Microscale 3D Collagen Cell Culture Assays in Conventional Flat-Bottom 384-Well Plates
Bibliographic record
Abstract
Three-dimensional (3D) culture systems such as cell-laden hydrogels are superior to standard two-dimensional (2D) monolayer cultures for many drug-screening applications. However, their adoption into high-throughput screening (HTS) has been lagging, in part because of the difficulty of incorporating these culture formats into existing robotic liquid handling and imaging infrastructures. Dispensing cell-laden prepolymer solutions into 2D well plates is a potential solution but typically requires large volumes of reagents to avoid evaporation during polymerization, which (1) increases costs, (2) makes drug penetration variable and (3) complicates imaging. Here we describe a technique to efficiently produce 3D microgels using automated liquid-handling systems and standard, nonpatterned, flat-bottomed, 384-well plates. Sub-millimeter-diameter, cell-laden collagen gels are deposited on the bottom of a ~2.5 mm diameter microwell with no concerns about evaporation or meniscus effects at the edges of wells, using aqueous two-phase system patterning. The microscale cell-laden collagen-gel constructs are readily imaged and readily penetrated by drugs. The cytotoxicity of chemotherapeutics was monitored by bioluminescence and demonstrated that 3D cultures confer chemoresistance as compared with similar 2D cultures. Hence, these data demonstrate the importance of culturing cells in 3D to obtain realistic cellular responses. Overall, this system provides a simple and inexpensive method for integrating 3D culture capability into existing HTS infrastructure.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.008 |
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".