Highly efficient non-viral delivery of macromolecules to unactivated human T cells using microfluidic transfection
Bibliographic record
Abstract
Abstract Traditional methods for transfecting unactivated human T cells are limited in their delivery efficiency while maintaining cell viability, phenotype, and function. To address these challenges, we developed the CellPore™ Transfection System. This microfluidic platform creates transient disruptions in the plasma membrane via pressure-induced mechanical deformation, allowing for cytosolic delivery. First, we developed a workflow to optimize delivery pressure to T cells using FITC-dextran. This optimized pressure was used to deliver mRNA for expression of eGFP (91.8%) and mCherry (92.9%), and Cas9 RNP complexes targeting B2M and TRAC, with knockout efficiencies of 74.0% MHC-I and 93.9% TCRαβ, respectively. Importantly, while electroporation of T cells resulted in increased expression of the activation marker CD69 (41.2%) and proinflammatory cytokines (77.7 pg/mL IL-2, 8.8 pg/mL IFNγ), CellPore™-transfected T cells maintained an unactivated phenotype (0.8% CD69+, 0.1 pg/ml IL-2 and 0.2 pg/ml IFNγ) and were capable of downstream activation/expansion via CD3, CD28, and CD2 cross-linking. Using this optimized workflow, efficient cytosolic delivery of FITC-dextran to human CD4 (97.4%), CD8 (96.6%) and regulatory (98.3%) T cells was also achieved. The CellPore™ Transfection System enables robust and efficient delivery of cargo to T cells without impacting cell function in a simple workflow that can easily be integrated into research protocols.
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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 | 0.000 |
| 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".