Biology subjects

De Smet, F.

Publications and source records attributed to De Smet, F..

15 recordsLinked to original sources

bioRxiv2026

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

Glioblastoma (GBM) is characterized by marked heterogeneity, glioma stem-like cells (GSCs), and resistance to therapy. Because GSCs share features with neural progenitor cells (NPCs), we investigated whether neurodevelopmental programs contribute to their response to irradiation. Transcriptional profiling of four patient-derived GSC lines revealed cell line-specific responses, with radiosensitivity correlating with the magnitude of p53 activation and basal expression of its negative regulator, MDM2. Despite this heterogeneity, radiation consistently activated p53-dependent pathways and suppressed cell-cycle programs. Among these, genes associated with primary hereditary microcephaly (MCPH) that regulate NPC proliferation were coordinately repressed. Single-cell RNA sequencing localized this response to G2/M-cycling cells. FOXM1 was similarly reduced following irradiation, emerged as a candidate regulator of a subset of MCPH genes, and correlated with their expression in GBM tumors. Pharmacological inhibition of FOXM1 reduced expression of selected MCPH genes and enhanced radiosensitivity in U251 cells. Together, these findings identify coordinated suppression of a FOXM1-associated MCPH program as part of the GBM radiation response, while suggesting that the radiosensitizing effects of pharmacological FOXM1 inhibition extend beyond this transcriptional axis.

cancer biology↗

bioRxiv2026

Unravelling resistance mechanisms of oncolytic viruses in glioblastoma

Glioblastoma (GBM) is an aggressive primary brain tumor with a major unmet medical need. Oncolytic viruses (OVs) show promise for GBM treatment, but complete remissions remain rare. The intratumoral heterogeneity of GBM drives therapeutic escape and emergence of OV-resistant subclones. Beyond the well-characterized interferon-mediated antiviral response, mechanisms driving OV resistance remain poorly understood. To identify new markers of tumor-intrinsic OV resistance in GBM, we exposed 14 GBM patient-derived cell lines (GBM-PDCLs) to 6 OVs and generated virus-resistant subpopulations from surviving cells. Focusing on Sindbis (SINV)- and H1-parvovirus (H1PV)-resistant cells, we showed that resistance is associated with impaired viral replication. Gene set enrichment analysis of transcriptomic profiles revealed that resistance to both SINV and H1PV correlated with downregulated glutamate receptor signaling. In contrast, collagen fibril organization was downregulated in SINV-resistant GBM PDCLs but upregulated in H1PV-resistant cells. Functional validation confirmed opposing effects of collagen degradation on SINV and H1PV oncolytic activity. One SINV-resistant GBM-PDCL showed cross-resistance to multiple OVs, which was associated with increased expression of antiviral immunity genes and increased dependence on type I interferon signaling for survival. Together, these findings reveal shared and virus-specific cellular processes driving OV resistance in GBM, providing a basis for strategies to overcome resistance.

cancer biology↗

bioRxiv2026

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

Glioblastoma (GBM) cell states reflect spatial microenvironmental interactions. Here, using COMET spatial proteomics and RNAscope across multiregional human GBM tissue, spanning tumor cores with pseudopalisading regions, infiltrative margins and peripheral regions, together with multiplex spatial profiling of 202 specimens from 33 patients with matched primary and recurrent tumors, we identify full-length gasdermin E (GSDME-FL) as a macrophage-instructed regulator of malignant cell plasticity. Integration with single-cell transcriptomics and functional perturbation shows that GSDME-high tumor cells localize to macrophage-rich perivascular niches and are associated with delayed recurrence and longer patient survival. Mechanistically, macrophage-derived S100A4 activates EGFR-Sp1 signaling to induce GSDME-FL in neighboring GBM cells. GSDME-FL restrains hypoxia-associated mesenchymal plasticity and shapes macrophage-induced tumor state transitions independently of caspase activation. During immunogenic cell death (ICD), cleaved GSDME promotes pre-lytic swelling, early ATP efflux, and the release of canonical ICD-associated cytokines and chemokines. NLRP3 signaling further supports ATP release and licenses macrophage phagocytosis of dying GBM cells, whereas NINJ1-dependent membrane rupture enables terminal HMGB1 release. Vaccination with ICD-treated glioma cells elicits tumor rejection in a prophylactic intracranial challenge model. Notably, co-expression of GSDME and NINJ1 in macrophage-rich perivascular niches provides a spatial correlate of pathway convergence in patient tumors. Thus, our findings define how a spatial macrophage niche induces GSDME-FL, thereby coupling malignant cell state plasticity to the inflammatory properties of GBM cell death.

