No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Lessons from genetics: interpreting complex phenotypes in RNAi screens
Abstract
Mammalian cell biology is witnessing a new era in which cellular processes are explained through dynamic networks of interacting cellular components. In this fast-pacing field, where image-based RNAi screening is taking a central role, there is a strong need to improve ways to capture such interactions in space and time. Cell biologists traditionally depict these events by confining themselves to the level of a single cell, or to many population-averaged cells. Similarly, classical geneticists observe and interpret phenotypes in a single organism to delineate signaling processes, but have also described genetic phenomena in populations of organisms. The analogy in the two approaches inspired us to draw parallels with, and take lessons from concepts in classical genetics.
... These techniques in conjunction with fluorescent microscopy are frequently exploited for functional analyses at the scale of high-throughput studies. 2 Currently, a variety of numerical features are extracted from images that are further analyzed with classification strategies. 3,4 Features represent any measured property derived from the image, such as total/ mean/standard deviation of fluorescence intensity, texture, Zernike shape descriptions, and so forth. ...
... Density-based screening may inherit several advantages of our probabilistic image analysis method 7,8 : (1) Unlike state-of-the-art high-content/ high-throughput analysis on unrestricted cells having massive cell-to-cell variation, statistically significant results are obtained with only several tens of cells per condition. (2) Density-based screening estimates all required test parameters directly from the data and do not require computationally intensive analytical techniques such as classification. As image acquisition facilities are developed at an accelerated speed and advanced microscopes acquire thousands of images daily, there is a rising need for automated image analysis tools. ...
A screening procedure was developed that takes advantage of the cellular normalization by micropatterning and a novel quantitative organelle mapping approach that allows unbiased and automated cell morphology comparison using black-box statistical testing. Micropatterns of extracellular matrix proteins force cells to adopt a reproducible shape and distribution of intracellular compartments avoiding strong cell-to-cell variation that is a major limitation of classical culture conditions. To detect changes in cell morphology induced by compound treatment, fluorescently labeled intracellular structures from several tens of micropatterned cells were transformed into probabilistic density maps. Then, the similarity or difference between two given density maps was quantified using statistical testing that evaluates differences directly from the data without additional analysis or any subjective decision. The versatility of this organelle mapping approach for different magnifications and its performance for different cell shapes has been assessed. Density-based analysis detected changes in cell morphology due to compound treatment in a small-scale proof-of-principle screen demonstrating its compatibility with high-throughput screening. This novel tool for high-content and high-throughput cellular phenotyping can potentially be used for a wide range of applications from drug screening to careful characterization of cellular processes.
... Why might the maintenance and culture history of cells matter so much? It is clear that cells in culture are not uniform and the individual cells in any population display natural variation in cellular states and phenotypic response [25][26][27][28][29]. It is probable that this variation contributes to some of the observed variation in response to siRNA challenge (see below) [30]. ...
... Within a cell population, the many possible distinct cellular states that co-exist could determine the measured penetrance of the phenotype in that population [28]. All of these aspects could contribute to the day-to-day variability of assays and might therefore contribute to the failure of some primary screen 'hits' to reproduce even in the hands of the same researcher. ...
RNAi technology is now a well-established and widely employed research technique that has been adopted by many researchers
for use in large-scale screening campaigns. Here, we offer our experience of genome-wide siRNA screening from the perspective
of a facility providing screening as a service to a wide range of researchers with diverse interests and approaches. We have
experienced the emotional rollercoaster of screening from the exuberant early promise of a screen, the messy reality of the
data through to the recognition of screen data as a potential information goldmine. Here, we use some of the questions we
most frequently encounter to highlight the initial concerns of many researchers embarking on a siRNA screen and conclude that
an informed view of what can be reasonably expected from a screen is essential to the most effective implementation of the
technology. Along the way, we suggest that for this area of research at least, either centralization of the resources or close
and open collaboration between interested parties offers distinct advantages.
... For RNAi assays or other perturbation experiments involving a lower number of knockdowns, high-dimensional readouts e.g. using microarrays (Boutros et al., 2002) are feasible. High-content, high-throughput image-based screens are rapidly developing, and offer opportunities for highdimensional readouts at a genome-wide scale (Sacher et al., 2008). ...
