Phosphite (Phi) and phosphate (Pi) share the same root uptake system, but Phi acts as a biostimulant that modulates plant growth and disease resistance in a species‑ and Pi‑dependent manner. In Arabidopsis, Phi induces hypersensitive‑like cell death and enhances resistance to Plectosphaerella cucumerina, while in rice it counteracts Pi‑induced susceptibility to Magnaporthe oryzae and Fusarium fujikuroi, accompanied by extensive transcriptional reprogramming.

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 authors compiled and standardized published data on Rubisco dark inhibition for 157 flowering plant species, categorizing them into four inhibition levels and analyzing phylogenetic trends. Their meta‑analysis reveals a complex, uneven distribution of inhibition across taxa, suggesting underlying chloroplast microenvironment drivers and providing a new resource for future photosynthesis improvement efforts.

PlantCV v4 is an open-source Python framework that simplifies image-based plant phenotyping by providing extensive tutorials and streamlined installation, enabling users with limited coding skills to automate trait extraction. The release adds support for fluorescence, thermal, and hyperspectral imaging and introduces a new subpackage for morphological measurements such as leaf angle, which is validated against manual data collection methods.

The study combined high-throughput image-based phenotyping with genome-wide association studies to uncover the genetic architecture of tolerance to the spittlebug Aeneolamia varia in 339 interspecific Urochloa hybrids. Six robust QTL were identified for plant damage traits, explaining up to 21.5% of variance, and candidate genes linked to hormone signaling, oxidative stress, and cell‑wall modification were highlighted, providing markers 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 introduces a deep‑learning based image analysis pipeline that scores blackleg disease severity from stem cross‑section images of canola species, achieving greater consistency than median expert raters while preserving comparable heritability of susceptibility traits. This standardized scoring method aims to improve selection of resistant varieties in breeding programs.

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.