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1.  RNAi screen of Salmonella invasion shows role of COPI in membrane targeting of cholesterol and Cdc42 
A genome wide RNAi screen identifies 72 host cell genes affecting S. Typhimurium entry, including actin regulators and COPI. This study implicates COPI-dependent cholesterol and sphingolipid localization as a common mechanism of infection by bacterial and viral pathogens.
Genome-scale RNAi screen identifies 72 host genes affecting S. Typhimurium host cell invasion.Step-specific follow-up assays assign the phenotypes to specific steps of the invasion process.COPI effects on host cell binding, ruffling and invasion were traced to a key role of COPI in membrane targeting of cholesterol, sphingolipids, Rac1 and Cdc42.This new role of COPI explains why COPI is required for host cell infection by numerous bacterial and viral pathogens.
Pathogens are not only a menace to public health, but they also provide excellent tools for probing host cell function. Thus, studying infection mechanisms has fueled progress in cell biology (Ridley et al, 1992; Welch et al, 1997). In the presented study, we have performed an RNAi screen to identify host cell genes required for Salmonella host cell invasion. This screen identified proteins known to contribute to Salmonella-induced actin rearrangements (e.g., Cdc42 and the Arp2/3 complex; reviewed in Schlumberger and Hardt, 2006) and vesicular traffic (e.g., Rab7) as well as unexpected hits, such as the COPI complex. COPI is a known organizer of Golgi-to-ER vesicle transport (Bethune et al, 2006; Beck et al, 2009). Here, we show that COPI is also involved in plasma membrane targeting of cholesterol, sphingolipids and the Rho GTPases Cdc42 and Rac1, essential host cell factors required for Salmonella invasion. This explains why COPI depletion inhibits infection by S. Typhimurium and illustrates how combining bacterial pathogenesis and systems approaches can promote cell biology.
Salmonella Typhimurium is a common food-borne pathogen and worldwide a major public health problem causing severe diarrhea. The pathogen uses the host's gut mucosa as a portal of entry and gut tissue invasion is a key event leading to the disease. This explains the intense interest from medicine and basic biology in the mechanism of Salmonella host cell invasion.
Tissue culture infection models have delineated a sequence of events leading host cell invasion (Figure 1; Schlumberger and Hardt, 2006): (i) pathogen binding to the host cell surface; (ii) activation of a syringe-like apparatus (‘Type III secretion system 1', T1) of the bacterium and injection of a bacterial toxin cocktail into the host cell. These toxins include SopE, a key virulence factor triggering invasion (Hardt et al, 1998), which was analyzed in our study; (iii) toxin-triggered membrane ruffling. To a significant extent, this is facilitated by SopE-triggered activation of Cdc42 and Rac1 and subsequent actin polymerization at the site of infection; (iv) engulfment of the pathogen within a vesicular compartment (SCV) and (v) maturation of the SCV, a process driven by a second Type III secretion system (T2), which is expressed by the pathogen upon bacterial entry (Figure 1). This sequence of events mediates Salmonella invasion into the gut epithelium and illustrates that this pathogen can be used for probing mechanisms of host cell actin control, membrane biogenesis, vesicle formation and vesicular trafficking.
SopE is a key virulence factor of invasion and triggers the activation of Cdc42 and Rac1 and subsequent actin polymerization at the site of infection. We have employed a SopE-expressing S. Typhimurium strain and RNAi screening technology to identify host cell factors affecting invasion. First, we developed an automated fluorescence microscopy assay to quantify S. Typhimurium entry in a high-throughput format (Figure 1C). This assay was based on a GFP reporter expressed by the pathogen after invasion and maturation of the SCV. Using this assay, we screened a ‘druggable genome' siRNA library (6978 genes, 3 oligos each, 1 oligo per well) and identified 72 invasion hits. These included established regulators of the actin cytoskeleton (Cdc42, Arp2/3, Nap1; Schlumberger and Hardt, 2006), some of which have not been implicated so far in Salmonella entry (Pfn1, Cap1), as well as proteins not previously thought to influence infection (Atp1a1, Rbx1, COPI complex). Potentially, these hits could affect any step of the invasion process (Figure 1A).
In the second stage of the study, we have assigned each ‘invasion hit' to particular steps of the invasion process. For this purpose, we developed step-specific assays for Salmonella binding, injection, ruffling and membrane engulfment and re-screened the genes found as hits in the first screen (four siRNAs per gene). As expected, a significant number of ‘hits' affected binding to the host cell, others affected binding and ruffling (e.g., Pfn1, Itgβ5, Cap1), a few were specific for the ruffling step (e.g., Cdc42) and some affected SCV maturation, namely Rab7a, the trafficking protein Vps39 and the vacuolar proton pump Atp6ap2. Thus, our experimental strategy allowed mechanistic interpretation and linked novel hits to particular phenotypes, thus providing a basis for further studies (Figure 1).
COPI depletion impaired effector injection and ruffling. This was surprising, as the COPI complex was known to regulate retrogade Golgi-to-ER transport, but was not expected to affect pathogen interactions at the plasma membrane. Therefore, we have investigated the underlying mechanism. We have observed that COPI depletion entailed dramatic changes in the plasma membrane composition (Figure 6). Cholesterol and sphingolipids, which form domains (‘lipid rafts') in the plasma membrane, were depleted from the cell surface and redirected into a large vesicular compartment. The same was true for the Rho GTPases Rac1 and Cdc42. This strong decrease in the amount of cholesterol-enriched microdomains and Rho GTPases in the plasma membrane explained the observed defects in S. Typhimurium host cell invasion and assigned a novel role for COPI in controlling mammalian plasma membrane composition. It should be noted that other viral and bacterial pathogens do show a similar dependency on host cellular COPI and plasma membrane lipids. This includes notorious pathogens such as Staphylococcus aureus (Ramet et al, 2002; Potrich et al, 2009), Listeria monocytogenes (Seveau et al, 2004; Agaisse et al, 2005; Cheng et al, 2005; Gekara et al, 2005), Mycobacterium tuberculosis (Munoz et al, 2009), Chlamydia trachomatis (Elwell et al, 2008), influenza virus (Hao et al, 2008; Konig et al, 2010), hepatitis C virus (Tai et al, 2009; Popescu and Dubuisson, 2010) and the vesicular stomatitis virus (presented study) and suggests that COPI-mediated control of host cell plasma membrane composition might be of broad importance for pathogenesis. Future work will have to address whether this might offer starting points for developing anti-infective therapeutics with a very broad spectrum of activity.