... Most previous work on inferring networks from RNAi data focuses on clustering of phenotypes to generate phenoclusters of genes showing similar effects upon perturbation (Sacher et al., 2008). Such methods are based on an underlying distance measure between phenotypes, which typically weighs each feature of a phenotype in the same, fixed way in determining the distance. ...
Motivation:
The reconstruction of signaling pathways from gene knockdown data is a novel research field enabled by developments in RNAi screening technology. However, while RNA interference is a powerful technique to identify genes related to a phenotype of interest, their placement in the corresponding pathways remains a challenging problem. Difficulties are aggravated if not all pathway components can be observed after each knockdown, but readouts are only available for a small subset. We are then facing the problem of reconstructing a network from incomplete data.
Results:
We infer pathway topologies from gene knockdown data using Bayesian networks with probabilistic Boolean threshold functions. To deal with the problem of underdetermined network parameters, we employ a Bayesian learning approach, in which we can integrate arbitrary prior information on the network under consideration. Missing observations are integrated out. We compute the exact likelihood function for smaller networks, and use an approximation to evaluate the likelihood for larger networks. The posterior distribution is evaluated using mode hopping Markov chain Monte Carlo. Distributions over topologies and parameters can then be used to design additional experiments. We evaluate our approach on a small artificial dataset, and present inference results on RNAi data from the Jak/Stat pathway in a human hepatoma cell line.
... Accurate subcellular object segmentation is very important in image analysis. For instance, it is required to quantify and characterize different parameters associated with tiny organelles in the cell and to set very high requirements for the accuracy of analysis of the image in order to correctly interpret cellular phenotypes [3]. In addition, images containing cellular structures that are obtained using FCM require accurate detection to help analyze accurately [4,5]. ...
Confocal microscope imaging has become popular in biotechnology labs. Confocal imaging technology utilizes fluorescence optics, where laser light is focused onto a specific spot at a defined depth in the sample. A considerable number of images are produced regularly during the process of research. These images require methods of unbiased quantification to have meaningful analyses. Increasing efforts to tie reimbursement to outcomes will likely increase the need for objective data in analyzing confocal microscope images in the coming years. Utilizing visual quantification methods to quantify confocal images with naked human eyes is an essential but often underreported outcome measure due to the time required for manual counting and estimation. The current method (visual quantification methods) of image quantification is time-consuming and cumbersome, and manual measurement is imprecise because of the natural differences among human eyes’ abilities. Subsequently, objective outcome evaluation can obviate the drawbacks of the current methods and facilitate recording for documenting function and research purposes. To achieve a fast and valuable objective estimation of fluorescence in each image, an algorithm was designed based on machine vision techniques to extract the targeted objects in images that resulted from confocal images and then estimate the covered area to produce a percentage value similar to the outcome of the current method and is predicted to contribute to sustainable biotechnology image analyses by reducing time and labor consumption. The results show strong evidence that t-designed objective algorithm evaluations can replace the current method of manual and visual quantification methods to the extent that the Intraclass Correlation Coefficient (ICC) is 0.9.
... The more siRNAs for the same gene are identified as having a similar cell population profile, the more reliably this gene can be regarded as a hit. Beyond this, the derived values of a regression analysis of frequency distribution profiles of cellular features are not affected by experimental bias to the same degree as population-averaged approaches [22], leading to more reproducible results. We used a cell-based chemical compound and RNAi screen of cell cycle progression to validating the SOPRA workflow. ...
Background
High-content screening (HCS) experiments generate complex data from multiple object features for each cell within a treated population. Usually, these data are analyzed by using population-averaged values of the features of interest, increasing the amount of false positives and the need for intensive follow-up validation. Therefore, there is a strong need for novel approaches with reproducible hit prediction by identifying significantly altered cell populations.
