Biology subjects

Charville, G.

Publications and source records attributed to Charville, G..

4 recordsLinked to original sources

bioRxiv2026

Hyaluronan and CD44 targeting reverses early matrix changes, proinflammatory signals and fibrosis in primary sclerosing cholangitis

Primary sclerosing cholangitis (PSC) is a rare, progressive liver disease characterized by biliary inflammation and bile duct strictures and no approved medical therapy. Despite its clinical severity, the pathological mechanisms underlying PSC remain poorly understood, largely due to early diagnostic challenges. Here we provide complementary evidence in human PSC samples, transcriptomic data, mouse models, and 3D cholangiocyte cultures that underscore the importance of hyaluronic acid (HA) and its cognate receptor CD44 in PSC pathogenesis. HA is a glycosaminoglycan abundant in the extracellular matrix in inflammatory disorders, yet its role in PSC has not been well characterized. We demonstrate that in early-stage PSC, cholangiocytes aberrantly produce high molecular weight HA that accumulates in the peribiliary matrix, increasing local tissue stiffness. This mechanical signal is transduced by a CD44/Integrin {beta}1 receptor complex in cholangiocytes, driving cell proliferation, YAP mechanosignaling, pro-inflammatory cytokine production with a transition to a ductular reactive phenotype. CD44 knockdown in cholangiocyte cell lines and mouse models significantly lowered stiffness, and attenuated inflammation. Together, these findings reveal a mechano-inflammatory axis in which HA-driven matrix stiffening perpetuates biliary inflammation and disease progression, identifying HA targeting and CD44 as promising therapeutic strategies. One Sentence SummaryHyaluronan and CD44 mediate matrix changes and progressive fibrosis in primary sclerosing cholangitis.

pathology↗

bioRxiv2025

Modeling patient tissues at molecular resolution with Eva

Tissue structure is essential to function and homeostasis in all organs, and disruptions to structure usually indicate disease. Modeling relationships between structural, molecular, and clinical aspects of tissues could advance new diagnostics and treatment strategies. Although profiling techniques like spatial proteomics can capture these relationships, the data remain challenging to extract insight from. Here, we present Eva, a foundation model for tissue imaging data that learns multi-scale spatial representations of tissues at the molecular, cellular, and sample level. Eva uses a novel vision transformer architecture and is pre-trained on masked reconstruction of over 40 million matched spatial proteomics and histopathology images. We show that Eva excels at a variety of tasks, including cross-modal inference from H&E to proteomics stains, quality control, data annotation, zero-shot retrieval, survival modeling, and patient stratification. Extensive evaluations on held-out validation data demonstrate the versatility and generalizability of the learned embeddings. We anticipate that Eva will accelerate translational science by bridging basic research and clinical practice.

bioinformatics↗

bioRxiv2025

Multidimensional Single-Cell Transcriptomic Profiling of Uterine Leiomyosarcomas Identifies Molecular Subtypes with Distinct Therapeutic Vulnerabilities

Uterine leiomyosarcoma (ULMS) is an orphan disease that frequently recurs and metastasizes, with patients undergoing multiple lines of chemotherapy due to lack of effective therapeutic targets. To address this gap, we used single-cell RNA sequencing and spatial transcriptomic analysis to comprehensively profile ULMS. We uncovered multiple states of tumor cells, including tumor cells with mesenchyme-like features, ischemic tumor cells defined by a MYC program, inflammatory tumor cells with active interferon signaling, and stem cell-like hormone receptor-positive cells. The inferred spatial correlates of these tumor cell states demonstrated unique localization patterns. By correlating these signatures to bulk RNA sequencing data, we demonstrate the relevance of these findings to clinical outcomes. Finally, using the single-cell integration and drug response prediction algorithm (scIDUC), we propose drug predictions that may target specific tumor states. Our findings suggest new avenues for further exploration of individualized and multifaceted therapeutic strategies to treat ULMS.

cancer biology↗

bioRxiv2023

A Spatiotemporal and Machine-Learning Platform Accelerates the Manufacturing of hPSC-derived Esophageal Mucosa

Human pluripotent stem cell-derived tissue engineering offers great promise in designer cell-based personalized therapeutics. To harness such potential, a broader approach requires a deeper understanding of tissue-level interactions. We previously developed a manufacturing system for the ectoderm-derived skin epithelium for cell replacement therapy. However, it remains challenging to manufacture the endoderm-derived esophageal epithelium, despite both possessing similar stratified structure. Here we employ single cell and spatial technologies to generate a spatiotemporal multi-omics cell atlas for human esophageal development. We illuminate the cellular diversity, dynamics and signal communications for the developing esophageal epithelium and stroma. Using the machine-learning based Manatee, we prioritize the combinations of candidate human developmental signals for in vitro derivation of esophageal basal cells. Functional validation of the Manatee predictions leads to a clinically-compatible system for manufacturing human esophageal mucosa. Our approach creates a versatile platform to accelerate human tissue manufacturing for future cell replacement therapies to treat human genetic defects and wounds.

developmental biology↗