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 evaluated 11 intermediate phenotypes linked to stalk lodging resistance in a diverse panel of 566 maize (Zea mays L.) inbred lines across four environments, preserving individual stalk identity to capture plant-level variation. This high-density phenotypic dataset enabled statistical genomics, predictive modeling, and machine learning to uncover genetic factors underlying lodging resistance, offering insights applicable to other grass species.

Phylogenetic analysis reveals that non‑seed plants, exemplified by the liverwort Marchantia polymorpha, possess a streamlined repertoire of cyclin and CDK genes, with only three cyclins active in a phase‑specific manner during vegetative development. Single‑cell RNA‑seq and fluorescent reporter assays, combined with functional overexpression studies, demonstrate the distinct, non‑redundant roles of MpCYCD;1, MpCYCA, and MpCYCB;1 in G1 entry, S‑phase progression, and G2/M transition, respectively.

The study utilizes explainable artificial intelligence (XAI) combined with machine learning to assess how inter‑annual weather variability influences oilseed sunflower yields across the United States from 1976 to 2022. Key climate predictors, especially summer maximum temperature and total precipitation, were identified, and predictive models were projected under various Shared Socioeconomic Pathways to 2080, revealing region‑specific yield declines.