The pathogen Salmonella Typhimurium is a common cause of diarrhea and invades the gut tissue by injecting a cocktail of virulence factors into epithelial cells, triggering actin rearrangements, membrane ruffling and pathogen entry. One of these factors is SopE, a G-nucleotide exchange factor for the host cellular Rho GTPases Rac1 and Cdc42. How SopE mediates cellular invasion is incompletely understood. Using genome-scale RNAi screening we identified 72 known and novel host cell proteins affecting SopE-mediated entry. Follow-up assays assigned these ‘hits' to particular steps of the invasion process; i.e., binding, effector injection, membrane ruffling, membrane closure and maturation of the Salmonella-containing vacuole. Depletion of the COPI complex revealed a unique effect on virulence factor injection and membrane ruffling. Both effects are attributable to mislocalization of cholesterol, sphingolipids, Rac1 and Cdc42 away from the plasma membrane into a large intracellular compartment. Equivalent results were obtained with the vesicular stomatitis virus. Therefore, COPI-facilitated maintenance of lipids may represent a novel, unifying mechanism essential for a wide range of pathogens, offering opportunities for designing new drugs.
doi:10.1038/msb.2011.7
PMCID: PMC3094068  PMID: 21407211
coatomer; HeLa; Salmonella; siRNA; systems biology
2.  Clustering phenotype populations by genome-wide RNAi and multiparametric imaging 
How to predict gene function from phenotypic cues is a longstanding question in biology.Using quantitative multiparametric imaging, RNAi-mediated cell phenotypes were measured on a genome-wide scale.On the basis of phenotypic ‘neighbourhoods', we identified previously uncharacterized human genes as mediators of the DNA damage response pathway and the maintenance of genomic integrity.The phenotypic map is provided as an online resource at http://www.cellmorph.org for discovering further functional relationships for a broad spectrum of biological module
Genetic screens for phenotypic similarity have made key contributions for associating genes with biological processes. Aggregating genes by similarity of their loss-of-function phenotype has provided insights into signalling pathways that have a conserved function from Drosophila to human (Nusslein-Volhard and Wieschaus, 1980; Bier, 2005). Complex visual phenotypes, such as defects in pattern formation during development, greatly facilitated the classification of genes into pathways, and phenotypic similarities in many cases predicted molecular relationships. With RNA interference (RNAi), highly parallel phenotyping of loss-of-function effects in cultured cells has become feasible in many organisms whose genome have been sequenced (Boutros and Ahringer, 2008). One of the current challenges is the computational categorization of visual phenotypes and the prediction of gene function and associated biological processes. With large parts of the genome still being in unchartered territory, deriving functional information from large-scale phenotype analysis promises to uncover novel gene–gene relationships and to generate functional maps to explore cellular processes.
In this study, we developed an automated approach using RNAi-mediated cell phenotypes, multiparametric imaging and computational modelling to obtain functional information on previously uncharacterized genes. To generate broad, computer-readable phenotypic signatures, we measured the effect of RNAi-mediated knockdowns on changes of cell morphology in human cells on a genome-wide scale. First, the several million cells were stained for nuclear and cytoskeletal markers and then imaged using automated microscopy. On the basis of fluorescent markers, we established an automated image analysis to classify individual cells (Figure 1A). After cell segmentation for determining nuclei and cell boundaries (Figure 1C), we computed 51 cell descriptors that quantified intensities, shape characteristics and texture (Figure 1F). Individual cells were categorized into 1 of 10 classes, which included cells showing protrusion/elongation, cells in metaphase, large cells, condensed cells, cells with lamellipodia and cellular debris (Figure 1D and E). Each siRNA knockdown was summarized by a phenotypic profile and differences between RNAi knockdowns were quantified by the similarity between phenotypic profiles. We termed the vector of scores a phenoprint (Figure 3C) and defined the phenotypic distance between a pair of perturbations as the distance between their corresponding phenoprints.
To visualize the distribution of all phenoprints, we plotted them in a genome-wide map as a two-dimensional representation of the phenotypic similarity relationships (Figure 3A). The complete data set and an interactive version of the phenotypic map are available at http://www.cellmorph.org. The map identified phenotypic ‘neighbourhoods', which are characterized by cells with lamellipodia (WNK3, ANXA4), cells with prominent actin fibres (ODF2, SOD3), abundance of large cells (CA14), many elongated cells (SH2B2, ELMO2), decrease in cell number (TPX2, COPB1, COPA), increase in number of cells in metaphase (BLR1, CIB2) and combinations of phenotypes such as presence of large cells with protrusions and bright nuclei (PTPRZ1, RRM1; Figure 3B).
To test whether phenotypic similarity might serve as a predictor of gene function, we focused our further analysis on two clusters that contained genes associated with the DNA damage response (DDR) and genomic integrity (Figure 3A and C). The first phenotypic cluster included proteins with kinetochore-associated functions such as NUF2 (Figure 3B) and SGOL1. It also contained the centrosomal protein CEP164 that has been described as an important mediator of the DNA damage-activated signalling cascade (Sivasubramaniam et al, 2008) and the largely uncharacterized genes DONSON and SON. A second phenotypically distinct cluster included previously described components of the DDR pathway such as RRM1 (Figure 3A–C), CLSPN, PRIM2 and SETD8. Furthermore, this cluster contained the poorly characterized genes CADM1 and CD3EAP.
Cells activate a signalling cascade in response to DNA damage induced by exogenous and endogenous factors. Central are the kinases ATM and ATR as they serve as sensors of DNA damage and activators of further downstream kinases (Harper and Elledge, 2007; Cimprich and Cortez, 2008). To investigate whether DONSON, SON, CADM1 and CD3EAP, which were found in phenotypic ‘neighbourhoods' to known DDR components, have a role in the DNA damage signalling pathway, we tested the effect of their depletion on the DDR on γ irradiation. As indicated by reduced CHEK1 phosphorylation, siRNA knock down of DONSON, SON, CD3EAP or CADM1 resulted in impaired DDR signalling on γ irradiation. Furthermore, knock down of DONSON or SON reduced phosphorylation of downstream effectors such as NBS1, CHEK1 and the histone variant H2AX on UVC irradiation. DONSON depletion also impaired recruitment of RPA2 onto chromatin and SON knockdown reduced RPA2 phosphorylation indicating that DONSON and SON presumably act downstream of the activation of ATM. In agreement to their phenotypic profile, these results suggest that DONSON, SON, CADM1 and CD3EAP are important mediators of the DDR. Further experiments demonstrated that they are also required for the maintenance of genomic integrity.
In summary, we show that genes with similar phenotypic profiles tend to share similar functions. The power of our computational and experimental approach is demonstrated by the identification of novel signalling regulators whose phenotypic profiles were found in proximity to known biological modules. Therefore, we believe that such phenotypic maps can serve as a resource for functional discovery and characterization of unknown genes. Furthermore, such approaches are also applicable for other perturbation reagents, such as small molecules in drug discovery and development. One could also envision combined maps that contain both siRNAs and small molecules to predict target–small molecule relationships and potential side effects.
Genetic screens for phenotypic similarity have made key contributions to associating genes with biological processes. With RNA interference (RNAi), highly parallel phenotyping of loss-of-function effects in cells has become feasible. One of the current challenges however is the computational categorization of visual phenotypes and the prediction of biological function and processes. In this study, we describe a combined computational and experimental approach to discover novel gene functions and explore functional relationships. We performed a genome-wide RNAi screen in human cells and used quantitative descriptors derived from high-throughput imaging to generate multiparametric phenotypic profiles. We show that profiles predicted functions of genes by phenotypic similarity. Specifically, we examined several candidates including the largely uncharacterized gene DONSON, which shared phenotype similarity with known factors of DNA damage response (DDR) and genomic integrity. Experimental evidence supports that DONSON is a novel centrosomal protein required for DDR signalling and genomic integrity. Multiparametric phenotyping by automated imaging and computational annotation is a powerful method for functional discovery and mapping the landscape of phenotypic responses to cellular perturbations.
doi:10.1038/msb.2010.25
PMCID: PMC2913390  PMID: 20531400
DNA damage response signalling; massively parallel phenotyping; phenotype networks; RNAi screening
3.  Integration of biological data by kernels on graph nodes allows prediction of new genes involved in mitotic chromosome condensation 
Molecular Biology of the Cell  2014;25(16):2522-2536.
