A microfluidic platform for complete mammalian cell culture
Bibliographic record
Abstract
We introduce the first lab-on-a-chip platform for complete mammalian cell culture. The new method is powered by digital microfluidics (DMF), a technique in which nanolitre-sized droplets are manipulated on an open surface of an array of electrodes. This is the first application of DMF to adherent cell culture and analysis, and more importantly, represents the first microfluidic platform capable of implementing all of the steps required for mammalian cell culture-cell seeding, growth, detachment, and re-seeding on a fresh surface. Three key innovations were required to implement complete cell culture on a microfluidic device: (1) a technique for growing cells on patterned islands (or "adhesion pads") positioned on an array of DMF actuation electrodes; (2) a method for rapidly and efficiently exchanging media and other reagents on cells grown on adhesion pads; and (3) a system capable of detachment and collection of cells from an (old) origin site and delivery to a (new) destination site for subculture. The new technique was applied to cells from several different lines which were seeded and repeatedly subcultured for weeks at a time in 150 nL droplets. Cells handled in this manner exhibited growth characteristics and morphology comparable to those cultured in standard tissue culture vessels. To illustrate an application for this system, a microfluidic method was developed to implement transient transfection-we propose that the combination of this technique with multigenerational culture allows for "on-demand" generation of transiently transfected cells. Broadly, we anticipate that the automated cell microculture technique presented here will be useful in myriad applications that would benefit from automated mammalian cell culture.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.006 | 0.005 |
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".