Results
Here we describe SOPRA, a workflow for analyzing image-based HCS data based on regression analysis of non-averaged object features from cell populations, which can be run on hundreds of samples using different cell features. Following plate-wise normalization, the values are counted within predetermined binning intervals, generating unique frequency distribution profiles (histograms) for each population, which are then normalized to control populations (control-based normalization). These control-normalized frequency distribution profiles are analyzed using the Bioconductor R-package maSigPro, originally developed to analyze time profiles. However, statistically significant altered frequency distributions are also identified by maSigPro when integrating it into the SOPRA workflow. Finally, significantly changed profiles can be used to generate a heatmap from which altered cell populations with similar phenotypes can be identified, enabling the detection of siRNAs and compounds with the same ‘on-target’ profile and reducing the number of false positive hits.
Conclusions
SOPRA is a novel analysis workflow for the detection of statistically significant normalized frequency distribution profiles of cellular features generated in high-throughput RNAi screens. For the validation of the SOPRA software workflow, a screen for cell cycle progression was used. We were able to identify such profiles for siRNA-mediated gene perturbations and chemical inhibitors of different cell cycle stages. The SOPRA software is freely available from Github.
... RNAi uses small double-stranded RNA (dsRNA) molecules to direct homology-dependent degradation of the target mRNA [14,15]. Although these techniques have been widely used as gold standards for the modulation of target proteins, these approaches face a fundamental limitation, i.e., waiting for the natural clearance of the target proteins from the cell [16]. ...
Proteins are fundamental biomolecules of living cells, and their expression levels depend on the balance between the synthesis and degradation. Researchers often aim to control protein expression levels for the investigation of protein function and its relationship with physiological phenomena. The genetic manipulation of the target protein using CRISPR/Cas9, Cre/loxP, tetracyclin system, and RNA interference, are widely used for the regulation of proteins at the DNA, transcriptional, or mRNA level. However, the significant time delay in controlling protein levels is a limitation of these techniques; the knockout or knockdown effects cannot be observed until the previously transcribed and synthesized protein is degraded. Recently, researchers have developed various types of molecular tools for the regulation of protein expression at the post-translational level, which rely on harnessing cellular proteolytic machinery including ubiquitin–proteasome pathway, autophagy-lysosome pathway, and endocytosis. The post-translational control of protein expression using small molecules, antibodies, and light can offer significant advantages regarding speed, tunability, and reversibility. These technologies are expected to be applied to pharmacotherapy and cell therapy, as well as research tools for fundamental biological studies. Here, we review the established and recently developed technologies, provide an update on their applications, and anticipate potential future directions.
... Most inducible loss-of-function approaches act at the transcriptional or post-transcriptional levels and often suffer from slow and incomplete protein removal [28][29][30][31] . Since PRDM14 is one of the core pluripotency factors in hESCs 13 , and exhibits dynamic changes during hPGCLC induction, conventional knockout approaches are unsuitable to study its functions in hPGCLCs. ...
PRDM14 is a crucial regulator of mouse primordial germ cells (mPGCs), epigenetic reprogramming and pluripotency, but its role in the evolutionarily divergent regulatory network of human PGCs (hPGCs) remains unclear. Besides, a previous knockdown study indicated that PRDM14 might be dispensable for human germ cell fate. Here, we decided to use inducible degrons for a more rapid and comprehensive PRDM14 depletion. We show that PRDM14 loss results in significantly reduced specification efficiency and an aberrant transcriptome of hPGC-like cells (hPGCLCs) obtained in vitro from human embryonic stem cells (hESCs). Chromatin immunoprecipitation and transcriptomic analyses suggest that PRDM14 cooperates with TFAP2C and BLIMP1 to upregulate germ cell and pluripotency genes, while repressing WNT signalling and somatic markers. Notably, PRDM14 targets are not conserved between mouse and human, emphasising the divergent molecular mechanisms of PGC specification. The effectiveness of degrons for acute protein depletion is widely applicable in various developmental contexts.
... Image-based screens are particularly advantageous for inference of biological functions as they provide spatial and context information at the single-cell level which allow capturing the emergent behaviours in biological systems (Lock & Strömblad, 2010). Singlecell data are critical for identifying loss-of-function phenotypes that are dependent on cellular state or manifest in only a small subpopulation of cells (partial penetrance) (Sacher et al, 2008). However, even for a widely used marker such as DAPI, phenotypic information on nuclear morphology and organisation of cells is often not fully utilised. ...