A gene function prediction method suitable for the design of targeted RNAi libraries is described and used to predict chromosome condensation genes. Systematic experimental validation of candidate genes in a focused RNAi screen by automated microscopy and quantitative image analysis reveals many new chromosome condensation factors.
The advent of genome-wide RNA interference (RNAi)–based screens puts us in the position to identify genes for all functions human cells carry out. However, for many functions, assay complexity and cost make genome-scale knockdown experiments impossible. Methods to predict genes required for cell functions are therefore needed to focus RNAi screens from the whole genome on the most likely candidates. Although different bioinformatics tools for gene function prediction exist, they lack experimental validation and are therefore rarely used by experimentalists. To address this, we developed an effective computational gene selection strategy that represents public data about genes as graphs and then analyzes these graphs using kernels on graph nodes to predict functional relationships. To demonstrate its performance, we predicted human genes required for a poorly understood cellular function—mitotic chromosome condensation—and experimentally validated the top 100 candidates with a focused RNAi screen by automated microscopy. Quantitative analysis of the images demonstrated that the candidates were indeed strongly enriched in condensation genes, including the discovery of several new factors. By combining bioinformatics prediction with experimental validation, our study shows that kernels on graph nodes are powerful tools to integrate public biological data and predict genes involved in cellular functions of interest.
doi:10.1091/mbc.E13-04-0221
PMCID: PMC4142622  PMID: 24943848
4.  Using iterative cluster merging with improved gap statistics to perform online phenotype discovery in the context of high-throughput RNAi screens 
BMC Bioinformatics  2008;9:264.
Background
The recent emergence of high-throughput automated image acquisition technologies has forever changed how cell biologists collect and analyze data. Historically, the interpretation of cellular phenotypes in different experimental conditions has been dependent upon the expert opinions of well-trained biologists. Such qualitative analysis is particularly effective in detecting subtle, but important, deviations in phenotypes. However, while the rapid and continuing development of automated microscope-based technologies now facilitates the acquisition of trillions of cells in thousands of diverse experimental conditions, such as in the context of RNA interference (RNAi) or small-molecule screens, the massive size of these datasets precludes human analysis. Thus, the development of automated methods which aim to identify novel and biological relevant phenotypes online is one of the major challenges in high-throughput image-based screening. Ideally, phenotype discovery methods should be designed to utilize prior/existing information and tackle three challenging tasks, i.e. restoring pre-defined biological meaningful phenotypes, differentiating novel phenotypes from known ones and clarifying novel phenotypes from each other. Arbitrarily extracted information causes biased analysis, while combining the complete existing datasets with each new image is intractable in high-throughput screens.
Results
Here we present the design and implementation of a novel and robust online phenotype discovery method with broad applicability that can be used in diverse experimental contexts, especially high-throughput RNAi screens. This method features phenotype modelling and iterative cluster merging using improved gap statistics. A Gaussian Mixture Model (GMM) is employed to estimate the distribution of each existing phenotype, and then used as reference distribution in gap statistics. This method is broadly applicable to a number of different types of image-based datasets derived from a wide spectrum of experimental conditions and is suitable to adaptively process new images which are continuously added to existing datasets. Validations were carried out on different dataset, including published RNAi screening using Drosophila embryos [Additional files 1, 2], dataset for cell cycle phase identification using HeLa cells [Additional files 1, 3, 4] and synthetic dataset using polygons, our methods tackled three aforementioned tasks effectively with an accuracy range of 85%–90%. When our method is implemented in the context of a Drosophila genome-scale RNAi image-based screening of cultured cells aimed to identifying the contribution of individual genes towards the regulation of cell-shape, it efficiently discovers meaningful new phenotypes and provides novel biological insight. We also propose a two-step procedure to modify the novelty detection method based on one-class SVM, so that it can be used to online phenotype discovery. In different conditions, we compared the SVM based method with our method using various datasets and our methods consistently outperformed SVM based method in at least two of three tasks by 2% to 5%. These results demonstrate that our methods can be used to better identify novel phenotypes in image-based datasets from a wide range of conditions and organisms.
Conclusion
We demonstrate that our method can detect various novel phenotypes effectively in complex datasets. Experiment results also validate that our method performs consistently under different order of image input, variation of starting conditions including the number and composition of existing phenotypes, and dataset from different screens. In our findings, the proposed method is suitable for online phenotype discovery in diverse high-throughput image-based genetic and chemical screens.
doi:10.1186/1471-2105-9-264
PMCID: PMC2443381  PMID: 18534020
5.  RNAi Screening: New Approaches, Understandings and Organisms 
RNA interference (RNAi) leads to sequence-specific knockdown of gene function. The approach can be used in large-scale screens to interrogate function in various model organisms and an increasing number of other species. Genome-scale RNAi screens are routinely performed in cultured or primary cells or in vivo in organisms such as C. elegans. High-throughput RNAi screening is benefitting from the development of sophisticated new instrumentation and software tools for collecting and analyzing data, including high-content image data. The results of large-scale RNAi screens have already proved useful, leading to new understandings of gene function relevant to topics such as infection, cancer, obesity and aging. Nevertheless, important caveats apply and should be taken into consideration when developing or interpreting RNAi screens. Some level of false discovery is inherent to high-throughput approaches and specific to RNAi screens, false discovery due to off-target effects (OTEs) of RNAi reagents remains a problem. The need to improve our ability to use RNAi to elucidate gene function at large scale and in additional systems continues to be addressed through improved RNAi library design, development of innovative computational and analysis tools and other approaches.
doi:10.1002/wrna.110
PMCID: PMC3249004  PMID: 21953743
RNAi; high-throughput screens; high-content imaging; cell-based assays
6.  A Computational model for compressed sensing RNAi cellular screening 
BMC Bioinformatics  2012;13:337.
Background
RNA interference (RNAi) becomes an increasingly important and effective genetic tool to study the function of target genes by suppressing specific genes of interest. This system approach helps identify signaling pathways and cellular phase types by tracking intensity and/or morphological changes of cells. The traditional RNAi screening scheme, in which one siRNA is designed to knockdown one specific mRNA target, needs a large library of siRNAs and turns out to be time-consuming and expensive.
Results
In this paper, we propose a conceptual model, called compressed sensing RNAi (csRNAi), which employs a unique combination of group of small interfering RNAs (siRNAs) to knockdown a much larger size of genes. This strategy is based on the fact that one gene can be partially bound with several small interfering RNAs (siRNAs) and conversely, one siRNA can bind to a few genes with distinct binding affinity. This model constructs a multi-to-multi correspondence between siRNAs and their targets, with siRNAs much fewer than mRNA targets, compared with the conventional scheme. Mathematically this problem involves an underdetermined system of equations (linear or nonlinear), which is ill-posed in general. However, the recently developed compressed sensing (CS) theory can solve this problem. We present a mathematical model to describe the csRNAi system based on both CS theory and biological concerns. To build this model, we first search nucleotide motifs in a target gene set. Then we propose a machine learning based method to find the effective siRNAs with novel features, such as image features and speech features to describe an siRNA sequence. Numerical simulations show that we can reduce the siRNA library to one third of that in the conventional scheme. In addition, the features to describe siRNAs outperform the existing ones substantially.
