Tunable PhenoCycler imaging of the murine pre-clinical tumour microenvironments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: The tumour microenvironment (TME) consists of tumour-supportive immune cells, endothelial cells, and fibroblasts. PhenoCycler, a high-plex single cell spatial biology imaging platform, is used to characterize the complexity of the TME. Researchers worldwide harvest and bank tissues from mouse models which are employed to model a plethora of human disease. With the explosion of interest in spatial biology, these panoplies of archival tissues provide a valuable resource to answer new questions. Here, we describe our protocols for developing tunable PhenoCycler multiplexed imaging panels and describe our open-source data analysis pipeline. Using these protocols, we used PhenoCycler to spatially resolve the TME of 8 routinely employed pre-clinical models of lymphoma, breast cancer, and melanoma preserved as FFPE. RESULTS: Our data reveal distinct TMEs in the different cancer models that were imaged and show that cell-cell contacts differ depending on the tumour type examined. For instance, we found that the immune infiltration in a murine model of melanoma is altered in cellular organization in melanomas that become resistant to αPD-1 therapy, with depletions in a number of cell-cell interactions. CONCLUSIONS: This work presents a valuable resource study seamlessly adaptable to any field of research involving murine models. The methodology described allows researchers to address newly formed hypotheses using archival materials, bypassing the new to perform new mouse studies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it