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Doornbos, C.

Publications and source records attributed to Doornbos, C..

3 recordsLinked to original sources

Dissecting the sources of variation in neuronally differentiated iPSC lines through multi-omics analysis

Induced pluripotent stem cells (iPSCs) are widely used as patient-specific disease models, yet substantial unexplained variability in molecular and functional readouts limits their reliability. Here, we systematically investigated the sources of variation in iPSC-derived neurons for three rare genetic disorders: Myotonic Dystrophy Type 1, chromodomain-DNA-helicase-binding protein 2-related disorder and N-acetylneuraminic acid synthase deficiency. This was performed by profiling multi-omics layers: genomics, epigenomics, transcriptomics, proteomics, metabolomics and lipidomics. Our study found that clonal variability was comparable to inter-patient differences and that neuronal differentiation state and nutrient-driven metabolic activity emerged as dominant contributors to variability observed across omics layers. Clonal differences could partly be attributed to stochastic differences in DNA methylation established during reprogramming. By modeling and correcting the observed variation, we improved the detection of disease-associated molecular signatures. Our study provides guidelines for improved study design and data analysis to minimize variability, enabling robust biomarker discovery and reliable iPSC-based disease modeling.

cell biology↗

GSEA and co-expression network approach to identify molecular processes affected in Porto-sinusoidal Vascular Disease

11.1 BackgroundPorto-sinusoidal vascular disease (PSVD) is a complex, rare liver disease characterized by the absence of cirrhosis, with or without the presence of portal hypertension or histological lesions. Given the knowledge gaps in the mechanisms involved in this disease with unknown etiology, we used omics-based approaches to further elucidate the pathways affected by PSVD, facilitating improvements in the prognosis, diagnosis, and treatment options for these patients. 1.2 MethodsWe applied gene set enrichment analysis (GSEA) and weighted gene coexpression network analysis (WGCNA) to identify pathways dysregulated in PSVD. Network construction and visualization were performed in Cytoscape to explore interconnectivity among enriched processes. Within key modules, candidate genes were prioritized by ranking approaches and cross-referenced with findings from previous studies. 1.3 Results and ConclusionThis study revealed that PSVD is characterized by coordinated dysregulation, with immune and signaling pathways activated alongside the suppression of metabolic, ribosomal, and mitochondrial programs. Alterations in ribosomal proteins, ATP synthase subunits, and serpin family members highlight translational, bioenergetic, and anticoagulant dysfunction as core mechanisms. Together, these findings define PSVD as a disorder of integrated immune, vascular, and metabolic imbalance.

bioinformatics↗

Integrative analysis of CAKUT multi-omics data

Congenital Anomalies of the Kidney and Urinary Tract (CAKUT) is the leading cause of childhood end-stage renal disease and a significant cause of chronic kidney disease in adults. Genetic and environmental factors are known to influence CAKUT development, but the currently known disease mechanism remains incomplete. Our goal is to identify affected pathways and networks in CAKUT, and thereby aid in getting a better understanding of its pathophysiology. Multi-omics experiments, including amniotic fluid miRNome, peptidome, and proteome analyses, can shed light on foetal kidney development in non-severe CAKUT patients compared to severe CAKUT cases. We performed FAIRification of these omics data sets to facilitate their integration with external data resources. Furthermore, we analysed and integrated the omics data sets using three different bioinformatics strategies. The three bioinformatics analyses provided complementary features, but all pointed towards an important role for collagen in CAKUT development. We published the three analysis strategies as containerized workflows. These workflows can be applied to other FAIR data sets and help gaining knowledge on other rare diseases.

systems biology↗