利用无标签 4D 显微镜自动重建细胞系。

IF 3.3 3区 生物学 Q2 GENETICS & HEREDITY
Genetics Pub Date : 2024-10-07 DOI:10.1093/genetics/iyae135
Matthew Waliman, Ryan L Johnson, Gunalan Natesan, Neil A Peinado, Shiqin Tan, Anthony Santella, Ray L Hong, Pavak K Shah
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引用次数: 0

摘要

世系后裔模式在后生动物胚胎的发育过程中起着至关重要的作用。在产生固定数量体细胞的中生代生物中,其细胞系拓扑结构的不变性为我们提供了一个强大的机会,可以利用经验重复性来研究不同个体的发育事件。利用秀丽隐杆线虫对胚胎发育的研究推动了这一发现。这些研究在很大程度上依赖于四维荧光显微镜和强大的计算机视觉管道所实现的高通量品系追踪。对于一系列应用而言,使用四维无标记显微镜进行计算机辅助但人工的谱系追踪仍然是一种必不可少的工具。近年来,荧光显微镜中细胞检测和跟踪的深度学习方法取得了长足的进步,但在致密组织和胚胎的三维无标记成像中实现细胞检测和跟踪自动化的解决方案仍然遥不可及。在这里,我们介绍了一种深度学习管道 embGAN,它能解决无标记三维延时成像中细胞自动检测和跟踪的难题。embGAN 无需手动标注数据进行训练,能学习到稳健的检测,表现出高度的尺度不变性,并能很好地泛化到在多个实验室、多台仪器上获取的图像。embGAN 在细胞检测和跟踪方面的性能接近最先进水平,无需荧光报告或转基因就能进行高通量的细胞系研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automated cell lineage reconstruction using label-free 4D microscopy.

Patterns of lineal descent play a critical role in the development of metazoan embryos. In eutelic organisms that generate a fixed number of somatic cells, invariance in the topology of their cell lineage provides a powerful opportunity to interrogate developmental events with empirical repeatability across individuals. Studies of embryonic development using the nematode Caenorhabditis elegans have been drivers of discovery. These studies have depended heavily on high-throughput lineage tracing enabled by 4D fluorescence microscopy and robust computer vision pipelines. For a range of applications, computer-aided yet manual lineage tracing using 4D label-free microscopy remains an essential tool. Deep learning approaches to cell detection and tracking in fluorescence microscopy have advanced significantly in recent years, yet solutions for automating cell detection and tracking in 3D label-free imaging of dense tissues and embryos remain inaccessible. Here, we describe embGAN, a deep learning pipeline that addresses the challenge of automated cell detection and tracking in label-free 3D time-lapse imaging. embGAN requires no manual data annotation for training, learns robust detections that exhibits a high degree of scale invariance, and generalizes well to images acquired in multiple labs on multiple instruments. We characterize embGAN's performance using lineage tracing in the C. elegans embryo as a benchmark. embGAN achieves near-state-of-the-art performance in cell detection and tracking, enabling high-throughput studies of cell lineage without the need for fluorescent reporters or transgenics.

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来源期刊
Genetics
Genetics GENETICS & HEREDITY-
CiteScore
6.90
自引率
6.10%
发文量
177
审稿时长
1.5 months
期刊介绍: GENETICS is published by the Genetics Society of America, a scholarly society that seeks to deepen our understanding of the living world by advancing our understanding of genetics. Since 1916, GENETICS has published high-quality, original research presenting novel findings bearing on genetics and genomics. The journal publishes empirical studies of organisms ranging from microbes to humans, as well as theoretical work. While it has an illustrious history, GENETICS has changed along with the communities it serves: it is not your mentor''s journal. The editors make decisions quickly – in around 30 days – without sacrificing the excellence and scholarship for which the journal has long been known. GENETICS is a peer reviewed, peer-edited journal, with an international reach and increasing visibility and impact. All editorial decisions are made through collaboration of at least two editors who are practicing scientists. GENETICS is constantly innovating: expanded types of content include Reviews, Commentary (current issues of interest to geneticists), Perspectives (historical), Primers (to introduce primary literature into the classroom), Toolbox Reviews, plus YeastBook, FlyBook, and WormBook (coming spring 2016). For particularly time-sensitive results, we publish Communications. As part of our mission to serve our communities, we''ve published thematic collections, including Genomic Selection, Multiparental Populations, Mouse Collaborative Cross, and the Genetics of Sex.
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