Biology subjects

Naert, T.

Publications and source records attributed to Naert, T..

6 recordsLinked to original sources

bioRxiv2026

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↗

bioRxiv2024

Pythia: Non-random DNA repair allows predictable CRISPR/Cas9 integration and gene editing

CRISPR-based genome engineering holds enormous promise for basic science and therapeutic applications. Integrating and editing DNA sequences is still challenging in many cellular contexts, largely due to insufficient control of the repair process. We find that repair at the genome-cargo interface is predictable by deep-learning models and adheres to sequence context specific rules. Based on in silico predictions, we devised a strategy of triplet base-pair repeat repair arms that correspond to microhomologies at double-strand breaks (trimologies), which facilitated integration of large cargo (>2 kb) and protected the targeted locus and transgene from excessive damage. Successful integrations occurred in >30 loci in human cells and in in vivo models. Germline transmissible transgene integration in Xenopus, and endogenous tagging of tubulin in adult mice brains demonstrated integration during early embryonic cleavage and in non-dividing differentiated cells. Further, optimal repair arms for single- or double nucleotide edits were predictable, and facilitated small edits in vitro and in vivo using oligonucleotide templates. We provide a design-tool (Pythia, pythia-editing.org) to optimize custom integration, tagging or editing strategies. Pythia will facilitate genomic integration and editing for experimental and therapeutic purposes for a wider range of target cell types and applications.

molecular biology↗

bioRxiv2023

The Benchtop mesoSPIM: a next-generation open-source light-sheet microscope for large cleared samples

In 2015, we launched the mesoSPIM initiative (www.mesospim.org), an open-source project for making light-sheet microscopy of large cleared tissues more accessible. Meanwhile, the demand for imaging larger samples at higher speed and resolution has increased, requiring major improvements in the capabilities of light-sheet microscopy. Here, we introduce the next-generation mesoSPIM ("Benchtop") with significantly increased field of view, improved resolution, higher throughput, more affordable cost and simpler assembly compared to the original version. We developed a new method for testing objectives, enabling us to select detection objectives optimal for light-sheet imaging with large-sensor sCMOS cameras. The new mesoSPIM achieves high spatial resolution (1.5 {micro}m laterally, 3.3 {micro}m axially) across the entire field of view, a magnification up to 20x, and supports sample sizes ranging from sub-mm up to several centimetres, while being compatible with multiple clearing techniques. The new microscope serves a broad range of applications in neuroscience, developmental biology, and even physics.

neuroscience↗

bioRxiv2022

Reflective multi-immersion microscope objectives inspired by the Schmidt telescope

Rapid advances in tissue clearing protocols have begun to outpace the capabilities of existing microscope objectives: High-resolution imaging inside cm-sized cleared samples is often not possible as it requires multi-immersion objectives with high numerical aperture (NA > 0.7), long working distance (WD > 10 mm) and a large field-of-view (FOV > 1 mm). Here, we introduce a novel mirror-based optical design, the "Schmidt objective", which meets all these criteria despite containing only two optical elements. It consists of a spherical mirror in contact with the immersion medium and an aspherical correction plate. We showcase a multi-photon variant of a Schmidt objective that reaches NA 1.08 at an refractive index of 1.56 and demonstrate its versatility by imaging fixed samples in a wide range of immersion media ranging from air and water to BABB, DBE, and ECI. In addition, we demonstrate in vivo imaging by recording neuronal activity in larval zebrafish.

neuroscience↗

bioRxiv2019

CRISPR-NSID: an in vivo CRISPR/Cas9 negative selection screen reveals EZH2 as a druggable dependency factor in a genetic desmoid tumor model

Cancer precision medicine implies identification of tumor-specific vulnerabilities associated with defined oncogenic pathways. Desmoid tumors are soft-tissue neoplasms strictly driven by Wnt signaling network hyperactivation. Despite this clearly defined genetic etiology and the strict and unique implication of the Wnt/{beta}-catenin pathway, no specific molecular targets for these tumors have been identified. To address this caveat, we developed fast and semi-high throughput genetic Xenopus tropicalis desmoid tumor models to identify and characterize novel drug targets. We used multiplexed CRISPR/Cas9 genome editing in these models to simultaneously target a tumor suppressor gene (apc) and candidate dependency genes. Our methodology CRISPR/Cas9 Selection mediated Identification of Dependencies (CRISPR-SID) uses calculated deviations between experimentally observed gene editing outcomes and deep-learning-predicted double strand break repair patterns, to identify genes under negative selection during tumorigenesis. This revealed EZH2 and SUZ12, both encoding polycomb repressive complex 2 components, and the transcription factor CREB3L1, as genetic dependencies for desmoid tumors. In vivo EZH2 inhibition by Tazemetostat induced partial regression of established autochthonous tumors. In vitro models of patient desmoid tumor cells revealed a direct effect of Tazemetostat on Wnt pathway activity. CRISPR-SID represents a potent novel approach for in vivo mapping of tumor vulnerabilities and drug target identification. Significance StatementCRISPR-SID was established in the diploid frog Xenopus tropicalis for in vivo elucidation of cancer cell vulnerabilities. CRISPR-SID uses deep learning predictions and binomial theory to identify genes under positive or negative selection during autochthonous tumor development. Using CRISPR-SID in a genetic model for desmoid tumors, treatment-recalcitrant mesenchymal tumors driven by hyper-activation of the Wnt signaling pathway, we identified EZH2 and SUZ12, both encoding critical components of the polycomb repressive complex 2, as dependency genes for desmoid. Finally, we demonstrate the promise of EZH2 inhibition as a novel therapeutic strategy for desmoid tumors. With the simplicity of CRISPR sgRNA multiplexing in Xenopus embryos the CRISPR-SID method may be applicable to reveal vulnerabilities in other tumor types.

cancer biology↗

bioRxiv2019

RBL1 (p107) functions as tumor suppressor in glioblastoma and small-cell pancreatic neuroendocrine carcinoma

Alterations of the retinoblastoma and/or the p53 signaling network are associated with specific cancers such as high-grade astrocytoma/glioblastoma, small cell lung cancer (SCLC), choroid plexus tumors and small-cell pancreatic neuroendocrine carcinoma (SC-PaNEC). However, the intricate functional compensation between RB1 and the related pocket proteins RBL1/p107 and RBL2/p130 in suppressing tumorigenesis remains poorly understood. Here we performed lineage-restricted parallel inactivation of rb1 and rbl1 by multiplex CRISPR/Cas9 genome editing in the true diploid Xenopus tropicalis to gain insight into these in vivo compensatory mechanisms. We show that while rb1 inactivation is sufficient to induce choroid plexus papilloma, combined rb1 and rbl1 inactivation is required and sufficient to drive SC-PaNEC, retinoblastoma and astrocytoma. Further, using a novel Li-Fraumeni syndrome-mimicking tp53 mutant X. tropicalis line, we demonstrate increased malignancy of retinoblastoma-mutant neural malignancies upon concomitant inactivation of tp53. Interestingly, although clinical SC-PaNEC samples are characterized by abnormal p53 expression or localization, in the current experimental models, the tp53 status had little effect on the establishment and growth of SC-PaNEC, but may rather be essential for maintaining chromosomal stability. SCLC was only rarely observed in our experimental set-up, indicating requirement of additional or alternative oncogenic insults. In conclusion, we used CRISPR/Cas9 to delineate the tumor suppressor properties of Rbl1 and generate new insights in functional compensation within the retinoblastoma protein family in suppressing pancreatic and specific neural cancers.

cancer biology↗