The study investigates the gene regulatory network (GRN) controlling flowering time in the allotetraploid crop Brassica napus by comparing its transcriptome to that of Arabidopsis thaliana. While most orthologous gene pairs show conserved expression dynamics, several flowering‑time genes display regulatory divergence, especially under cold conditions, indicating subfunctionalisation among paralogues. Despite these differences, the overall GRN topology remains similar to Arabidopsis, likely due to retention of multiple paralogues.

The authors used a bottom‑up thermodynamic modelling framework to investigate how plants decode calcium signals, starting from Ca2+ binding to EF‑hand proteins and extending to higher‑order decoding modules. They identified six universal Ca2+-decoding modules that can explain variations in calcium sensitivity among kinases and provide a theoretical basis for interpreting calcium signal amplitude and frequency in plant cells.

The study used comparative transcriptomics of dorsal and ventral petals across development, alongside expression profiling in floral symmetry mutants, to identify genes linked to dorsal (AmCYC-dependent) and ventral (AmDIV-dependent) identities in Antirrhinum majus. In situ hybridisation validated axis‑specific and boundary‑localized expression patterns, revealing that a conserved NGATHA‑LIKE1‑BRASSINAZOLE‑RESISTANT1‑miR164 module has been co‑opted to regulate AmDIV targets and shape the corolla. These findings delineate regulatory modules coordinating dorsoventral and proximal‑distal patterning in zygomorphic flowers.

The study sequenced genomes of ericoid mycorrhiza‑forming liverworts and experimentally reconstituted the symbiosis, revealing a nutrient‑regulated state that supports intracellular colonization. Comparative transcriptomics identified an ancestral gene module governing intracellular symbiosis, and functional validation in Marchantia paleacea through genetic manipulation, phylogenetics, and transactivation assays confirmed its essential role. The findings suggest plants have retained and independently recruited this ancestral module for diverse intracellular symbioses.

The study evaluates the use of single-cell RNA sequencing (scRNA-seq) data to predict plant metabolic pathway genes (MPGs) in Arabidopsis thaliana, comparing five multi-label machine‑learning algorithms against traditional bulk RNA‑seq approaches. scRNA‑seq generated co‑expression networks that, while different, yielded significantly higher MPG classification accuracy, especially when data were split by genetic background or tissue type, and deep learning outperformed classical methods. The authors conclude that scRNA‑seq offers superior predictive power and should be incorporated into future MPG discovery pipelines.

The study used single‑cell transcriptomics to compare Arabidopsis thaliana leaf cell responses during pattern‑triggered and effector‑triggered immunity, revealing that core defense modules are broadly shared but differ in timing, intensity, and cell‑type specific receptor dynamics. Distinct mesophyll subpopulations showed divergent resilience patterns, and gene regulatory network analysis identified WRKY‑regulated and salicylic‑acid biosynthesis modules, with the cue1-6 mutant confirming robustness of core immune responses while exposing cryptic sucrose‑responsive pathways.

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 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 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.