Medium-throughput processing of whole mount in situ hybridisation experiments into gene expression domains.

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

A quantitative validated model reveals two phases of transcriptional regulation for the gap gene giant in Drosophila

Understanding eukaryotic transcriptional regulation and its role in development and pattern formation is one of the big challenges in biology today. Most attempts at tackling this problem either focus on the molecular details of transcription factor binding, or aim at genome-wide prediction of expression patterns from sequence through bioinformatics and mathematical modelling. Here we bridge the gap between these two complementary approaches by providing an integrative model of cis-regulatory elements governing the expression of the gap gene giant (gt) in the blastoderm embryo of Drosophila melanogaster. We use a reverse-engineering method, where mathematical models are fit to quantitative spatio-temporal reporter gene expression data to infer the regulatory mechanisms underlying gt expression in its anterior and posterior domains. These models are validated through prediction of gene expression in mutant backgrounds. A detailed analysis of our data and models reveals that gt is regulated by domain-specific CREs at early stages, while a late element drives expression in both the anterior and the posterior domains. Initial gt expression depends exclusively on inputs from maternal factors. Later, gap gene cross-repression and gt auto-activation become increasingly important. We show that auto-regulation creates a positive feedback, which mediates the transition from early to late stages of regulation. We confirm the existence and role of gt auto-activation through targeted mutagenesis of Gt transcription factor binding sites. In summary, our analysis provides a comprehensive picture of spatio-temporal gene regulation by different interacting enhancer elements for an important developmental regulator.A.H. received funding from the La Caixa Foundation to conduct her PhD project at the CRG. The laboratory of J.J. is funded by the MEC-EMBL agreement for the EMBL/CRG Research Unit in Systems Biology. The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007–2013) under grant agreement FP7-KBBE-2011-5/289434 (BioPreDyn), by Grant 153 (MOPDEV) of the ERANet: ComplexityNET programme, by AGAUR SGR Grant 406, as well as grants BFU2009-10184 and BFU2012-33775 from the Spanish Ministry of the Economy and Competitiveness (MINECO, formerly MICINN). The Centre for Genomic Regulation (CRG) acknowledges support from MINECO, 'Centro de Excelencia Severo Ochoa 2013-2017', SEV-2012-0208

Scatter search applied to the inference of a development gene network

Efficient network inference is one of the challenges of current-day biology. Its application to the study of development has seen noteworthy success, yet a multicellular context, tissue growth, and cellular rearrangements impose additional computational costs and prohibit a wide application of current methods. Therefore, reducing computational cost and providing quick feedback at intermediate stages are desirable features for network inference. Here we propose a hybrid approach composed of two stages: exploration with scatter search and exploitation of intermediate solutions with low temperature simulated annealing. We test the approach on the well-understood process of early body plan development in flies, focusing on the gap gene network. We compare the hybrid approach to simulated annealing, a method of network inference with a proven track record. We find that scatter search performs well at exploring parameter space and that low temperature simulated annealing refines the intermediate results into excellent model fits. From this we conclude that for poorly-studied developmental systems, scatter search is a valuable tool for exploration and accelerates the elucidation of gene regulatory networks.We thank Johannes Jaeger for critical feedback and scientific advice. We thankfully acknowledge the computer resources, technical expertise and assistance provided by the Barcelona Supercomputing Center, which is part of the Red Española de Supercomputación. We thank SURFsara (www.surfsara.nl) for the support in using the Lisa Compute Cluster. The Centre for Genomic Regulation (CRG) acknowledges support from the Spanish Ministry of Economy and Competitiveness, ‘Centro de Excelencia Severo Ochoa 2013-2017’, SEV-2012-0208. AC kindly acknowledges Fondation Bettencourt Schueller

Reverse-engineering post-transcriptional regulation of gap genes in Drosophila melanogaster