Conclusions
This csRNAi system is very promising in saving both time and cost for large-scale RNAi screening experiments which may benefit the biological research with respect to cellular processes and pathways.
doi:10.1186/1471-2105-13-337
PMCID: PMC3544734  PMID: 23270311
7.  Single-cell analysis of population context advances RNAi screening at multiple levels 
A large set of high-content RNAi screens investigating mammalian virus infection and multiple cellular activities is analysed to reveal the impact of population context on phenotypic variability and to identify indirect RNAi effects.
Cell population context determines phenotypes in RNAi screens of multiple cellular activities (including virus infection, cell size regulation, endocytosis, and lipid homeostasis), which can be accounted for by a combination of novel image analysis and multivariate statistical methods.Accounting for cell population context-mediated effects strongly changes the reproducibility and consistency of RNAi screens across cell lines as well as of siRNAs targeting the same gene.Such analyses can identify the perturbed regulation of population context dependent cell-to-cell variability, a novel perturbation phenotype.Overall, these methods advance the use of large-scale RNAi screening for a systems-level understanding of cellular processes.
Isogenic cells in culture show strong variability, which arises from dynamic adaptations to the microenvironment of individual cells. Here we study the influence of the cell population context, which determines a single cell's microenvironment, in image-based RNAi screens. We developed a comprehensive computational approach that employs Bayesian and multivariate methods at the single-cell level. We applied these methods to 45 RNA interference screens of various sizes, including 7 druggable genome and 2 genome-wide screens, analysing 17 different mammalian virus infections and four related cell physiological processes. Analysing cell-based screens at this depth reveals widespread RNAi-induced changes in the population context of individual cells leading to indirect RNAi effects, as well as perturbations of cell-to-cell variability regulators. We find that accounting for indirect effects improves the consistency between siRNAs targeted against the same gene, and between replicate RNAi screens performed in different cell lines, in different labs, and with different siRNA libraries. In an era where large-scale RNAi screens are increasingly performed to reach a systems-level understanding of cellular processes, we show that this is often improved by analyses that account for and incorporate the single-cell microenvironment.
doi:10.1038/msb.2012.9
PMCID: PMC3361004  PMID: 22531119
cell-to-cell variability; image analysis; population context; RNAi; virus infection
8.  RNA Interference in Schistosoma mansoni Schistosomula: Selectivity, Sensitivity and Operation for Larger-Scale Screening 
Background
The possible emergence of resistance to the only available drug for schistosomiasis spurs drug discovery that has been recently incentivized by the availability of improved transcriptome and genome sequence information. Transient RNAi has emerged as a straightforward and important technique to interrogate that information through decreased or loss of gene function and identify potential drug targets. To date, RNAi studies in schistosome stages infecting humans have focused on single (or up to 3) genes of interest. Therefore, in the context of standardizing larger RNAi screens, data are limited on the extent of possible off-targeting effects, gene-to-gene variability in RNAi efficiency and the operational capabilities and limits of RNAi.
Methodology/Principal Findings
We investigated in vitro the sensitivity and selectivity of RNAi using double-stranded (ds)RNA (approximately 500 bp) designed to target 11 Schistosoma mansoni genes that are expressed in different tissues; the gut, tegument and otherwise. Among the genes investigated were 5 that had been previously predicted to be essential for parasite survival. We employed mechanically transformed schistosomula that are relevant to parasitism in humans, amenable to screen automation and easier to obtain in greater numbers than adult parasites. The operational parameters investigated included defined culture media for optimal parasite maintenance, transfection strategy, time- and dose- dependency of RNAi, and dosing limits. Of 7 defined culture media tested, Basch Medium 169 was optimal for parasite maintenance. RNAi was best achieved by co-incubating parasites and dsRNA (standardized to 30 µg/ml for 6 days); electroporation provided no added benefit. RNAi, including interference of more than one transcript, was selective to the gene target(s) within the pools of transcripts representative of each tissue. Concentrations of dsRNA above 90 µg/ml were directly toxic. RNAi efficiency was transcript-dependent (from 40 to >75% knockdown relative to controls) and this may have contributed to the lack of obvious phenotypes observed, even after prolonged incubations of 3 weeks. Within minutes of their mechanical preparation from cercariae, schistosomula accumulated fluorescent macromolecules in the gut indicating that the gut is an important route through which RNAi is expedited in the developing parasite.
Conclusions
Transient RNAi operates gene-selectively in S. mansoni newly transformed schistosomula yet the sensitivity of individual gene targets varies. These findings and the operational parameters defined will facilitate larger RNAi screens.
Author Summary
RNA interference (RNAi) is a technique to selectively suppress mRNA of individual genes and, consequently, their cognate proteins. RNAi using double-stranded (ds) RNA has been used to interrogate the function of mainly single genes in the flatworm, Schistosoma mansoni, one of a number of schistosome species causing schistosomiasis. In consideration of large-scale screens to identify candidate drug targets, we examined the selectivity and sensitivity (the degree of suppression) of RNAi for 11 genes produced in different tissues of the parasite: the gut, tegument (surface) and otherwise. We used the schistosomulum stage prepared from infective cercariae larvae which are accessible in large numbers and adaptable to automated screening platforms. We found that RNAi suppresses transcripts selectively, however, the sensitivity of suppression varies (40%–>75%). No obvious changes in the parasite occurred post-RNAi, including after targeting the mRNA of genes that had been computationally predicted to be essential for survival. Additionally, we defined operational parameters to facilitate large-scale RNAi, including choice of culture medium, transfection strategy to deliver dsRNA, dose- and time-dependency, and dosing limits. Finally, using fluorescent probes, we show that the developing gut allows rapid entrance of dsRNA into the parasite to initiate RNAi.
doi:10.1371/journal.pntd.0000850
PMCID: PMC2957409  PMID: 20976050
9.  Phenotype Recognition with Combined Features and Random Subspace Classifier Ensemble 
BMC Bioinformatics  2011;12:128.
Background
Automated, image based high-content screening is a fundamental tool for discovery in biological science. Modern robotic fluorescence microscopes are able to capture thousands of images from massively parallel experiments such as RNA interference (RNAi) or small-molecule screens. As such, efficient computational methods are required for automatic cellular phenotype identification capable of dealing with large image data sets. In this paper we investigated an efficient method for the extraction of quantitative features from images by combining second order statistics, or Haralick features, with curvelet transform. A random subspace based classifier ensemble with multiple layer perceptron (MLP) as the base classifier was then exploited for classification. Haralick features estimate image properties related to second-order statistics based on the grey level co-occurrence matrix (GLCM), which has been extensively used for various image processing applications. The curvelet transform has a more sparse representation of the image than wavelet, thus offering a description with higher time frequency resolution and high degree of directionality and anisotropy, which is particularly appropriate for many images rich with edges and curves. A combined feature description from Haralick feature and curvelet transform can further increase the accuracy of classification by taking their complementary information. We then investigate the applicability of the random subspace (RS) ensemble method for phenotype classification based on microscopy images. A base classifier is trained with a RS sampled subset of the original feature set and the ensemble assigns a class label by majority voting.
