The study generated a single‑cell transcriptomic atlas of tomato adventitious root development, revealing that vascular tissues retain high developmental potential and that the DOF11‑LEA3 regulatory axis drives this process. Cross‑species integration shows tomato AR‑initiating cells share transcriptional programs with woody dicots but not Arabidopsis, suggesting AR competence is an ancestral vascular identity module. These results highlight tomato as a more representative model for AR biology and provide targets for improving vegetative propagation.

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 chemically induced effector-triggered immunity combined with single-cell transcriptomics to map immune responses across all leaf cell types in Arabidopsis, revealing that while a core defense program is universally activated, individual cell types deploy distinct transcriptional modules. Functional assays showed that epidermis‑specific transcriptional regulators are essential for preventing pathogen penetration, indicating a spatial division of immune functions within the leaf.

The study co‑expressed BABY BOOM and WUSCHEL2 in maize embryos and used single‑cell transcriptomics to infer cell‑type‑specific gene regulatory networks underlying induced somatic embryogenesis. By prioritizing and functionally validating four novel transcription factors, the authors enhanced maize transformation efficiency and produced fertile transgenic plants.

The study combined long‑read whole‑genome assembly, multi‑omics profiling (DNA methylation, mRNA, ribosome‑associated transcripts, tRNA abundance, and protein levels) in two Arabidopsis thaliana accessions to evaluate how genomic information propagates through the Central Dogma. Codon usage in gene sequences emerged as the strongest predictor of both mRNA and protein abundance, while methylation, tRNA levels, and ribosome‑associated transcripts contributed little additional information under stable conditions.

The study performed a comprehensive computational analysis of the Arabidopsis thaliana proteome, classifying 48,359 proteins by melting temperature (Tm) and melting temperature index (TI) and linking thermal stability to amino acid composition, molecular mass, and codon usage. Machine‑learning and evolutionary analyses revealed that higher molecular mass and specific codon pairs correlate with higher Tm, and that gene duplication has driven the evolution of high‑Tm proteins, suggesting a genomic basis for stress resilience.