof-of-concept method for how increasingly complex single-cell RNA-seq datasets can be visualized in a simple and cohesive manner.