Development of a Human Photoacoustic Imaging Reporter Gene Using the Clinical Dye Indocyanine Green
Bibliographic record
Abstract
Purpose To develop a photoacoustic imaging (PAI) reporter gene that has high translational potential. Previous research has shown that human organic anion–transporting polypeptide 1b3 (OATP1B3) promotes the uptake of the near-infrared fluorescent dye indocyanine green (ICG). In this study, the authors have established OATP1B3 and ICG as a reporter gene–probe pair for in vivo PAI. Materials and Methods Human breast cancer cells were engineered to express OATP1B3. Control cells (not expressing OATP1B3) or OATP1B3-expressing cells were incubated with or without ICG, placed in a breast-mimicking phantom, and imaged with PAI. Control (n = 6) or OATP1B3-expressing (n = 5) cells were then implanted orthotopically into female mice. Full-spectrum PAI was performed before and 24 hours after ICG administration. One-way analysis of variance was performed, followed by Tukey posthoc multiple comparisons, to assess statistical significance. Results OATP1B3-expressing cells incubated with ICG exhibited a 2.7-fold increase in contrast-to-noise ratio relative to all other controls in vitro (P < .05). In mice, PAI signals after ICG administration were increased 2.3-fold in OATP1B3 tumors relative to those in controls (P < .05). Conclusion OATP1B3 operates as an in vivo PAI reporter gene based on its ability to promote the cellular uptake of ICG. Benefits include the human derivation of OATP1B3, combined with the use of wavelengths in the near-infrared region, high extinction coefficient, low quantum yield, and clinical approval of ICG. The authors posit that this system will be useful for localized monitoring of emerging gene- and cell-based therapies in clinical applications. © RSNA, 2019 Keywords: Animal Studies, Molecular Imaging, Molecular Imaging-Clinical Translation, Molecular Imaging-Reporter Gene Imaging, Optical Imaging Supplemental material is available for this article.
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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 | 0.000 | 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".