Dynamic instability in nanoscale lipid domains revealed by contact mode high speed AFM: effect of amyloid-β and cholesterol content
Bibliographic record
Abstract
Cellular membranes are an essential feature of life, the composition and structure of which is important in governing cellular processes and is linked to multiple disorders. Of particular interest is the role that the lipid membrane plays in amyloidogenic diseases such as Alzheimer's disease (AD), including the role of lipid composition and cholesterol in mediating amyloid toxicity. To mimic neuronal membranes, we used 3-component (DPPC/DOPC/Chol) and 5-component (DPPC/POPC/Chol/sphingomyelin/GM1) model membranes. Atomic force microscopy (AFM) is a key tool in studying the structures of lipid membranes and their interactions with amyloid. Recent advances in contact mode high-speed AFM (HS-AFM) have made it possible to capture dynamic processes at video rate. We used a unique custom-built contact mode HS-AFM to image model lipid membranes and study amyloid-β interactions in liquid. We demonstrate the advantage of using HS-AFM coupled with spatiotemporal variability analysis to capture the dynamic interaction of Aβ 1-42 monomers and oligomers with phase separated lipid bilayers to elucidate the role of nanoscale domains in amyloid-membrane interactions. We show that amyloid oligomer complexes induce greater dynamic instability than monomers, and that low cholesterol membranes are more susceptible to destabilization. Overall, we demonstrate the advantage of HS-AFM to image biological processes on biologically relevant soft samples and discuss tip-sample interactions at high-speed operation in contact mode on lipid membrane models in a liquid environment.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".