16 páginas, 6 figuras, 1 tablaSystems biology proceeds through repeated cycles of experiment and modeling. One way to implement this is reverse engineering, where models are fit to data to infer and analyse regulatory mechanisms. This requires rigorous methods to determine whether model parameters can be properly identified. Applying such methods in a complex biological context remains challenging. We use reverse engineering to study post-transcriptional regulation in pattern formation. As a case study, we analyse expression of the gap genes Krüppel, knirps, and giant in Drosophila melanogaster. We use detailed, quantitative datasets of gap gene mRNA and protein expression to solve and fit a model of post-transcriptional regulation, and establish its structural and practical identifiability. Our results demonstrate that post-transcriptional regulation is not required for patterning in this system, but is necessary for proper control of protein levels. Our work demonstrates that the uniqueness and specificity of a fitted model can be rigorously determined in the context of spatio-temporal pattern formation. This greatly increases the potential of reverse engineering for the study of development and other, similarly complex, biological processesThis collaborative project was carried out in the context of the BioPreDyn consortium, which is co-ordinated by JJ and JRB, and funded by European Commission grant FP7-KBBE-2011-5/289434. The laboratory of JJ is funded by the MEC-EMBL agreement for the EMBL/CRG Research Unit in Systems Biology. Additional financial support was provided by SGR Grant 406 from the Catalan funding agency AGAUR, and by grants BFU2009- 10184 and 273 BFU2009-09168 from the Spanish Ministerio de Economia y Competitividad (MINECO). The group at IIM-CSIC acknowledges financial support from MINECO and the European Regional Development Fund (ERDF; project “MultiScales”, DPI2011-28112-C04-03). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer reviewe

Determination of the 10% strip for profile extraction along the A–P axis.

<p>The embryo mask (A) is used to calculate the morphological skeleton shown in (B). Along this skeleton we position 5 equidistant points (red dots in C), though which we draw a cubic spline (solid black line in C). This spline is extended to the embryo borders using Lagrange extrapolation. It is then used to determine a band (or strip) that extends 10% along the minor (or dorso-ventral, D–V) axis of the embryo (5% above and below the spline; red lines in C). Expression profiles are extracted from the bright-field image by measuring the average staining intensity of vertical pixel columns that fall within the strip (D).</p

High-resolution gene expression data from blastoderm embryos of the scuttle fly Megaselia abdita

Gap genes are involved in segment determination during early development in dipteran insects (flies, midges, and mosquitoes). We carried out a systematic quantitative comparative analysis of the gap gene network across different dipteran species. Our work provides mechanistic insights into the evolution of this pattern-forming network. As a central component of our project, we created a high-resolution quantitative spatio-temporal data set of gap and maternal co-ordinate gene expression in the blastoderm embryo of the non-drosophilid scuttle fly, Megaselia abdita. Our data include expression patterns in both wild-type and RNAi-treated embryos. The data-covering 10 genes, 10 time points, and over 1,000 individual embryos-consist of original embryo images, quantified expression profiles, extracted positions of expression boundaries, and integrated expression patterns, plus metadata and intermediate processing steps. These data provide a valuable resource for researchers interested in the comparative study of gene regulatory networks and pattern formation, an essential step towards a more quantitative and mechanistic understanding of developmental evolution.This work was funded by the MEC-EMBL agreement for the EMBL/CRG Research Unit in Systems Biology, by European Commission grant FP7-KBBE-2011-5/289434 (BioPreDyn), by Grant 153 (MOPDEV) of the ERANet: ComplexityNET programme, by AGAUR SGR Grant 406, as well as grants BFU2009-10184 and BFU2012-33775 from the Spanish Ministry of the Economy and Competitiveness (MINECO, formerly MICINN). The Centre for Genomic Regulation (CRG) acknowledges support from MINECO, 'Centro de Excelencia Severo Ochoa 2013-2017', SEV-2012-0208

FlyGUI: analysing positional variability.

<p>Screenshot displaying the ‘Analysis: Variability’ tab of our FlyGUI. This tab allows us to plot sets of expression boundaries for specific genes and time classes. Individual slopes and medians can be displayed together (main panel), or separately as median-only (A), or as slopes-only (B) graphs. Either entire gene expression patterns (main panel), or individual slopes (C) can be plotted. Median slopes for multiple genes can be combined (not shown). See text for details.</p

Generating the embryo mask.

<p>The top row displays the four raw images obtained by microscopy: (A) DIC, (B) bright-field, (C) nuclear counterstain, and (D) detailed membrane morphology. The DIC image is used to create the binary embryo mask. This is achieved through a series of processing steps (1–16), which are described in detail in the Materials and Methods section of the main text.</p