Robotic manipulation of cardiomyocytes to identify gap junction modifiers for arrhythmogenic cardiomyopathy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Arrhythmogenic cardiomyopathy (ACM) is a leading cause of sudden cardiac death among young adults. Aberrant gap junction remodeling has been linked to disease-causative mutations in plakophilin-2 ( PKP2 ). Although gap junctions are a key therapeutic target, measurement of gap junction function in preclinical disease models is technically challenging. To quantify gap junction function with high precision and high consistency, we developed a robotic cell manipulation system with visual feedback from digital holographic microscopy for three-dimensional and label-free imaging of human induced pluripotent stem cell–derived cardiomyocytes (iPSC-CMs). The robotic system can accurately determine the dynamic height changes in the cells’ contraction and resting phases, microinject drug-treated healthy and diseased iPSC-CMs in their resting phase with constant injection depth across all cells, and deposit a membrane-impermeable dye that solely diffuses between cells through gap junctions for measuring the gap junction diffusion function. The robotic system was applied toward a targeted drug screen to identify gap junction modulators and potential therapeutics for ACM. Five compounds were found to dose-dependently enhance gap junction permeability in cardiomyocytes with PKP2 knockdown. In addition, PCO 400 (pinacidil) reduced beating irregularity in a mouse model of ACM expressing mutant PKP2 (R735X). These results highlight the utility of the robotic cell manipulation system to efficiently assess gap junction function in a relevant preclinical disease model, thus providing a technique to advance drug discovery for ACM and other gap junction–mediated diseases.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it