Characterising context-dependent gene functions is crucial for understanding the genetic bases of health and disease. To date, inference of gene functions from large-scale genetic perturbation screens is based on ad hoc analysis pipelines involving unsupervised clustering and functional enrichment. We present Knowledge- and Context-driven Machine Learning (KCML), a framework that systematically predicts multiple context-specific functions for a given gene based on the similarity of its perturbation phenotype to those with known function. As a proof of concept, we test KCML on three datasets describing phenotypes at the molecular, cellular and population levels and show that it outperforms traditional analysis pipelines. In particular, KCML identified an abnormal multicellular organisation phenotype associated with the depletion of olfactory receptors, and TGFβ and WNT signalling genes in colorectal cancer cells. We validate these predictions in colorectal cancer patients and show that olfactory receptors expression is predictive of worse patient outcomes. These results highlight KCML as a systematic framework for discovering novel scale-crossing and context-dependent gene functions. KCML is highly generalisable and applicable to various large-scale genetic perturbation screens.
... Because the depletion time is usually longer than the time required for one round of the cell cycle in mammalian cells, in some cases the initial defect was obscured or complemented by secondary effects caused by the loss of the POI (119,121). It was also difficult to analyze proteins with pleiotropic functions by siRNA because the phenotype is complicated by multiple defects caused by slow depletion (91). Moreover, the off-target effects and depletion efficiency of siRNA are variable (46,50). ...
The conditional depletion of a protein of interest (POI) is useful not only for loss-of-function studies, but also for the modulation of biological pathways. Technologies that work at the level of DNA, mRNA, and protein are available for temporal protein depletion. Compared with technologies targeting the pretranslation steps, direct protein depletion (or protein knockdown approaches) is advantageous in terms of specificity, reversibility, and time required for depletion, which can be achieved by fusing a POI with a protein domain called a degron that induces rapid proteolysis of the fusion protein. Conditional degrons can be activated or inhibited by temperature, small molecules, light, or the expression of another protein. The conditional degron-based technologies currently available are described and discussed.
... Most applications involving end point highresolution imaging are low dimensional, with the exception of live tracing of cell lineages and quantification of gene expression in embryos 15,27,28 . However, the rich information encoded in fluorescence images of multicellular models has not been fully exploited at high resolution (that is, characterization of subcellular features within a living multicellular organism, thus missing the identification and characterization of phenotypic changes of weak alleles 29,30 ). Although different approaches have been used to identify chemically or genetically induced phenotypes, these have typically either screened for severe changes or have focused on behavioural or anatomical changes 5,[31][32][33][34][35][36][37][38] . ...
Discovering mechanistic insights from phenotypic information is critical for the understanding of biological processes. For model organisms, unlike in cell culture, this is currently bottlenecked by the non-quantitative nature and perceptive biases of human observations, and the limited number of reporters that can be simultaneously incorporated in live animals. An additional challenge is that isogenic populations exhibit significant phenotypic heterogeneity. These difficulties limit genetic approaches to many biological questions. To overcome these bottlenecks, we developed tools to extract complex phenotypic traits from images of fluorescently labelled subcellular landmarks, using C. elegans synapses as a test case. By population-wide comparisons, we identified subtle but relevant differences inaccessible to subjective conceptualization. Furthermore, the models generated testable hypotheses of how individual alleles relate to known mechanisms or belong to new pathways. We show that our model not only recapitulates current knowledge in synaptic patterning but also identifies novel alleles overlooked by traditional methods.
... (3) Automated fluorescent microscopy provides an image-based analysis that is divided into two stages, image acquisition and computational analysis. Fluorescent microscopy allows a comprehensive analysis of GFP signal and morphology of individual cells (Figure 3.2A) (Sacher et al., 2008;Conrad and Gerlich, 2010). This method can be applied for simple quantification of the number of GFP-positive cell in small-scale screens, or for comprehensive analysis (e.g., cell morphology changes) for validated gene hits. ...
