Cell Cycle Phases, Their Effect on Cell Mechanical Properties, and the Impact on <i>Candida</i> -Host–Cell Interactions
Bibliographic record
Abstract
Cell mechanics is essential in many biological phenomena such as cell division and migration. Further, cell mechanobiological measurements can distinguish between healthy and diseased cells; thus, investigations of cell mechanics have led to the development of tools to study the elasticity and the viscosity of cells. Cell mechanics can be affected by factors such as cell morphology, cytoskeletal remodelling, and cell intrinsic factors, and these are important in understanding disease progression. Here, we use an atomic force microscopy (AFM)-microrheology with a colloidal probe for a dynamic mechanical analysis of host cell elasticity and viscosity at 6 frequencies ranging from 1 to 200 Hz. Epithelial cells exhibit a more “liquid-like” behavior as the frequency increases, whereas cancerous cells transition into this viscous, fluid-like state at lower frequencies (48 and 63 Hz) compared to normal cells (92 Hz). Cell mechanical measurements inherently exhibit heterogeneity due to physical factors, such as cell shape and the position of the probe on the cell surface. In addition to this physical variability, biological parameters─notably the cell cycle phases─also contribute to mechanical heterogeneity. In this study, we specifically investigated the influence of cell cycle phases on the cell mechanical properties. Using chemically synchronized normal and cancerous cells in different phases of the cell cycle shows that the actin cytoskeleton undergoes rapid reorganization as the cell cycle progresses. Results show that as actin becomes disorganized, the elastic moduli decreases and the loss tangent is larger coupled with a lower phase shift frequency. Cells in the G 1 and S phase had the lowest elastic moduli ( G ′) meaning they were softer than cells in the G 2 / M phase. During disease onset, the pathogen adheres and invades the host, a process that leads to cytoskeleton arrangement and thus changes in cell elasticity, and thus, to understand the impact of the cell cycle on host invasion, we probed the interaction of Candida albicans with HeLa, HCT 116, and HaCaT cells using AFM in the single-cell force spectroscopy mode. There was a significant increase in force of interaction during the S phase which could be attributed to the disorganized cytoskeleton. This shows the importance of cytoskeletal organization and cell cycle phase in cell mechanical properties and pathogen–host interaction.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".