Investigating cell mechanics with atomic force microscopy
Bibliographic record
Abstract
Transmission of mechanical force is crucial for normal cell development and functioning. However, the process of mechanotransduction cannot be studied in isolation from cell mechanics. Thus, in order to understand how cells 'feel', we must first understand how they deform and recover from physical perturbations. Owing to its versatility, atomic force microscopy (AFM) has become a popular tool to study intrinsic cellular mechanical properties. Used to directly manipulate and examine whole and subcellular reactions, AFM allows for top-down and reconstitutive approaches to mechanical characterization. These studies show that the responses of cells and their components are complex, and largely depend on the magnitude and time scale of loading. In this review, we generally describe the mechanotransductive process through discussion of well-known mechanosensors. We then focus on discussion of recent examples where AFM is used to specifically probe the elastic and inelastic responses of single cells undergoing deformation. We present a brief overview of classical and current models often used to characterize observed cellular phenomena in response to force. Both simple mechanistic models and complex nonlinear models have been used to describe the observed cellular behaviours, however a unifying description of cell mechanics has not yet been resolved.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".