Magnetic Particle Imaging is a sensitive <i>in vivo</i> imaging modality for the quantification of dendritic cell migration
Bibliographic record
Abstract
Abstract Immunotherapies, such as dendritic cell- (DC-)based therapies, are useful for treating cancer as an alternative to or in combination with traditional therapies. Cells must migrate to lymphoid organs to be effective and the magnitude of the ensuing T cell response is proportional to the number of lymph node-migrated DC. With less than 10% of cells expected to reach their destination, there is a need for an imaging modality capable of sensitively and quantitatively detecting cells. MRI has been used to track DC using iron and 19F methods, with limitations. Quantification of iron-induced signal loss is indirect and challenging; 19F signal is directly quantifiable but lacks sensitivity. Magnetic Particle Imaging (MPI) directly detects superparamagnetic iron oxide nanoparticles (SPIO) and enables quantitation of low numbers of SPIO-labeled cells. Here we describe the first study using MPI to track and quantify the migration of DC, injected into the footpads of C57BL/6 mice, to the popliteal lymph nodes (pLNs). As DC migrate from the site of injection to the lymph nodes, we measured a decrease in signal in the footpads and an increase in signal at the pLNs. The presence of SPIO-labeled DC in nodes was validated by ex vivo MPI and histology. By measuring the iron mass per cell in samples of labeled cells, we were able to provide an estimate of cell number for each source of signal and we report a sensitivity of approximately 4000 cells in vivo and 2000 cells ex vivo . For some mice, MPI was compared to cellular MRI. We also bring attention to the issue of resolving unequal signals within close proximity, a challenge for many pre-clinical studies using a highly concentrated tracer bolus that over shadows nearby lower signals. This study demonstrates the clear advantage of MPI to detect and quantify cells in vivo , bridging the gap left by cellular MRI, and all other in vivo imaging modalities, and opening the door for quantitative imaging of cellular immunotherapies.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".