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 investigates the role of the chromatin regulator MpSWI3, a core subunit of the SWI/SNF complex, in the liverwort Marchantia polymorpha. A promoter mutation disrupts male gametangiophore development and spermiogenesis, causing enhanced vegetative propagation, and transcriptomic analysis reveals that MpSWI3 regulates genes controlling reproductive initiation, sperm function, and asexual reproduction, highlighting its ancient epigenetic role in balancing vegetative and reproductive phases.

The study examined drought tolerance across nine provenances of the conifer Pinus nigra using high‑throughput phenotyping combined with metabolomic and transcriptomic analyses under controlled soil‑drying conditions. Drought tolerance, measured by the decline in Fv/Fm, varied among provenances but was not linked to a climatic gradient and was independent of growth, with tolerant provenances showing distinct flavonoid and diterpene profiles and provenance‑specific gene expression patterns. Integrating phenotypic and molecular data revealed metabolic signatures underlying drought adaptation in this non‑model conifer.

RNA sequencing of the halophyte Salicornia europaea revealed that combined hypoxia‑salt stress triggers a unique transcriptional response, with 16% of genes specifically altered and distinct synergistic, antagonistic, and additive effects across functional pathways. Metabolic analyses indicated enhanced sucrose and trehalose metabolism, a shift toward lactate fermentation, and increased proline synthesis, highlighting complex regulatory strategies for coping with concurrent stresses.

The study examined how the bioinoculant Trichoderma afroharzianum T22 influences Pisum sativum growth under iron-sufficient versus iron-deficient conditions, finding pronounced benefits—enhanced photosynthesis, Fe/N accumulation, and stress‑related gene expression—only during iron deficiency. RNA‑seq revealed distinct gene expression patterns tied to symbiosis, iron transport, and redox pathways, and microbiome profiling showed T22 reshapes the root bacterial community under deficiency, suggesting context‑dependent mutualism.

In a controlled dry-down experiment, Arabis sagittata showed significantly higher recovery from drought than the endangered Arabis nemorensis, a difference that could not be traced to a single major QTL, indicating a polygenic basis. Transcriptome and small‑RNA sequencing revealed that A. sagittata mounts a stronger transcriptional response, including species‑specific regulation of the conserved drought miRNA miR408, and machine‑learning identified distinct cis‑regulatory motif patterns underlying these divergent stress‑response networks.

The study characterizes tissue-specific elimination of B chromosomes in Sorghum purpureosericeum during embryo development, identifying 28 candidate genes linked to this process. Integrated in situ visualization, genome sequencing, and transcriptomic analyses reveal that the B chromosome originates from multiple A chromosomes, harbors unique repeats, and expresses divergent kinetochore components that likely mediate its selective removal.

The study evaluated whether integrating genomic, transcriptomic, and drone-derived phenomic data improves prediction of 129 maize traits across nine environments, using both linear (rrBLUP) and nonlinear (SVR) models. Multi-omics models consistently outperformed single-omics models, with transcriptomic data especially enhancing cross‑environment predictions and capturing genotype‑by‑environment interactions. The results highlight the added value of combining transcriptomics and phenomics with genotypes for more accurate and generalizable trait prediction in maize.

Phytoplasma infection in sesame (Sesamum indicum) triggers tissue-specific alterations in gene expression and metabolite composition, with floral organs adopting leaf-like traits and distinct changes in porphyrin, brassinosteroid, and phenylpropanoid pathways. Integrated transcriptomic and metabolomic analyses, supported by biochemical, histological, and qRT-PCR assays, reveal differential stress and secondary metabolite responses between infected leaves and flowers.