This protocol describes the batch isolation of tissue-specific chromatin for immunoprecipitation (BiTS-ChIP) for analysis of histone modifications, transcription factor binding, or polymerase occupancy within the context of a multicellular organism or tissue. Embryos expressing a cell type-specific nuclear marker are formaldehyde cross-linked and then subjected to dissociation. Fixed nuclei are isolated and sorted using FACS on the basis of the cell type-specific nuclear marker. Tissue-specific chromatin is extracted, sheared by sonication and used for ChIP-seq or other analyses. The key advantages of this method are the covalent cross-linking before embryo dissociation, which preserves the transcriptional context, and the use of FACS of nuclei, yielding very high purity. The protocol has been optimized for Drosophila, but with minor modifications should be applicable to any model system. The full protocol, including sorting, immunoprecipitation and generation of sequencing libraries, can be completed within 5 d.

Cell fate decisions are driven through the integration of inductive signals and tissue-specific transcription factors (TFs), although the details on how this information converges in cis remain unclear. Here, we demonstrate that the five genetic components essential for cardiac specification in Drosophila, including the effectors of Wg and Dpp signaling, act as a collective unit to cooperatively regulate heart enhancer activity, both in vivo and in vitro. Their combinatorial binding does not require any specific motif orientation or spacing, suggesting an alternative mode of enhancer function whereby cooperative activity occurs with extensive motif flexibility. A fraction of enhancers co-occupied by cardiogenic TFs had unexpected activity in the neighboring visceral mesoderm but could be rendered active in heart through single-site mutations. Given that cardiac and visceral cells are both derived from the dorsal mesoderm, this "dormant" TF binding signature may represent a molecular footprint of these cells' developmental lineage.

Chromatin modifications are associated with many aspects of gene expression, yet their role in cellular transitions during development remains elusive. Here, we use a new approach to obtain cell type-specific information on chromatin state and RNA polymerase II (Pol II) occupancy within the multicellular Drosophila melanogaster embryo. We directly assessed the relationship between chromatin modifications and the spatio-temporal activity of enhancers. Rather than having a unique chromatin state, active developmental enhancers show heterogeneous histone modifications and Pol II occupancy. Despite this complexity, combined chromatin signatures and Pol II presence are sufficient to predict enhancer activity de novo. Pol II recruitment is highly predictive of the timing of enhancer activity and seems dependent on the timing and location of transcription factor binding. Chromatin modifications typically demarcate large regulatory regions encompassing multiple enhancers, whereas local changes in nucleosome positioning and Pol II occupancy delineate single active enhancers. This cell type-specific view identifies dynamic enhancer usage, an essential step in deciphering developmental networks.

Accurately assessing the binding of transcription factors to cis-regulatory elements in vivo is an essential step toward understanding the mechanisms that govern embryonic development. Genome-wide transcription factor location analysis has been facilitated by the development of high-density tiling arrays (ChIP-on-chip), and more recently by next-generation sequencing technologies, which are used to sequence the DNA fragments obtained from chromatin immunoprecipitation experiments (ChIP-seq). This chapter provides a detailed protocol of the different steps required to generate a successful ChIP-seq library, starting from embryo collection and fixation to chromatin preparation, immunoprecipitation, and finally library preparation. The protocol is optimized for Drosophila embryos, but can be adapted to any organism. The obtained library is suitable for sequencing on an Illumina GAIIx platform.

Understanding how complex patterns of temporal and spatial expression are regulated is central to deciphering genetic programs that drive development. Gene expression is initiated through the action of transcription factors and their cofactors converging on enhancer elements leading to a defined activity. Specific constellations of combinatorial occupancy are therefore often conceptualized as rigid binding codes that give rise to a common output of spatio-temporal expression. Here, we assessed this assumption using the regulatory input of two essential transcription factors within the Drosophila myogenic network. Mutations in either Myocyte enhancing factor 2 (Mef2) or the zinc-finger transcription factor lame duck (lmd) lead to very similar defects in myoblast fusion, yet the underlying molecular mechanism for this shared phenotype is not understood. Using a combination of ChIP-on-chip analysis and expression profiling of loss-of-function mutants, we obtained a global view of the regulatory input of both factors during development. The majority of Lmd-bound enhancers are co-bound by Mef2, representing a subset of Mef2's transcriptional input during these stages of development. Systematic analyses of the regulatory contribution of both factors demonstrate diverse regulatory roles, despite their co-occupancy of shared enhancer elements. These results indicate that Lmd is a tissue-specific modulator of Mef2 activity, acting as both a transcriptional activator and repressor, which has important implications for myogenesis. More generally, this study demonstrates considerable flexibility in the regulatory output of two factors, leading to additive, cooperative, and repressive modes of co-regulation.

