Visualizing cell–cell communication using synthetic notch activated MRI
Bibliographic record
Abstract
Cell-cell communication plays a fundamental role in multicellular organisms. Cell-based cancer immunotherapies rely on the ability of innate or engineered receptors on immune cells to engage specific antigens on cancer cells to induce tumor kill. To improve the development and translation of these therapies, imaging tools capable of noninvasively and spatiotemporally visualizing immune-cancer cell interactions would be highly valuable. Using the synthetic Notch (SynNotch) system, we engineered T cells that upon interaction with a chosen antigen (CD19) on neighboring cancer cells induce the expression of optical reporter genes and the human-derived, magnetic resonance imaging (MRI) reporter gene organic anion transporting polypeptide 1B3 (OATP1B3). Administration of engineered T cells induced the antigen-dependent expression of all our reporter genes in mice bearing CD19-positive tumors but not CD19-negative tumors. Notably, due to the high spatial resolution and tomographic nature of MRI, contrast-enhanced foci within CD19-positive tumors representing OATP1B3-expressing T cells were clearly visible and their distribution was readily mapped. We then extended this technology onto human natural killer-92 (NK-92) cells, observing similar CD19-dependent reporter activity in tumor-bearing mice. Furthermore, we show that when delivered intravenously, engineered NK-92 cells can be detected via bioluminescence imaging in a systemic cancer model. With continued work, this highly modular imaging strategy could aid in the monitoring of cell therapies in patients and, beyond this, augment our understanding of how different cell populations interact within the body during normal physiology or disease.
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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".