Fluorophore-Conjugated Iron Oxide Nanoparticle Labeling and Analysis of Engrafting Human Hematopoietic Stem Cells
Bibliographic record
Abstract
The use of nanometer-sized iron oxide particles combined with molecular imaging techniques enables dynamic studies of homing and trafficking of human hematopoietic stem cells (HSC). Identifying clinically applicable strategies for loading nanoparticles into primitive HSC requires strictly defined culture conditions to maintain viability without inducing terminal differentiation. In the current study, fluorescent molecules were covalently linked to dextran-coated iron oxide nanoparticles (Feridex) to characterize human HSC labeling to monitor the engraftment process. Conjugating fluorophores to the dextran coat for fluorescence-activated cell sorting purification eliminated spurious signals from nonsequestered nanoparticle contaminants. A short-term defined incubation strategy was developed that allowed efficient labeling of both quiescent and cycling HSC, with no discernable toxicity in vitro or in vivo. Transplantation of purified primary human cord blood lineage-depleted and CD34(+) cells into immunodeficient mice allowed detection of labeled human HSC in the recipient bones. Flow cytometry was used to precisely quantitate the cell populations that had sequestered the nanoparticles and to follow their fate post-transplantation. Flow cytometry endpoint analysis confirmed the presence of nanoparticle-labeled human stem cells in the marrow. The use of fluorophore-labeled iron oxide nanoparticles for fluorescence imaging in combination with flow cytometry allows evaluation of labeling efficiencies and homing capabilities of defined human HSC subsets.
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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) | 0.000 | 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 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".