Development is driven by tightly coordinated spatio-temporal patterns of gene expression, which are initiated through the action of transcription factors (TFs) binding to cis-regulatory modules (CRMs). Although many studies have investigated how spatial patterns arise, precise temporal control of gene expression is less well understood. Here, we show that dynamic changes in the timing of CRM occupancy is a prevalent feature common to all TFs examined in a developmental ChIP time course to date. CRMs exhibit complex binding patterns that cannot be explained by the sequence motifs or expression of the TFs themselves. The temporal changes in TF binding are highly correlated with dynamic patterns of target gene expression, which in turn reflect transitions in cellular function during different stages of development. Thus, it is not only the timing of a TF's expression, but also its temporal occupancy in refined time windows, which determines temporal gene expression. Systematic measurement of dynamic CRM occupancy may therefore serve as a powerful method to decode dynamic changes in gene expression driving developmental progression.

We present a computational method for identifying potential targets of a transcription factor (TF) using wild-type gene expression time series data. For each putative target gene we fit a simple differential equation model of transcriptional regulation, and the model likelihood serves as a score to rank targets. The expression profile of the TF is modeled as a sample from a Gaussian process prior distribution that is integrated out using a nonparametric Bayesian procedure. This results in a parsimonious model with relatively few parameters that can be applied to short time series datasets without noticeable overfitting. We assess our method using genome-wide chromatin immunoprecipitation (ChIP-chip) and loss-of-function mutant expression data for two TFs, Twist, and Mef2, controlling mesoderm development in Drosophila. Lists of top-ranked genes identified by our method are significantly enriched for genes close to bound regions identified in the ChIP-chip data and for genes that are differentially expressed in loss-of-function mutants. Targets of Twist display diverse expression profiles, and in this case a model-based approach performs significantly better than scoring based on correlation with TF expression. Our approach is found to be comparable or superior to ranking based on mutant differential expression scores. Also, we show how integrating complementary wild-type spatial expression data can further improve target ranking performance.

Development requires the establishment of precise patterns of gene expression, which are primarily controlled by transcription factors binding to cis-regulatory modules. Although transcription factor occupancy can now be identified at genome-wide scales, decoding this regulatory landscape remains a daunting challenge. Here we used a novel approach to predict spatio-temporal cis-regulatory activity based only on in vivo transcription factor binding and enhancer activity data. We generated a high-resolution atlas of cis-regulatory modules describing their temporal and combinatorial occupancy during Drosophila mesoderm development. The binding profiles of cis-regulatory modules with characterized expression were used to train support vector machines to predict five spatio-temporal expression patterns. In vivo transgenic reporter assays demonstrate the high accuracy of these predictions and reveal an unanticipated plasticity in transcription factor binding leading to similar expression. This data-driven approach does not require previous knowledge of transcription factor sequence affinity, function or expression, making it widely applicable.

Development is regulated by dynamic patterns of gene expression, which are orchestrated through the action of complex gene regulatory networks (GRNs). Substantial progress has been made in modeling transcriptional regulation in recent years, including qualitative 'coarse-grain' models operating at the gene level to very 'fine-grain' quantitative models operating at the biophysical 'Transcription Factor-DNA level'. Recent advances in genome-wide studies have revealed an enormous increase in the size and complexity or GRNs. Even relatively simple developmental processes can involve hundreds of regulatory molecules, with extensive interconnectivity and cooperative regulation. This leads to an explosion in the number of regulatory functions, effectively impeding Boolean-based qualitative modeling approaches. At the same time, the lack of information on the biophysical properties for the majority of transcription factors within a global network restricts quantitative approaches. In this review we explore the current challenges in moving from modeling medium scale well-characterized networks to more poorly characterized global networks. We suggest to integrate coarse- and find-grain approaches to model gene regulatory networks in cis. We focus on two very well studied examples from Drosophila, which likely represent typical developmental regulatory modules across metazoans.