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

Area of Science:

• Cardiovascular Research
• Stem Cell Biology
• Computational Biology

Background:

• Human induced pluripotent stem cell (hiPSC)-derived cardiomyocytes (CM) represent a heterogeneous cell population, including ventricular, atrial, and nodal-like subtypes.
• This cellular heterogeneity complicates the reliable study of chamber-specific cardiac diseases and electrophysiological mechanisms.
• Previous methods for classifying CM phenotypes based on action potential morphology lacked defined criteria, limiting their utility.

Purpose of the Study:

• To develop an automated, in silico approach for discriminating electrophysiological differences among hiPSC-CM.
• To establish objective criteria for classifying hiPSC-CM phenotypes.
• To enhance the translational relevance of hiPSC-CM for studying cardiac arrhythmias and drug screening.

Main Methods:

• Utilized the dynamic clamp (DC) technique to inject a specific I_K1 current into hiPSC-CM.
• Derived nine electrical biomarkers to characterize cell electrophysiology.
• Applied unsupervised learning algorithms and principal component analysis for blind CM phenotype classification and visualization.
• Conducted pharmacological validation using specific ion channel blockers and receptor agonists.

Main Results:

• Successfully discriminated electrophysiological differences between hiPSC-CM subtypes.
• Developed an automated classification system for hiPSC-CM phenotypes.
• Demonstrated the utility of the derived electrical biomarkers and classification approach.
• Pharmacological validation confirmed the robustness of the CM classification.

Conclusions:

• The proposed dynamic clamp-based approach with machine learning provides an automated and reliable method for classifying hiPSC-CM electrophysiologically.
• This method improves the translational relevance of hiPSC-CM models for investigating inherited or acquired atrial arrhythmias.
• The approach is valuable for screening anti-arrhythmic agents and advancing the study of human cardiac electrophysiology.