Mandibular dysmorphology due to abnormal embryonic osteogenesis in FGFR2-related craniosynostosis mice

Perrine, Susan M. Motch; Wu, Meng; Stephens, Nicholas B.; Kriti, Divya; Bakel, Harm van; Jabs, Ethylin Wang; Richtsmeier, Joan T.

doi:10.1242/dmm.038513

Cited by 20 publications

(26 citation statements)

References 67 publications

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“…Purified RNA was stored at −80°C. RNA concentration and purity (RNA Integrity Number, or RIN) was determined using an Agilent Bioanalyzer 2100 and the RNA Pico kit as described by the manufacturer ( Motch Perrine et al, 2019 ; Wu et al, 2019 ).…”

Section: Methodsmentioning

confidence: 99%

“…Tissue was collected in 50 mL of Arcturus Picopure extraction buffer (Thermo Fisher Scientific, KIT0204) in the cap of a 0.5 mL Eppendorf collection tube, immediately frozen on dry ice, and stored at À80 C. RNA was purified using the Arcturus Picopure kit using the Macrocap procedure, with on-column DNase digestion (QIAGEN, 79254), as described by the manufacturer, and eluted with 20 mL of elution buffer. Purified RNA was stored at À80 C. RNA concentration and purity (RNA Integrity Number, or RIN) was determined using an Agilent Bioanalyzer 2100 and the RNA Pico kit as described by the manufacturer (Motch Perrine et al, 2019;Wu et al, 2019).…”

Section: Laser Capture Microdissectionmentioning

confidence: 99%

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Integrated Transcriptome and Network Analysis Reveals Spatiotemporal Dynamics of Calvarial Suturogenesis

et al. 2020

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SUMMARY Craniofacial abnormalities often involve sutures, the growth centers of the skull. To characterize the organization and processes governing their development, we profile the murine frontal suture, a model for sutural growth and fusion, at the tissue- and single-cell level on embryonic days (E)16.5 and E18.5. For the wild-type suture, bulk RNA sequencing (RNA-seq) analysis identifies mesenchyme-, osteogenic front-, and stage-enriched genes and biological processes, as well as alternative splicing events modifying the extracellular matrix. Single-cell RNA-seq analysis distinguishes multiple subpopulations, of which five define a mesenchymeosteoblast differentiation trajectory and show variation along the anteroposterior axis. Similar analyses of in vivo mouse models of impaired frontal suturogenesis in Saethre-Chotzen and Apert syndromes, Twist1 +/− and Fgfr2 +/S252W , demonstrate distinct transcriptional changes involving angiogenesis and ribogenesis, respectively. Co-expression network analysis reveals gene expression modules from which we validate key driver genes regulating osteoblast differentiation. Our study provides a global approach to gain insights into suturogenesis.

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Section: Methodsmentioning

confidence: 99%

Section: Laser Capture Microdissectionmentioning

confidence: 99%

Integrated Transcriptome and Network Analysis Reveals Spatiotemporal Dynamics of Calvarial Suturogenesis

et al. 2020

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“…28 A comparison of Meckel cartilage between these three genotypes again found significantly increased cartilage thickness only in Fgfr2 +/S252W and Fgfr2 +/P253R mutants. 29 Genetic and phenotypic variability exists within craniosynostosis syndromes. 13,30 As our understanding of genotype-phenotype relationships improves, genetic screening of these patients may become increasingly useful for prognostication, genetic counseling, and treatment decision making.…”

Section: Discussionmentioning

confidence: 99%

Genotype–Phenotype Correlation of Tracheal Cartilaginous Sleeves and Fgfr2 Mutations in Mice

