Miniaturized scalable arrayed CRISPR screening in primary cells enables discovery at the single donor resolution
Bibliographic record
Abstract
High-efficiency gene editing in primary human cells is critical for advancing therapeutic development and functional genomics, yet conventional electroporation platforms often require high cell input and are poorly suited to parallelized experiments. Here we introduce a next-generation digital microfluidics (DMF) electroporation platform that enables high-throughput, low-input genome engineering using discrete droplets manipulated on a planar electrode array. The system supports 48 independently programmable reaction sites and integrates seamlessly with laboratory automation, allowing efficient delivery of CRISPR-Cas9 RNPs and mRNA cargo into as few as 3,000 primary human cells per condition. The platform was validated across diverse primary human cell types and cargo modalities, demonstrating efficient delivery of various cargo, with high rates of transfection, gene knockout via non-homologous end joining, and precise knock-in through homology-directed repair. To showcase its utility in functional genomics, we applied the platform to an arrayed CRISPR-Cas9 screen in chronically stimulated human CD4⁺ T cells, identifying novel regulators of exhaustion, including epigenetic and transcriptional modulators. These findings establish our DMF-based electroporation platform as a powerful tool for miniaturized genome engineering in rare or precious cell populations and provide a scalable framework for high-content genetic screening in primary human cells.
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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 | 0.001 | 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".