Project II - 3D Quantitative Analysis of Mouse Models of Structural Birth Defects Through Computational Anatomy

项目总结 E15.5来自85个SCI-RNA-seq系的6个纯合子突变体的胎儿将从项目1获得 在建立扩散碘增强扫描后，通过高分辨率MicroCT进行成像 胎鼠的治疗方案。还将对多达100个未受影响的产仔胎儿进行成像，以获得 标准样本的合成总体模板(图1)，并将用于评估“正常” E15.5小鼠发育的表型变异(目标1)。模板将被分割为最精细的 可能的解剖细节，并将包含比现有KOMP2胎儿更多的解剖结构和细节 阿特拉斯。将评估每个sci-rna-seq系的总体表型变异性(图3)，然后 进行区域特定统计(图4)组织体积和形状差异分析(目标2)。我们 还将训练深度学习模型，实现对胎鼠的自动分割，并比较结果 到目前最先进的、基于模板的分析(目标3)。 我们的目标是利用3D成像、计算机视觉和机器学习方面的最新进展，使 小鼠突变体的3D形态特征更加准确、定量、重复性和 无障碍。结合分子表型和解剖表型将提供全面的 具有与人类结构性出生缺陷相关的突变的小鼠的特征。

PROJECT SUMMARY E15.5 fetuses of 6 homozygous mutants from each of the 85 sci-RNA-seq lines will be acquired from Project 1 to be imaged via high-resolution microCT following established diffusible iodine contrast enhanced scan protocols for fetal mice. Up to hundred fetuses of unaffected littermates will also be imaged to derive a synthetic population template of normative samples (Figure1) and will be used to assess the ‘normal’ phenotypic variation in mouse development at E15.5 (Aim 1). The template will be segmented to the finest anatomical detail possible and will contain more anatomical structure and detail than the existing KOMP2 fetal atlas. Each sci-RNA-seq line will be assesses for overall phenotypic variability (Figure 3) and then be subjected to region-specific statistical (Figure 4) analysis of tissue volume and shape differences (Aim 2). We will also train a deep-learning model to achieve automated segmentation of fetal mice and compare the results to the current-state-of-the-art, template-based analysis (Aim 3). Our goal is to leverage recent advances in the 3D imaging, computer vision and machine-learning to make the 3D morphological characterization of mouse mutants to be more accurate, quantitative, reproducible and accessible. Combining molecular phenotype and anatomical phenotypes will provide a comprehensive characterization of mice with mutations relevant to human structural birth defects.