π° "Somatic cells non-autonomously control germline incomplete cytokinesis through FGF signaling"
https://doi.org/doi:10.64898/2026.06.04.729929https://pubmed.ncbi.nlm.nih.gov/42282524/ #Mitosis #CellSomatic cells non-autonomously control germline incomplete cytokinesis through FGF signaling
Across species, germ cells divide and differentiate as interconnected units, termed cysts. These cysts are generated through reiterative rounds of mitosis followed by incomplete cytokinesis to generate stable ring canals (RCs). Despite the ubiquity of germ cell incomplete cytokinesis, it is still unclear how this program is mechanistically regulated across multiple cell cycles to retain integrity of the cyst. Here, by leveraging longitudinal live imaging of the Drosophila testis we have identified a critical, non-autonomous role for somatic support cells in maintenance of germline RC stability. We find that F-actin at RCs is stable throughout interphase but is dynamically disassembled and reassembled at each reiterative mitotic entrance and exit. Importantly, we find that somatic cells regulate the stability of interphase RC F-actin through the secreted growth factor, FGF. Genetic or pharmacological inhibition of FGF signaling induces disassembly of RC F-actin during interphase. Persistent clearance of F-actin from the RC leads to failure of incomplete cytokinesis and cyst abscission, suggesting that stable F-actin at RCs is required for the robust maintenance of incomplete cytokinesis through multiple rounds of germ cell divisions. Finally, we mechanistically link FGF signaling to germline activity of the non-receptor tyrosine kinase, Src64, which is known to regulate RC F-actin through Arp2/3. Taken together, we find a previously unappreciated role for somatic support cells in controlling an essential aspect of germ cell biology in the mitotically dividing spermatogonial pool.
Summary Statement Somatic cells of the gonad secrete FGF ligand, Pyramus, which is required for maintenance of F-actin at germline ring canals and integrity of germline incomplete cytokinesis.
### Competing Interest Statement
The authors have declared no competing interest.
National Institutes of Health, R01 GM138705
Hevolution Foundation, HF-GRO-23-1199154-38
π° "Somatic cells non-autonomously control germline incomplete cytokinesis through FGF signaling"
https://www.biorxiv.org/content/10.64898/2026.06.04.729929v1?rss=1 #Mitosis #Cellπ° "Functional Interaction of SKA and NDC80 Complexes at Kinetochores Promoting Anaphase Onset in Mitosis"
https://doi.org/doi:10.64898/2026.05.22.727258https://pubmed.ncbi.nlm.nih.gov/42239309/ #Mitosis #Cellπ° "LFCT: A Benchmark Dataset for Low-Frame-Rate Cell Tracking in Long-Term Live-Cell Microscopy"
https://www.biorxiv.org/content/10.64898/2026.05.30.728955v1?rss=1 #Mitosis #Cellπ° "Semi supervised GAN for smart microscopy, fast and data efficient cell cycle classification"
https://arxiv.org/abs/2604.20615 #Q-Bio.Qm
#Mitosis #Cell
Semi supervised GAN for smart microscopy, fast and data efficient cell cycle classification
Modern optical microscopes are fully motorised; however, transforming them into truly smart systems requires real-time adjustment of acquisition settings in response to detected objects and dynamic biological events. At the core are classification algorithms that commonly depend on customised software and are generally designed for narrowly-defined biological applications. In addition, they often require substantial annotated datasets for effective training. We introduce a semi-supervised generative adversarial network (SGAN) for robust cell-cycle stage classification under low-resource conditions, adaptable to diverse cellular structures. The framework combines unlabelled microscopy images with synthetically generated samples to mitigate limited annotation, while preserving stable performance even when the unlabelled subset is class-imbalanced. Tested on the Mitocheck dataset, which features five mitosis classes, the model achieved $93 \pm 2\%$ accuracy using only 80 labelled per class and 600 unlabelled images. The proposed algorithm is generic and can be readily adapted to new labeling schemes, classification targets, cell lines, or microscopy modalities through transfer learning. SGAN is well suited for integration into automated microscopes, enabling efficient and adaptable image analysis across diverse biological and microscopy applications.
π° "Genetic control of mitotic-to-meiotic transition regulates germline cell survival"
https://www.biorxiv.org/content/10.64898/2026.05.28.728457v1?rss=1 #Mitosis #Cellπ° "A Novel Drug Candidate that Selectively Targets the Critical Androgen Receptor-ELK1 Growth Axis in Advanced and Drug-Resistant Prostate Cancer"
https://www.biorxiv.org/content/10.64898/2026.05.26.727474v1?rss=1 #Mitosis #Cellπ° "ATR kinase inhibitors induce mitochondrial fission in CD8+ T cells and impair immune memory in vivo"
https://www.biorxiv.org/content/10.64898/2026.05.25.727628v1?rss=1 #Mitosis #Cell
Rxiv mechanobio