Abstract

The advent of induced pluripotent stem cell (iPSC) technology has provided an attractive avenue to study neurological disorders, such as Parkinson’s disease (PD) or amyotrophic lateral sclerosis (ALS), in living human tissue. Neuronal cultures, derived from patient iPSCs, retain the unique genetic signatures of their donors and hence allow the study of specific disease aspects that cannot be inferred from genetic or single-cell level investigations alone (Ardhanareeswaran et al. 2017). Pharmaceutical companies have started to use iPSCs as disease models for pre-clinical drug screenings and biobanks with patient-derived samples are currently being set up. High-density microelectrode arrays (HD-MEAs) provide a fitting methodology to record from iPSC-derived neurons at both, high spatial resolution and high throughput ​ (Obien et al. 2014)​ . Moreover, HD-MEAs allow for tracking of neuronal-network activity across development and to dissect neuronal function using pharmacological or genetic challenges. Combining human iPSC technology with HD-MEA recordings provides a state-of-the-art phenotypic screening platform, which could accelerate ​ in vitro drug discovery and help personalize treatment strategies ​ (Fink and Levine 2018)​ . Although, the field has realized the potential of human iPSC-derived neuronal cultures for biomarker discovery, there are currently no widely-accepted analytical tools or standardized assays available to thoroughly assess the functionality of iPSC-derived neurons. To address this need, we will use machine learning algorithms to infer essential features, i.e., biomarkers indicative of the disease state, in neuronal activity and connectivity that allow identification of disease phenotypes and evaluation of pharmacological interventions.
The overall aim of the project is to provide a toolkit for the systematic study of cellular and network phenotypes of neuronal cultures derived from human iPSCs, and to develop biological markers for neurological disorders based on HD-MEA electrophysiological recordings. Both, the data used for this project and the developed analytical tools will be made publicly available, thus providing a resource to researchers in this and related fields.