Multi-Modal Multi-Spectral Intravital Microscopic Imaging of Signaling Dynamics in Real-Time during Tumor–Immune Interactions
Bibliographic record
Abstract
Intravital microscopic imaging (IVM) allows for the study of interactions between immune cells and tumor cells in a dynamic, physiologically relevant system in vivo. Current IVM strategies primarily use fluorescence imaging; however, with the advances in bioluminescence imaging and the development of new bioluminescent reporters with expanded emission spectra, the applications for bioluminescence are extending to single cell imaging. Herein, we describe a molecular imaging window chamber platform that uniquely combines both bioluminescent and fluorescent genetically encoded reporters, as well as exogenous reporters, providing a powerful multi-plex strategy to study molecular and cellular processes in real-time in intact living systems at single cell resolution all in one system. We demonstrate that our molecular imaging window chamber platform is capable of imaging signaling dynamics in real-time at cellular resolution during tumor progression. Importantly, we expand the utility of IVM by modifying an off-the-shelf commercial system with the addition of bioluminescence imaging achieved by the addition of a CCD camera and demonstrate high quality imaging within the reaches of any biology laboratory.
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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 | 0.000 | 0.000 |
| 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".