Heterochromatin is enriched for specific epigenetic factors including Heterochromatin Protein 1a (HP1a), and is essential for many organismal functions. To elucidate heterochromatin organization and regulation, we purified Drosophila melanogaster HP1a interactors, and performed a genome-wide RNAi screen to identify genes that impact HP1a levels or localization. The majority of the over four hundred putative HP1a interactors and regulators identified were previously unknown. We found that 13 of 16 tested candidates (83%) are required for gene silencing, providing a substantial increase in the number of identified components that impact heterochromatin properties. Surprisingly, image analysis revealed that although some HP1a interactors and regulators are broadly distributed within the heterochromatin domain, most localize to discrete subdomains that display dynamic localization patterns during the cell cycle. We conclude that heterochromatin composition and architecture is more spatially complex and dynamic than previously suggested, and propose that a network of subdomains regulates diverse heterochromatin functions.
Polycomb group (PcG) proteins dynamically define cellular identities through epigenetic repression of key developmental genes. PcG target gene repression can be stabilized through the interaction in the nucleus at PcG foci. Here, we report the results of a high-resolution microscopy genome-wide RNAi screen that identifies 129 genes that regulate the nuclear organization of Pc foci. Candidate genes include PcG components and chromatin factors, as well as many protein-modifying enzymes, including components of the SUMOylation pathway. In the absence of SUMO, Pc foci coagulate into larger aggregates. Conversely, loss of function of the SUMO peptidase Velo disperses Pc foci. Moreover, SUMO and Velo colocalize with PcG proteins at PREs, and Pc SUMOylation affects its chromatin targeting, suggesting that the dynamic regulation of Pc SUMOylation regulates PcG-mediated silencing by modulating the kinetics of Pc binding to chromatin as well as its ability to form Polycomb foci.
Cell-based high content screening (HCS) is becoming an important and increasingly favored approach in therapeutic drug discovery and functional genomics. In HCS, changes in cellular morphology and biomarker distributions provide an information-rich profile of cellular responses to experimental treatments such as small molecules or gene knockdown probes. One obstacle that currently exists with such cell-based assays is the availability of image processing algorithms that are capable of reliably and automatically analyzing large HCS image sets. HCS images of primary neuronal cell cultures are particularly challenging to analyze due to complex cellular morphology. Here we present a robust method for quantifying and statistically analyzing the morphology of neuronal cells in HCS images. The major advantages of our method over existing software lie in its capability to correct non-uniform illumination using the contrast-limited adaptive histogram equalization method; segment neuromeres using Gabor-wavelet texture analysis; and detect faint neurites by a novel phase-based neurite extraction algorithm that is invariant to changes in illumination and contrast and can accurately localize neurites. Our method was successfully applied to analyze a large HCS image set generated in a morphology screen for polyglutamine-mediated neuronal toxicity using primary neuronal cell cultures derived from embryos of a Drosophila Huntington's Disease (HD) model.
RNA interference has re-energized the field of functional genomics by enabling genome-scale loss-of-function screens in cultured cells. Looking back on the lessons that have been learned from the first wave of technology developments and applications in this exciting field, we provide both a user's guide for newcomers to the field and a detailed examination of some more complex issues, particularly concerning optimization and quality control, for more advanced users. From a discussion of cell lines, screening paradigms, reagent types and read-out methodologies, we explore in particular the complexities of designing optimal controls and normalization strategies for these challenging but extremely powerful studies.
The pairing of homologous chromosomes is a fundamental feature of the meiotic cell. In addition, a number of species exhibit homolog pairing in nonmeiotic, somatic cells as well, with evidence for its impact on both gene regulation and double-strand break (DSB) repair. An extreme example of somatic pairing can be observed in Drosophila melanogaster, where homologous chromosomes remain aligned throughout most of development. However, our understanding of the mechanism of somatic homolog pairing remains unclear, as only a few genes have been implicated in this process. In this study, we introduce a novel high-throughput fluorescent in situ hybridization (FISH) technology that enabled us to conduct a genome-wide RNAi screen for factors involved in the robust somatic pairing observed in Drosophila. We identified both candidate "pairing promoting genes" and candidate "anti-pairing genes," providing evidence that pairing is a dynamic process that can be both enhanced and antagonized. Many of the genes found to be important for promoting pairing are highly enriched for functions associated with mitotic cell division, suggesting a genetic framework for a long-standing link between chromosome dynamics during mitosis and nuclear organization during interphase. In contrast, several of the candidate anti-pairing genes have known interphase functions associated with S-phase progression, DNA replication, and chromatin compaction, including several components of the condensin II complex. In combination with a variety of secondary assays, these results provide insights into the mechanism and dynamics of somatic pairing.
Genome-wide, cell-based screens using high-content screening (HCS) techniques and automated fluorescence microscopy generate thousands of high-content images that contain an enormous wealth of cell biological information. Such screens are key to the analysis of basic cell biological principles, such as control of cell cycle and cell morphology. However, these screens will ultimately only shed light on human disease mechanisms and potential cures if the analysis can keep up with the generation of data. A fundamental step toward automated analysis of high-content screening is to construct a robust platform for automatic cellular phenotype identification. The authors present a framework, consisting of microscopic image segmentation and analysis components, for automatic recognition of cellular phenotypes in the context of the Rho family of small GTPases. To implicate genes involved in Rac signaling, RNA interference (RNAi) was used to perturb gene functions, and the corresponding cellular phenotypes were analyzed for changes. The data used in the experiments are high-content, 3-channel, fluorescence microscopy images of Drosophila Kc167 cultured cells stained with markers that allow visualization of DNA, polymerized actin filaments, and the constitutively activated Rho protein Rac(V12). The performance of this approach was tested using a cellular database that contained more than 1000 samples of 3 predefined cellular phenotypes, and the generalization error was estimated using a cross-validation technique. Moreover, the authors applied this approach to analyze the whole high-content fluorescence images of Drosophila cells for further HCS-based gene function analysis.
