Jameson Daniel, Turner David A, Ankers John, Kennedy Stephnie, Ryan Sheila, Swainston Neil, Griffiths Tony, Spiller David G, Oliver Stephen G, White Michael R H, Kell Douglas B, Paton Norman W
Manchester Centre for Integrative Systems Biology, School of Chemistry, and Manchester Interdisciplinary Biocentre, University of Manchester, 131, Princess St, Manchester, M1 7DN, UK.
BMC Bioinformatics. 2009 Jul 21;10:226. doi: 10.1186/1471-2105-10-226.
High content live cell imaging experiments are able to track the cellular localisation of labelled proteins in multiple live cells over a time course. Experiments using high content live cell imaging will generate multiple large datasets that are often stored in an ad-hoc manner. This hinders identification of previously gathered data that may be relevant to current analyses. Whilst solutions exist for managing image data, they are primarily concerned with storage and retrieval of the images themselves and not the data derived from the images. There is therefore a requirement for an information management solution that facilitates the indexing of experimental metadata and results of high content live cell imaging experiments.
We have designed and implemented a data model and information management solution for the data gathered through high content live cell imaging experiments. Many of the experiments to be stored measure the translocation of fluorescently labelled proteins from cytoplasm to nucleus in individual cells. The functionality of this database has been enhanced by the addition of an algorithm that automatically annotates results of these experiments with the timings of translocations and periods of any oscillatory translocations as they are uploaded to the repository. Testing has shown the algorithm to perform well with a variety of previously unseen data.
Our repository is a fully functional example of how high throughput imaging data may be effectively indexed and managed to address the requirements of end users. By implementing the automated analysis of experimental results, we have provided a clear impetus for individuals to ensure that their data forms part of that which is stored in the repository. Although focused on imaging, the solution provided is sufficiently generic to be applied to other functional proteomics and genomics experiments. The software is available from: fhttp://code.google.com/p/livecellim/
高内涵活细胞成像实验能够在一段时间内追踪多个活细胞中标记蛋白的细胞定位。使用高内涵活细胞成像的实验会生成多个大型数据集，这些数据集通常是以临时方式存储的。这阻碍了对可能与当前分析相关的先前收集数据的识别。虽然存在管理图像数据的解决方案，但它们主要关注图像本身的存储和检索，而不是从图像中派生的数据。因此，需要一种信息管理解决方案，以促进高内涵活细胞成像实验的实验元数据和结果的索引。
我们为通过高内涵活细胞成像实验收集的数据设计并实现了一个数据模型和信息管理解决方案。许多要存储的实验测量单个细胞中荧光标记蛋白从细胞质到细胞核的转位。通过添加一种算法，该数据库的功能得到了增强，当这些实验的结果上传到存储库时，该算法会自动用转位时间和任何振荡转位周期对其进行注释。测试表明，该算法在处理各种以前未见过的数据时表现良好。
我们的存储库是一个功能齐全的示例，展示了如何有效地索引和管理高通量成像数据以满足最终用户的需求。通过对实验结果进行自动分析，我们为个人提供了明确的动力，以确保他们的数据成为存储在存储库中的数据的一部分。虽然专注于成像，但提供的解决方案足够通用，可应用于其他功能蛋白质组学和基因组学实验。该软件可从以下网址获得：fhttp://code.google.com/p/livecellim/
