[Intrinsic steady-state pattern of mouse cardiac electrophysiology: analysis using a characterized quantitative electrocardiogram strategy]

OBJECTIVE

To explore the intrinsic steady-state electrophysiological properties of mouse heart under physiological conditions by high-resolution quantitative analysis.

METHODS

Twenty-two young adult C57BL/6 mice with a 1:1 male-to-female ratio were used. The limbs of the mice were fixed without anesthesia, and electrocardiographic waveforms, including characteristic P-waves, R-waves, and ST-waves, were recorded using a sensitive 12-lead electrophysiological recorder (ECGsqa) under spontaneous breathing. LabScribe software was used to extract and quantify high-resolution time course and amplitude parameters within a single cardiac cycle from the V3 precordial lead. Pearson correlation test combined with simple linear regression was used to generate a scatter plot of ECG parameter fitting. The common and unique correlation parameters were separately identified by joint associations for profiling the quantitative association network.

RESULTS

ECGsqa analysis identified and quantified 14 characteristic ECG parameters, 28.6% of which showed statistical differences between the groups. Compared to male mice, female mice exhibited higher amplitudes and velocities of R and ST waves. Among the 51 association pairs identified in primary association analysis, 47.1% were positively correlated, including shared (29.2%), male-specific (29.2%), and female-specific (41.7%) association groups. Second-order clustering of the association pairs revealed that the amplituderate association pairs of each waveform voltage in both male and female mouse hearts were strongly correlated. The male mice showed an atrioventricular interconnection pattern, while the female mice showed a unique atrial conduction system quality dependence. The distribution network characteristics of the association groups showed that sex-specific and common correlation sets formed a certain series pattern.

CONCLUSION

We discovered a novel intrinsic correlation network of cardiac electrophysiological traits in male and female mice, which reveals the key internal quantitative characteristics and gender difference of both atrial and ventricular conduction systems.