Vision, challenges and opportunities for a Plant Cell Atlas

Jha, Suryatapa Ghosh; Borowsky, Alexander T.; Cole, Benjamin; Fahlgren, Noah; Farmer, Andrew; Huang, Shao‐shan Carol; Karia, Purva; Libault, Marc; Provart, Nicholas J.; Rice, Sarah E.; Saura-Sánchez, Maite; Agarwal, Pinky; Ahkami, Amir H.; Anderton, Christopher; Briggs, Steven P.; Brophy, Jennifer A N; Denolf, Peter; Costanzo, Luigi Di; Expósito-Alonso, Moisés; Giacomello, Stefania; Gomez-Cano, Fabio; Kaufmann, Kerstin; Ko, Dae Kwan; Kumar, Sagar; Malkovskiy, Andrey V.; Nakayama, Naomi; Obata, Toshihiro; Otegui, Marisa S.; Pálfalvi, Gergo; Quezada-Rodríguez, Elsa H; Singh, Rajveer; Uhrig, R. Glen; Waese, Jamie; Wijk, Klaas J. van; Wright, R Clay; Ehrhardt, David; Birnbaum, Kenneth D.; Rhee, Seung Y.

doi:10.7554/elife.66877

Cited by 33 publications

(10 citation statements)

References 161 publications

(172 reference statements)

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“…It has been proposed that the plant science community come together around the goal of creating a complete plant cell atlas, which should incorporate transcriptomic information about all cells in an entire plant 10 . scRNA-seq is considered a foundational technology for this goal, but the technology has limitations that prevent it being a full solution for the plant cell atlas.…”

Section: Introductionmentioning

confidence: 99%

Spatially Resolved Transcriptomic Analysis of the Germinating Barley Grain

Whelan

Lewsey

Peirats‐Llobet

et al. 2023

Preprint

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Seeds, which provide a major source of calories for humans, are a unique stage of a flowering plant's lifecycle. During seed germination the embryo reactivates rapidly and goes through major developmental transitions to become a seedling. This requires extensive and complex spatiotemporal coordination of cell and tissue activity. Existing gene expression profiling methods, such as laser capture microdissection followed by RNA-seq and single-cell RNA-seq, suffer from either low throughput or the loss of spatial information about the cells analysed. Spatial transcriptomics methods couple high throughput analysis of gene expression simultaneously with the ability to record the spatial location of each individual region analysed. We developed a spatial transcriptomics workflow for germinating barley grain to better understand the spatiotemporal control of gene expression within individual seed cell types. More than 14,000 genes were differentially regulated across 0, 1, 3, 6 and 24 hours after imbibition. This approach enabled us to observe that many functional categories displayed specific spatial expression patterns that could be resolved at a sub-tissue level. Individual aquaporin gene family members, important for water and ion transport, had specific spatial expression patterns over time, as well as genes related to cell wall modification, membrane transport and transcription factors. Using spatial autocorrelation algorithms, we were able to identify auxin transport genes that had increasingly focused expression within subdomains of the embryo over germination time, suggestive of a role in establishment of the embryo axis. Together, our data provides an unprecedented spatially resolved cellular map for barley grain germination and specific genes to target for functional genomics to define cellular restricted processes in tissues during germination. The data can be viewed at https://spatial.latrobe.edu.au/.

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Section: Introductionmentioning

confidence: 99%

Spatially Resolved Transcriptomic Analysis of the Germinating Barley Grain

Whelan

Lewsey

Peirats‐Llobet

et al. 2023

Preprint

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“…To that end, we developed a metadata annotation system within PeptideAtlas. These metadata are critical to determine cell-type or subcellular specific protein accumulation patterns and help accomplish the long-term goal of the Arabidopsis community to develop a detailed Arabidopsis Plant Cell Atlas …”

Section: Introductionmentioning

confidence: 99%

Detection of the Arabidopsis Proteome and Its Post-translational Modifications and the Nature of the Unobserved (Dark) Proteome in PeptideAtlas

van Wijk,

Leppert,

Sun

et al. 2023

J. Proteome Res.

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This study describes a new release of the Arabidopsis thaliana PeptideAtlas proteomics resource (build 2023−10) providing protein sequence coverage, matched mass spectrometry (MS) spectra, selected post-translational modifications (PTMs), and metadata. 70 million MS/MS spectra were matched to the Araport11 annotation, identifying ∼0.6 million unique peptides and 18,267 proteins at the highest confidence level and 3396 lower confidence proteins, together representing 78.6% of the predicted proteome. Additional identified proteins not predicted in Araport11 should be considered for the next Arabidopsis genome annotation. This release identified 5198 phosphorylated proteins, 668 ubiquitinated proteins, 3050 N-terminally acetylated proteins, and 864 lysine-acetylated proteins and mapped their PTM sites. MS support was lacking for 21.4% (5896 proteins) of the predicted Araport11 proteome: the "dark" proteome. This dark proteome is highly enriched for E3 ligases, transcription factors, and for certain (e.g., CLE, IDA, PSY) but not other (e.g., THIONIN, CAP) signaling peptides families. A machine learning model trained on RNA expression data and protein properties predicts the probability that proteins will be detected. The model aids in discovery of proteins with short half-life (e.g., SIG1,3 and ERF-VII TFs) and for developing strategies to identify the missing proteins. PeptideAtlas is linked to TAIR, tracks in JBrowse, and several other community proteomics resources.

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“…The recent advances in single-cell RNA sequencing (scRNA-seq) have revolutionized the studies in plant developmental biology and provided an unprecedented opportunity to access the differentiation trajectory of plant organs at single-cell resolution [ 12 , 13 ]. For a successful scRNA-seq experiment, the prerequisite is to generate high-quality and viable cell suspensions, which relies on breaking down the cell wall matrixes using hydrolytic buffers from tissues of interest [ 14 ]. However, the plant cell wall differs in thickness and composition depending on plant species, tissues, developmental stages, and environmental conditions [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

An Efficient and Universal Protoplast Isolation Protocol Suitable for Transient Gene Expression Analysis and Single-Cell RNA Sequencing

Wang

et al. 2022

IJMS

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The recent advent of single-cell RNA sequencing (scRNA-seq) has enabled access to the developmental landscape of a complex organ by monitoring the differentiation trajectory of every specialized cell type at the single-cell level. A main challenge in this endeavor is dissociating plant cells from the rigid cell walls and some species are recalcitrant to such cellular isolation. Here, we describe the establishment of a simple and efficient protocol for protoplast preparation in Chirita pumila, which includes two consecutive digestion processes with different enzymatic buffers. Using this protocol, we generated viable cell suspensions suitable for an array of expression analyses, including scRNA-seq. The universal application of this protocol was further tested by successfully isolating high-quality protoplasts from multiple organs (petals, fruits, tuberous roots, and gynophores) from representative species on the key branches of the angiosperm lineage. This work provides a robust method in plant science, overcoming barriers to isolating protoplasts in diverse plant species and opens a new avenue to study cell type specification, tissue function, and organ diversification in plants.

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Vision, challenges and opportunities for a Plant Cell Atlas

Cited by 33 publications

References 161 publications

Spatially Resolved Transcriptomic Analysis of the Germinating Barley Grain

Spatially Resolved Transcriptomic Analysis of the Germinating Barley Grain

Detection of the Arabidopsis Proteome and Its Post-translational Modifications and the Nature of the Unobserved (Dark) Proteome in PeptideAtlas

An Efficient and Universal Protoplast Isolation Protocol Suitable for Transient Gene Expression Analysis and Single-Cell RNA Sequencing

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