Visualization of Collagen–Mineral Arrangement using Atom Probe Tomography
Bibliographic record
Abstract
Abstract Bone is a complex, hierarchical structure comprised of two distinct phases: the organic, collagen– rich phase and the inorganic mineral–rich phase. This collagen–mineral arrangement has implications for bone function, aging, and disease. However, strategies to extract a single mineralized collagen fibril to investigate the interplay between components with sufficient resolution have been mostly confined to in vitro systems that only approximate the biological environment or transmission electron microscopy studies with lower spatial and chemical resolution. Therefore, there is extensive debate over the location of mineral with respect to collagen in in vivo mineralized tissues as visualization and quantification of the mineral in a living system is difficult or impossible. Herein, we have developed an approach to artificially extract a single mineralized collagen fibril from bone to analyze its composition and structure atom-by-atom with 3D resolution and sub-nanometer accuracy using atom probe tomography. This enables, for the first time, a method to probe fibril-level mineralization and collagen–mineral arrangement from an in vivo system with both the spatial and chemical precision required to comment on collagen– mineral arrangement. Using atom probe tomography, 4D (spatial + chemical) reconstructed volumes of leporine bone were generated with accuracy from correlative scanning electron microscopy. Distinct, winding collagen fibrils were identified with mineralized deposits both encapsulating and incorporated into the collagenous structures. This work demonstrates a novel fibril-level detection method that can be used to probe structural and chemical changes of bone and contribute new insights to the debate on collagen–mineral arrangement in mineralized tissues such as bones, and teeth.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".