Biology subjects

Wilbertz, J. H.

Publications and source records attributed to Wilbertz, J. H..

8 recordsLinked to original sources

bioRxiv2026

SNCA triplication shapes neuronal extracellular vesicle biology and promotes microglial activation in patient-derived iPSC-based models of Parkinson's disease

Extracellular vesicles (EVs) are emerging as key mediators of intercellular communication and potential biomarkers in Parkinson's disease (PD), yet how disease-causing genetic alterations shape neuronal EV biology remains incompletely understood. Here, we used PD-patient iPSC-derived midbrain dopaminergic neurons (mDANs) harboring SNCA triplication (SNCA-4x), gene-corrected controls (SNCA-GC), and SNCA knockout (SNCA-KO) to investigate the impact of -synuclein overexpression on neuronal-enriched EV (nEV) biology and neuron to microglia communication. SNCA-4x mDANs exhibited marked transcriptional alterations in pathways related to vesicle trafficking and extracellular matrix organization. Using an optimized isolation workflow, SNCA-4x neurons released significantly more and smaller nEVs enriched in -synuclein, mitochondrial DNA (mtDNA), and PARK7/DJ-1 mRNA, while displaying reduced acetylcholinesterase activity. Functionally, SNCA-4x-derived nEVs were taken up more efficiently by isogenic control iPSC-derived microglia than SNCA-GC nEVs and induced stronger pro-inflammatory activation than both SNCA-GC nEVs and untreated microglia, characterized by altered microglial morphology and increased TNF- and IL-1{beta}, consistent with damage-associated molecular pattern (DAMP)-mediated signaling. Together, these findings demonstrate that SNCA-4x reshapes the properties and molecular cargo of nEVs, enhancing their capacity to trigger microglial activation, and identify EV-associated -synuclein, mtDNA, and PARK7 mRNA as candidate mechanistic and biomarker features in PD.

neuroscience↗

bioRxiv2025

Mapping human microglial morphological diversity via handcrafted and deep learning-derived image features

Microglia play critical roles in brain health and disease by adopting a spectrum of dynamic activation states. However, capturing this continuous heterogeneity in a scalable way remains a major challenge. To address this, we developed an imaging and data analysis framework to map the activation landscape of human iPSC-derived microglia (iMG) at single-cell resolution. We combined high-content imaging using two complementary strategies: a hypothesis-driven immunofluorescence (IF) panel targeting key activation markers (NF-{kappa}B, ASC, CD45) and a discovery-oriented, pan-morphological Cell Painting (CP) assay. Diverse phenotypes were captured through handcrafted and representation learning-based features. To classify cells, we applied Gaussian Mixture Models (GMMs) to image-derived features, enabling soft probabilistic assignments that capture transitional states between phenotypes. Compared to graph-based methods like the Leiden algorithm, GMMs provided comparable classification performance while offering a more nuanced and biologically interpretable view of microglial heterogeneity. We demonstrate that deep learning features from the targeted IF panel are most powerful, achieving high classification accuracy and strong correlation with biological states such as functional NLRP3 inflammasome activation. Our model system provides a robust and scalable platform for quantifying microglial heterogeneity, offering a new tool to identify novel disease-associated states and compounds that precisely modulate microglial phenotypes for therapeutic discovery.

neuroscience↗

bioRxiv2024

Morphological profiling in human dopaminergic neurons identifies mitochondrial uncoupling as a neuroprotective effect

Parkinsons disease (PD) involves multiple pathological processes in midbrain dopaminergic (mDA) neurons, including protein degradation defects, vesicular trafficking disruption, endolysosomal dysfunction, mitochondrial issues, and oxidative stress. Current PD models often lack complexity and focus on single phenotypes. We used patient-derived SNCA triplication (SNCA-4x) and isogenic control (SNCA-corr) mDA neurons, applying high-content imaging-based morphological profiling to identify and rescue multiple phenotypes. Screening 1,020 compounds, we identified top-scoring compounds that restored healthy profiles in SNCA-4x neurons, increasing Tyrosine hydroxylase (TH) and decreasing -synuclein (Syn) levels. Several hits were linked to mitochondrial biology. Tyrphostin A9, a mitochondrial uncoupler, and several of its structural analogues decreased ROS levels, normalized mitochondrial membrane potential, and increased respiration. Western blotting confirmed that Tyrphostin A9 reduces Syn levels. Our study highlights the neuroprotective potential of mild mitochondrial uncoupling in mDA neurons.

neuroscience↗

bioRxiv2023

Pharmacological modulation of developmental and synaptic phenotypes in human SHANK3 deficient stem cell-derived neuronal models

One sentence summaryThis study describes the use of SHANK3 deficient stem cell-derived neuronal models to screen and characterize small molecules that partially rescued developmental and synaptic defects related to Phelan-McDermid syndrome (PMDS). Phelan-McDermid syndrome (PMDS) arises from mutations in the terminal region of chromosome 22q13, impacting the SHANK3 gene. The resulting deficiency of the postsynaptic density scaffolding protein SHANK3 is associated with autism spectrum disorder (ASD). We examined 12 different PMDS patient and CRISPR-engineered stem cell-derived neuronal models and controls and found that reduced expression of SHANK3 leads to neuronal hyperdifferentiation, increased synapse formation, and decreased neuronal activity. We performed automated imaging-based screening of 7,120 target-annotated small molecules and identified three compounds that rescued SHANK3-dependent neuronal hyperdifferentiation. One compound, Benproperine, rescued the decreased colocalization of Actin Related Protein 2/3 Complex Subunit 2 (ARPC2) with {beta}-actin and rescued increased synapse formation in SHANK3 deficient neurons when administered early during differentiation. Neuronal activity was only mildly affected, highlighting Benproperines effects as a neurodevelopmental modulator. This study demonstrates that small molecular compounds that reverse developmental phenotypes can be identified in human neuronal PMDS models.

