Recovery of phenotypically sorted cells using droplet-digital microfluidics
Bibliographic record
Abstract
Droplet microfluidics has become a ubiquitous and powerful tool for high-throughput phenotypic screening at the single-cell level. Large numbers of cells can be sorted for a variety of functions, including the secretion of antibodies with tailored properties. The recovery of cells from sorted droplets is still very poor compared to droplet sorting, usually being limited to around 50% of all sorted hits. Here, we present a fully integrated droplet-digital microfluidic platform for the isolation and the recovery of rare single cells and applied our system to antibody discovery. From our work, we have achieved an 18-fold increase in the recovery rate of individual cells and beads from droplets, as compared to conventional methods. We believe that the combination of high-throughput droplet generation with the on-demand control features of digital microfluidics will improve the number of characterized hits in single-cell -omics, antibody screens, directed evolution of enzymes, and beyond.
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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 | 0.001 |
| 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".