Biology subjects

De Vries, M.

Publications and source records attributed to De Vries, M..

6 recordsLinked to original sources

bioRxiv2026

Pheno-MYCN maps the morphological footprint of MYCN amplification in paediatric neuroblastoma

MYCN amplification has long been a prognostic marker in paediatric neuroblastoma, yet is typically assayed in bulk, alongside rather than within the heterogeneous tissue architecture pathologists assess. This leaves a gap: MYCN status alone cannot localise MYCN-associated biology, while morphology alone cannot assign molecular risk. Motivated by our finding that the two together identify high-risk cases missed by either, we developed Pheno-MYCN, a weakly supervised framework linking slide-level MYCN prediction to interpretable morphological sub-populations on routine H&E whole-slide images. The aim is not a stronger classifier: prediction probes what MYCN amplification does to the tissue, its evidence open to pathological scrutiny. Across 189 slides, Pheno-MYCN resolved each into phenotypic clusters that expert review mapped to neuroblastoma morphologies. Cell-level profiling revealed MYCN amplification "marked" every sub-population, through a different feature in each: densely cellular yet disorganised tumour with sparser, less diverse networks; chiefly abundance in necrotic and haemorrhagic regions. MYCN-amplified-like tissue was identifiable per slide from these features alone (AUC 0.93-1.00, leave-one-slide-out) and traced as a continuous gradient within tumours. Thus MYCN amplification leaves a concrete, interpretable footprint that can be read and localised on routine H&E, offering a low-cost means to flag and map it where molecular testing is limited.

cancer biology↗

bioRxiv2024

Comprehensive Unbiased Analysis of Vascular Tissue Changes in Accelerated Atherosclerosis Using High-Resolution Ultrasound combined with Photoacoustic Imaging

Venous bypass grafts are commonly used to circumvent complex coronary or peripheral artery occlusions. The patency rates, however, are hampered due to accelerated buildup of atherosclerotic lesions in the vein graft wall. Identification of unstable plaques is crucial to guide clinical decision making. In this study, we employ advanced high-resolution ultrasound (US) coupled with spectral photoacoustic imaging (sPAI) to enhance the accurate visualization and analysis of tissue composition in vivo. By applying unbiased spectral analysis, we investigate the composition and plaque instability in a murine vein graft model. MethodMale hypercholesterolemic ApoE3*Leiden mice and normocholesterolemic C57BL/6 mice underwent vein graft surgery in which a caval vein from a donor mouse was interpositioned into the arterial circulation of a recipient at the sight of the right common carotid artery. US imaging with sPAI was conducted on 7, 14, 21, and 28 days after surgery. Spectral curves from the near-infrared (NIR) I region, spanning 680 to 970nm, were extracted using a data-driven approach. Component discovery and cross-correlation analysis were performed with Matlab, and ImageJ reconstructed the components within 3D images. At the endpoint histological analysis of the vein grafts was performed. ResultsAnalysis of the NIRI region revealed distinct components, with 7 and 10 components tested in the cross-correlation map. Relative abundance values identified melanin, oxidized hemoglobin, deoxygenized hemoglobin, lipids, and collagen. Lipids and collagen spectra accurately identified lipid and collagen-rich tissues in vivo. The sPAI analysis of of the vein graft wall in vivo resulted in a 8.7% lipids in the vein graft wall compared to 1.8% lipids in the histological analysis at t=28d. For vein grafts from ApoE*3-Leiden mice no differences in the lipid positive area was observed between the sPAI analysis or histological quantification. The percentages collagen present in the vein graft walls from both strains analyzed via sPAI and histological showed comparable results at t=28d. ConclusionOur study demonstrates that sPAI can be utilized for compositional analysis of murine tissue in an unbiased manner. This methodology can be used to enhance our understanding of vein graft dynamics and holds promise to advance non-invasive characterization of vascular diseases to ultimately guide clinical decision making.

bioinformatics↗

bioRxiv2022

3D single-cell shape analysis of cancer cells using geometric deep learning

AO_SCPLOWBSTRACTC_SCPLOWAberrations in 3D cell morphogenesis are linked to diseases such as cancer. Yet there is little systems-level understanding of cell shape determination in 3D, largely because there is a paucity of data-driven methods to quantify and describe 3D cell shapes. We have addressed this need using unsupervised geometric deep learning to learn shape representations of over 95,000 melanoma cells imaged by 3D high-throughput light-sheet microscopy. We used a dynamic graph convolutional foldingnet autoencoder with improved deep embedded clustering to simultaneously learn lower-dimensional representations and classes of 3D cell shapes. We describe a landscape of 3D cell morphology using deep learning-derived 3D quantitative morphological signatures (3DQMS) across different substrate geometries, following treatment by different clinically relevant small molecules and systematic gene depletion in high-throughput. By data integration, we predict modes of action for different small molecules providing mechanistic insights and blueprints for biological re-engineering. Finally, we provide explainability and interpretability for deep learning models.

