Miniaturized Microfluidic Formats for Cell-Based High-Throughput Screening
Bibliographic record
Abstract
Cell-based high-throughput screening (HTS) has become an important method used in pharmaceutical drug discovery, and is presently carried out using robots and micro-well plates. A microfluidic-based device for cell-based HTS using a traditional cell-culture protocol would be a key enabler in miniaturization and in increasing throughput without consequent detrimental effects on the physiological significance of the screen. In this paper, we illustrate the advances in miniaturization of cell-based HTS, especially using microfabrication and microfluidics. We also detail a novel microfluidic HTS device targeted for cell-based assays using traditional non-compartmentalized agar gel as a cell-culture medium and electric control over drug dose. The basic design of this device consists of a gel layer supported by a nanoporous membrane that is bonded to microchannels underneath it. The pores of the membrane are blocked everywhere except in selected regions that serve as fluidic interfaces between the microchannel below and the gel above. Upon application of an electric field, nanopores start to act as electrokinetic pumps. By selectively switching an array of such micropumps, a number of spots containing drug molecules are created simultaneously in the gel layer. By diffusion, drugs reach the top surface of the gel where cells are to be grown. Based on this principle, a number of different devices can be fabricated using microfabrication technology. The fabricated devices include a single drug spot-forming device, a multiple drug spot-forming device, and a microarray drug spot-forming device. By controlling the pumping potential and duration, spots sizes ranging from 200 mu;m to 6 mm in diameter and having inter-spot distances of 0.4 to 10 mm have been created. The absence of diffusional transport through the nanoporous interfaces without an electric field is demonstrated. A number of representative molecules, including surrogate drug molecules (trypan blue and methylene blue) and biomolecules (DNA and protein) were selected for demonstration purposes. A dosing range of 50 to 3000 mu;g and a spot density of 156 spots/cm2 were achieved. The drug spot density was found to be limited by molecular diffusion in gel, so a numerical study was carried to determine ways to increase density. Based on this simulation, a diffusion barrier was proposed, which uses a specially dimensioned (having shallow grooves) gel sheet to reduce diffusion.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".