Fabrication of thin‐layer matrigel‐based constructs for three‐dimensional cell culture
Bibliographic record
Abstract
Extracellular matrix-based hydrogels such as Matrigel are easy-to-use, commercially available, and offer environments for three-dimensional (3-D) cell culture that mimic native tissue. However, manipulating small volumes of these materials to produce thin-layer 3-D culture systems suitable for analysis is difficult because of air-liquid-substrate interfacial tension effects and evaporation. Here, we demonstrate two simple techniques that use standard liquid-handling tools and nontreated 96-well plates to produce uniform, thin-layer constructs for 3-D culture of cells in Matrigel. The first technique, the floating 3-D cell culture method, uses phase-separating polymers to form a barrier between the dispensed Matrigel, air, and cultureware surface to generate consistently thin hydrogels from volumes as low as 5 μL. These unanchored gels provide a useful assay for investigating airway smooth muscle cell contraction and may have future applications in studying asthma pathophysiology. The second technique, the fixed 3-D cell culture method, provides an anchored gel system for culturing noncontractile cells (e.g., neurons) where 20 μL of Matrigel is dispensed into the bottom of a well filled with culture medium to form a thin gel containing embedded cells. This technique has potential widespread applications as an accessible 3-D culture platform for high-throughput production of disease models for evaluation of novel drug therapies. © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 35: e2733, 2019.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".