Understanding mechanisms of gene regulation that are independent of the DNA sequence itself - epigenetics - has the potential to overthrow long-held views on central topics in biology, such as the biology of disease or the evolution of species. High throughput technologies reveal epigenetic mechanisms at a genome-wide level, giving rise to epigenomics as a new discipline with a distinct set of research questions and methods. Leading experts from academia, the biotechnology and pharmaceutical industries explain the role of epigenomics in a wide range of contexts, covering basic chromatin biology, imprinting at a genome-wide level, and epigenomics in disease biology and epidemiology. Details on assays and sequencing technology serve as an up-to-date overview of the available technological tool kit. A reliable guide for newcomers to the field as well as experienced scientists, this is a unique resource for anyone interested in applying the power of twenty-first-century genomics to epigenetic studies.
... Nevertheless, fluorescence microscopy-based approaches are considered to be highly advantageous over biochemical techniques in large-scale miRNA screenings. Features that make fluorescence microscopy ideal to analyse regulatory potential of miRNAs include (i) rapid collection of large amount of data, (ii) feasibility of phenotype multiplexing, (iii) possibility to acquire quantitative data on a single cell and/or population levels and (iv) detection of subtle phenotypes Sacher et al, 2008). The pioneers in applying this approach for functional miRNA investigation were Sirotkin and colleagues (Sirotkin et al, 2010), who performed a fluorescence microscopy-based miRNA screening in order to identify miRNAs regulating cell proliferation and apoptosis. ...
MicroRNAs (miRNAs) are a large family of small noncoding RNAs that extensively regulate gene expression in animals, plants and protozoa. The first miRNA was identified in the early 1990s, but it took almost a decade until miRNAs were recognized as key post-transcriptional regulators of gene expression. Despite the rapidly growing list of miRNA-regulated physiological and pathological processes, intracellular membrane trafficking has attracted little interest from scientific miRNA community. Membrane trafficking defines a complex network of pathways, including biosynthetic trafficking and endocytosis that are indispensable for normal cellular functions. Previous studies have analyzed a few miRNAs involved in insulin secretion, however, no systematic investigation of miRNAs as important regulators of membrane trafficking has been performed. The overall aim of this study was to identify miRNAs and their biologically relevant target genes involved in the regulation of membrane trafficking. As tools to modulate miRNA functions, we used synthetic miRNA mimics (pre-miRs) and inhibitors (anti-miRs) to enhance (gain-of-function) and to suppress (loss-of-function) the activity of cellular miRNAs, respectively. As proof of principle, we demonstrated that increased activity of miR-17 family miRNAs accelerates the biosynthetic cargo protein (ts-O45-G) transport and reduces the cellular internalization of DiI-LDL ligand. Taking the advantage of available technological platforms, we designed a gain-of-function large-scale screening to identify miRNAs that affect biosynthetic ts-O45-G transport rate. We showed that 44 out of 470 tested miRNAs induced significant changes in cargo trafficking. Using image analysis platform, we further identified eight miRNAs (miR-30b, -382, -432, -517a, -517b, -517c, -637 and -765) that also showed significant effects on Golgi complex integrity. Importantly, the majority of identified miRNAs are not endogenously expressed in HeLa cells, indicating the need for validation studies in other experimental systems. To identify functionally relevant target genes, we selected miR-17 and miR-517a and performed genome-wide transcriptome analysis 12h, 24h and 48h after transfection with the respective pre-miRs. We identified TBC1D2 and LDLR genes as novel functional miR-17 targets and confirmed that they exert the miR-17-mediated regulation of endocytosis. Further studies are needed to identify target genes responsible for the miR-17-governed acceleration of ts-O45-G to the plasma membrane. In case of miR-517a, we found a set of target genes with functions in 12 membrane trafficking system, however, their functional interplay with miR-517a remains to be confirmed. Bioinformatics analysis of transcriptome profiling data confirmed that the presence of miRNA seed binding site in the 3´UTRs of human mRNAs is an important determinant for functional miRNA:mRNA interaction. Additionally, we demonstrated that the sets of transcripts downregulated at early time points after transfection with pre-miRs have substantially higher fractions of transcripts with miRNA binding sites in their 3´UTRs compared to the transcripts downregulated at late time points. We believe that these findings could contribute to the development of more accurate miRNA target prediction tool, also allowing identification of nonconserved miRNA targets. In conclusion, we have established an experimental platform that consists of (i) a functional screening module to identify miRNAs that affect membrane trafficking, (ii) a microarray module to identify miRNA target genes, (iii) a statistics and bioinformatics module for data analysis and integration and (iv) a target validation module to validate functional links between targets and miRNAs. Using this platform, we identified numerous miRNAs with novel functions in membrane trafficking system. Moreover, we identified and confirmed TBC1D2 and LDLR genes as novel functional targets of miR-17.
