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Legumes are important food crops and play a central role in sustainable agriculture through their ability to form symbiosis with rhizobia, soil bacteria that fix atmospheric nitrogen. Recent advances in single-cell and spatial transcriptomics, along with single-cell epigenomics, have enabled high-resolution analysis of gene expression dynamics and the prediction of cell-type-specific regulatory networks. In this review, we highlight recent progress in the use of single-cell omics in legumes, with a particular focus on how genes functioning in distinct cell types contribute to plant development, responses to pathogens, stress-induced plasticity, and the establishment of root nodule symbioses. Case studies in Medicago truncatula, Lotus japonicus, Glycine max, and Arachis hypogaea illustrate the shift from bulk to single-cell multi-omics. We conclude by outlining current limitations and future directions for building integrated legume cell atlases that will support translational research and crop improvement. 

Understanding the intimate interactions between plants and their microbiota at the cellular level is essential for unlocking the full potential of plant holobionts in agricultural systems. Traditional bulk and microbial community-level sequencing approaches reveal broad community patterns but fail to resolve how distinct plant cell types interact with or regulate microbial colonization, as well as the diverse antagonistic and synergistic interactions and responses existing between various microbial populations. Recent advances in single-cell and spatial multi-omics have transformed our understanding of plant cell identities as well as gene regulatory programs and their dynamic regulation in response to environmental stresses and plant development. In this review, we highlight the single-cell discoveries that uncover the plant cell-type-specific microbial perception, immune activation, and symbiotic differentiation, particularly in roots, nodules, and leaves. We further discuss how integrating transcriptomic, epigenomic, and spatial data can reconstruct multilayered interaction networks that connect plant cell-type-specific regulatory states with microbial spatial niches and inter-kingdom signaling (e.g., ligand–receptor and metabolite exchange), providing a foundation for developing new strategies to engineer crop–microbiome interactions to support sustainable agriculture. We conclude by outlining key methodological challenges and future research priorities that point toward building a fully integrated cellular interactome of the plant holobiont. 

Summary Guard cells (GCs) regulate gas exchange and water loss in plants and have been extensively studied inArabidopsis thaliana. However, cross‐species comparisons at single‐cell resolution remain limited. To address this, we aimed to define conserved and divergent transcriptomic signatures of GCs by generating a comparative single‐cell atlas encompassing five species:A. thaliana, soybean (Glycine max), tomato (Solanum lycopersicum), rice (Oryza sativa), and sorghum (Sorghum bicolor).We performed single‐nucleus RNA sequencing (sNucRNA‐seq) on leaf tissues from each species, followed by integrative bioinformatic analyses to identify and annotate GC populations. Orthology‐informed transcriptomic comparisons and co‐expression analyses were applied to assess shared and species‐specific regulatory modules.Our analyses revealed a conserved core transcriptome in GCs across species, enriched for genes involved in stomatal movement, abscisic acid signaling, carbon dioxide signaling, reactive oxygen species metabolism, and ion transport. Conserved expression of transcription factors (e.g. MYB60, FAMA orthologs), transporters (e.g. KAT1, SLAC1), and stress‐responsive genes was observed, despite considerable evolutionary divergence.These findings provide a reference framework for cross‐species GC biology, reveal conserved regulatory features, and nominate candidate genes for functional validation. This work contributes molecular insights toward improving drought resilience and stomatal efficiency in crop species through targeted gene engineering. 

This paper summarizes the main results and contributions of the MagNet Challenge 2023, an open-source research initiative for data-driven modeling of power magnetic materials. The MagNet Challenge has (1) advanced the stateof-the-art in power magnetics modeling; (2) set up examples for fostering an open-source and transparent research community; (3) developed useful guidelines and practical rules for conducting data-driven research in power electronics; and (4) provided a fair performance benchmark leading to insights on the most promising future research directions. The competition yielded a collection of publicly disclosed software algorithms and tools designed to capture the distinct loss characteristics of power magnetic materials, which are mostly open-sourced. We have attempted to bridge power electronics domain knowledge with state-of-the-art advancements in artificial intelligence, machine learning, pattern recognition, and signal processing. The MagNet Challenge has greatly improved the accuracy and reduced the size of data-driven power magnetic material models. The models and tools created for various materials were meticulously documented and shared within the broader power electronics community.