Biology subjects

Fong, M.

Publications and source records attributed to Fong, M..

3 recordsLinked to original sources

bioRxiv2025

RBProximity-CLIP Enables Subcellular Mapping of RNA-Binding Protein Interactions at Nucleotide Resolution

RNA-binding proteins (RBPs) enable post-transcriptional gene regulation (PTGR) through specific interactions with RNA molecules, influencing processes ranging from nuclear processing and export to cytoplasmic localization, translation, storage and degradation. A key determinant of PTGR processes is the subcellular compartmentalization of RBPs, which dictates RNA targets they can access and the regulation performed in that environment. To characterize RBP-RNA interactions at subcellular resolution, we developed RBProximity-CLIP. RBProximity-CLIP enables compartment-specific isolation and profiling of individual RBP-RNA interactions by combining APEX2-based proximity labeling and 4-thiouridine-enhanced RNA-protein crosslinking, with sequential RBP- and biotin-affinity purifications. Using this approach, we profiled the RNA targets of three RBPs, AGO2, YBX1, and ELAVL1, across the cytoplasmic, nuclear, and nucleolar compartments, revealing nucleus-specific miRNA-mediated AGO2 targets, as well as subsets of YBX1 and ELAVL1 targets that differ by compartment, yet share identical binding motifs. RBProximity-CLIP enables specific and sensitive detection of compartment-specific RBP-RNA interactomes, thereby providing new insight into spatial gene regulation by RBPs.

cell biology↗

bioRxiv2025

Extended nuclear glycosylation is a common post-translational modification

In eukaryotes, glycans modify proteins in the secretory pathway and the extracellular space. Aside from nucleocytoplasmic O-GlcNAc, glycosylation is not considered a relevant post-translational modification in other cellular compartments. Here, we challenge this long-standing paradigm by showing that extended O-glycans are commonly found on intranuclear proteins. Through comprehensive genetic and biochemical analyses, we conclusively demonstrate that these O-glycans stem from the secretory pathway, yet are found on nuclear proteins across mammalian cell lines and primary cells. Using knock-out cell lines, we show mechanistically that nuclear glycans are shuttled to the nucleus via active vesicular transport. We identify several of these intranuclear glycoproteins as RNA-binding proteins, including KHSRP/FUBP2, RBM12, and RPP30. Lastly, we show that site-specific glycosylation of RPP30 is crucial for effective tRNA processing. Overall, our findings suggest a much broader role for glycosylation in regulating cellular functions and open up investigation into the role of glycans in more biological processes.

molecular 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↗