Combinatorial nanodot stripe assay to systematically study cell haptotaxis
Bibliographic record
Abstract
Abstract Haptotaxis is critical to cell guidance and development and has been studied in vitro using either gradients or stripe assays that present a binary choice between full and zero coverage of a protein cue. However, stripes offer only a choice between extremes, while for gradients, cell receptor saturation, migration history, and directional persistence confound the interpretation of cellular responses. Here, we introduce nanodot stripe assays (NSAs) formed by adjacent stripes of nanodot arrays with different surface coverage. Twenty-one pairwise combinations were designed using 0, 1, 3, 10, 30, 44 and 100% stripes and were patterned with 200 × 200, 400 × 400 or 800 × 800 nm2 nanodots. We studied the migration choices of C2C12 myoblasts that express neogenin on NSAs (and three-step gradients) of netrin-1. The reference surface between the nanodots was backfilled with a mixture of polyethylene glycol and poly-d-lysine to minimize nonspecific cell response. Unexpectedly, cell response was independent of nanodot size. Relative to a 0% stripe, cells increasingly chose the high-density stripe with up to ~90% of cells on stripes with 10% coverage and higher. Cell preference for higher vs. lower netrin-1 coverage was observed only for coverage ratios >2.3, with cell preference plateauing at ~80% for ratios ≥4. The combinatorial NSA enables quantitative studies of cell haptotaxis over the full range of surface coverages and ratios and provides a means to elucidate haptotactic mechanisms.
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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 | 0.000 |
| 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".