Bacterial association with metals enables <i>in vivo</i> tracking of microbiota using magnetic resonance imaging
Bibliographic record
Abstract
Abstract Bacteria constitute a significant part of the biomass of the human microbiota, but their interactions are complex and difficult to replicate outside the host. Exploiting the superior resolution of magnetic resonance imaging (MRI) to examine signal parameters of selected human isolates may allow tracking of their dispersion throughout the body. We investigated longitudinal and transverse MRI relaxation rates and found significant differences between several bacterial strains. Common commensal strains of lactobacilli display notably high MRI relaxation rates, partially explained by outstanding cellular manganese content, while other species contain more iron than manganese. Lactobacillus crispatus show particularly high values, 4-fold greater than any other species; over 10-fold greater signal than relevant tissue background; and a linear relationship between relaxation rate and fraction of live cells. Different bacterial strains have detectable, repeatable MRI relaxation rates that in future may enable tracking of their persistence in the human body for enhanced molecular imaging. IMPORTANCE To understand how spatial and temporal distribution of microbiota impact human health, dynamic tools for monitoring microbiota landscapes inside the host are needed. Particularly when considering the complexity of the gastrointestinal tract and the microbiota that dwell within, tools for monitoring deep segments of the gut non-invasively are required. Medical imaging provides solutions that enable the study of microorganisms in their preferred niche regardless of health status. To bootstrap this technology, we investigated the magnetic resonance imaging (MRI) properties of bacterial isolates and showed that outstanding signal detection is an inherent property of several strains. Among these, we showed that bacteria relying on manganese metabolism have an MRI characteristic that is distinct from mammalian cells. Our findings will lead to direct and safe imaging of bacteria; influence how we monitor both infection and gut health; and help direct the use of antibiotics to curtail the growing threat of antibiotic resistance.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".