A single-cell Arabidopsis root atlas reveals developmental trajectories in wild-type and cell identity mutants

Shahan, Rachel; Hsu, CY; Nolan, Trevor M.; Cole, Benjamin; Taylor, Isaiah; Greenstreet, Laura; Zhang, Stephen X.; Afanassiev, Anton; Vlot, Anna Hendrika Cornelia; Schiebinger, Geoffrey; Benfey, Philip N.; Ohler, Uwe

doi:10.1016/j.devcel.2022.01.008

Cited by 166 publications

(228 citation statements)

References 107 publications

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“…One possible reason is that the BULR markers are lowly expressed and cannot be detected in single-cell data, but they are highly specific to individual cell types. These results are also observed when evaluating these marker genes in Shahan et al (2022) (Fig. S20).…”

Section: Testing New Marker Genes With Independently Labelled Cell Ty...supporting

confidence: 67%

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

et al. 2022

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Summary An essential step in the analysis of single‐cell RNA sequencing data is to classify cells into specific cell types using marker genes. In this study, we have developed a machine learning pipeline called single‐cell predictive marker (SPmarker) to identify novel cell‐type marker genes in the Arabidopsis root. Unlike traditional approaches, our method uses interpretable machine learning models to select marker genes. We have demonstrated that our method can: assign cell types based on cells that were labelled using published methods; project cell types identified by trajectory analysis from one data set to other data sets; and assign cell types based on internal GFP markers. Using SPmarker, we have identified hundreds of new marker genes that were not identified before. As compared to known marker genes, the new marker genes have more orthologous genes identifiable in the corresponding rice single‐cell clusters. The new root hair marker genes also include 172 genes with orthologs expressed in root hair cells in five non‐Arabidopsis species, which expands the number of marker genes for this cell type by 35–154%. Our results represent a new approach to identifying cell‐type marker genes from scRNA‐seq data and pave the way for cross‐species mapping of scRNA‐seq data in plants.

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Section: Testing New Marker Genes With Independently Labelled Cell Ty...supporting

confidence: 67%

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

et al. 2022

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“…Both IPT5 and IPT7 genes are highly expressed in the pWOL :: GFP transgenic line, a marker for stele cells [ 64 ]. Using single-cell RNA-seq approaches, IPT7 was identified as a potential cell type-specific marker for the metaxylem [ 65 ]. The quadruple mutants ipt1ipt3ipt5ipt7 almost completely depleted endogenous cytokinin levels due to deficient biosynthesis show substantial growth retardation both of the shoot and root [ 66 ].…”

Section: Discussionmentioning

confidence: 99%

“…In the M0028 :: GFP line [ 68 ], a specific marker for gene expression in LRC, COL, CSCs, and QC cells, the cytokinin oxidase gene CKX4 was accumulated [ 6 , 20 ]. CKX4 is also a potential cell type-specific marker for columella cells [ 65 ]. Our results suggest that CKX4 is regulated by both ADA2b and GCN5 action by modulating H3K14 acetylation in its promoter.…”

Section: Discussionmentioning

confidence: 99%

ADA2b and GCN5 Affect Cytokinin Signaling by Modulating Histone Acetylation and Gene Expression during Root Growth of Arabidopsis thaliana

