A practical, solution-oriented synthesis of current bottlenecks across experimental and computational pipelines is provided, emerging strategies to overcome these limitations are highlighted, and a roadmap for community-driven protocol sharing, benchmarking, and integration across spatial and multi-omics modalities is proposed.
Abstract
Spatial omics technologies are providing new opportunities for plant biology by enabling molecular profiling within structurally intact tissues, revealing spatially organised cell states, developmental gradients, and regulatory interactions. While spatial transcriptomics has driven early advances, the field is rapidly expanding toward integrated spatial multi-omics by combining single-cell and spatial transcriptomic, epigenomic, proteomic, and metabolomic data. These approaches offer new opportunities to study development, physiology, and plant biotic and abiotic interactions in spatially preserved cellular contexts. However, despite rapid adoption, the field remains constrained by plant-specific challenges when applying technologies largely developed for animal systems. Compared with animal systems, plant tissues pose additional challenges due to rigid cell walls, and diverse chemistries, complicating sample preparation, cell and subcellular segmentation, signal detection, and data integration. As a result, many studies rely on bespoke protocols and analysis pipelines that are often difficult to reproduce or generalise. Here, we provide a practical, solution-oriented synthesis of current bottlenecks across experimental and computational pipelines, highlight emerging strategies to overcome these limitations, and propose a roadmap for community-driven protocol sharing, benchmarking, and integration across spatial and multi-omics modalities. Addressing these challenges will be essential to establish spatial omics as a routine and scalable tool for plant biology.
This review provides a physiology-centered blueprint for applying single-cell RNA sequencing, single-nucleus RNA sequencing, and spatial transcriptomics to non-model species and critically evaluates dissociation and preservation bias, genome annotation, seasonal and ecological variation, biological replication, pseudor...
Adnan Amin, W. Zaman· Functional & Integrative Gen...· 0 citations
This review synthesizes recent progress across diverse plant species and tissues, showing that gene expression is not only cell-type specific but also tightly organized by position within organs and developmental niches, establishing spatial gene expression as a fundamental organizing principle of plant development and...
Yiqing Wang, Zhengzhi Tan, Nicole A Freeman et al.· Plant Communications· 0 citations
The biological complexity of plants arises from highly coordinated cellular activities. We propose that a "cellular spatiotemporal dogma" governs the zygote's programmed development into a complete plant and its adaptation to various environmental stresses, representing the set of principles describing how gene express...
The integrated Plant single-cell Database (iPscDB), an integrated and multifunctional platform that facilitates the integration and analysis of plant single-cell data, is presented, offering a practical and accessible resource for researchers with diverse technical backgrounds.
Peng Lu, Jing-Jing Jin, Jie-Meng Tao et al.· Nucleic Acids Research· 0 citations
Single-cell and spatial transcriptomics are transforming our understanding of cellular heterogeneity and tissue organization, yet their analytical complexity remains a major bottleneck. Here, we present EISCA and EISTA, two standardized, end-to-end pipelines for single-cell RNA-seq and imaging-based spatial transcripto...
Hui-Hai Wu, Ashleigh Lister, Iain C. Macaulay et al.· bioRxiv· 0 citations
Spatial transcriptomics (ST) integrates spatial information with gene expression data to quantify mRNA levels across diverse genes within the structural context of tissues and cells. This field enables simultaneous capture of cellular gene expression at the transcriptomic level while preserving spatial localization inf...
Xiang-Hui Li, Ze-Wei Yang, Jing-Jing Li et al.· The FASEB Journal· 0 citations
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