Article Title: Extracting Fluorescent Reporter Time Courses of Cell Lineages from High-throughput Microscopy at Low Temporal Resolution Extracting Fluorescent Reporter Time Courses of Cell Lineages from High-throughput Microscopy at Low Temporal Resolution
Article Title: Extracting Fluorescent Reporter Time Courses of Cell Lineages from High-throughput Microscopy at Low Temporal Resolution Extracting Fluorescent Reporter Time Courses of Cell Lineages from High-throughput Microscopy at Low Temporal Resolution
复制标题
文章标题：从低时间分辨率的高通量显微镜中提取细胞谱系的荧光报告基因时间过程从低时间分辨率的高通量显微镜中提取细胞谱系的荧光报告基因时间过程
DOI：
--
复制
发表时间：
--
期刊：
影响因子：
--
通讯作者：
T. Bretschneider
中科院分区：
文献类型：
--
作者：
Mike Downey;D. Jeziorska;S. Ott;T. Tamai;G. Koentges;Keith W. Vance;T. Bretschneider
根据出版商政策，本文件可在线查阅。请向下滚动以查看文档本身。请参阅此项目的存储库记录以及存储库主页上的政策信息以了解更多信息。要查看本文的最终版本，请访问出版商的网站。访问发布的版本可能需要订阅。摘要荧光时间过程数据的提取是高通量活细胞显微镜的主要瓶颈。在这里，我们提出了一个可扩展的框架的基础上的开源图像分析软件ImageJ，其目的特别是在分析荧光报告通过细胞分裂的表达。跟踪单个细胞谱系的能力对于分析参与控制细胞命运和身份决定的基因调控因子是必不可少的。在我们的方法中，使用Hoechst识别细胞核，Hoechst荧光的特征性下降有助于检测分裂细胞。我们首先比较了不同分割方法的效率和准确性，然后提出了一种用于细胞跟踪的统计评分算法，该算法利用了各种特征的组合，例如核强度，面积或形状，以及重要的是，其动态变化。主成分分析用于确定最重要的特征，并执行全局参数搜索以确定各个特征的权重。我们的算法已经过优化，以科普大的细胞运动，我们能够半自动地提取三代细胞的细胞轨迹。基于ImageJ的MTrackJ插件，我们开发了一些工具来有效地验证轨迹，并通过连接损坏的轨迹和重新分配错误连接的单元位置来手动纠正它们。将发布一个由两个时间序列和15，000个验证职位组成的黄金标准，作为基准测试的宝贵资源。我们演示了我们的方法可以应用于分析荧光分布从小鼠干细胞转染含有Msx1基因，多能性的调节因子，在母亲和女儿细胞的转录控制元件的报告构建体。此外，我们通过跟踪表达FUCCI细胞周期标志物的斑马鱼PAC2细胞，我们的框架可以很容易地适应不同的细胞类型和荧光标志物。版权所有：2011年唐尼等人。这是一个开放获取的文章，根据知识共享署名许可证的条款分发，允许无限制地使用，分发，并在任何媒体上复制，提供原作者和来源。
This paper is made available online in accordance with publisher policies. Please scroll down to view the document itself. Please refer to the repository record for this item and our policy information available from the repository home page for further information. To see the final version of this paper please visit the publisher's website. Access to the published version may require a subscription. Abstract The extraction of fluorescence time course data is a major bottleneck in high-throughput live-cell microscopy. Here we present an extendible framework based on the open-source image analysis software ImageJ, which aims in particular at analyzing the expression of fluorescent reporters through cell divisions. The ability to track individual cell lineages is essential for the analysis of gene regulatory factors involved in the control of cell fate and identity decisions. In our approach, cell nuclei are identified using Hoechst, and a characteristic drop in Hoechst fluorescence helps to detect dividing cells. We first compare the efficiency and accuracy of different segmentation methods and then present a statistical scoring algorithm for cell tracking, which draws on the combination of various features, such as nuclear intensity, area or shape, and importantly, dynamic changes thereof. Principal component analysis is used to determine the most significant features, and a global parameter search is performed to determine the weighting of individual features. Our algorithm has been optimized to cope with large cell movements, and we were able to semi-automatically extract cell trajectories across three cell generations. Based on the MTrackJ plugin for ImageJ, we have developed tools to efficiently validate tracks and manually correct them by connecting broken trajectories and reassigning falsely connected cell positions. A gold standard consisting of two time-series with 15,000 validated positions will be released as a valuable resource for benchmarking. We demonstrate how our method can be applied to analyze fluorescence distributions generated from mouse stem cells transfected with reporter constructs containing transcriptional control elements of the Msx1 gene, a regulator of pluripotency, in mother and daughter cells. Furthermore, we show by tracking zebrafish PAC2 cells expressing FUCCI cell cycle markers, our framework can be easily adapted to different cell types and fluorescent markers. Copyright: ß 2011 Downey et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
影响因子：
1.8
作者：
Giuliano, KA;Cheung, WS;Taylor, DL
通讯作者：
Taylor, DL