Electrophysiological Phenotyping of hiPSC-Derived Atrial Cardiomyocytes Using Automated Patch-Clamp: A Platform for Studying Atrial Inherited Arrhythmias
Bibliographic record
Abstract
Human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) represent a robust platform for modelling inherited cardiac disorders. Comparative analysis of ion channel activity in patient-specific and isogenic control lines provides critical insights into the molecular mechanisms underlying channelopathies and arrhythmias. Atrial-specific hiPSC-CMs (hiPSC-aCMs) exhibit distinct electrophysiological properties governed by unique ion channel expression profiles, underscoring the need for optimized methodologies to record atrial ionic currents accurately. Here, we characterized the electrophysiological features of hiPSC-aCMs using the Nanion Patchliner automated patch-clamp system. An optimized cell dissociation protocol was developed to enhance cell integrity and seal formation, while tailored intra- and extracellular solutions were employed to isolate specific ionic currents. Using this approach, we reliably recorded major atrial currents, including the sodium current (INa), L-type calcium current (ICaL), transient outward potassium current (Ito), ultrarapid component of the delayed rectifier current (IKur), small-conductance calcium-activated potassium current (ISK), and pacemaker funny current (If). The resulting current profiles were reproducible and consistent with those observed in native atrial cardiomyocytes. These findings establish the feasibility of the automated electrophysiological characterization of ion channels in hiPSC-aCMs. This platform enables more efficient investigation of pathogenic variants and facilitates the development of targeted therapeutics for atrial arrhythmias and related channelopathies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".