Development of reporter gene imaging techniques for long-term assessment of human circulating angiogenic cells
Bibliographic record
Abstract
The use of biomaterials and tracking the long-term fate of the transplanted cells is expected to help improve the clinical translation of cell therapies for cardiac regeneration. To this end, reporter gene strategies are promising for monitoring the fate of cells transplanted with or without a delivery biomaterial; however, their application with primary adult progenitor cells (such as human circulating angiogenic cells (CACs)) has not been extensively evaluated. In this study, human CACs were transduced with reporter genes via one of two lentiviral (LV) vectors: LV-GFP-iresTK or LV-Fluc-RFP-tTK. The mean transduction efficiency was 15% (LV-GFP-iresTK) and 13% (LV-Fluc-RFP-tTK) at multiplicities of infection (MOI) of 10 and 50, respectively. Western blot analysis confirmed HSV1-tk protein expression in transduced CACs. There was no significant difference in viability between the transduced CACs and the untreated controls at a MOI of 50 or below. However, a reduction was observed in cell viability of CACs transduced with LV-Fluc-RFP-tTK at an MOI of 100. Cell migration and angiogenic potential were not affected by transduction protocol. After 4 weeks, 80.3 ± 8.4% of the labeled cells continued to express the reporters and could be visualized when embedded within a collagen matrix scaffold. Therefore, quiescent human CACs can be stably transduced to express reporter genes without affecting their function. This reporter gene technique is a promising approach to be further tested for tracking transplanted CACs (±delivery matrix) non-invasively and longitudinally.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".