Modeling sources of interlaboratory variability in electrophysiological properties of mammalian neurons
Bibliographic record
Abstract
Patch-clamp electrophysiology is widely used to characterize neuronal electrical phenotypes. However, there are no standard experimental conditions for in vitro whole cell patch-clamp electrophysiology, complicating direct comparisons between data sets. In this study, we sought to understand how basic experimental conditions differ among laboratories and how these differences might impact measurements of electrophysiological parameters. We curated the compositions of external bath solutions (artificial cerebrospinal fluid), internal pipette solutions, and other methodological details such as animal strain and age from 509 published neurophysiology articles studying rodent neurons. We found that very few articles used the exact same experimental solutions as any other, and some solution differences stem from recipe inheritance from advisor to advisee as well as changing trends over the years. Next, we used statistical models to understand how the use of different experimental conditions impacts downstream electrophysiological measurements such as resting potential and action potential width. Although these experimental condition features could explain up to 43% of the study-to-study variance in electrophysiological parameters, the majority of the variability was left unexplained. Our results suggest that there are likely additional experimental factors that contribute to cross-laboratory electrophysiological variability, and identifying and addressing these will be important to future efforts to assemble consensus descriptions of neurophysiological phenotypes for mammalian cell types. NEW & NOTEWORTHY This article describes how using different experimental methods during patch-clamp electrophysiology impacts downstream physiological measurements. We characterized how methodologies and experimental solutions differ across articles. We found that differences in methods can explain some, but not all, of the study-to-study variance in electrophysiological measurements. Explicitly accounting for methodological differences using statistical models can help correct downstream electrophysiological measurements for cross-laboratory methodology differences.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".