An Efficient and Flexible Cell Aggregation Method for 3D Spheroid Production
Bibliographic record
Abstract
Monolayer cell culture does not adequately model the in vivo behavior of tissues, which involves complex cell-cell and cell-matrix interactions. Three-dimensional (3D) cell culture techniques are a recent innovation developed to address the shortcomings of adherent cell culture. While several techniques for generating tissue analogues in vitro have been developed, these methods are frequently complex, expensive to establish, require specialized equipment, and are generally limited by compatibility with only certain cell types. Here, we describe a rapid and flexible protocol for aggregating cells into multicellular 3D spheroids of consistent size that is compatible with growth of a variety of tumor and normal cell lines. We utilize varying concentrations of serum and methyl cellulose (MC) to promote anchorage-independent spheroid generation and prevent the formation of cell monolayers in a highly reproducible manner. Optimal conditions for individual cell lines can be achieved by adjusting MC or serum concentrations in the spheroid formation medium. The 3D spheroids generated can be collected for use in a wide range of applications, including cell signaling or gene expression studies, candidate drug screening, or in the study of cellular processes such as tumor cell invasion and migration. The protocol is also readily adapted to generate clonal spheroids from single cells, and can be adapted to assess anchorage-independent growth and anoikis-resistance. Overall, our protocol provides an easily modifiable method for generating and utilizing 3D cell spheroids in order to recapitulate the 3D microenvironment of tissues and model the in vivo growth of normal and tumor 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".