Phenotypic targeting using magnetic nanoparticles for rapid characterization of cellular proliferation regulators
Bibliographic record
Abstract
Genome-wide CRISPR screens have provided a systematic way to identify essential genetic regulators of a phenotype of interest with single-cell resolution. However, most screens use live/dead readout of viability to identify factors of interest. Here, we describe an approach that converts cell proliferation into the degree of magnetization, enabling downstream microfluidic magnetic sorting to be performed. We performed a head-to-head comparison and verified that the magnetic workflow can identify the same hits from a traditional screen while reducing the screening period from 4 weeks to 1 week. Taking advantage of parallelization and performance, we screened multiple mesenchymal cancer cell lines for their dependency on cell proliferation. We found and validated pan- and cell-specific potential therapeutic targets. The method presented provides a nanoparticle-enabled approach means to increase the breadth of data collected in CRISPR screens, enabling the rapid discovery of drug targets for treatment.
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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 | 0.001 |
| 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) | 0.001 | 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".