A dynamic clamping approach using in silico IK1 current for discrimination of chamber-specific hiPSC-derived cardiomyocytes

Abstract

Human induced pluripotent stem cell (hiPSC)-derived cardiomyocytes (CM) constitute a mixed population of ventricular-, atrial-, nodal-like cells, limiting the reliability for studying chamber-specific disease mechanisms. Previous studies characterised CM phenotype based on action potential (AP) morphology, but the classification criteria were still undefined. Our aim was to use in silico models to develop an automated approach for discriminating the electrophysiological differences between hiPSC-CM. We propose the dynamic clamp (DC) technique with the injection of a specific I_K1 current as a tool for deriving nine electrical biomarkers and blindly classifying differentiated CM. An unsupervised learning algorithm was applied to discriminate CM phenotypes and principal component analysis was used to visualise cell clustering. Pharmacological validation was performed by specific ion channel blocker and receptor agonist. The proposed approach improves the translational relevance of the hiPSC-CM model for studying mechanisms underlying inherited or acquired atrial arrhythmias in human CM, and for screening anti-arrhythmic agents.

Fig. 1. Molecular and functional assessment of atrial-like hiPSC-CM at 30 days of differentiation.

a K⁺ channel KCNA5 and KCNJ3 mRNA relative expression in RA vs Std protocols (n = 9 technical replicates from 4 independent differentiations). b The percentage of atrial- vs ventricular-like cardiomyocytes (CM) was evaluated by immunofluorescence for the specific isoform of myosin light chain 2 (MLC2v, ventricular and MLC2a, atrial in red). Cells were counterstained for cardiac troponin T (TnT, in green) cells (n = 4 independent differentiations) scale bars = 200 μm. c Electrical (MEA, c_I) and mechanical (MUSCLEMOTION,c_II) recordings of RA-treated clusters of CMs compared to Std one. Higher beating rate and shorter FPDc was quantified from MEA (n = 10 and n = 17 clusters of Std and RA respectively, from 6 independent differentiations). Shorter contraction duration in mechanical RA events were analysed in MUSCLEMOTION recordings (n = 7 clusters of Std and RA, from 6 independent differentiations). d Overlapped recordings highlight different kinetic properties of excitation-contraction (EC) coupling in both conditions. Red dashed line showed electromechanical coupling in Std compared to RA cluster (e) Examples of ACh-elicited current recordings and analysis of peak steady-state, in single Std- (black) and RA-treated (red) CMs (n = 10 and n = 21, respectively from 2 independent differentiations). Percentage of ACh-responsive (yes) and not-responsive CMs (no) are quantified using both differentiation protocols. (Std standard protocol, RA retinoic acid, FPDc corrected field potential duration, Contr. ACh acetylcholine, I_KACh ACh-activated current). Data shown are mean ± SEM (*p < 0.05 paired t-test RA vs Std).

Fig. 2. G_K1 parameter setting of I_{K1_Atr} model.

a Examples of evoked shorter (atrial-like) and longer (ventricular-like) AP profiles following progressive increase of G_K1 ranging from 0.2 to 1 nS/pF (0.05 nS/pF step). Light colour code for low G_K1 values and dark colors code for high G_K1 values. b E_diast, APD₉₀, APD₂₀/APD₉₀ and APA changes yielded from all cardiomyocytes (n = 3 differentiations, n = 3-14 cells, depending on the cell stability at lower G_K1) are represented against G_K1 values. Red dashed line identifies the critical G_K1 value (vertical lines) to reach stable AP parameters (horizontal lines shows the steady state of specific parameter obtained injecting critical G_k1). APD₉₀ AP duration measured at 90% of the repolarisation phase, E_diast diastolic membrane potential, APD₂₀ AP duration measured at 20%, APD₂₀/APD₉₀ ratio, APA AP amplitude, G_K1, I_K1 conductance. Data are presented as mean ± SE.

