The study used high-resolution chemical imaging to map cell-type specific metabolic changes in plant roots inoculated with a nine-strain endophyte consortium under drought, revealing that endophytes differentially alter root metabolomes across spatial domains. Machine learning identified metabolites and exudates predictive of drought and endophyte treatment, and correlation analyses showed dynamic endophyte–metabolite relationships under stress.

The study presents a multiplexed technique that applies both single‑cell RNA sequencing (scRNA‑seq) and single‑cell mass spectrometry metabolomics (scMS) to the same plant cell, enabling direct, cell‑by‑cell correlation of gene expression with metabolite levels. This integrated approach uncovers qualitative and quantitative relationships between biosynthetic gene activity and metabolite accumulation, advancing understanding of complex plant natural product pathways.

The study generated high-resolution single-cell and single-nucleus RNA‑seq transcriptome atlases for two independently evolved C4 dicots, Gynandropsis gynandra (NAD-ME) and Flaveria bidentis (NADP-ME), enabling detailed comparison of leaf cell types. Bundle sheath cells in both species displayed a gene‑expression profile resembling C3 mesophyll cells and shared a conserved set of C2H2 and DOF transcription factors, revealing common regulatory features of C4 evolution.

The study introduces ENTRAP-seq, a high‑throughput in‑planta assay that couples protein‑coding libraries with a nuclear magnetic sorting‑based reporter to multiplexively assess transcriptional regulatory activity of thousands of protein variants. Using this platform and machine‑learning analysis, the authors screened 1,495 plant viral proteins, uncovering numerous novel regulatory domains, and applied machine‑guided, semi‑rational design to modify the activity of a plant transcription factor.

The study uses an imputation strategy that integrates deep single-cell RNA sequencing with spatial gene expression data to map transcriptional dynamics across barley inflorescence development at cellular resolution. By leveraging the BARVISTA web interface, the authors identify key transcriptional events in meristem founder cells, characterize complex branching mutants, and reconstruct spatio‑temporal trajectories of flower organogenesis, offering insights for targeted trait manipulation.

The study used phylogeny‑based analyses of 36 legume genomes and a newly created multiparent advanced generation intercross (MAGIC) population of common bean to predict and characterize genome‑wide deleterious mutations. Machine‑learning integration of conservation and protein features identified thousands of potentially deleterious sites, whose variation correlated negatively with flowering time, maturity, and yield, highlighting the impact of genetic load on breeding performance.

The authors introduce S²-PepAnalyst, a web-based tool that leverages plant-specific datasets and advanced machine learning to predict small signaling peptides (SSPs) with 99.5% accuracy and minimal false negatives. By integrating protein language models, geometric‑topological analysis, and reinforcement learning, the tool surpasses existing predictors such as SignalP 6.0 in classifying peptide families like CLE and RALF.

The study finely dissected shoot stem cell–enriched tissues from maize and Arabidopsis thaliana and optimized single‑cell RNA‑seq protocols to reliably capture CLAVATA3 and WUSCHEL‑expressing cells. Cross‑species comparison and functional validation, including spatial transcriptomics and mutant analyses, revealed conserved ribosome‑associated RNA‑binding proteins and sugar‑kinase families as key regulators linked to shoot development and yield traits.

The study used comparative transcriptomics across Erysimum species to identify two 2‑oxoglutarate‑dependent dioxygenases, CARD5 and CARD6, responsible for the 14β‑ and 21‑hydroxylation steps in cardenolide biosynthesis in Erysimum cheiranthoides. Knockout mutants lacking these genes accumulated pathway intermediates, and transient expression in Nicotiana benthamiana confirmed their enzymatic functions, while structural modeling pinpointed residues linked to neofunctionalization.

The study used comparative and de‑novo transcriptomic analyses in poplar to uncover plant and fungal gene regulons that govern ectomycorrhizal (ECM) compatibility, distinguishing general fungal‑sensing responses from ECM‑specific pathways. Key findings include modulation of jasmonic acid‑related defenses, coordinated regulation of secretory and cell‑wall remodeling genes, and dynamic expression of the Common Symbiosis Pathway during early and mature symbiosis stages.