... This is only possible if the system allows for manipulation of gene function, either through simple or sophisticated tools (Figures 3 and 4). One of the most prominent obstacles to this method, which we believe discourages the evo-devo community from applying it widely, is the pleiotropic nature of many developmental genes [54,55]. Although techniques such as RNAi are specific to the target gene, this specificity fails in discriminating between distinct tissues and developmental stages and may lead to the disruption of other potential functions of the same genes that are not associated with the trait under study. ...
Evolutionary developmental biology (evo-devo) has provided invaluable contributions to our understanding of the mechanistic relationship between genotypic and phenotypic change. Similarly, evolutionary ecology has greatly advanced our understanding of the relationship between the phenotype and the environment. To fully understand the evolution of organismal diversity, a thorough integration of these two fields is required. This integration remains highly challenging because model systems offering a rich ecological and evolutionary background, together with the availability of developmental genetic tools and genomic resources, are scarce. In this review, we introduce the semi-aquatic bugs (Gerromorpha, Heteroptera) as original models well suited to study why and how organisms diversify. The Gerromorpha invaded water surfaces over 200 mya and diversified into a range of remarkable new forms within this new ecological habitat. We summarize the biology and evolutionary history of this group of insects and highlight a set of characters associated with the habitat change and the diversification that followed. We further discuss the morphological, behavioral, molecular and genomic tools available that together make semi-aquatic bugs a prime model for integration across disciplines. We present case studies showing how the implementation and combination of these approaches can advance our understanding of how the interaction between genotypes, phenotypes and the environment drives the evolution of distinct morphologies. Finally, we explain how the same set of experimental designs can be applied in other systems to address similar biological questions.
... We have seen that population distributions change shape when a perturbation is applied; this suggests that different cells in the population might be in distinct states, causing them to respond differently to the perturbation. We can define this hidden 'cell state' [35] to be a feature with the following properties: (1) it is not itself affected by a perturbation; (2) it can be used predict the response of some other feature to that perturbation. We refer to such a cell-state feature as a ''classifier'', and to the feature being perturbed as the ''output''. ...
Any single-cell-resolved measurement generates a population distribution of phenotypes, characterized by a mean, a variance, and a shape. Here we show that changes in the shape of a phenotypic distribution can signal perturbations to cellular processes, providing a way to screen for underlying molecular machinery. We analyzed images of a Drosophila S2R+ cell line perturbed by RNA interference, and tracked 27 single-cell features which report on endocytic activity, and cell and nuclear morphology. In replicate measurements feature distributions had erratic means and variances, but reproducible shapes; RNAi down-regulation reliably induced shape deviations in at least one feature for 1072 out of 7131 genes surveyed, as revealed by a Kolmogorov-Smirnov-like statistic. We were able to use these shape deviations to identify a spectrum of genes that influenced cell morphology, nuclear morphology, and multiple pathways of endocytosis. By preserving single-cell data, our method was even able to detect effects invisible to a population-averaged analysis. These results demonstrate that cell-to-cell variability contains accessible and useful biological information, which can be exploited in existing cell-based assays.
... However, based on the results presented herein, this assumption is clearly incorrect. Cell context is critical to cell behavior, and cells change their genetic profile base on cellcell contacts, how close to the edge of a culture they are, and even cell density [39]. Similar to other mammalian cells, mesothelial cells have the ability to change phenotype [40], and it is possible that at high seeding concentrations the mesothelial cells are differentiating and are thus not as responsive to YARA. ...
