Intravital imaging of osteocytes in mouse calvaria using third harmonic generation microscopy
Bibliographic record
Abstract
Osteocytes are the most abundant cell in the bone, and have multiple functions including mechanosensing and regulation of bone remodeling activities. Since osteocytes are embedded in the bone matrix, their inaccessibility makes in vivo studies problematic. Therefore, a non-invasive technique with high spatial resolution is desired. The purpose of this study is to investigate the use of third harmonic generation (THG) microscopy as a noninvasive technique for high-resolution imaging of the lacunar-canalicular network (LCN) in live mice. By performing THG imaging in combination with two- and three-photon fluorescence microscopy, we show that THG signal is produced from the bone-interstitial fluid boundary of the lacuna, while the interstitial fluid-osteocyte cell boundary shows a weaker THG signal. Canaliculi are also readily visualized by THG imaging, with canaliculi oriented at small angles relative to the optical axis exhibiting stronger signal intensity compared to those oriented perpendicular to the optical axis (parallel to the image plane). By measuring forward- versus epi-detected THG signals in thinned versus thick bone samples ex vivo, we found that the epi-collected THG from the LCN of intact bone contains a superposition of backward-directed and backscattered forward-THG. As an example of a biological application, THG was used as a label-free imaging technique to study structural variations in the LCN of live mice deficient in both histone deacetylase 4 and 5 (HDAC4, HDAC5). Three-dimensional analyses were performed and revealed statistically significant differences between the HDAC4/5 double knockout and wild type mice in the number of osteocytes per volume and the number of canaliculi per lacunar surface area. These changes in osteocyte density and dendritic projections occurred without differences in lacunar size. This study demonstrates that THG microscopy imaging of the LCN in live mice enables quantitative analysis of osteocytes in animal models without the use of dyes or physical sectioning.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".