Pre-Labeling of Immune Cells in Normal Bone Marrow and Spleen for Subsequent Cell Tracking by MRI
Why this work is in the frame
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Bibliographic record
Abstract
Iron particles are intravenously (IV) administered to label cells in vivo during magnetic resonance imaging. This technique has been extensively used to monitor immune cells in the context of inflammatory diseases. Here, we have investigated whether resting immune cells can be labeled in vivo in healthy mice before disease onset or injury, thus allowing visualization of critical early cellular events. Using 1.5 T magnetic resonance imaging, we were able to detect signal loss in bone marrow, liver, and spleen as early as 1 hour after the IV injection of superparamagnetic iron oxide nanoparticles (Feridex; 80 to 120 nm in diameter) or larger micron-sized iron oxide particles (Bangs; 0.9 μm in diameter). Results were confirmed via histology. Further, flow cytometric analysis confirmed the presence of iron-labeled CD19+ B cells, CD3+ T cells, and CD11b+ myeloid cells within the spleen and the bone marrow. Extending this work to a murine model of multiple sclerosis, we IV administered superparamagnetic iron oxide to healthy mice 1 week before inducing experimental autoimmune encephalomyelitis. Images acquired 1 week after the onset of hindlimb paralysis showed regions of signal hypointensity in the mouse brain that corresponded with iron-labeled macrophages. In summary, we show that resting immune cells in the healthy mouse liver, spleen, and bone marrow can be prelabeled with iron oxide nanoparticles. Furthermore, iron oxide preloading of immune cells in the reticuloendothelial system can be used to detect cellular infiltration in the brains of experimental autoimmune encephalomyelitis mice.
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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