Non-inertial lift induced migration for label-free sorting of cells in a co-flowing aqueous two-phase system
Bibliographic record
Abstract
Label free sorting of circulating tumor cells (CTCs) often remains a challenge due to their rarity in peripheral blood and identical morphology to white blood cells. We present a novel label-free passive microfluidic technique for isolation of cancer cells (EpCAM+ and CD45-) from peripheral blood mononuclear cells (PBMCs) (CD45+ and EpCAM-) in aqueous two-phase system (ATPS). Our technique involves non-inertial lift induced lateral cell migration across liquid-liquid interface that is employed for sorting cells of different size and stiffness. The interplay between lift force and interfacial tension (IFT) force governs cell migration phenomena. We estimate the order of magnitude of the lift force and find it to be higher than the IFT for cancer cells above a critical strain rate parameter ([small gamma, Greek, dot above]/h). The effect of spreading parameter and viscoelastic force was found to have negligible effect on lateral migration of cells. We demonstrated isolation of two different types of cancer cells, namely, MCF-7 and MDA-MB-231 from PBMCs and quantify our sorting results by tagging the cells with EpCAM and CD45 and using fluorescence imaging. With 102-104 cancer cells in 105-107 PBMCs, we achieved a processing rate of >25 000 cells per min at a sorting efficiency of ∼99%. Moreover, we demonstrated that cancer cells isolated from PBMCs using the proposed technique remain viable and can be cultured for downstream 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".