High transfection efficiency, gene expression, and viability of monocyte-derived human dendritic cells after nonviral gene transfer
Bibliographic record
Abstract
Dendritic cells (DCs) are bone marrow-originated, professional antigen-capturing cells and APCs, which can function as vaccine carriers. Although efficient transfection of human DCs has been achieved with viral vectors, viral gene products may influence cellular functions. In contrast, nonviral methods have generally resulted in inefficient gene transfer, low levels of gene expression, and/or low cell viability. Monocyte-derived DCs are the most common source of DCs for in vitro studies and for in vivo applications. We hypothesized that reduction of the time to generate immature DCs (iDCs) might result in higher viability after transfection. Therefore, we established a protocol to generate human iDCs from CD14(+) monocytes within 3 days. These "fast" iDCs were phenotypically and functionally indistinguishable from conventional iDCs, showing high endocytic ability and low antigen-presenting capacity. Furthermore, the fast iDCs matured normally and had similar antigen-presenting capacity to conventional mature DCs. To optimize transfection of iDCs, we compared nonviral transfection of plasmid DNA and in vitro-transcribed (IVT) RNA with transfection reagents, electroporation, and nucleofection. Nucleofection of IVT RNA with the X1 program of an Amaxa Co. Nucleofector resulted in the most efficient transfection, with an average of 93% transfected iDCs, excellent long-term viability, and strong protein expression. Furthermore, the IVT RNA-transfected iDCs retained all phenotypic and functional characteristics of iDCs. This method is applicable to most purposes, including in vitro functional assays, in vivo DC immunotherapy, and DC-based vaccines.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".