Biology subjects

Decraene, B.

Publications and source records attributed to Decraene, B..

3 recordsLinked to original sources

bioRxiv2026

Longitudinal tumor ecosystem mapping defines glioblastoma treatment trajectories

Glioblastoma remains an invariably recurring and lethal brain tumor shaped by complex interactions between malignant and microenvironmental cells. How these interactions evolve under first-line standard-of-care (SOC) therapy remains unclear. We performed spatial single-cell profiling of 671 paired newly diagnosed and recurrent glioblastoma samples from 96 patients to map tumor-ecosystem evolution and its clinical relevance. We delineated five distinct patient subgroups, each characterized by unique ecosystem trajectories that correlated with clinical outcome. Patients whose tumors transitioned into oligodendrocyte-progenitor-like niches with enhanced vascular integrity, oxygenation and an IL10/CCL/MHCII immunomodulatory environment experienced improved clinical prognosis. In contrast, progression towards hypoxic mesenchymal/astrocyte-like niches dominated by strong LGALS1/SPP1/TGF{beta} immunosuppression predicted poor outcome. Furthermore, a subgroup of early-relapsing patients on SOC therapy that was characterized by a depletion of an antigen-presenting myeloid-cell-rich niche, exhibited diminished responsiveness to second-line lomustine therapy. Overall, mapping evolutions in glioblastoma ecosystems offers a novel framework for prognostic stratification and therapeutic guidance.

cancer biology↗

bioRxiv2024

QUAL-IF-AI: Quality Control of Immunofluorescence Images using Artificial Intelligence

Fluorescent imaging has revolutionized biomedical research, enabling the study of intricate cellular processes. Multiplex immunofluorescent imaging has extended this capability, permitting the simultaneous detection of multiple markers within a single tissue section. However, these images are susceptible to a myriad of undesired artifacts, which compromise the accuracy of downstream analyses. Manual artifact removal is impractical given the large number of images generated in these experiments, necessitating automated solutions. Here, we present QUAL-IF-AI, a multi-step deep learning-based tool for automated artifact identification and management. We demonstrate the utility of QUAL-IF-AI in detecting four of the most common types of artifacts in fluorescent imaging: air bubbles, tissue folds, external artifacts, and out-of-focus areas. We show how QUAL-IF-AI outperforms state-of-the-art methodologies in a variety of multiplexing platforms achieving over 85% of classification accuracy and more than 0.6 Intersection over Union (IoU) across all artifact types. In summary, this work presents an automated, accessible, and reliable tool for artifact detection and management in fluorescent microscopy, facilitating precise analysis of multiplexed immunofluorescence images.

bioinformatics↗

bioRxiv2023

Single-cell profiling and zebrafish avatars reveal LGALS1 as immunomodulating target in glioblastoma

Glioblastoma (GBM) remains the most malignant primary brain tumor, with a median survival rarely exceeding 2 years. Tumor heterogeneity and an immunosuppressive microenvironment are key factors contributing to the poor response rates of current therapeutic approaches. GBM-associated macrophages (GAMs) often exhibit immunosuppressive features that promote tumor progression. However, their dynamic interactions with GBM tumor cells remain poorly understood. Here, we used patient-derived GBM stem cell cultures and combined single-cell RNA sequencing of GAM-GBM co-cultures and real-time in vivo monitoring of GAM-GBM interactions in orthotopic zebrafish xenograft models to provide insight into the cellular, molecular, and spatial heterogeneity. Our analyses revealed substantial heterogeneity across GBM patients in GBM-induced GAM polarization and the ability to attract and activate GAMs - features that correlated with patient survival. Differential gene expression analysis, immunohistochemistry on original tumor samples, and knock-out experiments in zebrafish subsequently identified LGALS1 as a primary regulator of immunosuppression. Overall, our work highlights that GAM-GBM interactions can be studied in a clinically relevant way using co-cultures and avatar models, while offering new opportunities to identify promising immune-modulating targets.

cancer biology↗