Toward More Free-Floating Model Cell Membranes: Method Development and Application to Their Interaction with Nanoparticles
Bibliographic record
Abstract
Identifying the mechanisms of nanoparticle (NP) interactions with cell membranes is key to understanding potential NP cytotoxicity and applications as nanocarriers for targeted drug delivery. To elucidate these mechanisms of interaction, supported phospholipid bilayers (SPBs) are commonly used as models of cell membranes. However, SPBs are soft thin films, and, as such, their properties can be significantly affected by the underlying substrate. Free-floating cell membranes would be best modeled by weakly adhered SPBs; thus, we propose a method for tailoring the interfacial interaction of an electrically charged SPB-substrate system based on modulations in the solution chemistry. Using the dissipation signal of the quartz crystal microbalance with dissipation monitoring (QCM-D), we show that the method can be used to tailor SPB-substrate interactions without the loss of its structural integrity. To demonstrate the application of the method, SPBs are exposed to cationic and anionic polystyrene latex NPs. These studies reveal that the bilayer response to the modulations in the interfacial interaction with its underlying substrate can be used as a sensitive tool to probe the integrity of SPBs upon exposure to NPs. As expected, anionic NPs tend to impart no significant damage to the anionic bilayers, whereas cationic NPs can be detrimental to bilayer integrity. This is the first report of a QCM-D based method to probe bilayer integrity following exposure to NPs. Importantly, the degree of SPB interaction with its underlying substrate is shown to be a critical factor in the kinetics of bilayer disruption by cationic NPs, whereby weakly adhered bilayers are prone to significantly faster breakup. Since free-floating cell membranes are better represented by a weakly adhered SPB, the results of this work critically influence paradigms in experimental studies involving SPBs as models for cell membranes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".