Sensitive Spatiotemporal Tracking of Spontaneous Metastasis in Deep Tissues via a Genetically-Encoded Magnetic Resonance Imaging Reporter
Bibliographic record
Abstract
ABSTRACT Metastasis remains a poorly understood aspect of cancer biology and the leading cause of cancer-related death, yet most preclinical cancer studies do not examine metastasis, focusing solely on the primary tumor. One major factor contributing to this paradox is a gap in available tools for accurate spatiotemporal measurements of metastatic spread in vivo . Our objective was to develop an imaging reporter system that offers sensitive three-dimensional detection of cancer cells at high resolutions in live mice. We utilized organic anion-transporting polypeptide lb3 ( oatp1b3 ) as a magnetic resonance imaging (MRI) reporter gene to this end, and systematically optimized its framework for in vivo tracking of viable cancer cells in a spontaneous metastasis model. We were able to image metastasis on oatp1b3 -MRI at the single lymph node level and continued to track its progression over time as cancer cells spread to multiple lymph nodes and different organ systems in single animals. While initial single lesions were successfully imaged in parallel via bioluminescence, later metastases were obscured by light scatter from the initial node. Importantly, we demonstrate and validate that 100-μm isotropic resolution MR images could detect micrometastases in lung tissue estimated to contain fewer than 10 3 cancer cells. In summary, oatp1b3 -MRI enables precise determination of lesion size and location over time and offers a path towards deep-tissue tracking of any oatp1b3-engineered cell type with combined high resolution, high sensitivity, 3D spatial information, and surrounding anatomical context.
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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 | 0.001 |
| 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 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".