NanoMEA: a versatile platform for high-throughput analysis of structure-function relationships in human stem cell-derived excitable cells and tissues
Bibliographic record
Abstract
Abstract Somatic cells derived from human pluripotent stem cell (hPSC) sources hold significant potential as a means to improve current in vitro screening assays. However, their inconsistent ability to recapitulate the structural and functional characteristics of native cells has raised questions regarding their ability to accurately predict the functional behavior of human tissues when exposed to chemical or pathological insults. In addition, the lack of cytoskeletal organization within conventional culture platforms prevents analysis of how structural changes in human tissues affect functional performance. Using cation-permeable hydrogels, we describe the production of multiwell nanotopographically-patterned microelectrode arrays (nanoMEAs) for studying the effect of structural organization on hPSC-derived cardiomyocyte and neuronal function in vitro . We demonstrate that nanoscale topographic substrate cues promote the development of more ordered cardiac and neuronal monolayers while simultaneously enhancing cytoskeletal organization, protein expression patterns, and electrophysiological function in these cells. We then show that these phenotypic improvements act to alter the sensitivity of hPSC-derived cardiomyocytes to treatment with arrhythmogenic and conduction-blocking compounds that target structural features of the cardiomyocyte. Similarly, we demonstrate that neuron sensitivity to synaptic blockers is increased when cells are maintained on nanotopographically-patterned Nafion surfaces. The improved structural and functional capacity of hPSC-derived cardiomyocyte and neuronal populations maintained on nanoMEAs may have important implications for improving the predictive capabilities of cell-based electrophysiological assays used in preclinical screening applications.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".