Molecular imaging of endothelial progenitor cell engraftment using contrast-enhanced ultrasound and targeted microbubbles
Bibliographic record
Abstract
AIMS: Imaging methods to track the fate of progenitor cells after their delivery would be useful in assessing the efficacy of cell-based therapies. We hypothesized that contrast-enhanced ultrasound (CEU) using microbubbles targeted to a genetically engineered cell-surface marker on endothelial progenitor cells (EPCs) would allow the targeted imaging of vascular engraftment. METHODS AND RESULTS: Rodent bone marrow-derived EPCs were isolated, cultured, and transfected to express the marker protein, H-2Kk, on the cell surface. Non-transfected EPCs and EPCs transfected with either null plasmid or Firefly luciferase served as controls. Control microbubbles (MB(C)) and microbubbles targeted to H-2Kk expressed on EPCs (MB(H-2Kk)) were constructed. Binding of targeted microbubbles to EPCs was assessed in vitro using a parallel plate flow chamber system. CEU imaging of EPC-targeted microbubbles was assessed in vivo using subcutaneously implanted EPC-supplemented Matrigel plugs in rats. In flow chamber experiments, there was minimal attachment of microbubbles to plated control EPCs. Although numbers of adhered MB(C) were also low, there was greater and more diffuse attachment of MB(H-2Kk) to plated H-2Kk-transfected EPCs. Targeted CEU demonstrated marked contrast enhancement at the periphery of the H-2Kk-transfected EPC-supplemented Matrigel plug for MB(H-2Kk,) whereas contrast enhancement was low for MB(C). Contrast enhancement was also low for both microbubbles within control mock-transfected EPC plugs. The signal intensity within the H-2Kk-transfected EPC plug was significantly greater for MB(H-2Kk) when compared with MB(C). CONCLUSION: Microbubbles targeted to a genetically engineered cell-surface marker on EPCs exhibit specific binding to EPCs in vitro. These targeted microbubbles bind to engrafted EPCs in vivo within Matrigel plugs and can be detected by their enhancement on CEU imaging.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".