Novel biomechanical silicone assays to quantify cellular contractile forces
Bibliographic record
Abstract
Cell migration is a highly integrated process and basic to many biological functions ranging from development to immune response and wound healing, to metastasis in cancer. For cells to migrate, they must generate and transduce traction forces to their environment, however, no technology to date has provided a high throughput solution for measuring these traction forces. Here I present a new assay for measuring cellular contractile forces employing robust multiwell culture plates: using a 96-well plate format, soft (~2-80 kPa) elastic silicone rubbers with embedded fiduciary particles were fabricated. By measuring the cell-induced deformation of these particles the cellular traction stresses were calculated using Fourier transform cytometry. This system was utilized to quantify the traction mechanics underlying cellular migration in cancer metastasis. Additionally, a novel traction force assay utilizing nanocontact printing of fluorescent proteins is introduced. In this assay, uniform arrays of sub-micron sized fluorescent dots were printed on the surface of soft silicone substrate. Cells were cultured on this Digital Nanodot Assay and cell-induced distortion of these dot patterns was used for calculation of traction stresses.The use of a non-degrading soft silicone rubber creates simple and stable assays, and it is anticipated that both of these novel technologies will be of broad utility in diverse quantitative biological and health sciences.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".