Nanoparticle Amplification Labeling for High-Performance Magnetic Cell Sorting
Bibliographic record
Abstract
Magnetic cell sorting is an enabling tool for the isolation of specific cellular subpopulations for downstream applications and requires the cells to be labeled by a sufficient number of magnetic nanoparticles to leverage magnetophoresis for efficient separation. This requirement makes it challenging to target weakly expressed biomarkers. Here, we developed a new approach that selectively and efficiently amplifies the magnetic labeling on cells through sequentially connected antibodies and nanoparticles delivered to the surface or interior of the cell. Using this approach, we achieved amplification up to 100-fold for surface and intracellular markers. We also demonstrated the utility of this assay for enabling high-performance magnetic cell sorting when it is applied to the analysis of rare tumor cells for cancer diagnosis and the purification of transfected CAR T cells for immunotherapy. The data presented demonstrate a useful tool for the stratification of rare cell subpopulations.
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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".