NanoMEA: A Tool for High-Throughput, Electrophysiological Phenotyping of Patterned Excitable Cells
Bibliographic record
Abstract
Matrix nanotopographical cues are known to regulate the structure and function of somatic cells derived from human pluripotent stem cell (hPSC) sources. High-throughput electrophysiological analysis of excitable cells derived from hPSCs is possible via multielectrode arrays (MEAs) but conventional MEA platforms use flat substrates and do not reproduce physiologically relevant tissue-specific architecture. To address this issue, we developed a high-throughput nanotopographically patterned multielectrode array (nanoMEA) by integrating conductive, ion-permeable, nanotopographic patterns with 48-well MEA plates, and investigated the effect of substrate-mediated cytoskeletal organization on hPSC-derived cardiomyocyte and neuronal function at scale. Using our nanoMEA platform, we found patterned hPSC-derived cardiac monolayers exhibit both enhanced structural organization and greater sensitivity to treatment with calcium blocking or conduction inhibiting compounds when subjected to high-throughput dose-response studies. Similarly, hPSC-derived neurons grown on nanoMEA substrates exhibit faster migration and neurite outgrowth speeds, greater colocalization of pre- and postsynaptic markers, and enhanced cell-cell communication only revealed through examination of data sets derived from multiple technical replicates. The presented data highlight the nanoMEA as a new tool to facilitate high-throughput, electrophysiological analysis of ordered cardiac and neuronal monolayers, which can have important implications for preclinical analysis of excitable cell function.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".