A Thermo-responsive collapse system for controlling heterogeneous cell localization, ratio and interaction for three-dimensional solid tumor modeling
Bibliographic record
Abstract
models, however, poses a challenge to mechanistic studies attempting to approve existing approaches of effector cell-mediated cytotoxicity within a three-dimensional, solid tumor-like environment. To gain additional experimental control, we developed an approach for constructing three-dimensional (3D) culture models using smart polymers that form temperature responsive hydrogels. By embedding cells in these hydrogels, we constructed 3D models to organize multiple cell populations at specified ratios on-demand and gently position them by exploiting the hydrogel phase transition. These systems were amenable to imaging at low- and high-resolution to evaluate cell-to-cell interactions, as well as to dissociation to allow for single cell analyses. We have called this approach "thermal collapse of strata" (TheCOS) and demonstrated its use in creating complex cell assemblies on demand in both layers and spheroids. As an application, we utilized TheCOS to evaluate the impact of directionality of degranulation of natural killer (NK) cell lytic granules. Blocking lytic granule convergence and polarization by inhibiting dynein has been shown to induce bystander killing in single cell suspensions. Using TheCOS we showed that lytic granule dispersion induced by dynein inhibition can be sustained in 3D and results in a multi-directional killing including that of non-triggering bystander cells. By imaging TheCOS experiments, we were able to map a "kill zone" associated with multi-directional degranulation in simulated solid tumor environments. TheCOS should allow for the testing of approaches to alter the mechanics of cytotoxicity as well as to generate a wide-array of human tumor microenvironments to assist in the acceleration of tumor immunotherapy.
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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".