A Molecular Smart Surface for Spatio-Temporal Studies of Cell Mobility
Bibliographic record
Abstract
Active migration in both healthy and malignant cells requires the integration of information derived from soluble signaling molecules with positional information gained from interactions with the extracellular matrix and with other cells. How a cell responds and moves involves complex signaling cascades that guide the directional functions of the cytoskeleton as well as the synthesis and release of proteases that facilitate movement through tissues. The biochemical events of the signaling cascades occur in a spatially and temporally coordinated manner then dynamically shape the cytoskeleton in specific subcellular regions. Therefore, cell migration and invasion involve a precise but constantly changing subcellular nano-architecture. A multidisciplinary effort that combines new surface chemistry and cell biological tools is required to understand the reorganization of cytoskeleton triggered by complex signaling during migration. Here we generate a class of model substrates that modulate the dynamic environment for a variety of cell adhesion and migration experiments. In particular, we use these dynamic substrates to probe in real-time how the interplay between the population of cells, the initial pattern geometry, ligand density, ligand affinity and integrin composition affects cell migration and growth. Whole genome microarray analysis indicates that several classes of genes ranging from signal transduction to cytoskeletal reorganization are differentially regulated depending on the nature of the surface conditions.
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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 | 0.001 |
| 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".