Journal Article

DZNE-2025-00655

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png MEA-seqX: High-Resolution Profiling of Large-Scale Electrophysiological and Transcriptional Network Dynamics.

Emery, B. A. (First author)DZNE* ; Hu, X.DZNE* ; Klütsch, D.DZNE* ; Khanzada, S.DZNE* ; Larsson, L. ; Dumitru, I. ; Frisén, J. ; Lundeberg, J. ; Kempermann, G.DZNE* ; Amin, H. (Last author)DZNE*

2025
Wiley-VCH Weinheim

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Please use a persistent id in citations: doi:10.1002/advs.202412373

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.

Keyword(s): Animals (MeSH) ; Mice (MeSH) ; Hippocampus: physiology (MeSH) ; Hippocampus: metabolism (MeSH) ; Gene Regulatory Networks: genetics (MeSH) ; Electrophysiological Phenomena: physiology (MeSH) ; Gene Expression Profiling: methods (MeSH) ; Neuronal Plasticity: physiology (MeSH) ; Neurons: physiology (MeSH) ; Machine Learning (MeSH) ; AI machine‐learning ; connectome ; experience‐dependent plasticity ; large‐scale neural recordings ; predictive modeling ; spatial transcriptomics ; spatiotemporal dynamics

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Contributing Institute(s):

1. Biohybrid Neuroelectronics (BIONICS) (AG Amin)
2. Adult Neurogenesis (AG Kempermann)

Research Program(s):

1. 351 - Brain Function (POF4-351) (POF4-351)
2. 352 - Disease Mechanisms (POF4-352) (POF4-352)

Appears in the scientific report 2025

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