Results
Experimental results on the phenotype recognition from three benchmarking image sets including HeLa, CHO and RNAi show the effectiveness of the proposed approach. The combined feature is better than any individual one in the classification accuracy. The ensemble model produces better classification performance compared to the component neural networks trained. For the three images sets HeLa, CHO and RNAi, the Random Subspace Ensembles offers the classification rates 91.20%, 98.86% and 91.03% respectively, which compares sharply with the published result 84%, 93% and 82% from a multi-purpose image classifier WND-CHARM which applied wavelet transforms and other feature extraction methods. We investigated the problem of estimation of ensemble parameters and found that satisfactory performance improvement could be brought by a relative medium dimensionality of feature subsets and small ensemble size.
Conclusions
The characteristics of curvelet transform of being multiscale and multidirectional suit the description of microscopy images very well. It is empirically demonstrated that the curvelet-based feature is clearly preferred to wavelet-based feature for bioimage descriptions. The random subspace ensemble of MLPs is much better than a number of commonly applied multi-class classifiers in the investigated application of phenotype recognition.
doi:10.1186/1471-2105-12-128
PMCID: PMC3098787  PMID: 21529372
10.  Automated measurement of cell motility and proliferation 
BMC Cell Biology  2005;6:19.
Background
Time-lapse microscopic imaging provides a powerful approach for following changes in cell phenotype over time. Visible responses of whole cells can yield insight into functional changes that underlie physiological processes in health and disease. For example, features of cell motility accompany molecular changes that are central to the immune response, to carcinogenesis and metastasis, to wound healing and tissue regeneration, and to the myriad developmental processes that generate an organism. Previously reported image processing methods for motility analysis required custom viewing devices and manual interactions that may introduce bias, that slow throughput, and that constrain the scope of experiments in terms of the number of treatment variables, time period of observation, replication and statistical options. Here we describe a fully automated system in which images are acquired 24/7 from 384 well plates and are automatically processed to yield high-content motility and morphological data.
Results
We have applied this technology to study the effects of different extracellular matrix compounds on human osteoblast-like cell lines to explore functional changes that may underlie processes involved in bone formation and maintenance. We show dose-response and kinetic data for induction of increased motility by laminin and collagen type I without significant effects on growth rate. Differential motility response was evident within 4 hours of plating cells; long-term responses differed depending upon cell type and surface coating. Average velocities were increased approximately 0.1 um/min by ten-fold increases in laminin coating concentration in some cases. Comparison with manual tracking demonstrated the accuracy of the automated method and highlighted the comparative imprecision of human tracking for analysis of cell motility data. Quality statistics are reported that associate with stage noise, interference by non-cell objects, and uncertainty in the outlining and positioning of cells by automated image analysis. Exponential growth, as monitored by total cell area, did not linearly correlate with absolute cell number, but proved valuable for selection of reliable tracking data and for disclosing between-experiment variations in cell growth.
Conclusion
These results demonstrate the applicability of a system that uses fully automated image acquisition and analysis to study cell motility and growth. Cellular motility response is determined in an unbiased and comparatively high throughput manner. Abundant ancillary data provide opportunities for uniform filtering according to criteria that select for biological relevance and for providing insight into features of system performance. Data quality measures have been developed that can serve as a basis for the design and quality control of experiments that are facilitated by automation and the 384 well plate format. This system is applicable to large-scale studies such as drug screening and research into effects of complex combinations of factors and matrices on cell phenotype.
doi:10.1186/1471-2121-6-19
PMCID: PMC1097721  PMID: 15831094
11.  An image score inference system for RNAi genome-wide screening based on fuzzy mixture regression modeling 
With recent advances in fluorescence microscopy imaging techniques and methods of gene knock down by RNA interference (RNAi), genome-scale high-content screening (HCS) has emerged as a powerful approach to systematically identify all parts of complex biological processes. However, a critical barrier preventing fulfillment of the success is the lack of efficient and robust methods for automating RNAi image analysis and quantitative evaluation of the gene knock down effects on huge volume of HCS data. Facing such opportunities and challenges, we have started investigation of automatic methods towards the development of a fully automatic RNAi-HCS system. Particularly important are reliable approaches to cellular phenotype classification and image-based gene function estimation.
We have developed a HCS analysis platform that consists of two main components: fluorescence image analysis and image scoring. For image analysis, we used a two-step enhanced watershed method to extract cellular boundaries from HCS images. Segmented cells were classified into several predefined phenotypes based on morphological and appearance features. Using statistical characteristics of the identified phenotypes as a quantitative description of the image, a score is generated that reflects gene function. Our scoring model integrates fuzzy gene class estimation and single regression models. The final functional score of an image was derived using the weighted combination of the inference from several support vector-based regression models. We validated our phenotype classification method and scoring system on our cellular phenotype and gene database with expert ground truth labeling.
We built a database of high-content, 3-channel, fluorescence microscopy images of Drosophila Kc167 cultured cells that were treated with RNAi to perturb gene function. The proposed informatics system for microscopy image analysis is tested on this database. Both of the two main components, automated phenotype classification and image scoring system, were evaluated. The robustness and efficiency of our system were validated in quantitatively predicting the biological relevance of genes.
doi:10.1016/j.jbi.2008.04.007
PMCID: PMC2763194  PMID: 18547870
High-content screening; Image score inference
12.  A high-throughput cell migration assay using scratch wound healing, a comparison of image-based readout methods 
BMC Biotechnology  2004;4:21.
Background
Cell migration is a complex phenomenon that requires the coordination of numerous cellular processes. Investigation of cell migration and its underlying biology is of interest to basic scientists and those in search of therapeutics. Current migration assays for screening small molecules, siRNAs, or other perturbations are difficult to perform in parallel at the scale required to screen large libraries.
Results
We have adapted the commonly used scratch wound healing assay of tissue-culture cell monolayers to a 384 well plate format. By mechanically scratching the cell substrate with a pin array, we are able to create characteristically sized wounds in all wells of a 384 well plate. Imaging of the healing wounds with an automated fluorescence microscope allows us to distinguish perturbations that affect cell migration, morphology, and division. Readout requires ~1 hr per plate but is high in information content i.e. high content. We compare readouts using different imaging technologies, automated microscopy, scanners and a fluorescence macroscope, and evaluate the trade-off between information content and data acquisition rate.
Conclusions
The adaptation of a wound healing assay to a 384 well format facilitates the study of aspects of cell migration, tissue reorganization, cell division, and other processes that underlie wound healing. This assay allows greater than 10,000 perturbations to be screened per day with a quantitative, high-content readout, and can also be used to characterize small numbers of perturbations in detail.
doi:10.1186/1472-6750-4-21
PMCID: PMC521074  PMID: 15357872
13.  Intelligent Interfaces for Mining Large-Scale RNAi-HCS Image Databases 
Recently, High-content screening (HCS) has been combined with RNA interference (RNAi) to become an essential image-based high-throughput method for studying genes and biological networks through RNAi-induced cellular phenotype analyses. However, a genome-wide RNAi-HCS screen typically generates tens of thousands of images, most of which remain uncategorized due to the inadequacies of existing HCS image analysis tools. Until now, it still requires highly trained scientists to browse a prohibitively large RNAi-HCS image database and produce only a handful of qualitative results regarding cellular morphological phenotypes. For this reason we have developed intelligent interfaces to facilitate the application of the HCS technology in biomedical research. Our new interfaces empower biologists with computational power not only to effectively and efficiently explore large-scale RNAi-HCS image databases, but also to apply their knowledge and experience to interactive mining of cellular phenotypes using Content-Based Image Retrieval (CBIR) with Relevance Feedback (RF) techniques.
doi:10.1109/BIBE.2007.4375742
PMCID: PMC3028207  PMID: 21278820
14.  Searching for novel cell cycle regulators in Trypanosoma brucei with an RNA interference screen 
BMC Research Notes  2009;2:46.
