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Houbart, W.

Publications and source records attributed to Houbart, W..

2 recordsLinked to original sources

A CRISPR-based Xenopus tropicalis model for retroperitoneal liposarcoma with genetic control over the dedifferentiation process

Well- and dedifferentiated liposarcomas (WDLPS and DDLPS) are characterized by extensive copy- number alterations rather than recurrent gene-inactivating mutations, obscuring the molecular mechanisms that drive disease progression and, critically, the transition from well-differentiated to the more aggressive dedifferentiated tumor states. Despite marked differences in clinical behavior and prognosis, the regulatory events underlying adipocytic lineage destabilization in DDLPS remain poorly understood. Here, we establish an in vivo model of retroperitoneal liposarcoma in Xenopus tropicalis through early embryonic mosaic perturbation of p53 and Rb pathway components. Combined disruption reproducibly induced retroperitoneal WDLPS development, demonstrating that pathway-level deregulation of the MDM2-p53 and CDK4-Rb axes is sufficient to initiate liposarcoma development in vivo. Strikingly, additional perturbation of transcriptional co-activator ep300 in this context resulted in increased tumor dedifferentiation, yielding lesions composed of spatially coexisting well- and dedifferentiated adipocytic states. In contrast, direct targeted disruption of downstream adipogenic regulators recurrently lost in human DDLPS, including cebpa, g0s2, and dgat2, failed to induce dedifferentiation in the same genetic context in vivo. These findings indicate that dedifferentiation cannot be explained by loss of downstream adipocytic effectors alone but instead reflects destabilization of higher-order regulatory programs governing adipocytic identity. Together, these results establish an in vivo model that closely reflects the clinical situation on a pathway level and provides initial mechanistic insight into how adipocytic differentiation may become destabilized during disease progression. This framework offers a foundation for future studies leveraging higher-order and multi-omic approaches to dissect the molecular processes underlying the WDLPS-to-DDLPS transition.

cancer biology↗

Deep learning enables feature extraction of 3D collagen architecture in cleared fibrotic tissues

Light-sheet fluorescence microscopy enables deep optical sectioning of large, cleared biological tissues. However, effective clearing of collagen-rich tissues remains a persistent technical challenge. Moreover, standardized workflows integrating three-dimensional imaging with computational analysis of collagen architecture are currently unavailable. Here, we present an integrated pipeline combining optimized tissue clearing, volumetric light-sheet imaging, and deep learning-based feature extraction of collagen architecture. Using experimental desmoid tumors as a proxy for collagen-dense tissues, we optimised DISCO-based clearing incorporating Fast Green FCF for collagen labelling. We achieved optical transparency and full 3D visualization of collagen architectures in desmoid tumors, human skin biopsies, and fibrotic mouse lung & liver tissues, including FFPE samples. Using the Benchtop mesoSPIM platform, we acquired high-resolution volumetric datasets and validated multimodal collagen imaging through two-photon microscopy with the Schmidt-Voigt objective. To enable automated feature extraction from these large volumetric datasets, we developed ColNet, a U-Net model for automated collagen fiber segmentation. ColNet demonstrated robust generalization across diverse human and mouse tissues without retraining or hyperparameter adjustment. This integrated workflow provides a foundation for future quantitative assessment of cell-extracellular matrix dynamics in a fibrotic context.

cancer biology↗