cancer biology↗

bioRxiv2026

Microglial lipid signaling drives glioblastoma invasion and represents a therapeutic vulnerability

Glioblastoma (GBM) is characterized by diffuse infiltration into the surrounding brain, which precludes complete surgical resection, the strongest determinant of patient survival. The mechanisms that drive this invasive growth remain incompletely understood. Here we identify a lipid-mediated paracrine signaling axis through which microglia, the resident macrophages of the brain, promote glioma invasion. Integrating single-cell transcriptomics, spatial lipidomics, and functional perturbation across mouse models and human GBM samples, we show that invading tumor cells engage and reprogram microglia via CSF1R-PI3K signaling. This induces a metabolic switch in microglia, leading to the secretion of bioactive lipids, including lysophosphatidylcholines (LPCs) and lysophosphatidic acids (LPAs), which act as pro-invasive cues across GBM subtypes through distinct downstream pathways. Disruption of the microglia-GBM axis, either by inhibiting CSF1R signaling or by blocking lipid mobilization, reduces lipid secretion and suppresses tumor invasion. Targeting downstream LPA-LPAR or YAP/TAZ signaling further constrains invasion in a context-dependent manner. Together, these findings define a lipid-driven signaling circuit that links the tumor microenvironment to glioma invasion and identify therapeutic strategies to limit tumor infiltration and improve surgical resectability.

cancer biology↗

bioRxiv2025

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

Cyclic Multiplexed ImmunoFluorescence (cMIF) can profile dozens to hundreds of proteins at subcellular resolution, offering unprecedented insights into tissue architecture in health and disease. However, measured signals are affected by tissue autofluorescence (AF), which can confound molecular quantifications at the single-cell level. Existing computational strategies to subtract AF are not sensitive nor specific enough and often leave residual background or over subtract weak markers. To address current limitations, we introduce CLEAR-AF (Coordinate-transformed Local Estimation and Adaptive Removal of AutoFluorescence), a subtraction framework where AF is calibrated for each acquired signal using a reference image. CLEAR-AF maps these intensities into polar coordinates to disentangle AF from true signal, and applies adaptive, distribution-aware thresholding to estimate and remove AF locally. Across multiple cMIF technologies, CLEAR-AF yields cleaner marker channels with improved specificity, greater sensitivity, and higher reproducibility relative to common AF removal approaches. By delivering more accurate per-cell measurements without workflow changes to acquisition, CLEAR-AF provides a practical, platform-agnostic step towards more reliable spatial proteomics at scale.

bioinformatics↗

bioRxiv2025

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

High-dimensional cyclic multiplexed immunofluorescence (cMIF) enables single-cell phenotyping within intact tissues. Cell annotations rely on a multi-step pipeline involving normalization, sampling, dimensionality reduction, and clustering, but the absence of standardized benchmarks for method selection--especially at the clustering stage--leads to inconsistent and less reproducible phenotyping. To address this, we developed CONCLAVE, a consensus-clustering-based workflow that optimizes upstream steps and integrates results from multiple clustering algorithms retaining only those cell labels supported by at least two independent methods. Through in-silico simulations and real-world cMIF datasets, CONCLAVE consistently outperformed single-clustering-method approaches in accuracy, reproducibility, and robustness, with improvements becoming more evident when mapped within spatial tissue contexts. Additionally, CONCLAVE includes a scoring module that flags regions likely to contain unreliable or inconsistent data, facilitating targeted quality control. In summary, CONCLAVE offers a robust framework for cell annotation in cMIF datasets, enhancing the reliability of downstream spatial proteomics analyses. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=118 SRC="FIGDIR/small/688190v1_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@14ddfecorg.highwire.dtl.DTLVardef@1a83a17org.highwire.dtl.DTLVardef@17dfc74org.highwire.dtl.DTLVardef@493c07_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2025

