Development and validation of platforms with integrated ECIS sensing for application to in vitro biological barrier modeling
Bibliographic record
Abstract
Biological barriers, which separate body compartments and the external environment, play key roles in homeostasis and protection of the body, through regulating nutrient transport, maintaining concentration gradients, and excluding pathogens. A key parameter for assessing in vitro models of biological barriers is barrier integrity. Traditional methods to assess biological barriers – permeability tracer assays and transendothelial electrical resistance (TEER) – are disruptive to cell culture and only indirectly measure cell monolayers in co-culture. The need for non-invasive monitoring is particularly pronounced in organ-on-chip systems, which are designed with the purpose of providing a controlled microenvironment to recapitulate tissue-level functions. The overall objective of this thesis was to validate and develop in vitro platforms with integrated electrical sensing for modeling and non-invasively assessing biological barriers in real time. The sensing technology is based on electrical cell-substrate impedance sensing (ECIS), where cells are grown directly on electrodes. In the first aim, a cell culture insert platform that incorporates ECIS electrodes onto a porous membrane (PM-ECIS) to monitor cells in co-culture was characterized. The sensitivity of PM-ECIS was investigated by assessing the measurement outputs for three different electrode sizes during endothelial barrier formation and disruption. The method was also validated by benchmarking against traditional chopstick TEER values. In the second aim, PM-ECIS electrodes were integrated into a microfluidic platform to evaluate the method’s utility for organ-on-chip applications. Many organ-on-chip platforms incorporate biomaterials (e.g., hydrogels) and fluid flow, which can make the implementation of traditional barrier assessment methods challenging. Measurements taken with PM-ECIS electrodes were shown to be robust to the presence of hydrogel, in contrast to traditional chopstick TEER. The platform’s potential for organ-on-chip applications was further demonstrated through its capacity to support a multi-day co-culture model of the blood-brain barrier, as well as to provide measurements that were sensitive to endothelial barrier changes in response to perfusion. In summary, this work demonstrates the potential of an electrical sensing method which provides direct, non-invasive, and real-time assessment of cells cultured on porous membrane. These capabilities provide a promising alternative to conventional barrier assessment methods for both standard co-culture and microphysiological in vitro applications.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".