ABSTRACT
In this review we summarize original methods for the extraction quantitative information from the confocal images of gene expression patterns. These methods include image segmentation, extraction of quantitative numerical data on gene expression, removal of background signal and spatial registration. Finally it is possible to construct a spatiotemporal atlas of gene expression form individual images obtained at each developmental stage. Initially all methods were developed to extract quantitative numerical information form confocal images of segmentation gene expression in Drosophila melanogaster. Application of these methods to Drosophila images makes it possible to reveal new mechanisms of formation of segmentation gene expression domains, as well as to construct the quantitative atlas of segmentation gene expression. Most image processing procedures can be easily adapted to process a wide range of biological images.
Subject(s)
Gene Expression Profiling/methods , Gene Expression , Microscopy, Confocal , Animals , Drosophila Proteins/genetics , Drosophila melanogaster/embryology , Drosophila melanogaster/genetics , Drosophila melanogaster/ultrastructure , Embryo, Nonmammalian/ultrastructure , Gene Expression Regulation, Developmental , Genes, Insect , Image EnhancementABSTRACT
In this review, we summarize original methods for the extraction of quantitative information from confocal images of gene-expression patterns. These methods include image segmentation, the extraction of quantitative numerical data on gene expression, and the removal of background signal and spatial registration. Finally, it is possible to construct a spatiotemporal atlas of gene expression from individual images recorded at each developmental stage. Initially all methods were developed to extract quantitative numerical information from confocal images of segmentation gene expression in Drosophila melanogaster. The application of these methods to Drosophila images makes it possible to reveal new mechanisms in the formation of segmentation gene expression domains, as well as to construct a quantitative atlas of segmentation gene expression. Most image processing procedures can be easily adapted to process a wide range of biological images.
ABSTRACT
For registering data on the in situ expression of segmentation genes, a method of image registration was developed basing on the spline approximation. The reference points for the registration were the coordinates of extrema in one-dimensional patterns of gene expression. This registration method is characterized by a very high accuracy. A method of creating a generalized pattern of gene expression in single cells is proposed. Such patterns were constructed for nine segmentation genes belonging to the gap and pair-rule classes of genes.
