Efficient Nonviral Transfection of Human Bone Marrow Mesenchymal Stromal Cells Shown Using Placental Growth Factor Overexpression
Bibliographic record
Abstract
BACKGROUND: Human mesenchymal stromal/stem cells (hMSCs) hold great therapeutic potential due to their immunomodulatory and tissue regenerative properties. Enhancement of biological features of hMSCs by transfection has become a focus of investigation for cell- and gene-based therapies. However, many of the current transient transfection methods result in either low transfection efficiency or high cytotoxicity. METHODS: In order to find a transfection method that would address the current issues of low transfection efficiency and high cytotoxicity, 6 commercially available cationic lipid and polymer reagents were tested on human bone marrow-derived MSCs (hBM-MSCs) using GFP as a reporter gene. One transfection method using TransIT-2020 was selected and tested with an emphasis on cell quality (viability, identity, and yield), as well as efficacy with a human placental growth factor (PlGF) plasmid. RESULTS: TransIT-2020 yielded the highest fluorescence signal per cell out of the methods that did not decrease cell recovery. Transfecting GFP to 5 hBM-MSC donors using TransIT-2020 yielded 24-36% GFP-expressing cells with a viability of 85-96%. hBM-MSC identity was unaffected as CD90, CD105, and CD73 markers were retained (>95%+) after transfection. When this method was applied to PlGF expression, there was up to a 220-fold increase in secretion. Both growth and secretion of PlGF in overexpressing hBM-MSC were sustained over 7 days, confirming the sustainability and applicability of the TransIT-2020 transfection system. DISCUSSION: We report a simple and efficient method for transient transfection that has not been reported for hBM-MSCs, encompassing high levels of plasmid expression without significant changes to fundamental hBM-MSC characteristics.
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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) | 0.000 | 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) | 0.002 | 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".