Microfluidic assay for the on-chip electrochemical measurement of cell monolayer permeability
Bibliographic record
Abstract
Cell monolayers, including endothelial cells lining the vasculature and blood-brain barrier, and epithelial cells lining the lung airways and gut, form a semipermeable barrier across which transport of biomolecules is tightly regulated. The assessment of barrier function is therefore critical in in vitro models of barrier-forming tissues, including microfluidic organ-on-a-chip models. Cell monolayer barrier function is commonly assessed using a fluorescent tracer-based permeability assay in both conventional Transwell and organ-on-a-chip models, but this method requires laborious manual sampling, bulky instrumentation and offline sample processing. In this work, we introduce a novel on-chip microfluidic permeability assay that replaces the traditional fluorescent tracer with an electroactive tracer. Similar to methods such as TEER, the electrochemical permeability assay eliminates the need for manual sampling and complex optical instrumentation. We validated the method by demonstrating close agreement between experimental and numerically-simulated diffusive and convective transport in the microfluidic device. Different electroactive tracers were screened for efficient electron transfer, stability and inertness relative to the cell monolayer. The assay was then used to measure the permeability of endothelial cells cultured under both static and flow culture conditions, and after exposure to a permeability mediator. In summary, the electrochemical permeability assay combines the simplicity of tracer-based permeability methods with the benefits of on-chip integration, which will ultimately facilitate the robust multiparametric characterization of barrier function in microfluidic organs-on-chips.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".