Gradient-based parameter optimization method to determine membrane ionic current composition in human induced pluripotent stem cell-derived cardiomyocytes

Abstract

Premature cardiac myocytes derived from human induced pluripotent stem cells (hiPSC-CMs) show heterogeneous action potentials (APs), probably due to different expression patterns of membrane ionic currents. We developed a method for determining expression patterns of functional channels in terms of whole-cell ionic conductance (Gx) using individual spontaneous AP configurations. It has been suggested that apparently identical AP configurations can be obtained using different sets of ionic currents in mathematical models of cardiac membrane excitation. If so, the inverse problem of Gx estimation might not be solved. We computationally tested the feasibility of the gradient-based optimization method. For a realistic examination, conventional 'cell-specific models' were prepared by superimposing the model output of AP on each experimental AP recorded by conventional manual adjustment of Gxs of the baseline model. Gxs of 4–6 major ionic currents of the 'cell-specific models' were randomized within a range of ± 5–15% and used as an initial parameter set for the gradient-based automatic Gxs recovery by decreasing the mean square error (MSE) between the target and model output. Plotting all data points of the MSE–Gx relationship during optimization revealed progressive convergence of the randomized population of Gxs to the original value of the cell-specific model with decreasing MSE. The absence of any other local minimum in the global search space was confirmed by mapping the MSE by randomizing Gxs over a range of 0.1–10 times the control. No additional local minimum MSE was obvious in the whole parameter space, in addition to the global minimum of MSE at the default model parameter.

Cell-specific models of hiPSC-CMs developed by the gradient-based parameter optimization method fitting two different action potential waveforms

“…Although the PS parameters of the initial parameter set were randomized for each run of the PS method, the BPs were distributed within an obvious envelope and well converged at a normalized I xsf = 1 value in the baseline model. The minimum level of log(MSE) was mostly smaller than − 2 (MSE < 0.01 mV 2 ), which is in the same range as those obtained in single AP waveform tting (see Kohjitani et al 25 ).…”

“…2), a slight gap remained after the end of the PO run between the target (red trace) and the model output (black trace), most probably due to the irregular slow uctuations. Indeed, the nal magnitude of log(MSE) is comparable to results obtained in the single AP wave tting in EM mode in our previous study 25 . The result of the multi-run test shown in the right half of Fig.…”

“…Indeed, in the study of Kohjitani et al 25 , it was demonstrated that the dispersion of the parameters was much improved by xing one of the parameters included in the co-relationship. In the simultaneous tting to two waveforms in the PS method, variable co-relations will be added.…”

Human induced pluripotent stem cell-derived closed-loop cardiac tissue for drug assessment

“…In the future, the adoption of Single-cell RNA sequencing (scRNA-seq) holds the promise of providing more precise genetic information and enhanced resolution of maturation states for each of these subpopulations.” In addition, there may be variation between different batches of experiment, for example, the GJA1 expression in the adult group, adapted from previous report 60 is lower than those in TW and control group ( Figure 2 D), and the GJA1 expression level of adult heart (Normalized to TPM) varies a lot among the different articles. 61 , 62 , 63 In the future, it may be more appropriate to perform the comparison within the same batch to minimize the variation.…”

Single shot detection of alterations across multiple ionic currents from assimilation of cell membrane dynamics

“…In this work, we demonstrate a powerful method based on statistical data assimilation (DA) that extracts information on multiple ionic currents simultaneously from chaotically driven current-clamp recordings. The method synchronizes a Hodgkin–Huxley-like model 20 to the membrane voltage oscillations of a hippocampal neuron to estimate ion channel parameters 21 – 24 such as maximal conductances 25 , voltage thresholds, slopes of activation curves, and recovery time constants that constitute the fingerprints of individual ion channels 26 – 28 . The predictive power of our approach is based on the observation that ionic currents reconstructed from estimated parameters carry an uncertainty three times lower than the parameters themselves.…”