Johannes H. Wilbertz

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

This study investigates how SNCA triplication affects neuronal extracellular vesicle biology and microglial activation in Parkinson's disease using iPSC-derived models.

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

The paper develops an imaging and analysis framework to map human microglial activation states using handcrafted and deep learning features, showing deep learning features achieve high classification accuracy and correlate with biological states.

Generating Synthetic Task-based Brain Fingerprints for Population Neuroscience Using Deep Learning

The paper introduces DeepTaskGen, a deep learning method that generates synthetic task-based contrast maps from resting-state fMRI data, outperforming benchmarks and enabling biomarker development for cognitive tasks.

Morphological profiling reveals neuroprotection via mitochondrial uncoupling in human dopaminergic neurons

The paper identifies compounds that restore morphology in Parkinson's disease neurons by targeting mitochondrial uncoupling.

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

This study screens small molecules in SHANK3-deficient neuronal models to identify compounds that rescue developmental and synaptic defects in Phelan-McDermid syndrome.

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

This study uses machine learning to identify subcellular phenotypic changes in human dopaminergic neurons exposed to bisphenols and perfluoroalkyls, showing increased alpha-synuclein and decreased neurite length.

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

The paper uses machine learning to classify Parkinson's disease patient-derived midbrain dopaminergic neurons based on multiple image-based phenotypes and identifies treatable targets.

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

The paper identifies small molecules that restore mutant Huntingtin conformational flexibility in patient-derived cells using a TR-FRET assay.

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

The paper uses single-molecule imaging to study mRNA localization in stress granules and processing bodies during the integrated stress response and finds similar translation and degradation rates for mRNA in these compartments compared to cytosol.