Spatial quantitation of antibiotics in bone tissue compartments by laser-capture microdissection coupled with UHPLC-tandem mass spectrometry
Bibliographic record
Abstract
Bones are the site of multiple diseases requiring chemotherapy, including cancer, arthritis, osteoporosis and infections. Yet limited methodologies are available to investigate the spatial distribution and quantitation of small molecule drugs in bone compartments, due to the difficulty of sectioning undecalcified bones and the interference of decalcification methods with spatially resolved drug quantitation. To measure drug concentrations in distinct anatomical bone regions, we have developed a workflow that enables spatial quantitation of thin undecalcified bone sections by laser-capture microdissection coupled to HPLC/tandem mass spectrometry, and spatial mapping on adjacent sections by mass spectrometry imaging. The adhesive film and staining methods were optimized to facilitate histology staining on the same sections used for mass spectrometry image acquisition, revealing drug accumulation in the underlying bone tissue architecture, for the first time. Absolute spatial concentrations of rifampicin, bedaquiline, doxycycline, vancomycin and several of their active metabolites are shown for both small rodent bones and larger rabbit bones that more closely resemble human bone density. Overlaid MALDI mass spectrometry images of drugs and histology staining enabled the generation of semi-quantitative data from regions of interest within anatomical bone compartments. These data correlated with absolute drug concentrations determined by HPLC-MS/MS in laser-capture microdissection samples. Collectively, these techniques enable semi- and fully quantitative drug distribution investigations within bone tissue compartments for the first time. Our workflow can be translated to image and quantify not only drugs but also biomarkers of disease to investigate drug penetration as well as mechanisms underlying bone disorders.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".