bioinformatics↗

bioRxiv2022

Age-associated Differences in the Human Lung Extracellular Matrix

IntroductionExtracellular matrix (ECM) remodelling has been associated with chronic lung diseases. However, information about specific age-associated differences in lung ECM is currently limited. In this study we aimed to identify and localize age-associated ECM differences in human lung using comprehensive transcriptomic, proteomic and immunohistochemical analyses. MethodsOur previously identified age-associated gene expression signature of the lung was re-analysed limiting it to an aging signature based on 270 control patients (37-80 years) and focused on the Matrisome core geneset using geneset enrichment analysis. To validate the age-associated transcriptomic differences on protein level, we compared the age-associated ECM genes (F <0.05) with a profile of age-associated proteins identified from a lung tissue proteomics dataset from 9 control patients (49-76 years) (FDR<0.05). Extensive immunohistochemical analysis was used to localize the age-associated ECM differences in lung tissues from control patients (9-82 years). ResultsComparative analysis of transcriptomic and proteomic data identified 7 ECM proteins with higher expression with age at both gene and protein level: COL1A1, COL6A1, COL6A2, COL14A1, FBLN2, LTBP4 and LUM. With immunohistochemistry we demonstrated higher protein expression with age for COL6A2 in whole tissue, parenchyma, airway wall and blood vessel, for COL14A1 in bronchial epithelium and blood vessel, and for FBLN2 and COL1A1 in lung parenchyma. ConclusionOur study revealed that higher age is associated with lung ECM remodelling, with specific differences occurring in defined regions within the lung. These differences may affect lung structure and physiology with aging and as such may increase susceptibility for developing chronic lung diseases. Key messagesO_ST_ABSWhat is already known on this topicC_ST_ABSsummarise the state of scientific knowledge on this subject before you did your study and why this study needed to be done. In animal models, it has been demonstrated that aging alters the composition of the lung ECM, with more deposition of collagen and degradation of elastin. Similar ECM differences have been observed in age-associated chronic lung diseases, including COPD; moreover, we observed in lung tissue that several ECM genes associate differently with age in COPD patients compared to non-COPD controls(1). Detailed knowledge on age-associated changes in specific ECM proteins as well as regional differences within the lung is lacking. What this study addssummarise what we now know as a result of this study that we did not know before. We identified 7 age-associated ECM proteins i.e. COL1A1, COL6A1, COL6A2 COL14A1, FBLN2, LTBP4 and LUM with higher transcript and protein levels in human lung tissue with age. Extensive immunohistochemical analysis revealed significant age-associated differences for 3 of these ECM proteins in specific compartments of the lung, with the most notable differences in the blood vessels and parenchyma. How this study might affect research, practice, or policysummarise the implications of this study. The identification of age-associated differences in specific human lung ECM proteins lays a new foundation for the investigation of ECM differences in age-associated chronic lung diseases. Additionally, examining the function of these age-associated ECM proteins and their cellular interactions in lung injury and repair responses may provide novel insight in mechanisms underlying chronic lung diseases.

molecular biology↗

bioRxiv2020

The cellular modifier MOAG-4/SERF drives amyloid formation through charge complementation

While aggregation-prone proteins are known to accelerate ageing and cause age-related diseases, the cellular mechanisms that drive their cytotoxicity remain unresolved. The orthologous proteins MOAG-4, SERF1A and SERF2 have recently been identified as cellular modifiers of such cytotoxicity. Using a peptide array screening approach on human amyloidogenic proteins, we found that SERF2 interacted with specific patterns of negatively charged and hydrophobic, aromatic amino acids. The absence of such patterns, or the neutralization of the positive charge in SERF2, prevented these interactions and abolished the amyloid-promoting activity of SERF2. In a protein aggregation model in the nematode C. elegans, protein aggregation was suppressed by mutating the endogenous locus of MOAG-4 to neutralize charge. Our data indicate that charge interactions are required for MOAG-4 and SERF2 to promote aggregation. Such charged interactions might accelerate the primary nucleation of amyloid by initiating structural changes and by decreasing colloidal stability. Our finding that negatively charged segments are overrepresented in amyloid-forming proteins suggests that inhibition of charge interactions deserves exploration as a strategy to target age-related protein toxicity. Significance StatementHow aging causes relatively common diseases such as Alzheimers and Parkinsons is still a mystery. Since toxic structural changes in proteins are likely to be responsible, we investigated biological mechanisms that could drive such changes. We made use of a modifying factor called SERF2, which accelerates structural changes and aggregation of several disease-related proteins. Through a peptide-binding screen, we found that SERF2 acts on negatively charged protein regions. The abundance of such regions in the disease-related proteins explains why SERF has its effect. Removing positive charge in SERF was sufficient to suppress protein aggregation in models for disease. We propose that blocking charge-interactions with SERF or other modifiers could serve as a general approach to treat age-related protein toxicity.

molecular biology↗

bioRxiv2016

Architecture and dynamics of the jasmonic acid gene regulatory network

The phytohormone jasmonic acid (JA) is a critical regulator of plant growth and defense. To significantly advance our understanding of the architecture and dynamics of the JA gene regulatory network, we performed high-resolution RNA-Seq time series analyses of methyl JA-treated Arabidopsis thaliana. Computational analysis unraveled in detail the chronology of events that occur during the early and later phases of the JA response. Several transcription factors, including ERF16 and bHLH27, were uncovered as early components of the JA gene regulatory network with a role in pathogen and insect resistance. Moreover, analysis of subnetworks surrounding the JA-induced transcription factors ORA47, RAP2.6L, and ANAC055 provided novel insights into their regulatory role of defined JA network modules. Collectively, our work illuminates the complexity of the JA gene regulatory network, pinpoints to novel regulators, and provides a valuable resource for future studies on the function of JA signaling components in plant defense and development.

plant biology↗