Background
The protozoan parasite, Trypanosoma brucei, is spread by the tsetse fly and causes Human African Trypanosomiasis. Its cell cycle is complex and not fully understood at the molecular level. The T. brucei genome contains over 6000 protein coding genes with >50% having no predicted function. A small scale RNA interference (RNAi) screen was carried out in Trypanosoma brucei to evaluate the prospects for identifying novel cycle regulators.
Results
Procyclic form T. brucei were transfected with a genomic RNAi library and 204 clones isolated. However, only 76 RNAi clones were found to target a protein coding gene of potential interest. These clones were screened for defects in proliferation and cell cycle progression following RNAi induction. Sixteen clones exhibited proliferation defects upon RNAi induction, with eight clones displaying potential cell cycle defects. To confirm the phenotypes, new RNAi cell lines were generated and characterised for five genes targeted in these clones. While we confirmed that the targeted genes are essential for proliferation, we were unable to unambiguously classify them as cell cycle regulators.
Conclusion
Our study identified genes essential for proliferation, but did not, as hoped, identify novel cell cycle regulators. Screening of the RNAi library for essential genes was extremely labour-intensive, which was compounded by the suboptimal quality of the library. For such a screening method to be viable for a large scale or genome wide screen, a new, significantly improved RNAi library will be required, and automated phenotyping approaches will need to be incorporated.
doi:10.1186/1756-0500-2-46
PMCID: PMC2674452  PMID: 19309510
15.  GenomeRNAi: a database for cell-based RNAi phenotypes 
Nucleic Acids Research  2006;35(Database issue):D492-D497.
RNA interference (RNAi) has emerged as a powerful tool to generate loss-of-function phenotypes in a variety of organisms. Combined with the sequence information of almost completely annotated genomes, RNAi technologies have opened new avenues to conduct systematic genetic screens for every annotated gene in the genome. As increasing large datasets of RNAi-induced phenotypes become available, an important challenge remains the systematic integration and annotation of functional information. Genome-wide RNAi screens have been performed both in Caenorhabditis elegans and Drosophila for a variety of phenotypes and several RNAi libraries have become available to assess phenotypes for almost every gene in the genome. These screens were performed using different types of assays from visible phenotypes to focused transcriptional readouts and provide a rich data source for functional annotation across different species. The GenomeRNAi database provides access to published RNAi phenotypes obtained from cell-based screens and maps them to their genomic locus, including possible non-specific regions. The database also gives access to sequence information of RNAi probes used in various screens. It can be searched by phenotype, by gene, by RNAi probe or by sequence and is accessible at
doi:10.1093/nar/gkl906
PMCID: PMC1747177  PMID: 17135194
16.  A Computational Framework for Ultrastructural Mapping of Neural Circuitry 
PLoS Biology  2009;7(3):e1000074.
Circuitry mapping of metazoan neural systems is difficult because canonical neural regions (regions containing one or more copies of all components) are large, regional borders are uncertain, neuronal diversity is high, and potential network topologies so numerous that only anatomical ground truth can resolve them. Complete mapping of a specific network requires synaptic resolution, canonical region coverage, and robust neuronal classification. Though transmission electron microscopy (TEM) remains the optimal tool for network mapping, the process of building large serial section TEM (ssTEM) image volumes is rendered difficult by the need to precisely mosaic distorted image tiles and register distorted mosaics. Moreover, most molecular neuronal class markers are poorly compatible with optimal TEM imaging. Our objective was to build a complete framework for ultrastructural circuitry mapping. This framework combines strong TEM-compliant small molecule profiling with automated image tile mosaicking, automated slice-to-slice image registration, and gigabyte-scale image browsing for volume annotation. Specifically we show how ultrathin molecular profiling datasets and their resultant classification maps can be embedded into ssTEM datasets and how scripted acquisition tools (SerialEM), mosaicking and registration (ir-tools), and large slice viewers (MosaicBuilder, Viking) can be used to manage terabyte-scale volumes. These methods enable large-scale connectivity analyses of new and legacy data. In well-posed tasks (e.g., complete network mapping in retina), terabyte-scale image volumes that previously would require decades of assembly can now be completed in months. Perhaps more importantly, the fusion of molecular profiling, image acquisition by SerialEM, ir-tools volume assembly, and data viewers/annotators also allow ssTEM to be used as a prospective tool for discovery in nonneural systems and a practical screening methodology for neurogenetics. Finally, this framework provides a mechanism for parallelization of ssTEM imaging, volume assembly, and data analysis across an international user base, enhancing the productivity of a large cohort of electron microscopists.
Author Summary
Building an accurate neural network diagram of the vertebrate nervous system is a major challenge in neuroscience. Diverse groups of neurons that function together form complex patterns of connections often spanning large regions of brain tissue, with uncertain borders. Although serial-section transmission electron microscopy remains the optimal tool for fine anatomical analyses, the time and cost of the undertaking has been prohibitive. We have assembled a complete framework for ultrastructural mapping using conventional transmission electron microscopy that tremendously accelerates image analysis. This framework combines small-molecule profiling to classify cells, automated image acquisition, automated mosaic formation, automated slice-to-slice image registration, and large-scale image browsing for volume annotation. Terabyte-scale image volumes requiring decades or more to assemble manually can now be automatically built in a few months. This makes serial-section transmission electron microscopy practical for high-resolution exploration of all complex tissue systems (neural or nonneural) as well as for ultrastructural screening of genetic models.
A framework for analysis of terabyte-scale serial-section transmission electron microscopic (ssTEM) datasets overcomes computational barriers and accelerates high-resolution tissue analysis, providing a practical way of mapping complex neural circuitry and an effective screening tool for neurogenetics.
doi:10.1371/journal.pbio.1000074
PMCID: PMC2661966  PMID: 19855814
17.  Online GESS: prediction of miRNA-like off-target effects in large-scale RNAi screen data by seed region analysis 
BMC Bioinformatics  2014;15:192.
Background
RNA interference (RNAi) is an effective and important tool used to study gene function. For large-scale screens, RNAi is used to systematically down-regulate genes of interest and analyze their roles in a biological process. However, RNAi is associated with off-target effects (OTEs), including microRNA (miRNA)-like OTEs. The contribution of reagent-specific OTEs to RNAi screen data sets can be significant. In addition, the post-screen validation process is time and labor intensive. Thus, the availability of robust approaches to identify candidate off-targeted transcripts would be beneficial.
