MEA‐seqX: High‐Resolution Profiling of Large‐Scale Electrophysiological and Transcriptional Network Dynamics
Abstract Concepts of brain function imply congruence and mutual causal influence between molecular events and neuronal activity. Decoding entangled information from concurrent molecular and electrophysiological network events demands innovative methodology bridging scales and modalities. The MEA‐seq...
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| Format: | Article |
| Language: | English |
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Wiley
2025-05-01
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| Series: | Advanced Science |
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| Online Access: | https://doi.org/10.1002/advs.202412373 |
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| author | Brett Addison Emery Xin Hu Diana Klütsch Shahrukh Khanzada Ludvig Larsson Ionut Dumitru Jonas Frisén Joakim Lundeberg Gerd Kempermann Hayder Amin |
| author_facet | Brett Addison Emery Xin Hu Diana Klütsch Shahrukh Khanzada Ludvig Larsson Ionut Dumitru Jonas Frisén Joakim Lundeberg Gerd Kempermann Hayder Amin |
| author_sort | Brett Addison Emery |
| collection | DOAJ |
| description | Abstract Concepts of brain function imply congruence and mutual causal influence between molecular events and neuronal activity. Decoding entangled information from concurrent molecular and electrophysiological network events demands innovative methodology bridging scales and modalities. The MEA‐seqX platform, integrating high‐density microelectrode arrays, spatial transcriptomics, optical imaging, and advanced computational strategies, enables the simultaneous recording and analysis of molecular and electrical network activities at mesoscale spatial resolution. Applied to a mouse hippocampal model of experience‐dependent plasticity, MEA‐seqX unveils massively enhanced nested dynamics between transcription and function. Graph–theoretic analysis reveals an increase in densely connected bimodal hubs, marking the first observation of coordinated hippocampal circuitry dynamics at molecular and functional levels. This platform also identifies different cell types based on their distinct bimodal profiles. Machine‐learning algorithms accurately predict network‐wide electrophysiological activity features from spatial gene expression, demonstrating a previously inaccessible convergence across modalities, time, and scales. |
| format | Article |
| id | doaj-art-94e91c797bc44f45a9fc623de99498ed |
| institution | OA Journals |
| issn | 2198-3844 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Science |
| spelling | doaj-art-94e91c797bc44f45a9fc623de99498ed2025-08-20T02:02:16ZengWileyAdvanced Science2198-38442025-05-011220n/an/a10.1002/advs.202412373MEA‐seqX: High‐Resolution Profiling of Large‐Scale Electrophysiological and Transcriptional Network DynamicsBrett Addison Emery0Xin Hu1Diana Klütsch2Shahrukh Khanzada3Ludvig Larsson4Ionut Dumitru5Jonas Frisén6Joakim Lundeberg7Gerd Kempermann8Hayder Amin9German Center for Neurodegenerative Diseases (DZNE) Group “Biohybrid Neuroelectronics” Tatzberg 41 01307 Dresden GermanyGerman Center for Neurodegenerative Diseases (DZNE) Group “Biohybrid Neuroelectronics” Tatzberg 41 01307 Dresden GermanyGerman Center for Neurodegenerative Diseases (DZNE) Group “Biohybrid Neuroelectronics” Tatzberg 41 01307 Dresden GermanyGerman Center for Neurodegenerative Diseases (DZNE) Group “Biohybrid Neuroelectronics” Tatzberg 41 01307 Dresden GermanyScience for Life Laboratory Department of Gene Technology KTH Royal Institute of Technology Tomtebodavägen 23 17165 Stockholm SwedenDepartment of Cell and Molecular Biology Karolinska Institute Berzelius väg 35 17165 Stockholm SwedenDepartment of Cell and Molecular Biology Karolinska Institute Berzelius väg 35 17165 Stockholm SwedenScience for Life Laboratory Department of Gene Technology KTH Royal Institute of Technology Tomtebodavägen 23 17165 Stockholm SwedenGerman Center for Neurodegenerative Diseases (DZNE) Group “Adult Neurogenesis” Tatzberg 41 01307 Dresden GermanyGerman Center for Neurodegenerative Diseases (DZNE) Group “Biohybrid Neuroelectronics” Tatzberg 41 01307 Dresden GermanyAbstract Concepts of brain function imply congruence and mutual causal influence between molecular events and neuronal activity. Decoding entangled information from concurrent molecular and electrophysiological network events demands innovative methodology bridging scales and modalities. The MEA‐seqX platform, integrating high‐density microelectrode arrays, spatial transcriptomics, optical imaging, and advanced computational strategies, enables the simultaneous recording and analysis of molecular and electrical network activities at mesoscale spatial resolution. Applied to a mouse hippocampal model of experience‐dependent plasticity, MEA‐seqX unveils massively enhanced nested dynamics between transcription and function. Graph–theoretic analysis reveals an increase in densely connected bimodal hubs, marking the first observation of coordinated hippocampal circuitry dynamics at molecular and functional levels. This platform also identifies different cell types based on their distinct bimodal profiles. Machine‐learning algorithms accurately predict network‐wide electrophysiological activity features from spatial gene expression, demonstrating a previously inaccessible convergence across modalities, time, and scales.https://doi.org/10.1002/advs.202412373AI machine‐learningconnectomeexperience‐dependent plasticitylarge‐scale neural recordingspredictive modelingspatial transcriptomics |
| spellingShingle | Brett Addison Emery Xin Hu Diana Klütsch Shahrukh Khanzada Ludvig Larsson Ionut Dumitru Jonas Frisén Joakim Lundeberg Gerd Kempermann Hayder Amin MEA‐seqX: High‐Resolution Profiling of Large‐Scale Electrophysiological and Transcriptional Network Dynamics Advanced Science AI machine‐learning connectome experience‐dependent plasticity large‐scale neural recordings predictive modeling spatial transcriptomics |
| title | MEA‐seqX: High‐Resolution Profiling of Large‐Scale Electrophysiological and Transcriptional Network Dynamics |
| title_full | MEA‐seqX: High‐Resolution Profiling of Large‐Scale Electrophysiological and Transcriptional Network Dynamics |
| title_fullStr | MEA‐seqX: High‐Resolution Profiling of Large‐Scale Electrophysiological and Transcriptional Network Dynamics |
| title_full_unstemmed | MEA‐seqX: High‐Resolution Profiling of Large‐Scale Electrophysiological and Transcriptional Network Dynamics |
| title_short | MEA‐seqX: High‐Resolution Profiling of Large‐Scale Electrophysiological and Transcriptional Network Dynamics |
| title_sort | mea seqx high resolution profiling of large scale electrophysiological and transcriptional network dynamics |
| topic | AI machine‐learning connectome experience‐dependent plasticity large‐scale neural recordings predictive modeling spatial transcriptomics |
| url | https://doi.org/10.1002/advs.202412373 |
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