Efficient and Rapid Labeling of Transplanted Cell Populations with Superparamagnetic Iron Oxide Nanoparticles Using Cell Surface Chemical Biotinylation for in Vivo Monitoring by MRI
Bibliographic record
Abstract
Determination of the dynamics of specific cell populations in vivo is essential for the development of cell-based therapies. For cell tracking by magnetic resonance imaging (MRI), cells need to internalize, or be surface labeled with a MRI contrast agent, such as superparamagnetic iron oxide nanoparticles (SPIOs): SPIOs give rise to signal loss by gradient-echo and T(2)-weighted MRI techniques. In this study, cancer cells were chemically tagged with biotin and then magnetically labeled with anti-biotin SPIOs. No significant detrimental effects on cell viability or death were observed following cell biotinylation. SPIO-labeled cells exhibited signal loss compared to non-SPIO-labeled cells by MRI in vitro. Consistent with the in vitro MRI data, signal attenuation was observed in vivo from SPIO-labeled cells injected into the muscle of the hind legs, or implanted subcutaneously into the flanks of mice, correlating with iron detection by histochemical and X-ray fluorescence (XRF) methods. To further validate this approach, human mesenchymal stem cells (hMSCs) were also employed. Chemical biotinylation and SPIO labeling of hMSCs were confirmed by fluorescence microscopy and flow cytometry. The procedure did not affect proliferation and multipotentiality, or lead to increased cell death. The SPIO-labeled hMSCs were shown to exhibit MRI signal reduction in vitro and was detectable in an in vivo model. In this study, we demonstrate a rapid, robust, and generic methodology that may be a useful and practical adjuvant to existing methods of cell labeling for in vivo monitoring by MRI. Further, we have shown the first application of XRF to provide iron maps to validate MRI data in SPIO-labeled cell tracking studies.
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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 | 0.001 |
| 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".