Graphene microelectrode arrays, 4D structured illumination microscopy, and a machine learning-based spike sorting algorithm permit the analysis of ultrastructural neuronal changes during neuronal signalling in a model of Niemann-Pick disease type C

Lu, M.; Hui, E.; Brockhoff, M.; Trauble, J.; Fernandez-Villegas, A.; Burton, O. J.; Lamb, J.; Ward, E.; Hooper, P. J.; Tadbier, W.; Laubli, N. F.; Hofmann, S.; Kaminski, C. F.; Lombardo, A.; Kaminski Schierle, G. S.

doi:10.1101/2024.02.22.581570

bioRxiv · 10.1101/2024.02.22.581570

Graphene microelectrode arrays, 4D structured illumination microscopy, and a machine learning-based spike sorting algorithm permit the analysis of ultrastructural neuronal changes during neuronal signalling in a model of Niemann-Pick disease type C

Lu, M.·Hui, E.·Brockhoff, M.·Trauble, J.·Fernandez-Villegas, A.·Burton, O. J.·Lamb, J.·Ward, E.·Hooper, P. J.·Tadbier, W.·Laubli, N. F.·Hofmann, S.·Kaminski, C. F.·Lombardo, A.·Kaminski Schierle, G. S.

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Abstract

Simultaneously recording network activity and ultrastructural changes of the synapse is essential for advancing our understanding of the basis of neuronal functions. However, the rapid millisecond-scale fluctuations in neuronal activity and the subtle sub-diffraction resolution changes of synaptic morphology pose significant challenges to this endeavour. Here, we use graphene microelectrode arrays (G-MEAs) to address these challenges, as they are compatible with high spatial resolution imaging across various scales as well as high temporal resolution electrophysiological recordings. Furthermore, alongside G-MEAs, we deploy an easy-to-implement machine learning-based algorithm to efficiently process the large datasets collected from MEA recordings. We demonstrate that the combined use of G-MEAs, machine learning (ML)-based spike analysis, and four-dimensional (4D) structured illumination microscopy (SIM) enables the monitoring of the impact of disease progression on hippocampal neurons which have been treated with an intracellular cholesterol transport inhibitor mimicking Niemann-Pick disease type C (NPC) and show that synaptic boutons, compared to untreated controls, significantly increase in size, which leads to a loss in neuronal signalling capacity.

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Lu, M., Hui, E., Brockhoff, M., Trauble, J., Fernandez-Villegas, A., Burton, O. J., Lamb, J., Ward, E., Hooper, P. J., Tadbier, W., Laubli, N. F., Hofmann, S., Kaminski, C. F., Lombardo, A., Kaminski Schierle, G. S.. 2024-02-24. Graphene microelectrode arrays, 4D structured illumination microscopy, and a machine learning-based spike sorting algorithm permit the analysis of ultrastructural neuronal changes during neuronal signalling in a model of Niemann-Pick disease type C. https://doi.org/10.1101/2024.02.22.581570

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