Microfluidic diamagnetic water-in-water droplets: a biocompatible cell encapsulation and manipulation platform
Bibliographic record
Abstract
Droplet microfluidics enables cellular encapsulation for biomedical applications such as single-cell analysis, which is an important tool used by biologists to study cells on a single-cell level, and understand cellular heterogeneity in cell populations. However, most cell encapsulation strategies in microfluidics rely on random encapsulation processes, resulting in large numbers of empty droplets. Therefore, post-sorting of droplets is necessary to obtain samples of purely cell-encapsulating droplets. With the recent advent of aqueous two-phase systems (ATPS) as a biocompatible alternative of the conventional water-in-oil droplet systems for cellular encapsulation, there has also been a focus on integrating ATPS with droplet microfluidics. In this paper, we describe a new technique that combines ATPS-based water-in-water droplets with diamagnetic manipulation to isolate single-cell encapsulating water-in-water droplets, and achieve a purity of 100% in a single pass. We exploit the selective partitioning of ferrofluid in an ATPS of polyethylene glycol-polypropylene glycol-polyethylene glycol triblock copolymer (PEG-PPG-PEG) and dextran (DEX), to achieve diamagnetic manipulation of water-in-water droplets. A cell-triggered Rayleigh-Plateau instability in the dispersed phase thread results in a size distinction between the cell-encapsulating and empty droplets, enabling diamagnetic separation and sorting of the cell-encapsulating droplets from empty droplets. This is a simple and biocompatible all-aqueous platform for single-cell encapsulation and droplet manipulation, with applications in single-cell analysis.
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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 | 0.000 |
| 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".