Platform for High-Throughput Testing of the Effect of Soluble Compounds on 3D Cell Cultures
Bibliographic record
Abstract
In vitro 3D culture could provide an important model of tissues in vivo, but assessing the effects of chemical compounds on cells in specific regions of 3D culture requires physical isolation of cells and thus currently relies mostly on delicate and low-throughput methods. This paper describes a technique ("cells-in-gels-in-paper", CiGiP) that permits rapid assembly of arrays of 3D cell cultures and convenient isolation of cells from specific regions of these cultures. The 3D cultures were generated by stacking sheets of 200-μm-thick paper, each sheet supporting 96 individual "spots" (thin circular slabs) of hydrogels containing cells, separated by hydrophobic material (wax, PDMS) impermeable to aqueous solutions, and hydrophilic and most hydrophobic solutes. A custom-made 96-well holder isolated the cell-containing zones from each other. Each well contained media to which a different compound could be added. After culture and disassembly of the holder, peeling the layers apart "sectioned" the individual 3D cultures into 200-μm-thick sections which were easy to analyze using 2D imaging (e.g., with a commercial gel scanner). This 96-well holder brings new utilities to high-throughput, cell-based screening, by combining the simplicity of CiGiP with the convenience of a microtiter plate. This work demonstrated the potential of this type of assays by examining the cytotoxic effects of phenylarsine oxide (PAO) and cyclophosphamide (CPA) on human breast cancer cells positioned at different separations from culture media in 3D cultures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".