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

BackgroundImmunotherapies have improved outcomes in many cancers but show limited efficacy in glioblastoma (GBM). This study aimed to determine whether immunotherapy could be tailored to GBM by functional subtyping of vascular-immune landscapes. MethodsWe employed single-cell RNA sequencing, multiplex immunohistochemistry to characterize three distinct TIME subtypes in human and murine GBMs. We evaluated responses to combination of anti-angiogenic immunomodulating therapies (CD40 Ag, anti-PDL1, PI3K{gamma}/{delta} inhibition) in orthotopic syngeneic GBM mouse models. ResultsWe identified three distinct functional TIME subtypes with unique vascular-immune landscapes in human and murine GBM: TIME-low (immune-low/deserted, leaky vasculature), TIME-med (intermediate immune-infiltration, angiogenic), and TIME-high (heavily infiltrated with immunosuppressive myeloid cells and dysfunctional T cells). Representative mouse models of TIME-GBMs responded in subtype specific ways to anti-angiogenic immunomodulating therapies. TIME-low GBMs enhanced T-cell activity but relapsed due to emerging myeloid immunosuppression, concomitant with mesenchymal transition. TIME-med displayed the most immune-activated, yet angiogenic phenotype, and showed overall good responses to various anti-angiogenic immunomodulating therapies. TIME-high GBMs were mostly non-responsive but improved when the myeloid-cell PI3K{gamma} was targeted. However, CD40 agonist treatment, expected to enhance APC function, unexpectedly worsened survival by promoting angiogenesis and heightening immunosuppression, leading to dysfunctional T cells and reduced NK cell recruitment, and subsequent enhanced tumor propagation. ConclusionsOur study reveals three GBM TIME subtypes with distinct vascular-immune landscapes that require tailored therapies. TIME-med tumors are predicted to respond best to immunotherapies, TIME-low tumors show transient effects with anti-angiogenic immunomodulating therapies, while TIME-high tumors, due to their profound immunosuppression, can even have worse outcomes. Key pointsO_LIThree TIME subtypes were identified in GBM with distinct vascular-immune landscapes C_LIO_LITIME subtypes show divergent immunotherapy responses C_LIO_LITIME classification supports personalized treatment strategy for GBM immunotherapy C_LI Importance of the StudyThis study advances glioblastoma immunotherapy by providing the first comprehensive single-cell characterization of TIME subtypes, moving beyond bulk RNA-sequencing to reveal detailed functional states of immune cells. We establish clinically relevant murine models that recapitulate human TIME subtypes, enabling preclinical testing of TIME-targeted therapies. Our findings identify TIME-low GBM as immune deserted and TIME-med tumors as the most immunotherapy-responsive subtype that should be prioritized for clinical selection. We found that high immune infiltration correlates with non-responsiveness and even unexpected detrimental effects with CD40 agonist treatment in TIME-high tumors--critical information given ongoing clinical trials. Identifying distinct immunosuppressive mechanisms across TIME subtypes and differential treatment responses provides a framework for personalized immunotherapy selection. The immediate translational impact of this work highlights the importance of TIME classification for treatment stratification and the urgent need to consider TIME status in clinical trial design, potentially explaining variable patient responses in previous GBM immunotherapy trials.

cancer biology↗

bioRxiv2024

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

MotivationIn the past decade, deep learning algorithms have surpassed the performance of many conventional image segmentation pipelines. Powerful models are now available for segmenting cells and nuclei in diverse 2D image types, but segmentation in 3D cell systems remains challenging due to the high cell density, the heterogenous resolution and contrast across the image volume, and the difficulty in generating reliable and sufficient ground truth data for model training. Reasoning that most image processing applications rely on nuclear segmentation but do not necessarily require an accurate delineation of their shapes, we implemented PAC-MAP, a 3D U-net based method that predicts the position of nuclei centroids and their proximity to other nuclei. ResultsWe show that our model outperforms existing methods, predominantly by boosting recall, especially in conditions of high cell density. When trained from scratch PAC-MAP attained an average F1 score of 0.793 in dense spheroids. When pretraining using weakly supervised bulk data input and finetuning with few expert annotations the average F1 score could be significantly improved up to 0.817. We demonstrate the utility of our method for quantifying the cell content of spheroids and mapping the degree of glioblastoma multiforme infiltration in cerebral organoids. Availability and implementationThe code is available on GitHub, at https://github.com/DeVosLab/PAC-MAP. ContactWinnok H. De Vos (winnok.devos@uantwerpen.be)

