Biology subjects

Moseley, J.

Publications and source records attributed to Moseley, J..

2 recordsLinked to original sources

bioRxiv2026

Rho1 and Rgf3 regulate the expansion of the nuclear envelope during fission yeast mitosis/cytokinesis

The nuclear envelope (NE) surrounds the genetic material and is continuous with the endoplasmic reticulum (ER). In yeast and other organisms undergoing closed mitosis, nuclear envelope expansion (NME) is strictly required to accommodate spindle elongation and ensure proper chromosome segregation within a single nuclear compartment. Failure to expand the NE during mitosis leads to chromosome missegregation. Here, we show that deletion of the unstructured N-terminal domain of Rgf3, a Rho1-specific guanine nucleotide exchange factor (GEF), causes early mitotic defects that produce the characteristic "cut" phenotype of untimely cell division. The rgf3{Delta}N2 mutant displays spindle buckling, a hallmark of anaphase nuclei unable to properly expand the NE. From yeast to mammals, phosphatidic acid (PA)--a key precursor in phospholipid biosynthesis--is metabolized via two competing pathways, the cytidine diphosphate-diacylglycerol (CDP-DAG) and the Kennedy pathways, both contributing to lipid membrane homeostasis. We provide evidence that impaired Rho1 activation in rgf3{Delta}N2 selectively disrupts phospholipid synthesis through the CDP-choline branch of the Kennedy pathway. Thus, Rho1 promotes mitotic progression by modulating phospholipid biosynthesis to enable efficient NME during anaphase. HighlightsThe N-terminus of Rgf3 is required for proper nuclear envelope expansion (NME) during anaphase. The structurally flexible N-terminal domain of Rgf3 is essential for localized Rho1 activation. Active Rho1 drives mitotic membrane growth by modulating phospholipid synthesis through the Kennedy pathway.

Cell Biology↗

bioRxiv2021

Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning

Understanding the spatial organization of tissues is of critical importance for both basic and translational research. While recent advances in tissue imaging are opening an exciting new window into the biology of human tissues, interpreting the data that they create is a significant computational challenge. Cell segmentation, the task of uniquely identifying each cell in an image, remains a substantial barrier for tissue imaging, as existing approaches are inaccurate or require a substantial amount of manual curation to yield useful results. Here, we addressed the problem of cell segmentation in tissue imaging data through large-scale data annotation and deep learning. We constructed TissueNet, an image dataset containing >1 million paired whole-cell and nuclear annotations for tissue images from nine organs and six imaging platforms. We created Mesmer, a deep learning-enabled segmentation algorithm trained on TissueNet that performs nuclear and whole-cell segmentation in tissue imaging data. We demonstrated that Mesmer has better speed and accuracy than previous methods, generalizes to the full diversity of tissue types and imaging platforms in TissueNet, and achieves human-level performance for whole-cell segmentation. Mesmer enabled the automated extraction of key cellular features, such as subcellular localization of protein signal, which was challenging with previous approaches. We further showed that Mesmer could be adapted to harness cell lineage information present in highly multiplexed datasets. We used this enhanced version to quantify cell morphology changes during human gestation. All underlying code and models are released with permissive licenses as a community resource.

bioinformatics↗