Simultaneously recording network activity and ultrastructural changes of the synapse can significantly advance our understanding of the structural basis of neuronal functions. The intricate changes in neuronal activity at millisecond-scale and the minute structural modifications at the synapse that are smaller than the diffraction limit present considerable hurdles for this undertaking. Here, we introduce a multi-module recording system based on graphene microelectrode arrays (G-MEAs), which facilitate high-resolution imaging at different scales and permit electrophysiological recordings with high temporal precision. In conjunction with G-MEAs, we also apply a straightforward machine learning algorithm to streamline the analysis of extensive data gathered from microelectrode array recordings. We illustrate that the integration of G-MEAs, machine learning-based spike analysis, and four-dimensional structured illumination microscopy (4D-SIM) provides a powerful tool for observing the effects of disease progression on hippocampal neurons. Furthermore, treating neurons with an inhibitor of intracellular cholesterol transport to mimic Niemann-Pick disease type C (NPC) results in a significant enlargement of synaptic boutons compared to untreated neurons, ultimately impairing neuronal signaling capabilities.
Dr. Meng Lu is an Assistant Professor at the Institute of Advanced Clinical Medicine at Peking University, where he leads pioneering interdisciplinary research that integrates advanced imaging, artificial intelligence, and biomedical sciences. Before establishing his lab at Peking University, Dr. Lu earned his Ph.D. in Biotechnology from the University of Cambridge and advanced to Senior Research Associate (PI track) in 2022. His work centres on AI-driven scientific discovery, leveraging cutting-edge techniques in computer vision, large language models, and multi-modal models to integrate diverse data sources, delivering a holistic understanding of neuronal network dynamics and disease mechanisms.
Dr. Lu’s current research combines super-resolution imaging, electrophysiology, and deep learning to decode and model the intricate processes within neuronal networks. Focused on identifying diagnostic markers and therapeutic targets for neurodegenerative diseases, this approach provides unprecedented insights into disease pathology.
Simultaneously recording network activity and ultrastructural changes of the synapse can significantly advance our understanding of the structural basis of neuronal functions. The intricate changes in neuronal activity at millisecond-scale and the minute structural modifications at the synapse that are smaller than the diffraction limit present considerable hurdles for this undertaking. Here, we introduce a multi-module recording system based on graphene microelectrode arrays (G-MEAs), which facilitate high-resolution imaging at different scales and permit electrophysiological recordings with high temporal precision. In conjunction with G-MEAs, we also apply a straightforward machine learning algorithm to streamline the analysis of extensive data gathered from microelectrode array recordings. We illustrate that the integration of G-MEAs, machine learning-based spike analysis, and four-dimensional structured illumination microscopy (4D-SIM) provides a powerful tool for observing the effects of disease progression on hippocampal neurons. Furthermore, treating neurons with an inhibitor of intracellular cholesterol transport to mimic Niemann-Pick disease type C (NPC) results in a significant enlargement of synaptic boutons compared to untreated neurons, ultimately impairing neuronal signaling capabilities.
Dr. Meng Lu is an Assistant Professor at the Institute of Advanced Clinical Medicine at Peking University, where he leads pioneering interdisciplinary research that integrates advanced imaging, artificial intelligence, and biomedical sciences. Before establishing his lab at Peking University, Dr. Lu earned his Ph.D. in Biotechnology from the University of Cambridge and advanced to Senior Research Associate (PI track) in 2022. His work centres on AI-driven scientific discovery, leveraging cutting-edge techniques in computer vision, large language models, and multi-modal models to integrate diverse data sources, delivering a holistic understanding of neuronal network dynamics and disease mechanisms.
Dr. Lu’s current research combines super-resolution imaging, electrophysiology, and deep learning to decode and model the intricate processes within neuronal networks. Focused on identifying diagnostic markers and therapeutic targets for neurodegenerative diseases, this approach provides unprecedented insights into disease pathology.
