Frederik De Smet

Heterogeneous and conserved radiation responses reveal FOXM1-dependent regulation of microcephaly genes in glioblastoma

The paper identifies FOXM1-dependent regulation of microcephaly genes in glioblastoma radiation response, showing that FOXM1 inhibition enhances radiosensitivity.

Unravelling resistance mechanisms of oncolytic viruses in glioblastoma

This study identifies cellular processes driving oncolytic virus resistance in glioblastoma by analyzing resistant subclones from patient-derived cell lines.

Macrophage-instructed GSDME couples glioblastoma cell-state plasticity with inflammatory cell death

This paper identifies GSDME-FL as a macrophage-instructed regulator of glioblastoma cell-state plasticity and inflammatory cell death, showing its role in tumor progression and immune response.

Microglial lipid signaling drives glioblastoma invasion and represents a therapeutic vulnerability

This study identifies a lipid-mediated signaling pathway driven by microglia that promotes glioblastoma invasion and shows that targeting this pathway can suppress tumor invasion.

Longitudinal tumor ecosystem mapping defines glioblastoma treatment trajectories

This study maps glioblastoma tumor ecosystems over time using spatial single-cell profiling to identify prognostic subgroups and treatment responses.

Distinct tumor immune microenvironmental (TIME) landscapes drive divergent immunotherapy responses in glioblastoma

This study identifies three distinct tumor immune microenvironment (TIME) subtypes in glioblastoma (GBM) with divergent responses to immunotherapy, supporting personalized treatment strategies.

CLEAR-AF: Improved Autofluorescence Subtraction for Multiplexed Tissue Imaging via Polar Transformation and Gaussian Mixture Models

CLEAR-AF improves autofluorescence subtraction in multiplexed tissue imaging by using polar transformation and Gaussian Mixture Models, enhancing specificity, sensitivity, and reproducibility.

CONCLAVE: CONsensus CLustering with Annotation-Validation Extrapolation for cyclic multiplexed immunofluorescence data

CONCLAVE improves cell annotation in cyclic multiplexed immunofluorescence data by using consensus clustering and validation, enhancing accuracy and reproducibility.

PAC-MAP: Proximity Adjusted Centroid Mapping for Accurate Detection of Nuclei in Dense 3D Cell Systems

PAC-MAP improves nucleus detection in dense 3D cell systems by predicting centroid positions and proximity, achieving high recall and F1 scores in spheroids and organoids.

COLLAGE: COnsensus aLignment of muLtiplexing imAGEs

COLLAGE improves image alignment for multiplexed immunohistochemistry by breaking alignment into smaller puzzles, enabling parallel processing and reducing errors.

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

QUAL-IF-AI is a deep learning tool that detects and manages artifacts in multiplex immunofluorescence images, achieving high accuracy and IoU across common artifact types.