The study introduces a hybrid modeling framework that integrates a logistic ordinary differential equation with a Long Short-Term Memory neural network to form a Physics-Informed Neural Network (PINN) for predicting wheat plant height. Using only time and temperature as inputs, the PINN outperformed other longitudinal growth models, achieving the lowest average RMSE and reduced variability across multiple random initializations. The results suggest that embedding biological growth constraints within data‑driven models can substantially improve prediction accuracy for plant traits.

The review examines the genetic networks governing spikelet number per spike (SNS) in wheat, highlighting how the balance between inflorescence meristem activity and the timing of terminal spikelet transition determines yield potential. It discusses how mutations affecting meristem identity can create supernumerary spikelets, the trade-offs of such traits, and recent advances using spatial transcriptomics, single‑cell analyses, and multi‑omics to identify new SNS genes for breeding.

The study applied dual-species spatial transcriptomics at single-cell resolution to map plant and fungal gene activity in rice roots colonized by Rhizophagus irregularis, revealing transcriptional heterogeneity among morphologically similar arbuscules. By pioneering an AM-inducible TRAP-seq using stage‑specific promoters, the authors uncovered stage‑specific reprogramming of nutrient transporters and defence genes, indicating dynamic regulation of nutrient exchange and arbuscule lifecycle.

The study applied spatial transcriptomics to map the transcriptional landscape of wheat (Triticum aestivum) inflorescences during spikelet development, revealing two distinct regions—a RAMOSA2‑active primordium and an ALOG1‑expressing boundary. Developmental assays showed that spikelets arise from meristematic zones accompanied by vascular rachis formation, identifying key regulators that could be targeted to improve spikelet number and yield.

An optimized workflow was developed to apply the Xenium in situ sequencing platform to formalin‑fixed paraffin‑embedded (FFPE) sections of Medicago truncatula roots and nodules, incorporating customized tissue preparation, probe design, and imaging to overcome plant‑specific challenges such as cell wall autofluorescence. The protocol was validated across nodule developmental stages using both a 50‑gene panel for mature cell identity and an expanded 480‑gene panel covering multiple cell types, providing a scalable high‑resolution spatial transcriptomics method adaptable to other plant systems.

The study applied the STOmics spatial transcriptomics platform to map gene expression at subcellular resolution in developing wheat (Triticum aestivum) seeds during grain filling, analyzing over four million transcripts. Eight functional cellular groups were identified, including four distinct endosperm clusters with radial expression patterns and novel marker genes, and subgenome‑biased expression was observed among specific paralogs. These results highlight spatial transcriptomics as a powerful tool for uncovering tissue‑specific and polyploid‑specific gene regulation in seeds.

The study combined spatial transcriptomics and single-nuclei RNA sequencing to map soybean (Glycine max) responses to Asian soybean rust caused by Phakopsora pachyrhizi, revealing two distinct host cell states: pathogen‑occupied regions and adjacent non‑infected regions that show heightened defense gene expression. Gene co‑expression network analysis identified a key immune‑related module active in the stressed cells, highlighting a cell‑non‑autonomous defense mechanism.

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.