Transfection of hard-to-transfect primary human macrophages with<i>Bax</i>siRNA to reverse Resveratrol-induced apoptosis
Bibliographic record
Abstract
Small interfering RNA (siRNA) is a critical loss-of-function tool for elucidating the role of genes in biomedical studies. The effective use of siRNA needs transfection technology that delivers siRNA into the correct location of target cells, especially those which are extremely difficult to transfect. Macrophages, which play an important role in the pathogenesis of many diseases, are known to be extremely hard to transfect. Thus, to elucidate the functions of genes in human macrophage biology, it is essential to devise technology for efficient siRNA transfection. However, a fast and efficient method for siRNA transfection in primary human macrophages has not been reported. The siRNA transfection is a tug-of-war between transfection rate and cytotoxicity. A higher transfection rate is generally accompanied with increased cytotoxicity, therefore, choosing a transfection reagent that limits cell death while maintain a desirable transfection rate is important. In this study, we employed auto-analysis function of the IncuCyte® to devise a fast and cost-saving technology for efficient transfection of adherent cells and particularly human macrophages. We show that DharmaFECT3 transfection reagent from Dharmacon was the most efficient in transfecting primary human monocyte-derived macrophages and PMA-differentiated U937 cells, whereas other transfection reagents tested were cytotoxic. This method exhibited approximately 85% transfection efficiency in human macrophages. Moreover, siRNA silencing of Bax with this technique effectively protected primary human macrophages and PMA-differentiated U937 cells against Resveratrol-induced cell death. In addition, this method inherently takes the balance between transfection rate and cytotoxicity of siRNA transfection reagents into consideration.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".