Hydrogel Thin Film with Swelling-Induced Wrinkling Patterns for High-Throughput Generation of Multicellular Spheroids
Bibliographic record
Abstract
Three-dimensional (3D) multicellular spheroids (MCSs) mimic the structure and function of real tissue much better than the conventional 2D cell monolayers, however, their application was severely hindered by difficulties in their generation. An ideal method for MCS fabrication should produce spheroids with narrow size distribution and allow for control over their size. The method should also be simple, cheap, and scalable. Here, we use patterned nonadhesive poly(2-hydroxyethyl methacrylate) hydrogel films to guide the self-assembly of cells. The films were fabricated directly in the wells of cell culture plates. They were patterned spontaneously by swelling in water, without the use of any template or specialized facilities. When cell suspension is added, the cells settle down by gravity to the bottom. Because of the presence of the wrinkling pattern composed of uniformed microcaves, the cells accumulate to the center of the microcaves and gradually self-assemble into MCSs. Using this method, monodisperse MCSs were generated. The size of the spheroids can be facilely controlled by the number of cells seeded. The method is compatible with the conventional monolayer cell culture method. Thousands of spheroids can be generated in a single well. We expect this method will pave the way for the application of MCSs in various biomedical areas.
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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".