A Digital Microfluidic Platform for the Microscale Production of Functional Immune Cell Therapies
Bibliographic record
Abstract
Abstract Genetically engineering human immune cells has been shown to be an effective approach for developing novel cellular therapies to treat a wide range of diseases. To expand the scope of these cellular therapies while solving persistent challenges, extensive research and development is still required. Electroporation has recently emerged as one of the most popular techniques for inserting biological payloads into human immune cells to perform genetic engineering. However, several recent studies have reported that electroporation can negatively impact cell functionality. Additionally, the requirement to use large amounts of cells and expensive payloads to achieve efficient delivery can drive up the costs of development efforts. Here we use a digital microfluidic enabled electroporation system (referred to as triDrop) and compare them against two state-of-the-art commercially available systems for the engineering of human T cells. We describe the ability to use triDrop for highly viable, highly efficient transfection while using substantially fewer cells and payload. Subsequently, we perform transcriptomic analysis on cells engineered with each of the three systems and show that electroporation with triDrop lead to less dysregulation of several functionally relevant pathways. Finally, in a direct comparison of immunotherapeutic functionality, we show that T cells engineered with triDrop have an improved ability to mount an immune response when presented with tumor cells. These results show that the triDrop platform is uniquely suited to produce functionally engineered immune cells while also reducing the costs of cell engineering compared to other commercially available systems.
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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 | 0.000 | 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".