Mace Daniel L, Lee Ji-Young, Twigg Richard W, Colinas Juliette, Benfey Philip N, Ohler Uwe
Institute for Genome Science and Policy, Duke University, Durham, NC 27708, USA.
Bioinformatics. 2006 Jul 15;22(14):e323-31. doi: 10.1093/bioinformatics/btl228.
Confocal microscopy has long provided qualitative information for a variety of applications in molecular biology. Recent advances have led to extensive image datasets, which can now serve as new data sources to obtain quantitative gene expression information. In contrast to microarrays, which usually provide data for many genes at one time point, these image data provide us with expression information for only one gene, but with the advantage of high spatial and/or temporal resolution, which is often lostin microarray samples.
We have developed a prototype for the automatic analysis of Arabidopsis confocal images, which show the expression of a single transcription factor by means of GFP reporter constructs. Using techniques from image registration, we are able to address inherent problems of non-rigid transformation and partial mapping, and obtain relative expression values for 13 different tissues in Arabidopsis roots. This provides quantitative information with high spatial resolution, which accurately represents the underlying expression values within the organism. We validate our approach on a data set of 122 images depicting expression patterns of 30 transcription factors, both in terms of registration accuracy, as well as correlation with cell-sorted microarray data. Approaches like this will be useful to lay the groundwork to reconstruct regulatory networks on the level of tissues or even individual cells.
Upon request from the authors.
长期以来，共聚焦显微镜为分子生物学的各种应用提供了定性信息。最近的进展产生了大量的图像数据集，这些数据集现在可以作为新的数据来源来获取定量基因表达信息。与通常在一个时间点为许多基因提供数据的微阵列不同，这些图像数据仅为我们提供一个基因的表达信息，但具有高空间和/或时间分辨率的优势，而这在微阵列样本中常常会丢失。
我们开发了一个用于自动分析拟南芥共聚焦图像的原型，这些图像通过绿色荧光蛋白（GFP）报告构建体展示单个转录因子的表达。利用图像配准技术，我们能够解决非刚性变换和部分映射的固有问题，并获得拟南芥根中13种不同组织的相对表达值。这提供了具有高空间分辨率的定量信息，准确地反映了生物体内部潜在的表达值。我们在一个包含122张描绘30种转录因子表达模式的图像数据集上验证了我们的方法，包括配准精度以及与细胞分选微阵列数据的相关性。这样的方法将有助于为在组织甚至单个细胞水平上重建调控网络奠定基础。
可根据作者要求提供。
