A Tri‐Droplet Liquid Structure for Highly Efficient Intracellular Delivery in Primary Mammalian Cells Using Digital Microfluidics
Bibliographic record
Abstract
Automated techniques for mammalian cell engineering are needed to examine a wide range of unique genetic perturbations especially when working with precious patient samples. An automated and miniaturized technique making use of digital microfluidics to electroporate a minimal number of mammalian cells (≈40 000) at a time on a scalable platform is introduced. This system functions by merging three droplets into a continuous droplet chain, which is called a triDrop. In the triDrop configuration, the outer droplets are comprised of high‐conductive liquid while an inner or middle droplet comprising of low‐conductivity liquid that contains the cells and biological payloads. In this work, it is shown that applying a voltage to the outer droplets generates an effective electric field throughout the tri‐droplet structure allowing for insertion of the biological payload into the cells without sacrificing long‐term cell health. This technique is shown for a range of biological payloads including plasmids, mRNA, and fully formed proteins being inserted into adherent and suspension cells which include primary T‐cells. The unique features of flexibility and versatility of triDrop show that the platform can be used for the automation of multiplexed gene edits with the benefits of low reagent consumption and minimal cell numbers.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".