High-throughput microfluidic cell sorting platform (MICS)
Bibliographic record
Abstract
<title>Abstract</title> Genome-scale functional genetic screens can be used to interrogate determinants of protein expression modulation of a target of interest. Such phenotypic screening approaches typically require sorting of large numbers of cells (>10<sup>8</sup>). In conventional cell sorting techniques (i.e. fluorescence-activated cell sorting), sorting time, associated with high instrument and operating costs and loss of cell viability, are limiting to the scalability and throughput of these screens. We recently established a rapid and scalable high-throughput microfluidic cell sorting platform (MICS) using immunomagnetic nanoparticles to sort cells in parallel capable of sorting more than 10<sup>8</sup> HAP1 cells in under one hour while maintaining high levels of cell viability (Ref. 1). This protocol outlines how to set-up MICS for large-scale phenotypic screens in mammalian cells. We anticipate this platform being used for genome-wide functional genetic screens as well as other applications requiring the sorting of large numbers of cells based on protein expression.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".