Atomic Force Microscopy and High-Content Analysis: Two Innovative Technologies for Dissecting the Relationship Between Epithelial–Mesenchymal Transition-Related Morphological and Structural Alterations and Cell Mechanical Properties
Bibliographic record
Abstract
Epithelial-mesenchymal transition (EMT) is a complex series of cellular reprogramming events culminating in striking alterations in morphology towards an invasive mesenchymal phenotype. Increasingly, evidence suggests that EMT exerts a pivotal role in pathophysiological situations including fibrosis and cancer. Core to these dynamical changes in cellular polarity and plasticity is discrete modifications in cytoskeletal structure. In particular, newly established actin-stress fibres supplant a preceding system of highly organised cortical actin. Although cumulative studies have contributed to elucidation of the detailed signalling pathways that underpin this elaborate molecular process, there remains a deficiency regarding its precise contribution to cellular biomechanics. The advent of atomic force microscopy (AFM) and high-content analysis (HCA) provides two innovative technologies for dissecting the relationship between EMT-related morphological and structural alterations and cell mechanical properties. AFM permits acquisition of high resolution topographical images and detailed analysis of cellular viscoelasticity while HCA facilitates a comprehensive and perspicacious assessment of morphological changes. In combination, they offer the possibility of novel insights into the dynamic traits of transitioning cells. Herein, a detailed protocol describing AFM and HCA techniques for evaluation of transforming growth factor-β1-induced EMT of alveolar epithelial cells is provided.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".