Controlling cell–cell interactions using surface acoustic waves
Bibliographic record
Abstract
The interactions between pairs of cells and within multicellular assemblies are critical to many biological processes such as intercellular communication, tissue and organ formation, immunological reactions, and cancer metastasis. The ability to precisely control the position of cells relative to one another and within larger cellular assemblies will enable the investigation and characterization of phenomena not currently accessible by conventional in vitro methods. We present a versatile surface acoustic wave technique that is capable of controlling the intercellular distance and spatial arrangement of cells with micrometer level resolution. This technique is, to our knowledge, among the first of its kind to marry high precision and high throughput into a single extremely versatile and wholly biocompatible technology. We demonstrated the capabilities of the system to precisely control intercellular distance, assemble cells with defined geometries, maintain cellular assemblies in suspension, and translate these suspended assemblies to adherent states, all in a contactless, biocompatible manner. As an example of the power of this system, this technology was used to quantitatively investigate the gap junctional intercellular communication in several homotypic and heterotypic populations by visualizing the transfer of fluorescent dye between cells.
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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 | 0.001 | 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".