Amplified Micromagnetic Field Gradients Enable High-Resolution Profiling of Rare Cell Subpopulations
Bibliographic record
Abstract
Analyzing small collections of cells is challenging because of the need for extremely high levels of sensitivity. We recently reported a new approach, termed magnetic ranking cytometry (MagRC), to profile nanoparticle-labeled cells. Using antibody-functionalized magnetic nanoparticles, we label cells so that each cell's magnetization is proportional to its surface expression of a selected biomarker. Using a microfluidic device that sorts the cells into 100 different zones based on magnetic labeling levels, we generate profiles that report on the level and distribution of surface expression in small collections of cells. Here, we present a new set of studies investigating in depth parameters such as flow rate and magnetic nanoparticle size that affect device performance using both experiments and modeling. We present a model that further elucidates the mechanism of cell capture and use it to optimize device performance to efficiently capture rare cells. We show that this method has excellent specificity and can be used to characterize rare cells even in the presence of whole blood.
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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".