Journal: Advanced Science
Article Title: Deep Learning‐Based Ion Channel Kinetics Analysis for Automated Patch Clamp Recording
doi: 10.1002/advs.202404166
Figure Lengend Snippet: Overview of the proposed method for ion channel kinetics analysis. A) Ion channels on the cell membrane and the whole‐cell configuration for acquiring ion channel currents. B) Anomaly detection for filtering out anomalous recordings and neural networks for recording multi‐class classification. C) Six representative traces of whole‐cell voltage‐clamp recordings: (I) typical ion channel activity; (II) unsustainable outward currents of non‐inactivation activity; (III) slow‐rising current due to the absence of fast inactivation activity; (IV) the presence of slow activation/inactivation activity; (V) disorder channel activity with overlapping currents; and (VI) observation of hyperpolarization‐activated cyclic nucleotide‐gated activity. D) The artificial intelligence framework analyzed recordings to assess the inhibitory effects of memantine on endogenous ion channels, providing kinetics data for drug screening in neurodegenerative diseases. The evoked ion channel activities (red curves) and non‐evoked response currents (black curves) illustrate the effects of the drug. E) The artificial intelligence framework investigated nanomatrix‐induced NSCs differentiation by identifying neurophysiological properties. The activity of evoked sodium and potassium ion channels indicates the functional properties of the neuronal cells.
Article Snippet: To assess the effectiveness and generalizability of our framework in real‐world applications, we acquired newly unseen whole‐cell recordings from nanomatrix‐induced NSCs differentiation.
Techniques: Membrane, Activity Assay, Activation Assay, Drug discovery, Functional Assay
