Sensitivity and validation of porous membrane electrical cell substrate impedance spectroscopy (PM-ECIS) for measuring endothelial barrier properties
Bibliographic record
Abstract
ABSTRACT Conventional trans-endothelial electrical resistance (TEER) setups are invasive and cannot directly measure monolayer integrity in co-culture. These limitations are addressed by porous membrane electrical cell-substrate impedance sensing (PM-ECIS), which measures barrier integrity in cell monolayers grown directly on permeable membranes patterned with electrodes. Here we advance the design and utility of PM-ECIS by investigating its sensitivity to working electrode size and correlation to TEER. Gold electrodes were fabricated on porous membrane inserts using hot embossing and UV lithography, with working electrode diameters of 250, 500, and 750 µm within the same insert. Frequency scans of confluent primary endothelial monolayers showed normalized resistance peaking at 4 kHz, consistent with traditional solid substrate ECIS. Sensitivity to resistance changes (4 kHz) during endothelial barrier formation was inversely proportional to electrode size, with the smallest electrodes being the most sensitive (p<0.001). Similarly, smaller electrodes were most sensitive to changes in impedance (40 kHz) corresponding to cell spreading and proliferation (p<0.001). Barrier disruption with EGTA was detectable by 250 and 750 µm (p<0.01), and 500 µm electrodes (p=0.058). Resistances measured by PM-ECIS vs. TEER for sodium chloride solutions were positively and significantly correlated for all electrode sizes (r>0.9; p<0.0001), but only with 750 µm electrodes for endothelial monolayers (r=0.71; p=0.058). These data inform design and selection of PM-ECIS electrodes for specific applications, and support PM-ECIS as a promising alternative to conventional TEER due to its capacity for direct, non-invasive, and real-time assessment of cells cultured on porous membranes.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.000 |
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".