Quantitative Analysis of Cell Edge Dynamics during Cell Spreading
Bibliographic record
Abstract
Cell spreading is a dynamic process in which a cell suspended in media attaches to a substrate and flattens itself from a rounded to a thin and spread-out shape. Following the cell-substrate attachment, the cell forms a thin sheet of lamellipodia emanating from the cell body. In the lamellipodia, globular actin (G-actin) monomers polymerize into a dense filamentous actin (F-actin) meshwork that pushes against the plasma membrane, thereby providing the mechanical forces required for the cell to spread. Notably, the molecular players that control the actin polymerization in lamellipodia are essential for many other cellular processes, such as cell migration and endocytosis. Since spreading cells form continuous lamellipodia that span the entire cell periphery and persistently expand outward, cell spreading assays have become an efficient tool to assess the kinetics of lamellipodial protrusions. Although several technical implementations of the cell spreading assay have been developed, a detailed description of the workflow, which would include both a step-by-step protocol and computational tools for data analysis, is currently lacking. Here, we describe the experimental procedures of the cell spreading assay and present an open-source tool for quantitative and unbiased analysis of cell edge dynamics during spreading. When combined with pharmacological manipulations and/or gene-silencing techniques, this protocol is amenable to a large-scale screen of molecular players regulating lamellipodial protrusions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".