Magnetic Resonance Imaging of Cells Overexpressing MagA, an Endogenous Contrast Agent for Live Cell Imaging
Bibliographic record
Abstract
Molecular imaging with magnetic resonance imaging (MRI) may benefit from the ferrimagnetic properties of magnetosomes, membrane-enclosed iron biominerals whose formation in magnetotactic bacteria is encoded by multiple genes. One such gene is MagA, a putative iron transporter. We have examined expression of MagA in mouse neuroblastoma N2A cells and characterized their response to iron loading and cellular imaging by MRI. MagA expression augmented both Prussian blue staining and the elemental iron content of N2A cells, without altering cell proliferation, in cultures grown in the presence of iron supplements. Despite evidence for iron incorporation in both MagA and a variant, MagAE137V, only MagA expression produced intracellular contrast detectable by MRI at 11 Tesla. We used this stable expression system to model a new sequence for cellular imaging with MRI, using the difference between gradient and spin echo images to distinguish cells from artifacts in the field of view. Our results show that MagA activity in mammalian cells responds to iron supplementation and functions as a contrast agent that can be deactivated by a single point mutation. We conclude that MagA is a candidate MRI reporter gene that can exploit more fully the superior resolution of MRI in noninvasive medical imaging.
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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".