Understanding the structural biology of osteomalacia through multiscale 3D X-ray and electron tomographic imaging: a review of X-linked hypophosphatemia, the <i>Hyp</i> mouse model, and imaging methods
Bibliographic record
Abstract
Abstract Biomineralization in bones and teeth is a highly regulated extracellular event. In the skeleton, mineralization at the tissue level is controlled within the collagenous extracellular matrix by both circulating and local factors. While systemic regulation of mineral ion homeostasis has been well-studied over many decades, much less is known about the regulation of mineralization at the local level directly within the extracellular matrix. Some local regulators have been identified, such as tissue-nonspecific alkaline phosphatase (TNAP), phosphate-regulating endopeptidase homolog X-linked (PHEX), pyrophosphate, and osteopontin, and others are currently under investigation. Dysregulation of the actions of enzyme-inhibitor substrate pairs engaged in mineralization (as we describe by the Stenciling Principle for extracellular matrix mineralization) leads to osteomalacic “soft bone” diseases, such as hypophosphatasia (HPP) and X-linked hypophosphatemia (XLH). This review addresses how advances in 3D imaging tools and software now allow contextual and correlative viewing and interpretation of mineralized tissue structure across most length scales. Contextualized and integrated 3D multiscale data obtained from these imaging modalities have afforded an unprecedented structural biology view of bone from the macroscale to the nanoscale. Such correlated volume imaging data is highly quantitative, providing not only an integrated view of the skeleton in health, but also a means to observe alterations that occur in disease. In the context of the many hierarchical levels of skeletal organization, here we summarize structural features of bone over multiple length scales, with a focus on nano- and microscale features as viewed by X-ray and electron tomography imaging methods (submicron μCT and FIB-SEM). We additionally summarize structural changes observed after dysregulation of the mineralization pathway, focusing here on the Hyp mouse model for XLH. More specifically, we summarize how mineral patterns/packs at the microscale (3D crossfibrillar mineral tessellation), and how this is defective in Hyp mouse bone and Hyp enthesis fibrocartilage.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".