Internally Controlled and Dynamic Optical Measures of Functional Tumor Biology
Bibliographic record
Abstract
Imaging defined aspects of functional tumor biology with bioluminescent reporter transgenes is a popular approach in preclinical drug development as it is sensitive, relatively high-throughput and low cost. However, the lack of internal controls subject functional bioluminescence to a number of unpredictable variables that reduce this powerful tool to semi-quantitative interpretation of large-scale effects. Here, we report the generation of sensitive and quantitative live reporters for two key measures of functional cancer biology and pharmacologic stress: the cell cycle and oxidative stress. We developed a two-colored readout, where two independent enzymes convert a common imaging substrate into spectrally distinguishable light. The signal intensity of one color is dependent upon the biological state, whereas the other color is constitutively expressed. The ratio of emitted colored light corrects the functional signal for independent procedural variables, substantially improving the robustness and interpretation of relatively low-fold changes in functional signal intensity after drug treatment. The application of these readouts in vitro is highly advantageous, as peak cell response to therapy can now be readily visualized for single or combination treatments and not simply assessed at an arbitrary and destructive timepoint. Spectral imaging in vivo can be challenging, but we also present evidence to show that the reporters can work in this context as well. Collectively, the development and validation of these internally controlled reporters allow researchers to robustly and dynamically visualize tumor cell biology in response to treatment. Given the prevalence of bioluminescence imaging, this presents significant and much needed opportunities for preclinical therapeutic development.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".