Photoacoustic imaging of gene expression using tyrosinase as a reporter gene
Bibliographic record
Abstract
Optical reporter genes, such as green fluorescence protein, are powerful research tools that allow visualization of gene expression. We have successfully used tyrosinase as a reporter gene for photoacoustic imaging. Tyrosinase is the key regulatory enzyme in the production of melanin which has a broad optical absorption spectrum. MCF-7 cells were stably transfected with tyrosinase under the control of an inducible promoter. For photoacoustic experiments, MCF-7 cells were resuspended at 108 cells/mL and injected in 700 μm (inner diameter) plastic tubing. Photoacoustic signal of MCF-7 cells expressing tyrosinase were >20-fold greater than those of untransfected MCF-7 cells. Photoacoustic signal of tyrosinaseexpressing MCF-7 cells were approximately 2-fold lesser and greater than those of blood at 576 and 650 nm, respectively, suggesting that photoacoustic signal from blood and tyrosinase-expressing cells can be separated by dualwavelength analysis. Photoacoustic signal from tyrosinase-expressing MCF-7 cells covered by chicken tissue could even be detected at a laser penetration depth of 4 cm, suggesting that tyrosinase can be used to image gene expression in relatively deep tissues. The current data suggests that tyrosinase is a strong reporter gene for photoacoustic imaging.
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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 | 0.000 | 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 | 0.000 |
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 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".