Microgel Iron Oxide Nanoparticles for Tracking Human Fetal Mesenchymal Stem Cells Through Magnetic Resonance Imaging
Bibliographic record
Abstract
Stem cell transplantation for regenerative medicine has made significant progress in various injury models, with the development of modalities to track stem cell fate and migration post-transplantation being currently pursued rigorously. Magnetic resonance imaging (MRI) allows serial high-resolution in vivo detection of transplanted stem cells labeled with iron oxide particles, but has been hampered by low labeling efficiencies. Here, we describe the use of microgel iron oxide (MGIO) particles of diameters spanning 100-750 nm for labeling human fetal mesenchymal stem cells (hfMSCs) for MRI tracking. We found that MGIO particle uptake by hfMSCs was size dependent, with 600-nm MGIO (M600) particles demonstrating three- to sixfold higher iron loading than the clinical particle ferucarbotran (33-263 versus 9.6-42.0 pg iron/hfMSC; p < .001). Cell labeling with either M600 particles or ferucarbotran did not affect either cellular proliferation or tri-lineage differentiation into osteoblasts, adipocytes, and chondrocytes, despite differences in gene expression on a genome-wide microarray analysis. Cell tracking in a rat photothrombotic stroke model using a clinical 1.5-T MRI scanner demonstrated the migration of labeled hfMSCs from the contralateral cortex to the stroke injury, with M600 particles achieving a five- to sevenfold higher sensitivity for MRI detection than ferucarbotran (p < .05). However, model-related cellular necrosis and acute inflammation limited the survival of hfMSCs beyond 5-12 days. The use of M600 particles allowed high detection sensitivity with low cellular toxicity to be achieved through a simple incubation protocol, and may thus be useful for cellular tracking using standard clinical MRI scanners.
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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".