Robust Formation of Optimal Single Spheroids towards Cost-Effective In-Vitro 3-Dimensional Tumor Models
Bibliographic record
Abstract
Abstract Monolayer cell cultures, while useful for basic in vitro studies, are not physiologically relevant. Spheroids, a complex 3-dimensional (3D) structure resemble in vivo tumor growth more closely thereby allowing results obtained with spheroids relating to proliferation, cell death, differentiation, metabolism, and various anti-tumor therapies to be more predictive of in vivo outcomes. The protocol herein presents a rapid and high throughput method for the generation of single spheroids using various cancer cell lines including (U87 MG, SEBTA-027, SF188) brain cancer cells, (DU-145, TRAMP-C1) prostate cancer cells, and (BT-549, Py230) breast cancer cells in a 96-round bottom well plates. The proposed method is associated with significantly low costs (ca. £1) per plate without the need for refining or transferring and homogeneous compact spheroid morphology was evidenced as early as 1 day after following this protocol. By using confocal microscopy and the IncuCyte live imaging system, proliferating cells were traced in the rim while dead cells were found to be located inside the core region of the spheroid. H&E staining of spheroid sections was utilized to investigate the tightness of the cell packaging and Western blotting analyses revealed that these spheroids adopted a stem cell-like phenotype. This method was also used to obtain EC50 of the anti-cancer dipeptide carnosine on U87 MG 3D culture. The affordable easy-to-follow 5 step-protocol allows for robust generation of various uniform spheroids which show 3D morphology characteristics.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".