Rapid multicomponent optical protein patterning
Bibliographic record
Abstract
Cells sense spatial distributions of molecules which trigger signal transduction pathways that induce the cell to migrate or extend by remodelling the cytoskeleton. However, the influence of local and small variations of extracellular protein concentration on chemotaxis is not fully understood, due in part to the lack of simple and precise methods to pattern proteins in vitro. We recently developed a new technology to fabricate such patterns which relies on photobleaching fluorophores to adsorb proteins on a cell culture substrate: laser-assisted protein adsorption by photobleaching (LAPAP). Here we report several key improvements to LAPAP: we created arbitrary patterns made of several different proteins simultaneously, we reduced the fabrication time more than one order of magnitude and we used secondary antibodies to significantly enlarge the spectrum of proteins that can be employed. As a result, multicomponent protein gradients can be produced using reagents that are typically available in life science research laboratories on a standard inverted microscope equipped with a camera port.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".