Reproducible Manufacturing of SPOT as a High-throughput Scaffold-based Culture Platform
Bibliographic record
Abstract
Patient-derived organoids (PDOs) are becoming increasingly used in the field of cancer research to model tumors. They combine the benefits of in vivo and traditional in vitro models, recapitulating tissue complexity and heterogeneity while still consisting of human cells. The rise of physiologically representative PDO models has prompted a need for devices that enable straightforward analysis of organoid behavior. This protocol explains how to assemble and use the Scaffold-supported Platform for Organoid-based Tissues (SPOT), a high-throughput, three-dimensional (3D) organoid culture device. This platform eliminates the meniscus typically formed by seeded hydrogels to allow accurate, quick imaging, similar to the imaging process for two-dimensional (2D) cultures. As a result, organoids and co-cultures seeded in SPOT can easily be imaged using high-throughput microscopy. Designed for off-the-shelf use, SPOT is accessible to researchers with basic tissue culture experience and is compatible with both manual and automated seeding workflows. This protocol details the fabrication of SPOT in 96- and 384-well formats, including step-by-step instructions for assembly, seeding, quality control, and troubleshooting. While fabrication requires 3-4 h, excluding idle time, multiple plates can be produced simultaneously, improving scalability. By streamlining PDO culture and analysis, SPOT provides a robust tool for high-throughput drug screening and translational cancer research.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".