An AI-based Approach to High-Content Phenotypic Characterization of Human iPSC-Derived Neuronal Cells

While high-content imaging is an efficient tool to capture phenotypic changes in neurite morphology, quantitative image analysis is still a challenging task, due to the manifold changes in morphology and the complexity of analysis algorithms used. Deep learning (AI)-based image analysis can address these challenges by reducing the effort and expertise required to capture morphological changes.

In this webinar, we demonstrate a workflow, which integrates the ImageXpress^® system with the Ai-based Genedata Imagence^® platform to analyze neurotoxicity in human iPSC-derived neurons.
Key highlights:

• Screening of neurotoxicity and neurite outgrowth assays using the ImageXpress Micro Confocal system
• Overview of fully integrated imaging and analysis workflow
• Software Demo of the Gendata Imagence workflow
• Quantification of neurotoxicity using Genedata Imagence

Presenter:
Oksana Sirenko, PhD, Senior Research Scientist, Molecular Devices
Matthias Fassler, PhD, Scientific Account Manager, Genedata