Abstract
Understanding the function and evolution of developmental regulatory networks requires the characterisation and quantification of spatio-temporal gene expression patterns across a range of systems and species. However, most high-throughput methods to measure the dynamics of gene expression do not preserve the detailed spatial information needed in this context. For this reason, quantification methods based on image bioinformatics have become increasingly important over the past few years. Most available approaches in this field either focus on the detailed and accurate quantification of a small set of gene expression patterns, or attempt high-throughput analysis of spatial expression through binary pattern extraction and large-scale analysis of the resulting datasets. Here we present a robust, “medium-throughput” pipeline to process in situ hybridisation patterns from embryos of different species of flies. It bridges the gap between high-resolution, and high-throughput image processing methods, enabling us to quantify graded expression patterns along the antero-posterior axis of the embryo in an efficient and straightforward manner. Our method is based on a robust enzymatic (colorimetric) in situ hybridisation protocol and rapid data acquisition through wide-field microscopy. Data processing consists of image segmentation, profile extraction, and determination of expression domain boundary positions using a spline approximation. It results in sets of measured boundaries sorted by gene and developmental time point, which are analysed in terms of expression variability or spatio-temporal dynamics. Our method yields integrated time series of spatial gene expression, which can be used to reverse-engineer developmental gene regulatory networks across species. It is easily adaptable to other processes and species, enabling the in silico reconstitution of gene regulatory networks in a wide range of developmental contexts.
Highlights
One of the central challenges in biology today is to understand the structure, function, and evolution of gene regulatory networks involved in pattern formation during development
Raw data consists of four images acquired using a compound, wide-field fluorescence microscope: (A) a differential interference contrast (DIC) image (Fig. 2A), (B) a bright-field image (Fig. 2B), (C) a fluorescent image of the DAPI nuclear counterstain (Fig. 2C), and (D) a DIC image showing details of membrane morphology on the dorsal side of the embryo (Fig. 2D)
There are two graphical analysis methods looking at the variability and dynamics of expression boundaries, each with their own tabs (Figs. 9 and 10)
Summary
One of the central challenges in biology today is to understand the structure, function, and evolution of gene regulatory networks involved in pattern formation during development. In order to achieve this, we need to map and compare spatio-temporal patterns of gene expression across different species. With the advent of high-throughput methodology, the scale at which we can generate expression data has increased dramatically. RNA-seq, DNA microarrays, and quantitative PCR are among the best known methods used for this purpose. None of these ‘omics’ approaches provides detailed spatial information, which is crucial in this context. Quantitative techniques based on in situ hybridisation (or antibody staining) combined with microscopy and image-processing algorithms have become increasingly important over the past few years [1,2,3,4,5]
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