The study benchmarked over 20 web‑based gRNA on‑target efficiency prediction tools against an experimental plant CRISPR editing dataset, finding several machine‑learning based tools whose scores strongly correlated with observed InDel frequencies. Additionally, the performance of popular platforms such as CRISPOR and CRISPR‑P was assessed, offering guidance for improved gRNA design in plant genome editing.

The study generated deep proteome and phosphoproteome datasets from guard cell‑enriched tissue to examine how phosphorylation regulates stomatal movements. Comparative analysis revealed increased phosphorylation of endomembrane trafficking and vacuolar proteins in closed stomata, supporting a role for phospho‑regulated trafficking in stomatal dynamics.

The study examined seed maturation and germination in the endangered legume Paubrasilia echinata using proteomic and polyamine analyses at 4, 6, and 8 weeks post-anthesis, identifying over 2,000 proteins and linking specific polyamines to developmental stages. Mature seeds (6 weeks) showed elevated proteasome components, translation machinery, LEA proteins, and heat shock proteins, while polyamine dynamics revealed putrescine dominance in early development and spermidine/spermine association with desiccation tolerance and germination. These findings uncover dynamic molecular shifts underlying seed development and provide insights for conservation and propagation.

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 provides a comprehensive proteomic analysis of seed mitochondria from white lupin, revealing fully assembled OXPHOS complexes ready for immediate energy production upon imbibition. Quantitative mass‑spectrometry identified 1,162 mitochondrial proteins, highlighting tissue‑specific transporter and dehydrogenase profiles and dynamic remodeling during early germination, while many uncharacterized proteins suggest novel legume‑specific functions.

The study examined how prior light‑acclimation influences the fitness and rapid photoprotective reprogramming of Chlamydomonas during transitions between low and high diurnal light intensities. While high‑light‑acclimated cells struggled to grow and complete the cell cycle after shifting to low light, low‑light‑acclimated cells quickly remodeled thylakoid ultrastructure, enhanced photoprotective quenching, and altered photosystem protein levels, recovering chloroplast function within a single day. Transcriptomic and proteomic profiling revealed swift induction of stress‑response genes, indicating high flexibility in diurnal light acclimation.

The study introduces a native‑condition method combining cell fractionation and immuno‑isolation to purify autophagic compartments from Arabidopsis, followed by proteomic and lipidomic characterisation of the isolated phagophore membranes. Proteomic profiling identified candidate proteins linked to autophagy, membrane remodeling, vesicular trafficking and lipid metabolism, while lipidomics revealed a predominance of glycerophospholipids, especially phosphatidylcholine and phosphatidylglycerol, defining the unique composition of plant phagophores.

The study integrated weekly morphophysiological measurements with high-density genotyping-by-sequencing data and a machine‑learning pipeline to dissect flowering time variation in diverse Cannabis sativa landraces. By applying mutual information, recursive feature elimination, random forest, and support vector machine classifiers to over 234,000 combined genetic, phenotypic, and environmental features, the authors identified 53 key markers that classify early, medium, and late flowering types with 96.6% accuracy. Notable loci, including CsFT3 and CsCFL1, were highlighted as promising targets for breeding and smart‑crop strategies.

The study used comparative proteomics to examine how the emerging 15ADON/3ANX chemotype of Fusarium graminearum affects protein expression in both wheat and the fungus. It identified a core wheat proteome altered by infection, chemotype‑specific wheat proteins, and fungal proteins linked to virulence and ergosterol biosynthesis, revealing distinct molecular responses influencing disease severity.

The study used extensive gravimetric load‑cell and ambient sensor data collected over seven years from hundreds of greenhouse-grown crops to train machine‑learning models for predicting daily whole‑plant transpiration. Random Forest and XGBoost achieved the highest accuracy (R² up to 0.89), with ambient temperature identified as the dominant driver. These results highlight the promise of ML‑based tools for precise agricultural water management.