Results
Significant efforts have been made to eliminate false positive results attributable to sequence-specific OTEs associated with RNAi. These approaches have included improved algorithms for RNAi reagent design, incorporation of chemical modifications into siRNAs, and the use of various bioinformatics strategies to identify possible OTEs in screen results. Genome-wide Enrichment of Seed Sequence matches (GESS) was developed to identify potential off-targeted transcripts in large-scale screen data by seed-region analysis. Here, we introduce a user-friendly web application that provides researchers a relatively quick and easy way to perform GESS analysis on data from human or mouse cell-based screens using short interfering RNAs (siRNAs) or short hairpin RNAs (shRNAs), as well as for Drosophila screens using shRNAs. Online GESS relies on up-to-date transcript sequence annotations for human and mouse genes extracted from NCBI Reference Sequence (RefSeq) and Drosophila genes from FlyBase. The tool also accommodates analysis with user-provided reference sequence files.
Conclusion
Online GESS provides a straightforward user interface for genome-wide seed region analysis for human, mouse and Drosophila RNAi screen data. With the tool, users can either use a built-in database or provide a database of transcripts for analysis. This makes it possible to analyze RNAi data from any organism for which the user can provide transcript sequences.
doi:10.1186/1471-2105-15-192
PMCID: PMC4073188  PMID: 24934636
RNAi; Off-target effects; Data analysis; Seed region; miRNA; siRNA; shRNA; High-throughput screening
18.  Quantitative and Automated High-throughput Genome-wide RNAi Screens in C. elegans 
RNA interference is a powerful method to understand gene function, especially when conducted at a whole-genome scale and in a quantitative context. In C. elegans, gene function can be knocked down simply and efficiently by feeding worms with bacteria expressing a dsRNA corresponding to a specific gene 1. While the creation of libraries of RNAi clones covering most of the C. elegans genome 2,3 opened the way for true functional genomic studies (see for example 4-7), most established methods are laborious. Moy and colleagues have developed semi-automated protocols that facilitate genome-wide screens 8. The approach relies on microscopic imaging and image analysis.
Here we describe an alternative protocol for a high-throughput genome-wide screen, based on robotic handling of bacterial RNAi clones, quantitative analysis using the COPAS Biosort (Union Biometrica (UBI)), and an integrated software: the MBioLIMS (Laboratory Information Management System from Modul-Bio) a technology that provides increased throughput for data management and sample tracking. The method allows screens to be conducted on solid medium plates. This is particularly important for some studies, such as those addressing host-pathogen interactions in C. elegans, since certain microbes do not efficiently infect worms in liquid culture.
We show how the method can be used to quantify the importance of genes in anti-fungal innate immunity in C. elegans. In this case, the approach relies on the use of a transgenic strain carrying an epidermal infection-inducible fluorescent reporter gene, with GFP under the control of the promoter of the antimicrobial peptide gene nlp 29 and a red fluorescent reporter that is expressed constitutively in the epidermis. The latter provides an internal control for the functional integrity of the epidermis and nonspecific transgene silencing9. When control worms are infected by the fungus they fluoresce green. Knocking down by RNAi a gene required for nlp 29 expression results in diminished fluorescence after infection. Currently, this protocol allows more than 3,000 RNAi clones to be tested and analyzed per week, opening the possibility of screening the entire genome in less than 2 months.
doi:10.3791/3448
PMCID: PMC3399495  PMID: 22395785
Molecular Biology;  Issue 60;  C. elegans;  fluorescent reporter;  Biosort;  LIMS;  innate immunity;  Drechmeria coniospora
19.  Constraint factor graph cut–based active contour method for automated cellular image segmentation in RNAi screening 
Journal of microscopy  2008;230(Pt 2):177-191.
Summary
Image-based, high throughput genome-wide RNA interference (RNAi) experiments are increasingly carried out to facilitate the understanding of gene functions in intricate biological processes. Automated screening of such experiments generates a large number of images with great variations in image quality, which makes manual analysis unreasonably time-consuming. Therefore, effective techniques for automatic image analysis are urgently needed, in which segmentation is one of the most important steps. This paper proposes a fully automatic method for cells segmentation in genome-wide RNAi screening images. The method consists of two steps: nuclei and cytoplasm segmentation. Nuclei are extracted and labelled to initialize cytoplasm segmentation. Since the quality of RNAi image is rather poor, a novel scale-adaptive steerable filter is designed to enhance the image in order to extract long and thin protrusions on the spiky cells. Then, constraint factor GCBAC method and morphological algorithms are combined to be an integrated method to segment tight clustered cells. Compared with the results obtained by using seeded watershed and the ground truth, that is, manual labelling results by experts in RNAi screening data, our method achieves higher accuracy. Compared with active contour methods, our method consumes much less time. The positive results indicate that the proposed method can be applied in automatic image analysis of multi-channel image screening data.
doi:10.1111/j.1365-2818.2008.01974.x
PMCID: PMC2839415  PMID: 18445146
active contour; automatic image segmentation; constraint factor; fluorescent microscopy; genome-wide screening; graph cut; morphological algorithm; RNAi
20.  GUItars: A GUI Tool for Analysis of High-Throughput RNA Interference Screening Data 
PLoS ONE  2012;7(11):e49386.
Background
High-throughput RNA interference (RNAi) screening has become a widely used approach to elucidating gene functions. However, analysis and annotation of large data sets generated from these screens has been a challenge for researchers without a programming background. Over the years, numerous data analysis methods were produced for plate quality control and hit selection and implemented by a few open-access software packages. Recently, strictly standardized mean difference (SSMD) has become a widely used method for RNAi screening analysis mainly due to its better control of false negative and false positive rates and its ability to quantify RNAi effects with a statistical basis. We have developed GUItars to enable researchers without a programming background to use SSMD as both a plate quality and a hit selection metric to analyze large data sets.
Results
The software is accompanied by an intuitive graphical user interface for easy and rapid analysis workflow. SSMD analysis methods have been provided to the users along with traditionally-used z-score, normalized percent activity, and t-test methods for hit selection. GUItars is capable of analyzing large-scale data sets from screens with or without replicates. The software is designed to automatically generate and save numerous graphical outputs known to be among the most informative high-throughput data visualization tools capturing plate-wise and screen-wise performances. Graphical outputs are also written in HTML format for easy access, and a comprehensive summary of screening results is written into tab-delimited output files.
Conclusion
With GUItars, we demonstrated robust SSMD-based analysis workflow on a 3840-gene small interfering RNA (siRNA) library and identified 200 siRNAs that increased and 150 siRNAs that decreased the assay activities with moderate to stronger effects. GUItars enables rapid analysis and illustration of data from large- or small-scale RNAi screens using SSMD and other traditional analysis methods. The software is freely available at http://sourceforge.net/projects/guitars/.
doi:10.1371/journal.pone.0049386
PMCID: PMC3502531  PMID: 23185323
21.  Automated Quantitative Live Cell Fluorescence Microscopy 
Advances in microscopy automation and image analysis have given biologists the tools to attempt large scale systems-level experiments on biological systems using microscope image readout. Fluorescence microscopy has become a standard tool for assaying gene function in RNAi knockdown screens and protein localization studies in eukaryotic systems. Similar high throughput studies can be attempted in prokaryotes, though the difficulties surrounding work at the diffraction limit pose challenges, and targeting essential genes in a high throughput way can be difficult. Here we will discuss efforts to make live-cell fluorescent microscopy based experiments using genetically encoded fluorescent reporters an automated, high throughput, and quantitative endeavor amenable to systems-level experiments in bacteria. We emphasize a quantitative data reduction approach, using simulation to help develop biologically relevant cell measurements that completely characterize the cell image. We give an example of how this type of data can be directly exploited by statistical learning algorithms to discover functional pathways.