bioinformatics↗

bioRxiv2024

COLLAGE: COnsensus aLignment of muLtiplexing imAGEs

Multiplexed immunohistochemistry (mIHC) enables the high-dimensional single-cell interrogation of pathological tissue samples. mIHC is commonly based on the collection of high-resolution images from repeated staining cycles of the same tissue sample. Images of individual cycles typically consist of smaller tiles that need to be stitched into larger composite images, while images from serial rounds require alignment in a shared set of coordinates to enable pixel-perfect data integration. Current algorithms for stitching and registration require solving a single large puzzle consisting of billions of pixels making them computationally expensive but moreover forcing them to introduce errors to close the puzzle, which significantly impact the downstream results and the single-cell profiles. Here, we present the development and evaluation of COLLAGE (COnsensus aLignment of muLtiplexing imAGEs), an innovative stitching and registration method that leverages on the complementarity of these two steps in a divide and conquer approach: in contrast to other algorithms, COLLAGE breaks the process down into thousands of small puzzles, enabling extensive parallelisation and not forcing errors in its solution. Because COLLAGE also includes AlgnQC, a novel deep-learning-based evaluation metric of registration quality, the quality of the resulting image stacks is consistently maximised, while images with errors are flagged in an automated way. COLLAGE is available via www.disscovery.org. O_FIG O_LINKSMALLFIG WIDTH=148 HEIGHT=200 SRC="FIGDIR/small/603557v1_ufig1.gif" ALT="Figure 1"> View larger version (65K): org.highwire.dtl.DTLVardef@18d0f5borg.highwire.dtl.DTLVardef@1eb2756org.highwire.dtl.DTLVardef@163bb95org.highwire.dtl.DTLVardef@b04453_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

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

IL-1R signaling drives enteric glia-macrophage interaction in colorectal cancer progression

Enteric glial cells (EGCs) have been implicated in colorectal cancer (CRC) pathogenesis. However, their precise mechanisms of interaction with the CRC immune cell compartment and pro-tumorigenic role remain unclear. This study aimed to investigate the immunomodulatory effects of EGCs on tumor-associated macrophages (TAMs) and their involvement in CRC progression. Using EGC depletion and supplementation models, we assessed the impact of EGCs on the immunomodulation of orthotopic murine CRC. Furthermore, by making use of Bulk RNA-sequencing of CRC EGCs and single-cell sequencing of the tumor microenvironment, we identified the factors involved in the EGC-TAM crosstalk. Findings demonstrate that EGCs acquire a reactive and immunomodulatory phenotype in both murine CRC models and patients, influencing TAM differentiation. Mechanistically, secretion of IL-1 by tumor-infiltrating monocytes and macrophages triggers the phenotypic and functional switch of CRC EGCs via IL-1R. Consequently, tumor EGCs secrete IL-6, promoting the differentiation of monocytes into pro-tumorigenic SPP1+ TAMs. Importantly, the reactive tumor EGCs phenotype correlates with worse disease outcomes in preclinical models and CRC patients. Here we uncover a previously unexplored neuroimmune interaction between EGCs and TAMs within the colorectal tumor microenvironment, informing potential therapeutic strategies and enhancing our understanding of CRC progression. eTOC SummaryOur study unveils a novel neuroimmune interaction between enteric glia and TAMs in colon carcinoma. Monocyte/Macrophage-derived IL-1 activates enteric glia, leading to the differentiation of pro-tumorigenic SPP1+ TAMs via glial-derived IL-6. Blocking glial IL-1R-signaling reduces colonic tumor lesions, highlighting IL-1R as a potential therapeutic target.

cancer biology↗

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↗

bioRxiv2022

Single-cell molecular profiling using ex vivo functional readouts fuels precision oncology in glioblastoma

