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◆ Journal of Experimental Botany2026-05-08· Data science

Cross-species plant single-cell analysis: community challenges and shared solutions

Maryam Haghan, Tran N Chau, Razan Alajoleen, Chia‐Yu Chen, Sajib Acharjee Dip, Vandana Gurung, Che‐Wei Hsu, Chadinthon Kittivorawong, Sanchari Kundu, Xiaohui Li, Juncheng Luo, Mohammad Salehin, Mathew Simenc, Jie Yun, Bastiaan Bargmann, Song Li

原始摘要(英文原文)· Original 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.
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