Nanocontact Printing of Proteins on Physiologically Soft Substrates to Study Cell Haptotaxis
Bibliographic record
Abstract
Abstract Surface bound guidance cues and gradients are vital for directing cellular processes during development and repair. In vivo, these cues are often presented within a soft extracellular matrix with elastic moduli E < 10 kPa, but in vitro haptotaxis experiments have been conducted primarily on hard substrates with elastic moduli in the MPa to GPa range. Here, a technique is presented for patterning haptotactic proteins with nanometer resolution on soft substrates with physiological elasticity. A new nanocontact printing process was developed that circumvented the use of plasma activation that was found to alter the mechanical properties of the substrate. A dissolvable poly(vinyl alcohol) film was first patterned by lift-off nanocontact printing, and in turn printed onto the soft substrate, followed by dissolution of the film in water. An array of 100 unique digital nanodot gradients (DNGs), consisting of millions of 200 × 200 nm2 protein nanodots, was patterned in less than 5 min with with <5% average deviation from the original gradient design. DNGs of netrin-1, a known protein guidance cue, were patterned, and the unpatterned surface was backfilled with a reference surface consisting of 75% polyethylene glycol grafted with polylysine and 25% poly-d-lysine. Haptotaxis of C2C12 myoblasts demonstrated the functionality of the DNGs patterned on soft substrates. In addition, high densities of netrin-1 were observed to induce cell spreading, while live imaging of sinusoidal control gradients highlighted cell migration and navigation by “inching”. The nanopatterning technique developed here paves the way for studying haptotactic responses to diverse digital nanodot patterns on surfaces covering the full range of physiological elasticity, and is expected to be applicable to the study of both culture and primary cells, such as neutrophils and neurons.
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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".