Control of Cell Geometry through Infrared Laser Assisted Micropatterning
Bibliographic record
Abstract
Micropatterning is an established technique in the cell biology community used to study connections between the morphology and function of cellular compartments while circumventing complications arising from natural cell-to-cell variations. To standardize cell shape, cells are either confined in 3D molds or controlled for adhesive geometry through adhesive islands. However, traditional micropatterning techniques based on photolithography and deep UV etching heavily depend on clean rooms or specialized equipment. Here we present an infrared laser assisted micropatterning technique (microphotopatterning) modified from Doyle et al. that can be conveniently set up with commercially available imaging systems. In this protocol, we use a Nikon A1R MP+ imaging system to generate micropatterns with micron precision through an infrared (IR) laser that ablates preset regions on poly-vinyl alcohol coated coverslips. We employ a custom script to enable automated pattern fabrication with high efficiency and accuracy in systems not equipped with a hardware autofocus. We show that this IR laser assisted micropatterning (microphotopatterning) protocol results in defined patterns to which cells attach exclusively and take on the desired shape. Furthermore, data from a large number of cells can be averaged due to the standardization of cell shape. Patterns generated with this protocol, combined with high resolution imaging and quantitative analysis, can be used for relatively high throughput screens to identify molecular players mediating the link between form and 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".