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
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 synap...
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Wiley
2024-11-01
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| Series: | Advanced Science |
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| Online Access: | https://doi.org/10.1002/advs.202402967 |
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| author | Meng Lu Ernestine Hui Marius Brockhoff Jakob Träuble Ana Fernandez‐Villegas Oliver J Burton Jacob Lamb Edward Ward Philippa J Woodhams Wadood Tadbier Nino F Läubli Stephan Hofmann Clemens F Kaminski Antonio Lombardo Gabriele S Kaminski Schierle |
| author_facet | Meng Lu Ernestine Hui Marius Brockhoff Jakob Träuble Ana Fernandez‐Villegas Oliver J Burton Jacob Lamb Edward Ward Philippa J Woodhams Wadood Tadbier Nino F Läubli Stephan Hofmann Clemens F Kaminski Antonio Lombardo Gabriele S Kaminski Schierle |
| author_sort | Meng Lu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-6998b4607d234c0ebee01bcdbb3364c2 |
| institution | OA Journals |
| issn | 2198-3844 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Science |
| spelling | doaj-art-6998b4607d234c0ebee01bcdbb3364c22025-08-20T01:52:38ZengWileyAdvanced Science2198-38442024-11-011144n/an/a10.1002/advs.202402967Graphene 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 CMeng Lu0Ernestine Hui1Marius Brockhoff2Jakob Träuble3Ana Fernandez‐Villegas4Oliver J Burton5Jacob Lamb6Edward Ward7Philippa J Woodhams8Wadood Tadbier9Nino F Läubli10Stephan Hofmann11Clemens F Kaminski12Antonio Lombardo13Gabriele S Kaminski Schierle14Department of Chemical Engineering and Biotechnology University of Cambridge Philippa Fawcett Drive Cambridge CB3 0AS UKDepartment of Chemical Engineering and Biotechnology University of Cambridge Philippa Fawcett Drive Cambridge CB3 0AS UKDepartment of Chemical Engineering and Biotechnology University of Cambridge Philippa Fawcett Drive Cambridge CB3 0AS UKDepartment of Chemical Engineering and Biotechnology University of Cambridge Philippa Fawcett Drive Cambridge CB3 0AS UKDepartment of Chemical Engineering and Biotechnology University of Cambridge Philippa Fawcett Drive Cambridge CB3 0AS UKDepartment of Engineering University of Cambridge 9 JJ Thomson Ave Cambridge CB3 0FA UKDepartment of Chemical Engineering and Biotechnology University of Cambridge Philippa Fawcett Drive Cambridge CB3 0AS UKDepartment of Chemical Engineering and Biotechnology University of Cambridge Philippa Fawcett Drive Cambridge CB3 0AS UKDepartment of Chemical Engineering and Biotechnology University of Cambridge Philippa Fawcett Drive Cambridge CB3 0AS UKDepartment of Engineering University of Cambridge 9 JJ Thomson Ave Cambridge CB3 0FA UKDepartment of Chemical Engineering and Biotechnology University of Cambridge Philippa Fawcett Drive Cambridge CB3 0AS UKDepartment of Engineering University of Cambridge 9 JJ Thomson Ave Cambridge CB3 0FA UKDepartment of Chemical Engineering and Biotechnology University of Cambridge Philippa Fawcett Drive Cambridge CB3 0AS UKUniversity College London 17‐19 Gordon Street London WC1H 0AH UKDepartment of Chemical Engineering and Biotechnology University of Cambridge Philippa Fawcett Drive Cambridge CB3 0AS UKAbstract 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.https://doi.org/10.1002/advs.202402967electrophysiologygraphenemachine learningmicroelectrode arrayniemann‐pick disease type Cstructured illumination microscopy |
| spellingShingle | Meng Lu Ernestine Hui Marius Brockhoff Jakob Träuble Ana Fernandez‐Villegas Oliver J Burton Jacob Lamb Edward Ward Philippa J Woodhams Wadood Tadbier Nino F Läubli Stephan Hofmann Clemens F Kaminski Antonio Lombardo Gabriele S Kaminski Schierle 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 Advanced Science electrophysiology graphene machine learning microelectrode array niemann‐pick disease type C structured illumination microscopy |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | electrophysiology graphene machine learning microelectrode array niemann‐pick disease type C structured illumination microscopy |
| url | https://doi.org/10.1002/advs.202402967 |
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