Fig. 3. I_{K1_Atr}vs I_{K1_Ventr} formulation effect on action potential (AP) profile.

a I_{K1_Atr} and I_{K1_Ventr} computational model I/V relationships; Test-1 and Test-2 representing V_peak and I/V_decay changes in I_{K1_Ventr} toward those of I_{K1_Atr} (violet and green dotted lines, respectively). b AP profiles (b_I-III) recorded in three hiPSC-CM following the alternative injection of I_{K1_Ventr} (blue), I_{K1_Atr} (red), Test 1_V_peak (violet) and Test 2_V_decay (green) computational models (b_IV-VI). I-clamp recordings without I_K1 injection are represented as black traces in all panels. (n = 6–8 cells, p < 0.05 see Supplementary Tables 4-5).

Fig. 4. hiPSC-CMs AP phenotype cell classification by unsupervised learning algorithm.

a A K-means classification algorithm was applied to cluster cells according to 9 electrical parameters (cluster 1 vs cluster 2). b C_m, E_diast, E_diast + DC, APD₉₀, APD₅₀, APD₂₀, APD₂₀/APD₉₀, dV/dt_max, APA in cluster 1 vs cluster 2. The best cut-off discriminating cluster 1 vs cluster 2 was defined by ROC curve analysis (see also Supplementary Table 8) and indicated with a dashed line. Data shown are median interquartile range 25^th, 50^th, 75^th percentiles and analysed by the Mann–Whitney U test. C_m cell membrane capacitance, E_diast diastolic membrane potential, E_diast with DC, diastolic membrane potential with dynamic clamp; APD₉₀, APD₅₀, and APD₂₀, action potential duration measured at 90%, 50%, and 20% of the repolarisation phase, respectively, APD₂₀/APD₉₀ ratio between APD₂₀ and APD₉₀, dV/dt_max maximal AP phase 0 depolarisation velocity, APA AP amplitude; (n cells = 46, from 4 independent differentiations). (*p < 0.05; **p < 0.01; ***p ≤ 0.005).

Fig. 5. Based-model pharmacological validation and RA efficiency evaluation.

a Example of cut-off- sensitive and non-sensitive based atrial- and ventricular-like APs recorded during the baseline condition and the superfusion of I_Kur-specific blocker 4-aminopyridine (4-AP, 50 µM). b Distribution of APD₂₀/APD₉₀ values of 4-AP-sensitive and 4-AP non-sensitive CMs, with respect to the critical cut-off value (0.44). The “failing” cells compared to expectation are showed as green dots in the bar graph (n = 19 randomly selected cells from 4 independent differentiations) and AP waveforms are shown in the insets. c Atrial- and ventricular-like AP phenotypes represented in the panel and their quantitative change (%) with either the Std- or RA-treated cellular platform (8 independent experiments, 4 Std and 4 RA, each including 20–30 recorded cells). (Std standard protocol, RA retinoic acid, APD₂₀/APD₉₀ ratio between APD₂₀ and APD₉₀). Data shown are mean ± SEM (*p < 0.05 paired t-test RA vs Std).

Fig. 6. I_{K1_Ventr}vs I_{K1_Atr} application and repolarization stability.

a A series of 30 APs recorded under the injection of I_{K1_Atr} or I_{K1_Ventr} computational model either in atrial- or ventricular-like CMs. In parallel, the dispersion of relative APD₉₀ values is plotted around the identity line in Poincaré plots. b Linear STV/APD₉₀ correlations in each condition, resulting significantly higher with I_{K1_Ventr} vs I_{K1_Atr} model in atrial- and ventricular-like CMs (n = 10 and n = 5 cells respectively, from 4 independent differentiations) (*p < 0.05 paired t-test RA vs Std). c Variance comparison of atrial and ventricular (n = 16 and n = 7 cells respectively, from 2 independent differentiations) electrical biomarkers under the injection of two I_K1 models, showed as violin graphs. (APD₉₀, APD₅₀, APD₂₀, AP duration measured at 90, 50 and 20% of the repolarisation phase; APD₂₀/APD₉₀ ratio; I_{K1_Ventr}, ventricular I_K1 equation; I_{K1_Atr}, atrial I_K1 equation; STV, short-term variability). Levene’s test was used to quantify the equality of variance in triangulation parameters (*p < 0.05; **p < 0.01).