Molecular Mechanisms Driving Bistable Switch Behavior in Xylem Cell Differentiation

Abstract

SummaryPlant xylem cells conduct water and mineral nutrients. Although most plant cells are totipotent, xylem cells are unusual and undergo terminal differentiation. Many genes regulating this process are well characterized, including the VASCULAR-RELATED NAC DOMAIN7 (VND7), MYB46 and MYB83 transcription factors which are proposed to act in interconnected feed-forward loops. Much less is known regarding the dynamic behavior underlying the terminal transition to xylem cell differentiation. Here we utilize whole root and single cell data to mathematically model this relationship. These provide evidence for VND7 regulating bistable switching of cells in the root to a xylem cell identity, with additional features of hysteresis. We further determine that although MYB46 responds to VND7 induction, it is not inherently involved in executing the binary switch. A novel regulatory architecture is proposed that involves four downstream targets of VND7 that act in a cycle. These data provide an important model to study the emergent properties that may give rise to totipotency relative to terminal differentiation and reveal novel xylem cell subtypes.

Status and Potential of Single‐Cell Transcriptomics for Understanding Plant Development and Functional Biology

“…This study further revealed that two other xylem cell differentiation transcription factors, MYB46 and MYB83, do not respond to VND7 in a switch like fashion as it was previously thought [46]. Furthermore, the cell coverage used in the study was comparable to the previously conducted microscopy‐based analysis with an extra advantage of finding cellular quiescence centres, which were absent in earlier studies [46, 47]. This important finding not only cleared the ambiguity in the understanding of an important biological pathway but also provided an alternate pathway where VND7 was shown to work in a cycle by governing through four targets.…”

“…In another study by Turco et al [46], a Drop-seq-based isolation of single-cells from a bulk of ten plates with 200 roots per plate revealed a novel regulatory network of four targets controlled by a transcription factor called VASCULAR-RELATED NAC DOMAIN7 (VND7), which has a wellestablished role in terminal differentiation of xylem cells. This study further revealed that two other xylem cell differentiation transcription factors, MYB46 and MYB83, do not respond to VND7 in a switch like fashion as it was previously thought [46]. Furthermore, the cell coverage used in the study was comparable to the previously conducted microscopy-based analysis with an extra advantage of finding cellular quiescence centres, which were absent in earlier studies [46,47].…”

Identification of new marker genes from plant single-cell RNA-seq data using interpretable machine learning methods

“…Identification of cell types is a key step in the analysis and interpretation of scRNA-seq data (Luecken and Theis, 2019). Currently, approaches to define Arabidopsis root cell types fall into three major categories: (1) Calculate index of cell identity (ICI) using selected marker genes based on information theoretic scores from a published cell expression profiles (Efroni et al, 2015; Shulse et al, 2019; Turco et al, 2019); (2) Assign cell types by visualizing expression patterns using known marker genes (Jean-Baptiste et al, 2019; Ryu et al, 2019; Zhang et al, 2019b). (3) Compute correlation coefficient with published gene expression data (Jean-Baptiste et al, 2019; Shulse et al, 2019).…”

“…Currently, approaches to define Arabidopsis root cell types fall into three major categories: (1) ICI (index of cell identity) method. This approach uses selected marker genes based on information theoretic scores from published cell expression profiles (Efroni et al, 2015; Shulse et al, 2019; Turco et al, 2019); (2) Cluster-marker genes. This approach generates clusters of cells with unsupervised dimension reduction methods and assigns cell types by visualizing expression patterns using known marker genes (Jean-Baptiste et al, 2019; Ryu et al, 2019; Zhang et al, 2019b).…”