neuroscience↗

bioRxiv2023

Machine learning identifies phenotypic profile alterations of human dopaminergic neurons exposed to bisphenols and perfluoroalkyls

Parkinsons disease (PD) is the second most common neurodegenerative disease and is characterized by the loss of midbrain dopaminergic neurons. Endocrine disruptors (EDs) are active substances that interfere with hormonal signaling. Among EDs, bisphenols (BPs) and perfluoroalkyls (PFs) are chemicals leached from plastics and other household products, and humans are unavoidably exposed to these xenobiotics. Data from animal studies suggest that ED exposure may play a role in PD, but data about the effect of BPs and PFs on human models of the nervous system are lacking. Previous studies demonstrated that machine learning (ML) applied to microscopy data can classify different cell phenotypes based on image features. In this study, the effect of BPs and PFs at different concentrations within the real-life exposure range (0.01, 0.1, 1, and 2 M) on the phenotypic profile of human stem cell-derived midbrain dopaminergic neurons (mDANs) was analyzed. Cells exposed for 72 hours to the xenobiotics were stained with neuronal markers and evaluated using high content microscopy yielding 126 different phenotypic features. Two different ML models (XGBoost and LightGBM) were trained to classify ED-treated versus control mDANs. ED-treated mDANs were identified with high accuracy (0.92). Assessment of the phenotypic feature contribution to the classification showed that EDs induced a significant increase of alpha-synuclein (Syn) and tyrosine hydroxylase (TH) staining intensity within the neurons. Moreover, microtubule-associated protein 2 (MAP2) neurite length and branching were significantly diminished in treated neurons. Our study shows that human mDANs are adversely impacted by exposure to EDs, causing their phenotype to shift and exhibit more characteristics of PD. Importantly, ML-supported high-content imaging can identify concrete but subtle subcellular phenotypic changes that can be easily overlooked by visual inspection alone and that define EDs effects in mDANs, thus enabling further pathological characterization in the future.

neuroscience↗

bioRxiv2022

Machine learning-aided multidimensional phenotyping of Parkinson's disease patient stem cell-derived midbrain dopaminergic neurons

Combining multiple Parkinsons disease (PD) relevant cellular phenotypes might increase the accuracy of midbrain dopaminergic (mDA) in vitro models. We differentiated patient-derived induced pluripotent stem cells (iPSCs) with a LRRK2 G2019S mutation, isogenic control and genetically unrelated iPSCs into mDA neurons. Using automated fluorescence microscopy in 384-well plate format, we identified elevated levels of -synuclein and Serine 129 phosphorylation (pS129), reduced dendritic complexity, and mitochondrial dysfunction. Next, we measured additional image-based phenotypes and used machine learning (ML) to accurately classify mDA neurons according to their genotype. Additionally, we show that chemical compound treatments, targeting LRRK2 kinase activity or -synuclein levels, are detectable when using ML classification based on multiple image-based phenotypes. We validated our approach using a second isogenic patient derived SNCA gene triplication mDA neuronal model. This phenotyping and classification strategy improves the exploitability of mDA neurons for disease modelling and the identification of novel PD drug targets.

neuroscience↗

bioRxiv2021

Time-resolved FRET screening identifies small molecular modifiers of mutant Huntingtin conformational inflexibility in patient-derived cells

Huntingtons disease (HD) is the most common monogenic neurodegenerative disease and is fatal. CAG repeat expansions in mutant Huntingtin (mHTT) exon 1 encode for polyglutamine (polyQ) stretches and influence age of onset and disease severity, depending on their length. mHTT is more structured compared to wild-type (wt) HTT, resulting in a decreased N-terminal conformational flexibility. mHTT inflexibility may contribute to both gain of function toxicity, due to increased mHTT aggregation propensity, but also to loss of function phenotypes, due to decreased interactions with binding partners. High-throughput-screening techniques to identify mHTT flexibility states and potential flexibility modifying small molecules are currently lacking. Here, we propose a novel approach for identifying small molecules that restore mHTTs conformational flexibility in human patient fibroblasts. We applied an antibody-based time-resolved Forster resonance energy transfer (TR-FRET) immunoassay, measuring endogenous HTT flexibility using two validated HTT-specific antibodies. The ratio of TR-FRET signal at 4{degrees}C and 20{degrees}C differs between wtHTT and mHTT and allowed to perform a high-throughput screening using HTT flexibility as a read-out. We identified several small molecules that can partially rescue mHTT inflexibility, presumably by altering HTT post-translational modifications. This novel screening approach has the potential to identify previously unknown HD drugs and drug targets.

neuroscience↗

bioRxiv2018

Single-molecule imaging of mRNA localization and regulation during the integrated stress response

Biological phase transitions form membrane-less organelles that generate distinct cellular environments. How molecules are partitioned between these compartments and the surrounding cellular space and the functional consequence of this localization is not well understood. Here, we report the localization of mRNA to stress granules(SGs) and processing bodies(PBs), which are distinct biomolecular condensates, and its effect on translation and mRNA degradation during the integrated stress response. Using single mRNA imaging in living human cells, we find that the interactions of mRNAs with SGs and PBs have different dynamics and that specific RNA binding proteins can anchor mRNAs within these compartments. During recovery from stress, mRNAs that were within SGs and PBs are translated and degraded at similar rates as their cytosolic counterparts.

molecular biology↗