The Plant Single Cell RNA-Sequencing Database

High-throughput single cell RNA-Sequencing (scRNA-Seq) provides unprecedented power in understanding gene expression in complex tissues. The incomparable depth of data that scRNA-Seq affords means that this technology is swiftly becoming a standard for transcriptomic sequencing in plants.

This portal presents a user-friendly interface to query such data derived from plant tissues. With our recent Arabidopsis root scRNA-Seq dataset as a starting point ( Denyer, Ma et al., 2019 ), detailed expression information for individual genes or gene lists can be visualised and downloaded as: (i) t-SNE plots, showing expression profiles across the cluster cloud, (ii) violin plots describing the distribution of expression values within cells of a given cluster, and (iii) tables summarising mean expression per cell and percentage of cells expressing a given gene in each cluster. In addition, information of gene expression patterns across pseudotime-derived developmental trajectories can be visualised as scatterplot and heatmaps.

Accompanying cluster maps provide a first, comprehensive view of patterns of gene expression displayed in the t-SNE and violin plots. Tips on how to derive the detailed spatiotemporal expression information uniquely captured by scRNA-Seq, as well as details on specific datasets, can be found in the tutorial.

This website will continue to be updated with information from additional analyses and datasets. For technical advice on using the website, suggestions, or if you encounter problems using the scRNA-Seq browser, please do not hesitate to contact Marja Timmermans ( ).

If you make use of the data presented here, please cite the following article in addition to the primary data sources:

Ma, X., Denyer, T., and Timmermans, MCP. (2020). PscB: A Browser to Explore Plant Single Cell RNA-Sequencing Data Sets. Plant Physiol. 183, 464-467.

Enjoy searching for your favourite genes!

Click here to enter the Arabidopsis Root scRNA-Seq Atlas

Denyer, Ma et al., 2019

Additional datasets will be added here in time!


We thank Dieter Steinmetz and Andreas Keck for installation and maintenance of the server; Funding was provided by the Alexander von Humboldt Foundation.