The spontaneous and reversible formation of foci and filaments that contain proteins involved in different metabolic processes is common in both the nucleus and the cytoplasm. Stress granules (SGs) and processing bodies (PBs) belong to a novel family of cellular structures collectively known as mRNA silencing foci that harbour repressed mRNAs and their associated proteins. SGs and PBs are highly dynamic and they form upon stress and dissolve thus releasing the repressed mRNAs according to changes in cell physiology. In addition, aggregates containing abnormal proteins are frequent in neurodegenerative disorders. In spite of the growing relevance of these supramolecular aggregates to diverse cellular functions a reliable automated tool for their systematic analysis is lacking. Here we report a MATLAB Script termed BUHO for the high-throughput image analysis of cellular foci. We used BUHO to assess the number, size and distribution of distinct objects with minimal deviation from manually obtained parameters. BUHO successfully addressed the induction of both SGs and PBs in mammalian and insect cells exposed to different stress stimuli. We also used BUHO to assess the dynamics of specific mRNA-silencing foci termed Smaug 1 foci (S-foci) in primary neurons upon synaptic stimulation. Finally, we used BUHO to analyze the role of candidate genes on SG formation in an RNAi-based experiment. We found that FAK56D, GCN2 and PP1 govern SG formation. The role of PP1 is conserved in mammalian cells as judged by the effect of the PP1 inhibitor salubrinal, and involves dephosphorylation of the translation factor eIF2α. All these experiments were analyzed manually and by BUHO and the results differed in less than 5% of the average value. The automated analysis by this user-friendly method will allow high-throughput image processing in short times by providing a robust, flexible and reliable alternative to the laborious and sometimes unfeasible visual scrutiny.
While genetic screens have identified many genes essential for neurite outgrowth, they have been limited in their ability to identify neural genes that also have earlier critical roles in the gastrula, or neural genes for which maternally contributed RNA compensates for gene mutations in the zygote. To address this, we developed methods to screen the Drosophila genome using RNA-interference (RNAi) on primary neural cells and present the results of the first full-genome RNAi screen in neurons. We used live-cell imaging and quantitative image analysis to characterize the morphological phenotypes of fluorescently labelled primary neurons and glia in response to RNAi-mediated gene knockdown. From the full genome screen, we focused our analysis on 104 evolutionarily conserved genes that when downregulated by RNAi, have morphological defects such as reduced axon extension, excessive branching, loss of fasciculation, and blebbing. To assist in the phenotypic analysis of the large data sets, we generated image analysis algorithms that could assess the statistical significance of the mutant phenotypes. The algorithms were essential for the analysis of the thousands of images generated by the screening process and will become a valuable tool for future genome-wide screens in primary neurons. Our analysis revealed unexpected, essential roles in neurite outgrowth for genes representing a wide range of functional categories including signalling molecules, enzymes, channels, receptors, and cytoskeletal proteins. We also found that genes known to be involved in protein and vesicle trafficking showed similar RNAi phenotypes. We confirmed phenotypes of the protein trafficking genes Sec61alpha and Ran GTPase using Drosophila embryo and mouse embryonic cerebral cortical neurons, respectively. Collectively, our results showed that RNAi phenotypes in primary neural culture can parallel in vivo phenotypes, and the screening technique can be used to identify many new genes that have important functions in the nervous system.
Regulation of cell growth is a fundamental process in development and disease that integrates a vast array of extra- and intracellular information. A central player in this process is RNA polymerase I (Pol I), which transcribes ribosomal RNA (rRNA) genes in the nucleolus. Rapidly growing cancer cells are characterized by increased Pol I-mediated transcription and, consequently, nucleolar hypertrophy. To map the genetic network underlying the regulation of nucleolar size and of Pol I-mediated transcription, we performed comparative, genome-wide loss-of-function analyses of nucleolar size in Saccharomyces cerevisiae and Drosophila melanogaster coupled with mass spectrometry-based analyses of the ribosomal DNA (rDNA) promoter. With this approach, we identified a set of conserved and nonconserved molecular complexes that control nucleolar size. Furthermore, we characterized a direct role of the histone information regulator (HIR) complex in repressing rRNA transcription in yeast. Our study provides a full-genome, cross-species analysis of a nuclear subcompartment and shows that this approach can identify conserved molecular modules.
Biological networks are highly complex systems, consisting largely of enzymes that act as molecular switches to activate/inhibit downstream targets via post-translational modification. Computational techniques have been developed to perform signaling network inference using some high-throughput data sources, such as those generated from transcriptional and proteomic studies, but comparable methods have not been developed to use high-content morphological data, which are emerging principally from large-scale RNAi screens, to these ends. Here, we describe a systematic computational framework based on a classification model for identifying genetic interactions using high-dimensional single-cell morphological data from genetic screens, apply it to RhoGAP/GTPase regulation in Drosophila, and evaluate its efficacy. Augmented by knowledge of the basic structure of RhoGAP/GTPase signaling, namely, that GAPs act directly upstream of GTPases, we apply our framework for identifying genetic interactions to predict signaling relationships between these proteins. We find that our method makes mediocre predictions using only RhoGAP single-knockdown morphological data, yet achieves vastly improved accuracy by including original data from a double-knockdown RhoGAP genetic screen, which likely reflects the redundant network structure of RhoGAP/GTPase signaling. We consider other possible methods for inference and show that our primary model outperforms the alternatives. This work demonstrates the fundamental fact that high-throughput morphological data can be used in a systematic, successful fashion to identify genetic interactions and, using additional elementary knowledge of network structure, to infer signaling relations.