Min-Yao Jhu, Max Minne, Ziliang Luo, Hannah Dörpholz, Jie Yao, M Shahid Mukhtar, Fern Mathieu, Marta Peirats-Llobet, Travis Lee, Pau Formosa-Jordan, Siyu Song, Marc Libault, Che-Wei Hsu, Trevor M Nolan, Tatsuya Nobori, Christopher R Anderton, Robert J Schmitz, David Jackson, Miguel Moreno-Risueno, Hilde Nelissen, Rüdiger Simon, Rosangela Sozzani, Keiko Sugimoto
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.