Wells Stephen A, Morris Paul G, Taylor Joseph D, Nogaret Alain
Department of Physics, University of Bath, Bath, United Kingdom.
Front Comput Neurosci. 2025 Mar 4;19:1458878. doi: 10.3389/fncom.2025.1458878. eCollection 2025.
The identification of ion channels expressed in neuronal function and neuronal dynamics is critical to understanding neurological disease. This program calls for advanced parameter estimation methods that infer ion channel properties from the electrical oscillations they induce across the cell membrane. Characterization of the expressed ion channels would allow detecting channelopathies and help devise more effective therapies for neurological and cardiac disease. Here, we describe Recursive Piecewise Data Assimilation (RPDA), as a computational method that successfully deconvolutes the ionic current waveforms of a hippocampal neuron from the assimilation of current-clamp recordings. The strength of this approach is to simultaneously estimate all ionic currents in the cell from a small but high-quality dataset. RPDA allows us to quantify collateral alterations in non-targeted ion channels that demonstrate the potential of the method as a drug toxicity counter-screen. The method is validated by estimating the selectivity and potency of known ion channel inhibitors in agreement with the standard pharmacological assay of inhibitor potency (IC50).
识别在神经元功能和神经元动力学中表达的离子通道对于理解神经疾病至关重要。该计划需要先进的参数估计方法，从离子通道在细胞膜上诱导的电振荡中推断离子通道特性。对所表达离子通道的表征将有助于检测通道病，并有助于设计出针对神经和心脏疾病更有效的治疗方法。在此，我们描述递归分段数据同化（RPDA），这是一种计算方法，通过对电流钳记录进行同化，成功地解卷积了海马神经元的离子电流波形。这种方法的优势在于能从小规模但高质量的数据集中同时估计细胞中的所有离子电流。RPDA使我们能够量化非靶向离子通道中的并行变化，这证明了该方法作为药物毒性反筛选的潜力。通过估计已知离子通道抑制剂的选择性和效力，并与抑制剂效力的标准药理学测定（IC50）一致，验证了该方法。
