Diamagnetic droplet microfluidics applied to single-cell sorting
Bibliographic record
Abstract
The heterogeneity of diseased tissue causes major challenges in the detection and treatment of disease. Such challenges have motivated the development of tools for single-cell isolation and analysis. However, many cell isolation methods in microfluidics rely on the use of cell-labeling steps or expose cells to potentially harmful forces. Here, we present a microfluidic method for label-free control of cell-encapsulating biocompatible droplets using negative magnetophoresis. Our system is distinguished from previous microfluidic diamagnetic sorting approaches by the encapsulation of the cells inside droplets, which isolates the cells from the magnetic continuous phase. The droplet phase is comprised of cells suspended in their growth culture medium, and all of the magnetic content is contained in the oil-based continuous phase. At a flow-focusing junction, empty droplets and cell-encapsulating droplets are both generated and surrounded by the magnetic continuous phase. Cell encapsulation produces a size distinction between empty droplets and cell-encapsulated droplets. Through the application of a permanent magnet to the system, diamagnetic size-based sorting of empty droplets from cell-encapsulated droplets is achieved with a purity of ∼84% in a single pass. Additionally, since the encapsulated cells are completely isolated from the magnetic content in the continuous phase, 88% cell-viability is observed after a two-hour incubation period. If combined with a single-cell assay, this system can provide label-free isolation of viable cells at a high purity for subsequent downstream analysis.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".