Cross-Species Plant Single-Cell Analysis: Community Challenges and Shared Solutions

Single-cell genomics is rapidly reshaping plant biology, yet broader adoption is limited by plant-specific technical constraints, fragmented tools, and inconsistent analytical practices. Here we report outcomes from the 2025 Summer Workshop for Plant Single-Cell Analysis, which convened researchers to define community needs and design shared solutions. Participants identified five priority challenge areas: (1) improving data quality through imputation, simulation, and deep generative modeling; (2) developing automated, phylogenetically aware cell-type annotation frameworks; (3) reconstructing developmental trajectories and gene regulatory networks from single-cell and single-nucleus profiles; (4) creating visualization approaches that embed transcriptional states into anatomically grounded plant organ contexts; and (5) using Artificial Intelligence (AI) agents and foundation models to orchestrate end-to-end single-cell workflows. In response, we established PlantSCHub, a community-curated web portal that aggregates protocols, datasets, and tutorials to support reproducible plant single-cell analysis. We outline conceptual roadmaps for cross-species integration, multimodal trajectory and Gene Regulatory Networks (GRNs) inference, spatially anchored visualization, and AI scientist agents that dynamically coordinate analytical tools and literature. We discuss both scRNA-seq and scATAC-seq that are important for regulatory inference and cross-species analysis. Together, these efforts aim to transform isolated plant single-cell studies into an interoperable, evolving ecosystem that accelerates discovery and crop improvement.

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Publication Details

Journal
Journal of Experimental Botany
Published
2026-05-08
DOI
https://doi.org/10.1093/jxb/erag218
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
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article

Cross-Species Plant Single-Cell Analysis: Community Challenges and Shared Solutions

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Journal of Experimental Botany
Single-cell and spatial transcriptomics
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Cross-Species Plant Single-Cell Analysis: Community Challenges and Shared Solutions

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article en

Abstract

Single-cell genomics is rapidly reshaping plant biology, yet broader adoption is limited by plant-specific technical constraints, fragmented tools, and inconsistent analytical practices. Here we report outcomes from the 2025 Summer Workshop for Plant Single-Cell Analysis, which convened researchers to define community needs and design shared solutions. Participants identified five priority challenge areas: (1) improving data quality through imputation, simulation, and deep generative modeling; (2) developing automated, phylogenetically aware cell-type annotation frameworks; (3) reconstructing developmental trajectories and gene regulatory networks from single-cell and single-nucleus profiles; (4) creating visualization approaches that embed transcriptional states into anatomically grounded plant organ contexts; and (5) using Artificial Intelligence (AI) agents and foundation models to orchestrate end-to-end single-cell workflows. In response, we established PlantSCHub, a community-curated web portal that aggregates protocols, datasets, and tutorials to support reproducible plant single-cell analysis. We outline conceptual roadmaps for cross-species integration, multimodal trajectory and Gene Regulatory Networks (GRNs) inference, spatially anchored visualization, and AI scientist agents that dynamically coordinate analytical tools and literature. We discuss both scRNA-seq and scATAC-seq that are important for regulatory inference and cross-species analysis. Together, these efforts aim to transform isolated plant single-cell studies into an interoperable, evolving ecosystem that accelerates discovery and crop improvement.

Journal of Experimental Botany
University of California, Riverside (US), California Institute of Technology (US), Salk Institute for Biological Studies (US), University of Michigan (US), Yale University (US), North Carolina Agricultural and Technical State University (US), Institut thématique Génétique, génomique et bioinformatique (FR), Clemson University (US), Virginia Tech (US)
Openalex Percentile: Top 7%
Single-cell and spatial transcriptomics
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