et al. 2020

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Objectives: To characterize tracheal cartilage morphology in mouse models of fibroblast growth factor receptor (Fgfr2)related craniosynostosis syndromes. To establish relationships between specific Fgfr2 mutations and tracheal cartilaginous sleeve (TCS) phenotypes in these mouse models. Methods: Postnatal day 0 knock-in mouse lines with disease-specific genetic variations in the Fgfr2 gene (Fgfr2 C342Y/C342Y , Fgfr2 C342Y/+ , Fgfr2 +/Y394C , Fgfr2 +/S252W , and Fgfr2 +/P253R) as well as line-specific controls were utilized. Tracheal cartilage morphology as measured by gross analyses, microcomputed tomography (μCT), and histopathology were compared using Chi-squared and single-factor analysis of variance statistical tests. Results: A greater proportion of rings per trachea were abnormal in Fgfr2 C342Y/+ tracheas (63%) than Fgfr2 +/S252W (17%), Fgfr2 +/P253R (17%), Fgfr2 +/Y394C (12%), and controls (10%) (P < .001 for each vs. Fgfr2 C342Y/+). TCS segments were found only in Fgfr2 C342Y/C342Y (100%) and Fgfr2 C342Y/+ (72%) tracheas. Cricoid and first-tracheal ring fusion was noted in all Fgfr2 C342Y/C342Y and 94% of Fgfr2 C342Y/+ samples. The Fgfr2 C342Y/C342Y and Fgfr2 C342Y/+ groups were found to have greater areas and volumes of cartilage than other lines on gross analysis and μCT. Histologic analyses confirmed TCS among the Fgfr2 C342Y/C342Y and Fgfr2 C342Y/+ groups, without appreciable differences in cartilage morphology, cell size, or density; no histologic differences were observed among other Fgfr2 lines compared to controls. Conclusion: This study found TCS phenotypes only in the Fgfr2 C342Y mouse lines. These lines also had increased tracheal cartilage compared to other mutant lines and controls. These data support further study of the Fgfr2 mouse lines and the investigation of other Fgfr2 variants to better understand their role in tracheal development and TCS formation.

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“…Many publications report small changes in cell proliferation or apoptosis in response to a perturbation in a model system. However, these changes are often measured by manual or semi-automatic cell counting in a few stained tissue regions [2, 3, 4]. A major limitation of this approach is that it is difficult to investigate changes across a whole region of tissue or how these changes contribute to the overall anatomical development.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of Tissues and Proliferating Cells in Light-Sheet Microscopy Images using Convolutional Neural Networks

Vercio¹,

Rm²,

Robertson³

et al. 2021

Preprint

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Background and Objective: A variety of genetic mutations are known to affect cell proliferation and apoptosis during organism development, leading to structural birth defects such as facial clefting. Yet, the mechanisms how these alterations influence the development of the face remain unclear. Cell proliferation and its relation to shape variation can be studied in high detail using Light-Sheet Microscopy (LSM) imaging across a range of developmental time points. However, the large number of LSM images captured at cellular resolution precludes manual analysis. Thus, the aim of this work was to develop and evaluate automatic methods to segment tissues and proliferating cells in these images in an accurate and efficient way. Methods: We developed, trained, and evaluated convolutional neural networks (CNNs) for segmenting tissues, cells, and specifically proliferating cells in LSM datasets. We compared the automatically extracted tissue and cell annotations to corresponding manual segmentations for three specific applications: (i) tissue segmentation (neural ectoderm and mesenchyme) in nuclear-stained LSM images, (ii) cell segmentation in nuclear-stained LSM images, and (iii) segmentation of proliferating cells in Phospho-Histone H3 (PHH3)-stained LSM images. Results: The automatic CNN-based tissue segmentation method achieved a macro-average F-score of 0.84 compared to a macro-average F-score of 0.89 comparing corresponding manual segmentations from two observers. The automatic cell segmentation method in nuclear-stained LSM images achieved an F-score of 0.57, while comparing the manual segmentations resulted in an F-score of 0.39. Finally, the automatic segmentation method of proliferating cells in the PHH3-stained LSM datasets achieved an F-score of 0.56 for the automated method, while comparing the manual segmentations resulted in an F-score of 0.45. Conclusions: The proposed automatic CNN-based framework for tissue and cell segmentation leads to results comparable to the inter-observer agreement, accelerating the LSM image analysis. The trained CNN models can also be applied for shape or morphological analysis of embryos, and more generally in other areas of cell biology.

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Mandibular dysmorphology due to abnormal embryonic osteogenesis in FGFR2-related craniosynostosis mice

Cited by 20 publications

References 67 publications

Integrated Transcriptome and Network Analysis Reveals Spatiotemporal Dynamics of Calvarial Suturogenesis

Integrated Transcriptome and Network Analysis Reveals Spatiotemporal Dynamics of Calvarial Suturogenesis

Genotype–Phenotype Correlation of Tracheal Cartilaginous Sleeves and Fgfr2 Mutations in Mice

Segmentation of Tissues and Proliferating Cells in Light-Sheet Microscopy Images using Convolutional Neural Networks

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