Porous membrane electrical cell-substrate impedance spectroscopy for versatile assessment of biological barriers in vitro
Bibliographic record
Abstract
Abstract Cell culture models of endothelial and epithelial barriers typically use porous membrane inserts (e.g., Transwell inserts) as a permeable substrate on which barrier cells are grown, often in co-culture with other cell types on the opposite side of the membrane. Current methods to characterize barrier function in porous membrane inserts can disrupt the barrier or provide bulk measurements that cannot isolate barrier cell resistance alone. Electrical cell-substrate impedance sensing (ECIS) addresses these limitations but its implementation on porous membrane inserts has been limited by costly manufacturing and low sensitivity. Here we present porous membrane ECIS (PM-ECIS), a cost-effective method to adapt ECIS technology to porous substrate-based in vitro models. We demonstrate high fidelity patterning of electrodes on porous membranes that can be incorporated into well plates of a variety of sizes with excellent cell biocompatibility with mono- and co-culture set ups. PM-ECIS provided sensitive, real-time measurement of isolated changes in endothelial cell barrier impedance with cell growth and barrier disruption. Barrier function characterized by PM-ECIS resistance correlated well with permeability coefficients obtained from molecular tracer permeability assays performed on the same cultures, validating the device. Integration of ECIS into conventional porous cell culture inserts provides a versatile, sensitive, and automated alternative to current methods to measure barrier function in vitro, including molecular tracer assays and transepithelial/endothelial electrical resistance (TEER).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".