Tsilimigka

Poulios

Mallioura

et al. 2022

Plants

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In Arabidopsis thaliana, the histone acetyltransferase GCN5 and the associated coactivator ADA2b regulate root growth and affect gene expression. The cytokinin signaling reporter TCS::GFP was introduced into gcn5-1, ada2b-1, and ada2a-2, as well as the ada2a-2ada2b-1 mutants. The early root growth (4 to 7 days post-germination) was analyzed using cellular and molecular approaches. TCS signal accumulated from the fourth to seventh days of root growth in the wild-type columella cells. In contrast, ada2b-1 and gcn5-1 and ada2a-2ada2b-1 double mutants displayed reduced TCS expression relative to wild type. Gene expression analysis showed that genes associated with cytokinin homeostasis were downregulated in the roots of gcn5-1 and ada2b-1 mutants compared to wild-type plants. H3K14 acetylation was affected in the promoters of cytokinin synthesis and catabolism genes during root growth of Arabidopsis. Therefore, GCN5 and ADA2b are positive regulators of cytokinin signaling during root growth by modulating histone acetylation and the expression of genes involved in cytokinin synthesis and catabolism. Auxin application in the roots of wild-type seedlings increased TCS::GFP expression. In contrast, ada2b and ada2ada2b mutant plants do not show the auxin-induced TCS signal, suggesting that GCN5 and ADA2b are required for the auxin-induced cytokinin signaling in early root growth.

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“…While many different single‐cell sequencing techniques exist (for review, see Birnbaum, 2018), the rise of single‐cell investigations across eukaryotes in general, and in plants in particular, has been driven by the 10 × Genomics single‐cell microfluidics‐based platform (Birnbaum, 2018; McFaline‐Figueroa et al., 2020). Currently, cell‐type atlases that report plant cell identity exist for the Arabidopsis root (Denyer et al., 2019; Shahan et al., 2020; Shulse et al., 2019; Zhang et al., 2019), shoot (Kim et al., 2021; Lopez‐Anido et al., 2021; Xu et al., 2021a) and seed (Picard et al., 2021), as well as other plant species (Kajala et al., 2021; Liu et al., 2021; Xu et al., 2021b; Figure 1a). Importantly, these cell‐type atlases are generated through an unbiased clustering of single sequenced cells (or nuclei) using algorithms that exploit the variability between each cell’s transcriptome or epigenome (Satija et al., 2015).…”

Section: Developmental Pseudotime: Predicting the Past From Plant Cel...mentioning

confidence: 99%

“…For example, after single‐cell sequencing captured the transcriptomes of cell types within the Arabidopsis root tip, pseudotime was able to predict the developmental trajectories that connect these cell types together and parse them into meristematic, elongation and maturation zones (Shahan et al., 2020). This approach confirmed the well‐known mechanism that TFs SHORTROOT and SCARECROW play in early tissue patterning, as well as extended what is known about their mechanism of action by discovering that SHORTROOT can act to specify mature endodermal cell identity (Shahan et al., 2020). Indeed, along these lines pseudotime is helping revise the timing of well‐known molecular signaling mechanisms that drive development.…”

Section: Developmental Pseudotime: Predicting the Past From Plant Cel...mentioning

confidence: 99%

The biology of time: dynamic responses of cell types to developmental, circadian and environmental cues

et al. 2021

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As sessile organisms, plants are finely tuned to respond dynamically to developmental, circadian and environmental cues. Genome-wide studies investigating these types of cues have uncovered the intrinsically different ways they can impact gene expression over time. Recent advances in single-cell sequencing and time-based bioinformatic algorithms are now beginning to reveal the dynamics of these time-based responses within individual cells and plant tissues. Here, we review what these techniques have revealed about the spatiotemporal nature of gene regulation, paying particular attention to the three distinct ways in which plant tissues are time sensitive. (i) First, we discuss how studying plant cell identity can reveal developmental trajectories hidden in pseudotime. (ii) Next, we present evidence that indicates that plant cell types keep their own local time through tissue-specific regulation of the circadian clock. (iii) Finally, we review what determines the speed of environmental signaling responses, and how they can be contingent on developmental and circadian time. By these means, this review sheds light on how these different scales of timebased responses can act with tissue and cell-type specificity to elicit changes in whole plant systems.

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A single-cell Arabidopsis root atlas reveals developmental trajectories in wild-type and cell identity mutants

Cited by 166 publications

References 107 publications

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

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

ADA2b and GCN5 Affect Cytokinin Signaling by Modulating Histone Acetylation and Gene Expression during Root Growth of Arabidopsis thaliana

The biology of time: dynamic responses of cell types to developmental, circadian and environmental cues

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