Isolation of Phenotypically Distinct Cancer Cells Using Nanoparticle-Mediated Sorting
Bibliographic record
Abstract
Isolating subpopulations of heterogeneous cancer cells is an important capability for the meaningful characterization of circulating tumor cells at different stages of tumor progression and during the epithelial-to-mesenchymal transition. Here, we present a microfluidic device that can separate phenotypically distinct subpopulations of cancer cells. Magnetic nanoparticles coated with antibodies against the epithelial cell adhesion molecule (EpCAM) are used to separate breast cancer cells in the microfluidic platform. Cells are sorted into different zones on the basis of the levels of EpCAM expression, which enables the detection of cells that are losing epithelial character and becoming more mesenchymal. The phenotypic properties of the isolated cells with low and high EpCAM are then assessed using matrix-coated surfaces for collagen uptake analysis, and an NAD(P)H assay that assesses metabolic activity. We show that low-EpCAM expressing cells have higher collagen uptake and higher folate-induced NAD(P)H responses compared to those of high-EpCAM expressing cells. In addition, we tested SKBR3 cancer cells undergoing chemically induced hypoxia. The induced cells have reduced expression of EpCAM, and we find that these cells have higher collagen uptake and NAD(P)H metabolism relative to noninduced cells. This work demonstrates that nanoparticle-mediated binning facilitates the isolation of functionally distinct cell subpopulations and allows surface marker expression to be associated with invasiveness, including collagen uptake and metabolic activity.
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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 | 0.000 | 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 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".