Decoding Cellular Heterogeneity through Single-Cell and Spatial Multi-Omics Integration

Plant tissues are made from many different cell types, and even neighbouring cells can differ in their gene activity, regulation, and function. This cellular heterogeneity shapes how plants grow, differentiate, respond to environmental stress, and regulate processes such as nutrient cycling and dormancy. This Research Topic focuses on how single-cell and spatial multi-omics approaches can help us understand that heterogeneity in plant systems. Over the last few years, single-cell and single-nucleus RNA sequencing have begun to reveal just how varied the cell populations within roots, leaves, and other organs really are, uncovering cell states and developmental trajectories that were hidden in bulk measurements. Multi-omics methods have since added further layers of information, capturing chromatin accessibility, DNA methylation, and transcription factors binding in individual cells. Spatial technologies have taken this a step further, returning these molecular profiles to their positions within the tissue and showing how different cell types are arranged and how they interact. Even so, several difficulties remain. Plants are awkward material to work with: their rigid cell walls make cells hard to separate, their high metabolite content can interfere with profiling, and well-annotated reference atlases are still missing for most species. In practice, each type of data tends to be analyzed on its own, and we still lack well-tested computational frameworks to bring transcriptomic, epigenomic, and spatial datasets together. Reliable tools for integrating, aligning, and benchmarking these data are limited, which makes it hard to move from simply cataloguing cells to explaining how heterogeneity works. As a plant molecular biologist working on transcription factors, chromatin profiling, and gene regulatory networks, I see a clear and timely need to bring these efforts together. The field is advancing rapidly, yet plant systems remain underrepresented compared with animal and biomedical research. We are proposing this Research Topic to gather methodological advances, analytical tools, benchmarking studies, and biological applications focused on plants, encouraging cross-disciplinary exchange and helping to establish shared standards for single-cell and spatial multi-omics integration across diverse plant species.The central problem this Research Topic sets out to address is how to combine single-cell and spatial multi-omics data in plants into one coherent, biologically informative picture. Individual layers of information, including gene expression, chromatin state, DNA methylation, and metabolite distribution, are becoming easier to measure, but because they are often collected on different platforms and from different cells, integrating them is far from straightforward. Plant specific features such as rigid cell walls and variable cell size add to the difficulty and often mean that methods designed for animal tissues cannot be applied directly. There has, however, been real progress: spatially resolved multi-omic atlases and single-cell chromatin accessibility maps have now been produced for crops including soybean, rice, and wheat. Building on these advances, this Topic will gather studies that develop and test integration frameworks, adapt existing tools to plant biology, map single-cell data onto spatial coordinates, and infer gene regulatory networks at the level of individual cell types, with a particular welcome for work on non-model and crop species.This Research Topic covers the full span of single-cell and spatial multi-omics in plants, from the development of new experimental and computational methods to their use in answering long-standing biological questions. We are especially interested in studies that connect methodological advances with genuine biological insight, and that extend these technologies to non-model and crop species where they are still rarely used. We welcome contributions on themes including, but not limited to: - Computational and statistical approaches for integrating transcriptomic, epigenomic, and metabolomic data across single-cell and spatial platforms - Methods for mapping dissociated single-cell data back onto spatial coordinates, including deconvolution, label transfer, and joint embedding - Single-cell and spatial epigenomics, covering chromatin accessibility, DNA methylation, histone modifications, and transcription factor binding - Inference of cell-type-specific gene regulatory networks and how they change during development and stress - Benchmarking studies and practical workflows tailored to plant features such as rigid cell walls, variable cell size, and high metabolite content - Software, analysis pipelines, and reference cell atlases for model, non-model, and crop species - Biological applications in development, differentiation, biotic and abiotic stress responses, nutrient cycling, dormancy, and plant microbe interactions Both experimental and computational studies are welcome, as are papers that establish shared resources, standards, or reproducible workflows for the plant single-cell and spatial community. Topic editors: Shumayla ., Mandeep Kaur, Anil Kumar, Gerardo Alejo-Jacuinde Source: https://www.frontiersin.org/research-topics/84663/decoding-cellular-heterogeneity-through-single-cell-and-spatial-multi-omics-integration