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Biology subjects

Jena, R.

Publications and source records attributed to Jena, R..

2 recordsLinked to original sources

Comparative metabolomics of released pollen during dispersal reveals metabolic adaptations to cold and heat stress

Heat and cold stress can severely impair released pollen, reducing pollen viability and ultimately limiting fertilization and crop productivity. Following release, pollen is directly exposed to fluctuating environmental temperatures, necessitating adaptive metabolic mechanisms to sustain viability during dispersal. In response to temperature stress, pollen undergoes metabolic reprogramming that supports biochemical and physiological adaptation. However, the metabolic basis of pollen tolerance to extreme temperatures remains incompletely understood. In this study, biochemical assays combined with LC-MS-based untargeted metabolomic profiling were employed to investigate metabolic changes in released pollen exposed to low (15 {degrees}C) and high (35 {degrees}C) temperature stress. A total of 484 metabolites were detected, of which 147 were significantly altered between cold- and heat-stressed pollen, including 61 upregulated and 86 downregulated metabolites. Differentially regulated metabolites spanned multiple classes, including amino acids, flavonoids, long-chain fatty acids, sugars, and polyamines. Pathway enrichment analysis highlighted biologically relevant perturbations in amino acid and nucleic acid metabolism, including purine metabolism, arginine biosynthesis, and glutathione metabolism. Collectively, these findings demonstrate temperature-dependent metabolic adjustments in released pollen and provide new insights into the biochemical strategies underlying pollen tolerance to heat and cold stress. This work advances our understanding of pollen metabolic adaptation during dispersal and provides a foundation for identifying metabolic indicators relevant to crop fertility under changing climatic conditions. These findings provide a metabolomic framework for understanding pollen thermotolerance during dispersal and offer a resource for future studies on reproductive resilience under climate change.

plant biology↗

CenSegNet: a generalist high-throughput deep learning framework for centrosome phenotyping at spatial and single-cell resolution in heterogeneous tissues

Centrosome abnormalities (CA) are a hallmark of epithelial cancers, yet their spatial complexity and phenotypic heterogeneity remain poorly resolved due to limitations in conventional image analysis. We present CenSegNet (Centrosome Segmentation Network), a modular deep learning framework for high-resolution, context-aware segmentation of centrosomes and epithelial architecture across diverse tissue types. Integrating a dual-branch architecture with uncertainty-guided refinement, CenSegNet achieves state-of-the-art performance and generalisability across both immunofluorescence and immunohistochemistry modalities, outperforming existing models in accuracy and morphological fidelity. Applied to tissue microarrays (TMAs) containing 911 breast cancer sample cores from 127 patients, CenSegNet enables the first large-scale, spatially resolved quantification of numerical and structural CA at single-cell resolution. These CA subtypes are mechanistically uncoupled, exhibiting distinct spatial distributions, age-dependent dynamics, and associations with histological tumour grade, hormone receptor status, genomic alterations, and nodal involvement. Structural CA levels are additionally associated with overall survival, supporting the clinical relevance of spatially resolved CA patterns. Discordant CA profiles at tumour margins are linked to local aggressiveness and stromal remodelling. To support broad adoption and reproducibility, CenSegNet is released as an open-source Python library. Together, our findings establish CenSegNet as a scalable, generalisable platform for spatially resolved centrosome phenotyping in intact tissues, enabling systematic dissection of the biology of this organelle and its dysregulation in cancer and other epithelial diseases.

cell biology↗