Substrate-Bound Protein Gradients to Study Haptotaxis
Bibliographic record
Abstract
Cells navigate in response to inhomogeneous distributions of extracellular guidance cues. The cellular and molecular mechanisms underlying migration in response to gradients of chemical cues have been investigated for over a century. Following the introduction of micropipettes and more recently microfluidics for gradient generation, much attention and effort was devoted to study cellular chemotaxis, which is defined as guidance by gradients of chemical cues in solution. Haptotaxis, directional migration in response to gradients of substrate-bound cues, has received comparatively less attention; however, it is increasingly clear that in vivo many physiologically relevant guidance proteins - including many secreted cues - are bound to cellular surfaces or incorporated into extracellular matrix and likely function via a haptotactic mechanism. Here, we review the history of haptotaxis. We examine the importance of the reference surface, the surface in contact with the cell that is not covered by the cue, which forms a gradient opposing the gradient of the protein cue and must be considered in experimental designs and interpretation of results. We review and compare microfluidics, contact printing, light patterning, and 3D fabrication to pattern substrate-bound protein gradients in vitro. The range of methods to create substrate-bound gradients discussed herein makes possible systematic analyses of haptotactic mechanisms. Furthermore, understanding the fundamental mechanisms underlying cell motility will inform bioengineering approaches to program cell navigation and recover lost function.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".