Deep learning-based enhancement of fluorescence labeling for accurate cell lineage tracing during embryogenesis.

Zelin Li, Dongying Xie, Yiming Ma, Cunmin Zhao, Sicheng You, Hong Yan, Zhongying Zhao
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Abstract

Motivation: Automated cell lineage tracing throughout embryogenesis plays a key role in the study of regulatory control of cell fate differentiation, morphogenesis and organogenesis in the development of animals, including nematode Caenorhabditis elegans. However, automated cell lineage tracing suffers from an exponential increase in errors at late embryo because of the dense distribution of cells, relatively low signal-to-noise ratio (SNR) and imbalanced intensity profiles of fluorescence images, which demands a huge amount of human effort to manually correct the errors. The existing image enhancement methods are not sensitive enough to deal with the challenges posed by the crowdedness and low signal-to-noise ratio. An alternative method is urgently needed to assist the existing detection methods in improving their detection and tracing accuracy, thereby reducing the huge burden for manual curation.

Results: We developed a new method, termed as DELICATE, that dramatically improves the accuracy of automated cell lineage tracing especially during the stage post 350 cells of C. elegans embryo. DELICATE works by increasing the local SNR and improving the evenness of nuclei fluorescence intensity across cells especially in the late embryos. The method both dramatically reduces the segmentation errors by StarryNite and the time required for manually correcting tracing errors up to 550-cell stage, allowing the generation of accurate cell lineage at large-scale with a user-friendly software/interface.

Availability and implementation: All images and data are available at https://doi.org/10.6084/m9.figshare.26778475.v1. The code and user-friendly software are available at https://github.com/plcx/NucApp-develop.

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基于深度学习的荧光标记增强技术,实现胚胎发育过程中的精确细胞系追踪
动机在研究包括线虫在内的动物发育过程中细胞命运分化、形态发生和器官形成的调控过程中,对整个胚胎发生过程进行自动细胞系追踪起着关键作用。然而,在胚胎晚期,由于细胞分布密集、信噪比(SNR)相对较低以及荧光图像的强度分布不平衡,自动细胞系追踪的误差呈指数级增长,需要大量人力手动纠正错误。现有的图像增强方法不够灵敏,无法应对拥挤和低信噪比带来的挑战。我们迫切需要一种替代方法来帮助现有的检测方法提高检测和追踪的准确性,从而减轻人工纠错的巨大负担:我们开发了一种名为 DELICATE 的新方法,它能显著提高自动细胞系追踪的准确性,尤其是在秀丽隐杆线虫胚胎 350 个细胞之后的阶段。DELICATE 的工作原理是提高局部信噪比,改善细胞核荧光强度的均匀性,尤其是在胚胎晚期。该方法大大减少了 StarryNite 的分割误差,也减少了在 550 细胞阶段手动纠正追踪误差所需的时间,从而以用户友好的软件/界面大规模生成准确的细胞系:所有图像和数据可在 https://doi.org/10.6084/m9.figshare.26778475.v1 上获取。代码和用户友好型软件可在 https://github.com/plcx/NucApp-develop.Supplementary information 上获取:补充数据可在 Bioinformatics online 上获取。
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