The diffraction limit makes high-throughput fluorescence microscopy more challenging in prokaryotes, but approaches such as quantitative data reduction now allow systems-level analysis of bacteria by this technique.
doi:10.1101/cshperspect.a000455
PMCID: PMC2908775  PMID: 20591990
22.  Genome wide screening of RNAi factors of Sf21 cells reveal several novel pathway associated proteins 
BMC Genomics  2014;15(1):775.
Background
RNA interference (RNAi) leads to sequence specific knock-down of gene expression and has emerged as an important tool to analyse gene functions, pathway analysis and gene therapy. Although RNAi is a conserved cellular process involving common elements and factors, species-specific differences have been observed among different eukaryotes. Identification of components for RNAi pathway is pursued intensively and successful genome-wide screens have been performed for components of RNAi pathways in various organisms. Functional comparative genomics analysis offers evolutionary insight that forms basis of discoveries of novel RNAi-factors within related organisms. Keeping in view the academic and commercial utility of insect derived cell-line from Spodoptera frugiperda, we pursued the identification and functional analysis of components of RNAi-machinery of Sf21 cell-line using genome-wide application.
Results
The genome and transcriptome of Sf21 was assembled and annotated. In silico application of comparative genome analysis among insects allowed us to identify several RNAi factors in Sf21 line. The candidate RNAi factors from assembled genome were validated by knockdown analysis of candidate factors using the siRNA screens on the Sf21-gfp reporter cell-line. Forty two (42) potential factors were identified using the cell based assay. These include core RNAi elements including Dicer-2, Argonaute-1, Drosha, Aubergine and auxiliary modules like chromatin factors, RNA helicases, RNA processing module, signalling allied proteins and others. Phylogenetic analyses and domain architecture revealed that Spodoptera frugiperda homologs retained identity with Lepidoptera (Bombyx mori) or Coleoptera (Tribolium castaneum) sustaining an evolutionary conserved scaffold in post-transcriptional gene silencing paradigm within insects.
Conclusion
The database of RNAi-factors generated by whole genome association survey offers comprehensive outlook about conservation as well as specific differences of the proteins of RNAi machinery. Understanding the interior involved in different phases of gene silencing also offers impending tool for RNAi-based applications.
Electronic supplementary material
The online version of this article (doi:10.1186/1471-2164-15-775) contains supplementary material, which is available to authorized users.
doi:10.1186/1471-2164-15-775
PMCID: PMC4247154  PMID: 25199785
RNA interference; siRNA screening; Sf21 cells; Genome-wide screening; Insect RNAi; Spodoptera frugiperda
23.  Validation of a High-Content Screening Assay Using Whole-Well Imaging of Transformed Phenotypes 
Abstract
Automated microscopy was introduced two decades ago and has become an integral part of the discovery process as a high-content screening platform with noticeable challenges in executing cell-based assays. It would be of interest to use it to screen for reversers of a transformed cell phenotype. In this report, we present data obtained from an optimized assay that identifies compounds that reverse a transformed phenotype induced in NIH-3T3 cells by expressing a novel oncogene, KP, resulting from fusion between platelet derived growth factor receptor alpha (PDGFRα) and kinase insert domain receptor (KDR), that was identified in human glioblastoma. Initial image acquisitions using multiple tiles per well were found to be insufficient as to accurately image and quantify the clusters; whole-well imaging, performed on the IN Cell Analyzer 2000, while still two-dimensional imaging, was found to accurately image and quantify clusters, due largely to the inherent variability of their size and well location. The resulting assay exhibited a Z′ value of 0.79 and a signal-to-noise ratio of 15, and it was validated against known effectors and shown to identify only PDGFRα inhibitors, and then tested in a pilot screen against a library of 58 known inhibitors identifying mostly PDGFRα inhibitors as reversers of the KP induced transformed phenotype. In conclusion, our optimized and validated assay using whole-well imaging is robust and sensitive in identifying compounds that reverse the transformed phenotype induced by KP with a broader applicability to other cell-based assays that are challenging in HTS against chemical and RNAi libraries.
doi:10.1089/adt.2010.0342
PMCID: PMC3123874  PMID: 21182456
24.  Functional complementation of RNA interference mutants in trypanosomes 
BMC Biotechnology  2005;5:6.
Background
In many eukaryotic cells, double-stranded RNA (dsRNA) triggers RNA interference (RNAi), the specific degradation of RNA of homologous sequence. RNAi is now a major tool for reverse-genetics projects, including large-scale high-throughput screens. Recent reports have questioned the specificity of RNAi, raising problems in interpretation of RNAi-based experiments.
Results
Using the protozoan Trypanosoma brucei as a model, we designed a functional complementation assay to ascertain that phenotypic effect(s) observed upon RNAi were due to specific silencing of the targeted gene. This was applied to a cytoskeletal gene encoding the paraflagellar rod protein 2 (TbPFR2), whose product is essential for flagellar motility. We demonstrate the complementation of TbPFR2, silenced via dsRNA targeting its UTRs, through the expression of a tagged RNAi-resistant TbPFR2 encoding a protein that could be immunolocalized in the flagellum. Next, we performed a functional complementation of TbPFR2, silenced via dsRNA targeting its coding sequence, through heterologous expression of the TbPFR2 orthologue gene from Trypanosoma cruzi: the flagellum regained its motility.
Conclusions
This work shows that functional complementation experiments can be readily performed in order to ascertain that phenotypic effects observed upon RNAi experiments are indeed due to the specific silencing of the targetted gene. Further, the results described here are of particular interest when reverse genetics studies cannot be easily achieved in organisms not amenable to RNAi. In addition, our strategy should constitute a firm basis to elaborate functional-dissection studies of genes from other organisms.
doi:10.1186/1472-6750-5-6
PMCID: PMC549545  PMID: 15703078
25.  In Vivo RNAi Rescue in Drosophila melanogaster with Genomic Transgenes from Drosophila pseudoobscura 
PLoS ONE  2010;5(1):e8928.
Background
Systematic, large-scale RNA interference (RNAi) approaches are very valuable to systematically investigate biological processes in cell culture or in tissues of organisms such as Drosophila. A notorious pitfall of all RNAi technologies are potential false positives caused by unspecific knock-down of genes other than the intended target gene. The ultimate proof for RNAi specificity is a rescue by a construct immune to RNAi, typically originating from a related species.
Methodology/Principal Findings
We show that primary sequence divergence in areas targeted by Drosophila melanogaster RNAi hairpins in five non-melanogaster species is sufficient to identify orthologs for 81% of the genes that are predicted to be RNAi refractory. We use clones from a genomic fosmid library of Drosophila pseudoobscura to demonstrate the rescue of RNAi phenotypes in Drosophila melanogaster muscles. Four out of five fosmid clones we tested harbour cross-species functionality for the gene assayed, and three out of the four rescue a RNAi phenotype in Drosophila melanogaster.
Conclusions/Significance
The Drosophila pseudoobscura fosmid library is designed for seamless cross-species transgenesis and can be readily used to demonstrate specificity of RNAi phenotypes in a systematic manner.
doi:10.1371/journal.pone.0008928
PMCID: PMC2812509  PMID: 20126626

Results 1-25 (1059337)