BackgroundFunctional profiling of freshly isolated glioblastoma cells is being evaluated as a next-generation method for precision oncology. While promising, its success largely depends on the method to evaluate treatment activity which requires sufficient resolution and specificity. MethodsHere, we describe the precision oncology by single-cell profiling using ex vivo readouts of functionality (PROSPERO) assay to evaluate the intrinsic susceptibility of high- grade brain tumor cells to respond to therapy. Different from other assays, PROSPERO extends beyond life/death screening by rapidly evaluating acute molecular drug responses at single-cell resolution. ResultsThe PROSPERO assay was developed by correlating short-term single-cell molecular signatures using CyTOF to long-term cytotoxicity readouts in representative patient- derived glioblastoma cell cultures (n=14) that were exposed to radiotherapy and the small- molecule p53/MDM2 inhibitor AMG232. The predictive model was subsequently projected to evaluate drug activity in freshly resected GBM samples from patients (n=34). Here, PROSPERO revealed an overall limited capacity of tumor cells to respond to therapy, as reflected by the inability to induce key molecular markers upon ex vivo treatment exposure, while retaining proliferative capacity, insights that were validated in PDX models. This approach also allowed the investigation of cellular plasticity, which in PDCLs highlighted therapy-induced proneural-to-mesenchymal transitions, while in patients samples this was more heterogeneous. ConclusionPROSPERO provides a precise way to evaluate therapy efficacy by measuring molecular drug responses using specific biomarker changes in freshly resected brain tumor samples, in addition to providing key functional insights in cellular behavior, which may ultimately complement standard, clinical biomarker evaluations.

cancer biology↗

bioRxiv2021

Immunogenomic, single-cell and spatial dissection of CD8+T cell exhaustion reveals critical determinants of cancer immunotherapy

Tumoural-CD8+T cells exhibit exhausted or dysfunctional states. Contrary to immunotherapy-responsive exhausted-CD8+T cells, the clinical features of dysfunctional-CD8+T cells are disputed. Hence, we conducted large-scale multi-omics and multi-dimensional mapping of CD8+T cell-states across multiple cancer patient-cohorts. This identified tumour-specific continuum of CD8+T cell-states across 6 human cancers, partly imprinted by organ-specific immuno-modulatory niches. Herein, melanoma and glioblastoma enriched prototypical exhausted (CD8+TEXT) and severely-dysfunctional (CD8+TSDF) states, respectively. Contrary to CD8+TEXT, CD8+TSDF displayed transcriptomic and epigenetic effector/cytolytic dysfunctions, and dysregulated effector/memory single-cell trajectories, culminating into maladaptive prodeath stress and cell-cycle defects. Suboptimal antigen-priming underscored CD8+TSDF, which was distinct from immune-checkpoints "rich" CD8+TEXT, reflecting chronic antigen-stimulation. Continuum variation also existed on tumour spatial-level, with convergent (CD8+TEXT-supportive vascular regions) and divergent features (dysfunctional CD4+T::CD8+TSDFcell-to-cell interactions) between melanoma and glioblastoma. Globally, IFN{gamma}-IL2 disparities, paucity of intra-tumoural CD4+/CD8+T cells, and myeloid TGF{beta}/wound healing responses, distinguished CD8+TSDF-landscape. Within immuno-oncology clinical-trials, anti-PD1 immunotherapy failed to "reinvigorate" CD8+TSDF-landscape, and instead facilitated effector-dysfunction and TGF{beta}/wound healing. However, cellular immunotherapies (dendritic cell-vaccines, adoptive T-cell therapy) ameliorated assorted CD8+TSDF-landscape disparities, highlighting a roadmap for anti-glioblastoma multimodal-immunotherapy. Collectively, our study comprehensively expands clinical-knowledge on CD8+T cell-exhaustion and suggests that tumour-specific, pre-existing CD8+TEXT/TSDF-states, determine immunotherapy-responses.

immunology↗

bioRxiv2020

The tumor suppressor protein PTEN undergoes amyloid-like aggregation in tumor cells.

Protein aggregation is an underappreciated mechanism that may contribute to the loss- and oncogenic-gain-of-function of mutant tumor suppressors such as p53 and axin. In the present study, we describe amyloid-like aggregation behaviour of the second most frequently mutated tumor suppressor in human cancer, PTEN. In silico analysis revealed a particularly high aggregation vulnerability for this protein, which was corroborated by in vitro aggregation assays. In cultured tumor cells, we found that under stress conditions, PTEN readily undergoes amyloid-like aggregation as a result of mutation. However, we also show that severe dysregulation of protein homeostasis may lead to aggregation of wild-type PTEN. These observations were supported by a small survey of patient-derived uterine tumor tissues, which found that more than 25% of tumors analyzed displayed wild-type PTEN aggregation. Finally, in an exploratory clinical study we found that PTEN aggregation status was correlated with a decline in clinical outcome. Our findings establish that the tumor suppressor PTEN is highly aggregation-prone and our work suggests that protein aggregation might be an underestimated but prevalent component of cancer cell biology.

cancer biology↗