The suitability of high throughput automated patch clamp for physiological applications
Bibliographic record
Abstract
Abstract Although automated patch clamp (APC) devices have been around for many years and have become an integral part of many aspects of drug discovery, high throughput instruments with gigaohm seal data quality are relatively new. Experiments where a large number of compounds are screened against ion channels are ideally suited to high throughput APC, particularly when the amount of compound available is low. Here we evaluate different APC approaches using a variety of ion channels and screening settings. We have performed a screen of 1920 compounds on GluN1/GluN2A NMDA receptors for negative allosteric modulation using both the SyncroPatch 384 and FLIPR. Additionally, we tested the effect of 36 arthropod venoms on NaV1.9 using a single 384‐well plate on the SyncroPatch 384. As an example for mutant screening, a range of acid‐sensing ion channel variants were tested and the success rate increased through fluorescence‐activated cell sorting (FACS) prior to APC experiments. Gigaohm seal data quality makes the 384‐format accessible to recording of primary and stem cell‐derived cells on the SyncroPatch 384. We show recordings in voltage and current clamp modes of stem cell‐derived cardiomyocytes. In addition, the option of intracellular solution exchange enabled investigations into the effects of intracellular Ca2+ and cAMP on TRPC5 and HCN2 currents, respectively. Together, these data highlight the broad applicability and versatility of APC platforms and also outlines some limitations of the approach. Key points High throughput automated patch clamp (APC) can be used for a variety of applications involving ion channels. Lower false positive rates were achieved using automated patch clamp versus a fluorometric imaging plate reader (FLIPR) in a high throughput compound screen against NMDA receptors. Genetic variants and mutations can be screened on a single 384‐well plate to reduce variability of experimental parameters. Intracellular solution can be perfused to investigate effects of ions and second messenger systems without the need for excised patches. Primary cells and stem cell‐derived cells can be used on high throughput APC with reasonable success rates for cell capture, voltage clamp measurements and action potential recordings in current clamp mode.
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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 | 0.001 | 0.002 |
| 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 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".