Phosphorescence-optimized 2-photon lifetime and kinetic imaging reveals reanimation of tumor immune surveillance by hyper-oxygenation
Bibliographic record
Abstract
Abstract Lymphocytes encounter varying oxygen levels as they traverse through healthy and diseased tissue. Hypoxia is a hallmark of the tumor microenvironment and can affect the anti-tumor immune response. However, despite the importance of T cell adaptation to differing oxygen concentrations in the tumor niche, there has been no method to study T cell spatiotemporal dynamics in the context of oxygen in vivo. To this end, we developed phosphorescence-optimized 2-photon lifetime and kinetic (2pOLAK) microscopy which enables co-imaging of phosphorescence lifetimes and cellular dynamics in highly fluorescent biological reporter systems. In conjunction with the PtP-C343 oxygen probe, 2pOLAK microscopy revealed the tissue oxygen landscapes and individual oxygen "experiences" of T cells as they moved through tissues in syngeneic models of metastatic lung cancer and acute leukemia. We found that T cells experienced hypoxia in leukemic bone marrow, and that the motility of these cells was significantly decreased relative to non-hypoxic T-cells in healthy bone marrow. Inhibition of oxidative phosphorylation slowed non-hypoxic T cell motility to a level comparable with that of hypoxic T cells. T cell motility was also decreased in hypoxic lung tumor cores, and it was significantly lower than in the tumor margin, where T cells experienced higher oxygen. Supplemental oxygenation increased the oxygen experienced by T cells in the tumor core and reanimated T cell motility. These studies describe a novel method for co-imaging tissue oxygen and cellular behavior, shed light on the role that oxygen availability plays in T cell dynamics in vivo, and suggest that counteracting hypoxia can improve tumor immune surveillance by restarting T cell motility.
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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".