Merger of dynamic two-photon and phosphorescence lifetime microscopy reveals dependence of lymphocyte motility on oxygen in solid and hematological tumors
Bibliographic record
Abstract
BACKGROUND: Low availability of oxygen in tumors contributes to the hostility of the tumor microenvironment toward the immune system. However, the dynamic relationship between local oxygen levels and the immune surveillance of tumors by tumor infiltrating T-lymphocytes (TIL) remains unclear. This situation reflects a methodological difficulty in visualizing oxygen gradients in living tissue in a manner that is suitable for spatiotemporal quantification and contextual correlation with individual cell dynamics tracked by typical fluorescence reporter systems. METHODS: Here, we devise a regimen for intravital oxygen and cell dynamics co-imaging, termed 'Fast' Scanning Two-photon Phosphorescence Lifetime Imaging Microscopy (FaST-PLIM). Using FaST-PLIM, we image the cellular motility of T-lymphocytes in relation to the microscopic distribution of oxygen in mouse models of hematological and solid tumors, namely in bone marrow with or without B-cell acute lymphocytic leukemia (ALL), and in lungs with sarcoma tumors. RESULTS: below this level. In solid tumors, this relationship was reflected in slow migration of TIL in tumor cores compared to that in tumor margins. Remarkably, breathing 100% oxygen alleviated tumor core hypoxia and rapidly invigorated the motility of otherwise stalled tumor core TILs. CONCLUSIONS: This study demonstrates a versatile and highly contextual FaST-PLIM method for phosphorescence lifetime-based oxygen imaging in living animal tumor immunology models. The initial results of this method application to ALL and solid lung tumor models highlight the importance of oxygen supply for the maintenance of intratumoral T cell migration, define a 5 mmHg local oxygen concentration threshold for TIL motility, and demonstrate efficacy of supplementary oxygen breathing in TIL motility enhancement coincident with reduction of tumor hypoxia.
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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".