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 presents a deep‑learning pipeline that uses state‑of‑the‑art convolutional neural networks to automatically estimate the establishment of perennial groundcovers in agricultural research plots from smartphone images. By employing region‑of‑interest markers and deploying the models on AWS SageMaker with a lightweight Django web interface, the approach provides fast, objective, and reproducible assessments that can be adopted by researchers and growers across the Midwest.

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 used an interpretable convolutional neural network to predict ABA responsiveness from proximal promoter sequences in Arabidopsis thaliana, revealing both known ABF-binding motifs and novel regulatory elements. Model performance was boosted by advanced data augmentation, and predicted regulatory regions were experimentally validated using reporter lines, confirming the inferred cis‑regulatory code.

The study reveals that leaf wounding in Arabidopsis triggers localized cooling and activation of cold-responsive genes, with the subsequent loss of cooling marking wound healing. It identifies CBF transcription factors as mediators of this cooling signal and introduces a computer‑vision and deep‑learning workflow to quantitatively monitor healing dynamics.

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 examines how the SnRK1 catalytic subunit KIN10 integrates carbon availability with root growth regulation in Arabidopsis thaliana. Loss of KIN10 reduces glucose‑induced inhibition of root elongation and triggers widespread transcriptional reprogramming of metabolic and hormonal pathways, notably affecting auxin and jasmonate signaling under sucrose supplementation. These findings highlight KIN10 as a central hub linking energy status to developmental and environmental cues in roots.