Automated identification of disease mechanisms in hiPSC-derived neuronal networks using simulation-based inference
Nina Doorn*1, Michel van1,2, Monica Frega3
1Department of Clinical Neurophysiology, University of Twente, Enschede, The Netherlands
2Department of Neurology and Clinical Neurophysiology, Medisch Spectrum Twente, The Netherlands
3Department of Informatics, Bioengineering, Robotics and System Engineering, University of Genova, Italy
*Email: n.doorn-1@utwente.nl
Introduction
Human induced pluripotent stem cells (hiPSCs)-derived neuronal networks on multi-electrode arrays (MEAs) are a powerful tool to study neurological disordersin vitro[1]. The electric activity patterns of these networks differ between healthy and patient-derived neurons, reflecting underlying pathology (Fig. 1A). However, elucidating the underlying molecular mechanisms is challenging and requiresextensive, costly, and hypothesis-driven additional experiments.Biophysical models can link observable network activity to underlying molecular mechanisms by estimating model parameters that simulate the experimental observations. However, parameter estimation in such models is difficult due to stochasticity, non-linearity, and parameter degeneracy.
Methods
Here, we address this challenge using simulation-based inference (SBI), a machine-learning approach that allows efficient statistical inference of biophysical model parameters using only simulations [2]. We apply SBI to our previously validated biophysical model of hiPSC-derived neuronal networks on MEA[3], which includesHodgkin-Huxley-type neurons and detailed synaptic models (Fig. 1B). To train SBI, we simulated 300,000 network configurations, varying key parameters governing synaptic and intrinsic neuronal properties (Fig. 1C). We used a neural density estimator to infer posterior distributions of these model parameters given experimental MEA recordings from healthy, pharmacologically treated, and patient-derived networks (Fig 1D).
Results
SBI accurately inferred ground-truth parameters in synthetic data and successfully identified known disease mechanisms in patient-derived neuronal networks. In networks from patients with the genetic epilepsies Dravet Syndrome and GEFS+, SBI predicted reduced sodium and potassium conductances and increased synaptic depression, which was experimentally verified. InCACNA1Ahaploinsufficient networks, SBI correctly identified impaired connectivity. Additionally, SBI detected drug-induced changes, such as prolonged synaptic depression following Dynasore treatment.
Discussion
SBI enables automated and probabilistic inference of biophysical parameters, offering advantages over traditional parameter estimation methods, which can be time-consuming, lack uncertainty quantification, or cannot deal with parameter degeneracy. Our results show how SBI can be used with biophysical models to identify possible disease mechanisms from patient-derived neuronal data. Ourproposed analysis pipeline enables researchers to extract crucial mechanistic information from MEA measurements in a systematic, cost-effective, and rapid manner, paving the way for targeted experiments and novel insights into disease.
Figure 1. Figure 1. A) The activity of in vitro neuronal networks cultured from hiPSCs of healthy controls and patients is measured using MEAs. B) The computational model with biophysical parameters in blue. C) A Neural density estimator is trained on model simulations. Afterward, experimental data is passed through the estimator to approximate the D) posterior distributions. Adapted from [4].
Acknowledgements
This work was supported by the Netherlands Organisation for Health Research and Development ZonMW; BRAINMODEL PSIDER program 10250022110003 (to M.F.). We thank Eline van Hugte, Marina Hommersom, and Nael Nadif Kasri for providing MEA recordings from patient-derived and genome-editedin vitroneuronal networks.
References
● 1.https://doi.org/10.1016/J.STEMCR.2021.07.001
● 2.https://doi.org/10.7554/ELIFE.56261
● 3.https://doi.org/10.1016/J.STEMCR.2024.09.001
● 4.https://doi.org/10.1101/2024.05.23.595522
