Micropocket hydrogel devices for all-in-one formation, assembly, and analysis of aggregate-based tissues
Bibliographic record
Abstract
Multicellular aggregated tissues have grown critically important in benchtop biomedical research, both as stand-alone spheroids and when assembled into larger bioengineered constructs. However, typical systems for aggregate formation are limited in their capacity to reliably handle such cultures at various experimental stages in a broadly accessible, consistent, and scalable manner. In this work, we develop a broadly versatile all-in-one biofabrication strategy to form uniform, spherical, multicellular aggregates that can be maintained at precisely defined positions for analysis or transfer into a larger tissue. The 3D-printed MicroPocket Culture (MPoC) system consists of an array of simple geometry-based valves in a polyacrylamide hydrogel, and is able to produce hundreds of uniformly-sized aggregates in standard tissue culture well plates, using simple tools that are readily available in all standard biological wet-labs. The model breast cancer aggregates formed in these experiments are retained in defined positions on chip during all liquid handling steps required to stimulate, label, and image the experiment, enabling high-throughput studies on this culture model. Furthermore, MPoCs enable robust formation of aggregates in cell types that do not conventionally form such structures. Finally, we demonstrate that this single platform can also be used to generate complex 3D tissues from the precisely-positioned aggregate building blocks. To highlight the unique and broad versatility of this technique, we develop a simple 3D invasion assay and show that cancer cells preferentially migrate towards nearby model tumors; demonstrating the importance of spatial precision when engineering 3D tissues. Together, this platform presents a broadly accessible and uniquely capable system with which to develop advanced aggregate-based models for tissue engineering, fundamental research, and applied drug discovery.
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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".