<i>In vivo</i> tracking of adenoviral-transduced iron oxide-labeled bone marrow-derived dendritic cells using magnetic particle imaging
Bibliographic record
Abstract
ABSTRACT Background Despite widespread study of dendritic cell (DC)-based cancer immunotherapies, the in vivo post-injection fate of DC remains largely unknown. Due in part to a lack of quantifiable imaging modalities, this is troubling as the amount of DC migration to secondary lymphoid organs correlates with therapeutic efficacy. Preliminary studies have identified magnetic particle imaging (MPI) as a suitable modality to quantify in vivo migration of superparamagnetic iron oxide-(SPIO)-labeled DC. Herein, we describe a lymph node- (LN)-focused MPI scan to quantify DC in vivo migration accurately and consistently. Methods Both adenovirus (Ad)-transduced SPIO + (Ad SPIO + ) and SPIO + C57BL/6 bone marrow-derived DC were generated and assessed for viability and phenotype using flow cytometry. Ad SPIO + and SPIO + DC were fluorescently-labeled and injected into C57BL/6 mouse hind footpads (n=6). Two days later, in vivo DC migration was quantified using whole animal, popliteal LN- (pLN)-focused, and ex vivo pLN MPI scans. Results No significant differences in viability, phenotype and in vivo pLN migration were noted for Ad SPIO + and SPIO + DC. Day 2 pLN-focused MPI successfully quantified DC migration in all instances while whole animal MPI only quantified pLN migration in 75% of cases. Ex vivo MPI and fluorescence microscopy confirmed MPI signal was pLN-localized and due to originally-injected Ad SPIO + and SPIO + DC. Conclusions We overcame a reported limitation of MPI by using a pLN-focused MPI scan to quantify pLN-migrated Ad SPIO + and SPIO + DC in 100% of cases. With this improved method, we detected as few as 1000 DC (4.4 ng Fe) in vivo . MPI is a suitable pre-clinical imaging modality to assess DC-based cancer immunotherapeutic efficacy.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".