Graphene Microelectrode Arrays, 4D Structured Illumination Microscopy, and a Machine Learning Spike Sorting Algorithm Permit the Analysis of Ultrastructural Neuronal Changes During Neuronal Signaling in a Model of Niemann-Pick Disease Type C.

Journal: Advanced science (Weinheim, Baden-Wurttemberg, Germany)
PMID: 39340823

Abstract

Simultaneously recording network activity and ultrastructural changes of the synapse is essential for advancing 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 endeavor. Here, specially designed graphene microelectrode arrays (G-MEAs) are used, which are compatible with high spatial resolution imaging across various scales as well as permit high temporal resolution electrophysiological recordings to address these challenges. Furthermore, alongside G-MEAs, an easy-to-implement machine learning algorithm is developed to efficiently process the large datasets collected from MEA recordings. It is demonstrated that the combined use of G-MEAs, machine learning (ML) spike analysis, and 4D structured illumination microscopy (SIM) enables monitoring the impact of disease progression on hippocampal neurons which are 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, leading to a loss in neuronal signaling capacity.

Authors

  • Meng Lu
    Department of Electrical and Computer Engineering, Iowa State University, Ames, IA 50011, United States; Department of Mechanical Engineering, Iowa State University, Ames, IA 500110, United States. Electronic address: menglu@iastate.edu.
  • Ernestine Hui
    Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge, CB3 0AS, UK.
  • Marius Brockhoff
    Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge, CB3 0AS, UK.
  • Jakob Träuble
    Computational Molecular Medicine, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany.
  • Ana Fernandez-Villegas
    Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge, CB3 0AS, UK.
  • Oliver J Burton
    Department of Engineering, University of Cambridge, 9 JJ Thomson Ave, Cambridge, CB3 0FA, UK.
  • Jacob Lamb
    Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge, CB3 0AS, UK.
  • Edward Ward
    Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge, CB3 0AS, UK.
  • Philippa J Woodhams
    Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge, CB3 0AS, UK.
  • Wadood Tadbier
    Department of Engineering, University of Cambridge, 9 JJ Thomson Ave, Cambridge, CB3 0FA, UK.
  • Nino F Läubli
    Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge, CB3 0AS, UK.
  • Stephan Hofmann
    Department of Engineering, University of Cambridge, 9 JJ Thomson Ave, Cambridge, CB3 0FA, UK.
  • Clemens F Kaminski
    Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, United Kingdom.
  • Antonio Lombardo
    University College London, 17-19 Gordon Street, London, WC1H 0AH, UK.
  • Gabriele S Kaminski Schierle
    Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge, CB3 0AS, UK.

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