High-Throughput Optical Controlling and Recording Calcium Signal in iPSC-Derived Cardiomyocytes for Toxicity Testing and Phenotypic Drug Screening
Bibliographic record
Abstract
Understanding how excitable cells work in health and disease and how that behavior can be altered by small molecules or genetic manipulation is important. Genetically encoded calcium indicators (GECIs) with multiple emission windows can be combined (e.g., for simultaneous observation of distinct subcellular events) or used in extended applications with other light-dependent actuators in excitable cells (e.g., combining genetically encoded optogenetic control with spectrally compatible calcium indicators). Such approaches have been used in primary or stem cell-derived neurons, cardiomyocytes, and pancreatic beta-cells. However, it has been challenging to increase the throughput, or duration of observation, of such approaches due to limitations of the instruments, analysis software, indicator performance, and gene delivery efficiency. Here, a high-performance green GECI, mNeonGreen-GECO (mNG-GECO), and red-shifted GECI, K-GECO, is combined with optogenetic control to achieve all-optical control and visualization of cellular activity in a high-throughput imaging format using a High-Content Imaging System. Applications demonstrating cardiotoxicity testing and phenotypic drug screening with healthy and patient-derived iPSC-CMs are shown. In addition, multi-parametric assessments using combinations of spectral and calcium affinity indicator variants (NIR-GECO, LAR-GECO, and mtGCEPIA or Orai1-G-GECO) are restricted to different cellular compartments are also demonstrated in the iPSC-CM model.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".