A Reinvestigation of Murine Cranial Suture Biology: Microcomputed Tomography versus Histologic Technique

Stadler, James A.; Cortes, Wilberto; Zhang, Lin Ling; Hanger, Christopher C.; Gosain, Arun K.

doi:10.1097/01.prs.0000232979.78132.b2

Cited by 14 publications

(19 citation statements)

References 32 publications

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“…The normal morphology of the mouse posterior frontal (PF), sagittal (SAG) and coronal (COR) sutures has been well described [Markens, 1975;Jiang et al, 2002;Lenton et al, 2005;Sahar et al, 2005;Stadler et al, 2006]. In contrast, much remains to be understood regarding the tissue interactions and signaling factors necessary for the initiation of suture formation and maintenance of suture patency.…”

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confidence: 99%

Ex vivo Model of Cranial Suture Morphogenesis and Fate

Slater

Lenton

James

et al. 2009

Cells Tissues Organs

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Background/Aims: Craniosynostosis, the premature fusion of cranial sutures, is a common congenital defect. In vivo models for studying cranial suture biology impose inherent restrictions on tissue accessibility and manipulation. The present study was performed to investigate the utility of the renal capsule assay in overcoming these limitations and providing a reproducible model system for studying cranial suture morphogenesis and fate. Materials and Methods: The posterior frontal suture, which fuses physiologically, and the coronal and sagittal sutures, which remain patent, were dissected from postnatal and embryonic mouse calvaria and placed under the renal capsule of syngeneic recipient mice (n = 72 in total). Sutures were harvested from 1–14 days after transplantation for histological and morphometric analysis. Suture transplants were compared with nonmanipulated sutures at equivalent developmental stages. The derivation of cells associated with the growing transplants was analyzed using β-actin-GFP (green fluorescent protein) transgenic mice. Results: Sutures transplanted under the renal capsule maintained normal suture morphology and fate with the posterior frontal suture fusing and the coronal and sagittal sutures remaining patent. In posterior frontal suture transplants, the fusion process mimicked in vivo suture fusion with a delay of 1–2 days. In comparison to in vivo suture complexes, transplant thickness and trabeculation were significantly increased. In addition, we found that osteoblasts within the growing transplant were derived from the transplant itself rather than the host. Conclusion: The renal capsule supports the growth of cranial sutures. In this system transplanted sutures recapitulate the anatomical development and fate (fusion or patency) of cranial sutures in vivo. This model system will facilitate controlled ex vivo manipulations of both embryonic and postnatal sutures.

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confidence: 99%

Ex vivo Model of Cranial Suture Morphogenesis and Fate

Slater

Lenton

James

et al. 2009

Cells Tissues Organs

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“…These errors are very common with traditional segmentation techniques, such as a global threshold, because they do not take into account any neighboring pixels when defining the tissue of interest. 6,20,28 The algorithm developed overcomes these limitations by using a continuous Fourier series representation of the segmented boundary. This allows the algorithm to bridge these gaps in the suture border where there may be regions that are not completely mineralized.…”

Section: Discussionmentioning

confidence: 99%

“…20,28,29 Despite the growing use of CT, there are substantial limitations with traditional image processing algorithms when examining cranial development. The majority of the CT based analyses of cranial sutures in animal models has been performed using a global threshold or manual tracing for segmentation, which is the process of identifying a tissue of interest in an image.…”

Section: Introductionmentioning

confidence: 99%

“…The majority of the CT based analyses of cranial sutures in animal models has been performed using a global threshold or manual tracing for segmentation, which is the process of identifying a tissue of interest in an image. 6,19,20,25,28 Not only is manual tracing time consuming, but it also introduces the potential for user bias in the analysis. 6 Global threshold segmentation is limited when analyzing images that are heterogeneous or have varying intensity.…”

Section: Introductionmentioning

confidence: 99%

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Algorithm to Assess Cranial Suture Fusion with Varying and Discontinuous Mineral Density

et al. 2012

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One of the most difficult challenges in medical imaging is the accurate segmentation of mineralized tissues. This process is complicated when studying developmental or regenerative processes due to the changes in mineral density that these tissues undergo over time. To address these limitations an algorithm was developed to enable the use of computed tomography (CT) to study tissues of varying and heterogeneous mineralization. To examine and validate this algorithm a study was performed on the development of murine cranial sutures. C57Bl/6J mice ranging in age from 6 to 25 days were imaged using micro-CT (μCT). The algorithm was developed to segment the bones of both the posterior frontal (PF) and coronal (COR) sutures. For the curved COR suture, an addition to the algorithm was developed to reconstruct images that were perpendicular to the suture about all three axes. The algorithm showed excellent linear correlation (R (2) > 0.96) with serial histomorphometry and nearly a 1:1 relationship between all measures. The algorithm was validated with serial histology. The algorithm showed that the PF suture fused between days 12 and 20 but then showed a significant increase in bone volume after day 20. The algorithm developed provides an accurate method to segment the irregular sutures of the mouse calvaria.

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“…Recent advances in micro-computed tomography have enabled rapid, complete, and nondestructive three-dimensional analysis, making micro-computed tomography a powerful tool for studying cranial suture development. 8,9 Despite the potential of this imaging modality for analyzing the developing murine cranial sutures, traditional image segmentation algorithms are either not accurate enough, require subjective assessment, or are prohibitively time consuming because of changes in calvarial density that are seen during suture fusion. 9,10 To address this challenge, we previously developed a novel image-processing algorithm to analyze fusion of the murine cranial sutures.…”

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confidence: 99%