A microfluidic platform with integrated porous membrane cell-substrate impedance spectroscopy (PM-ECIS) for biological barrier assessment
Bibliographic record
Abstract
Abstract Traditionally, biological barriers are assessed in vitro by measuring trans-endothelial/epithelial electrical resistance (TEER) across a monolayer using handheld chopstick electrodes. Implementation of TEER into organ-on-chip (OOC) setups is a challenge however, due to non-uniform current distribution and interference from biomaterials typically found in such systems. In this work, we address the pitfalls of standard TEER measurement through the application of porous membrane electrical cell-substrate impedance sensing (PM-ECIS) to an OOC setup. Gold leaf electrodes (working electrode diameters = 250, 500, 750 µm) were incorporated onto porous membranes and combined with biocompatible tape to assemble microfluidic devices. PM-ECIS resistance at 4 kHz was not influenced by presence of collagen hydrogel in bottom channels, compared to TEER measurements in same devices, which showed a difference of 1723 ± 381.8 Ω (p=0.006) between control and hydrogel conditions. A proof of concept, multi-day co-culture model of the blood-brain barrier was also demonstrated in these devices. PM-ECIS measurements were robust to fluid shear (5 dyn/cm 2 ) in cell-free devices, yet were highly sensitive to flow-induced changes in an endothelial barrier model. Initiation of perfusion (0.06 dyn/cm 2 ) in HUVEC-seeded devices corresponded to significant decreases in impedance at 40 kHz (p<0.01 for 750 and 500 µm electrodes) and resistance at 4 kHz (p<0.05 for all electrode sizes) relative to static control cultures, with minimum values reached at 6.5 to 9.5 hours after induction of flow. Our microfluidic PM-ECIS platform enables sensitive, non-invasive, real-time measurements of barrier function in setups integrating critical OOC features like 3D co-culture, biomaterials and shear stress.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 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".