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

项目 II - 通过计算解剖学对结构性先天缺陷小鼠模型进行 3D 定量分析

基本信息

批准号：
10541192
负责人：
Ali Murat Maga
金额：
$ 32.82万
依托单位：
SEATTLE CHILDREN'S HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-01-11 至 2025-12-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10541192
关键词：
3-Dimensional Anatomy Atlases Biological Assay Communities Computer Vision Systems Consumption Data Data Analyses Data Set Detection Development Dimensions Etiology Fetal Development Fetus Genomics Genotype Goals Harvest Human Image Iodine Machine Learning Modeling Morphology Mus Mutant Strains Mice Mutation Organogenesis Phenotype Population Protocols documentation Publishing Random Allocation Reproducibility Resolution Sampling Scanning Shapes Structural Congenital Anomalies Structural defect Structure Technology Three-Dimensional Image Three-Dimensional Imaging Tissues Training Variant X-Ray Computed Tomography automated segmentation computational anatomy contrast enhanced deep learning deep learning model fetal imaging Segmentation insight interest machine learning model microCT molecular phenotype mouse development mouse model mutant next generation open source organ growth phenomics soft tissue statistics tool transcriptome sequencing user-friendly

项目摘要

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.

项目概要 85 个 sci-RNA-seq 系中每个系的 6 个纯合突变体的 E15.5 胎儿将从项目 1 中获得在建立扩散碘对比增强扫描后通过高分辨率 microCT 进行成像还将对多达 100 只未受影响的同窝小鼠的胎儿进行成像，以得出标准样本的合成总体模板（图1）将用于评估“正常” E15.5 小鼠发育的表型变异（目标 1）。可能具有解剖细节，并且比现有的 KOMP2 胎儿包含更多的解剖结构和细节将评估每个 sci-RNA-seq 系的整体表型变异性（图 3），然后进行分析。对组织体积和形状差异进行区域特异性统计（图 4）分析（目标 2）。还将训练深度学习模型，实现胎儿小鼠的自动分割并比较结果到当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领域的最新进展，使小鼠突变体的 3D 形态学表征更加准确、定量、可重复和结合分子表型和解剖表型将提供全面的。具有与人类结构出生缺陷相关的突变的小鼠的特征。