High-Resolution Imaging and Morphological Phenotyping of C. elegans through Stable Robotic Sample Rotation and Artificial Intelligence-Based 3-Dimensional Reconstruction.

IF 11 1区 综合性期刊 Q1 Multidisciplinary Research Pub Date : 2024-10-30 eCollection Date: 2024-01-01 DOI:10.34133/research.0513
Peng Pan, Pengsong Zhang, Sharanja Premachandran, Ran Peng, Shaojia Wang, Qigao Fan, Yu Sun, John A Calarco, Xinyu Liu
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Abstract

Accurate visualization and 3-dimensional (3D) morphological profiling of small model organisms can provide quantitative phenotypes benefiting genetic analysis and modeling of human diseases in tractable organisms. However, in the highly studied nematode Caenorhabditis elegans, accurate morphological phenotyping remains challenging because of notable decrease in image resolution of distant signal under high magnification and complexity in the 3D reconstruction of microscale samples with irregular shapes. Here, we develop a robust robotic system that enables the contactless, stable, and uniform rotation of C. elegans for multi-view fluorescent imaging and 3D morphological phenotyping via the precise reconstruction of 3D models. Contactless animal rotation accommodates a variety of body shapes and sizes found at different developmental stages and in mutant strains. Through controlled rotation, high-resolution fluorescent imaging of C. elegans structures is obtained by overcoming the limitations inherent in both widefield and confocal microscopy. Combining our robotic system with machine learning, we create, for the first time, precise 3D reconstructions of C. elegans at the embryonic and adult stages, enabling 3D morphological phenotyping of mutant strains in an accurate and comprehensive fashion. Intriguingly, our morphological phenotyping discovered a genetic interaction between 2 RNA binding proteins (UNC-75/CELF and MBL-1/MBNL), which are highly conserved between C. elegans and humans and implicated in neurological and muscular disorders. Our system can thus generate quantitative morphological readouts facilitating the investigation of genetic variations and disease mechanisms. More broadly, our method will also be amenable for 3D phenotypic analysis of other biological samples, like zebrafish and Drosophila larvae.

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通过稳定的机器人样本旋转和基于人工智能的三维重建技术,实现高分辨率成像和 elegans 形态表型。
对小型模式生物进行精确的可视化和三维(3D)形态分析可提供定量表型,有利于在可控生物体内对人类疾病进行遗传分析和建模。然而,对于研究较多的线虫--秀丽隐杆线虫(Caenorhabditis elegans)来说,精确的形态表型分析仍然具有挑战性,因为在高倍放大条件下,远处信号的图像分辨率明显下降,而且具有不规则形状的微尺度样本的三维重建非常复杂。在此,我们开发了一种坚固耐用的机器人系统,可实现优雅鼠的非接触、稳定和均匀旋转,通过精确重建三维模型进行多视角荧光成像和三维形态表型。非接触式动物旋转可适应不同发育阶段和突变株的各种体形和大小。通过控制旋转,可以克服宽视场显微镜和共聚焦显微镜固有的局限性,获得高分辨率的秀丽隐杆线虫结构荧光成像。结合我们的机器人系统和机器学习,我们首次在胚胎和成虫阶段精确地创建了秀丽隐杆线虫的三维重建,从而能够以准确和全面的方式对突变株进行三维形态表型分析。有趣的是,我们的形态表型发现了两种 RNA 结合蛋白(UNC-75/CELF 和 MBL-1/MBNL)之间的遗传相互作用,这两种蛋白在秀丽隐杆线虫和人类之间高度保守,并与神经和肌肉疾病有关。因此,我们的系统可以生成定量形态读数,为研究基因变异和疾病机制提供便利。更广泛地说,我们的方法还可用于其他生物样本(如斑马鱼和果蝇幼虫)的三维表型分析。
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来源期刊
Research
Research Multidisciplinary-Multidisciplinary
CiteScore
13.40
自引率
3.60%
发文量
0
审稿时长
14 weeks
期刊介绍: Research serves as a global platform for academic exchange, collaboration, and technological advancements. This journal welcomes high-quality research contributions from any domain, with open arms to authors from around the globe. Comprising fundamental research in the life and physical sciences, Research also highlights significant findings and issues in engineering and applied science. The journal proudly features original research articles, reviews, perspectives, and editorials, fostering a diverse and dynamic scholarly environment.
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