Velocity valleys enable efficient capture and spatial sorting of nanoparticle-bound cancer cells
Bibliographic record
Abstract
The development of strategies for isolating rare cells from complex matrices like blood is important for a wide variety of applications including the analysis of bloodborne cancer cells, infectious pathogens, and prenatal testing. Due to their high colloidal stability and surface-to-volume ratio, antibody-coated magnetic nanoparticles are excellent labels for cellular surface markers. Unfortunately, capture of nanoparticle-bound cells at practical flow rates is challenging due to the small volume, and thus low magnetic susceptibility, of magnetic nanoparticles. We have developed a means to capture nanoparticle-labeled cells using microstructures which create pockets of locally low linear velocity, termed velocity valleys. Cells that enter a velocity valley slow down momentarily, allowing the magnetic force to overcome the reduced drag force and trap the cells. Here, we describe a model for this mechanism of cell capture and use this model to guide the rational design of a device that efficiently captures rare cells and sorts them according to surface expression in complex matrices with greater than 10,000-fold specificity. By analysing the magnetic and drag forces on a cell, we calculate a threshold linear velocity for capture and relate this to the capture efficiency. We find that the addition of X-shaped microstructures enhances capture efficiency 5-fold compared to circular posts. By tuning the linear velocity, we capture cells with a 100-fold range of surface marker expression with near 100% efficiency and sort these cells into spatially distinct zones. By tuning the flow channel geometry, we reduce non-specific cell adhesion by 5-fold.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".