The scarcity of accessible sites that are dynamic or cell type-specific in plants may be due in part to tissue heterogeneity in bulk studies. To assess the effects of tissue heterogeneity, we apply single-cell ATAC-seq to Arabidopsis thaliana roots and identify thousands of differentially accessible sites, sufficient to resolve all major cell types of the root. We find that the entirety of a cell’s regulatory landscape and its transcriptome independently capture cell type identity. We leverage this shared information on cell identity to integrate accessibility and transcriptome data to characterize developmental progression, endoreduplication and cell division. We further use the combined data to characterize cell type-specific motif enrichments of transcription factor families and link the expression of family members to changing accessibility at specific loci, resolving direct and indirect effects that shape expression. Our approach provides an analytical framework to infer the gene regulatory networks that execute plant development.

In plants, chromatin accessibility – the primary mark of regulatory DNA – is relatively static across tissues and conditions. This scarcity of accessible sites that are dynamic or tissue-specific may be due in part to tissue heterogeneity in previous bulk studies. To assess the effects of tissue heterogeneity, we apply single-cell ATAC-seq to A. thaliana roots and identify thousands of differentially accessible sites, sufficient to resolve all major cell types of the root. However, even this vast increase relative to bulk studies in the number of dynamic sites does not resolve the poor correlation at individual loci between accessibility and expression. Instead, we find that the entirety of a cell’s regulatory landscape and its transcriptome each capture cell type identity independently. We leverage this shared information on cell identity to integrate accessibility and transcriptome data in order to characterize developmental progression, endoreduplication and cell division in the root. We further use the combined data to characterize cell type-specific motif enrichments of large transcription factor families and to link the expression of individual family members to changing accessibility at specific loci, taking the first steps toward resolving the direct and indirect effects that shape gene expression. Our approach provides an analytical framework to infer the gene regulatory networks that execute plant development.

Thousands of sequenced genomes are now publicly available capturing a significant amount of natural variation within plant species; yet, much of these data remain inaccessible to researchers without significant bioinformatics experience. Here, we present a webtool called ViVa (Visualizing Variation) which aims to empower any researcher to take advantage of the amazing genetic resource collected in the Arabidopsis thaliana 1001 Genomes Project ( http://1001genomes.org ). ViVa facilitates data mining on the gene, gene family, or gene network level. To test the utility and accessibility of ViVa, we assembled a team with a range of expertise within biology and bioinformatics to analyze the natural variation within the well‐studied nuclear auxin signaling pathway. Our analysis has provided further confirmation of existing knowledge and has also helped generate new hypotheses regarding this well‐studied pathway. These results highlight how natural variation could be used to generate and test hypotheses about less‐studied gene families and networks, especially when paired with biochemical and genetic characterization. ViVa is also readily extensible to databases of interspecific genetic variation in plants as well as other organisms, such as the 3,000 Rice Genomes Project ( http://snp-seek.irri.org/ ) and human genetic variation ( https://www.ncbi.nlm.nih.gov/clinvar/ ).

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