Feasibility of Using Adjunctive Optogenetic Technologies in Cardiomyocyte Phenotyping – from the Single Cell to the Whole Heart
Bibliographic record
Abstract
In 1791, Galvani established that electricity activated excitable cells. In the two centuries that followed, electrode stimulation of neuronal, skeletal and cardiac muscle became the adjunctive method of choice in experimental, electrophysiological, and clinical arenas. This approach underpins breakthrough technologies like implantable cardiac pacemakers that we currently take for granted. However, the contact dependence, and field stimulation that electrical depolarization delivers brings inherent limitations to the scope and experimental scale that can be achieved. Many of these were not exposed until reliable in vitro stem-cell derived experimental materials, with genotypes of interest, were produced in the numbers needed for multi-well screening platforms (for toxicity or efficacy studies) or the 2D or 3D tissue surrogates required to study propagation of depolarization within multicellular constructs that mimic clinically relevant arrhythmia in the heart or brain. Here the limitations of classical electrode stimulation are discussed. We describe how these are overcome by optogenetic tools which put electrically excitable cells under the control of light. We discuss how this enables studies in cardiac material from the single cell to the whole heart scale. We review the current commercial platforms that incorporate optogenetic stimulation strategies, and summarize the global literature to date on cardiac applications of optogenetics. We show that the advantages of optogenetic stimulation relevant to iPS-CM based screening include independence from contact, elimination of electrical stimulation artefacts in field potential measuring approaches such as the multi-electrode array, and the ability to print re-entrant patterns of depolarization at will on 2D cardiomyocyte monolayers.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".