In this study, the role of substrate stiffness on the endocytic uptake of a cell-penetrating peptide was investigated. The cell-penetrating peptide, an inhibitor of mitogen-activated protein kinase activated protein kinase II (MK2), enters a primary mesothelial cell line predominantly through caveolae. Using tissue culture polystyrene and polyacrylamide gels of varying stiffness for cell culture, and flow cytometry quantification and enzyme-linked immunoassays (ELISA) for uptake assays, we showed that the amount of uptake of the peptide is increased on soft substrates. Further, peptide uptake per cell increased at lower cell density. The improved uptake seen on soft substrates in vitro better correlates with in vivo functional studies where 10-100 µM concentrations of the MK2 inhibitor cell penetrating peptide demonstrated functional activity in several disease models. Additional characterization showed actin polymerization did not affect uptake, while microtubule polymerization had a profound effect on uptake. This work demonstrates that cell culture substrate stiffness can play a role in endocytic uptake, and may be an important consideration to improve correlations between in vitro and in vivo drug efficacy.
... For large-scale RNAi screens, the readouts are generally based on single reporters [1]. High-content, high-throughput image-based screens at a genome-wide scale are developing rapidly [15]. RNAi screens are used to investigate the downstream effects of a silenced gene. ...
Inference of topology of signaling networks from perturbation experiments is a challenging problem. Recently, the inference problem has been formulated as a reference network editing problem and it has been shown that finding the minimum number of edit operations on a reference network to comply with perturbation experiments is an NP-complete problem. In this paper, we propose an integer linear optimization (ILP) model for reconstruction of signaling networks from RNAi data and a reference network. The ILP model guarantees the optimal solution; however, is practical only for small signaling networks of size 10-15 genes due to computational complexity. To scale for large signaling networks, we propose a divide and conquer-based heuristic, in which a given reference network is divided into smaller subnetworks that are solved separately and the solutions are merged together to form the solution for the large network. We validate our proposed approach on real and synthetic data sets, and comparison with the state of the art shows that our proposed approach is able to scale better for large networks while attaining similar or better biological accuracy.
... For the analysis of microscopic images, single cell images are converted into a vector of 10-200 morphological descriptors [1][2][3][4]. These morphological descriptors are sufficiently rich to distinguish various physiological states of a cell, such as mitotic and apoptotic phases [5][6][7][8]. The purpose of these methods is the clustering of cells into meaningful, phenotypically distinct classes [9,10]. ...
Gene perturbation experiments in combination with fluorescence time-lapse cell imaging are a powerful tool in reverse genetics. High content applications require tools for the automated processing of the large amounts of data. These tools include in general several image processing steps, the extraction of morphological descriptors, and the grouping of cells into phenotype classes according to their descriptors. This phenotyping can be applied in a supervised or an unsupervised manner. Unsupervised methods are suitable for the discovery of formerly unknown phenotypes, which are expected to occur in high-throughput RNAi time-lapse screens.
We developed an unsupervised phenotyping approach based on Hidden Markov Models (HMMs) with multivariate Gaussian emissions for the detection of knockdown-specific phenotypes in RNAi time-lapse movies. The automated detection of abnormal cell morphologies allows us to assign a phenotypic fingerprint to each gene knockdown. By applying our method to the Mitocheck database, we show that a phenotypic fingerprint is indicative of a gene's function.
Our fully unsupervised HMM-based phenotyping is able to automatically identify cell morphologies that are specific for a certain knockdown. Beyond the identification of genes whose knockdown affects cell morphology, phenotypic fingerprints can be used to find modules of functionally related genes.
... Nevertheless, we consider fluorescence microscopy screens to be highly advantageous. Features that make such microscopy ideal to analyse regulatory potential of miRNAs include the following: (i) rapid collection of large amount of data, (ii) feasibility of phenotype multiplexing, (iii) the possibility to acquire quantitative data on a cell-by-cell basis and/or population-based basis, and (iv) detection of subtle phenotypes [133,134]. One of the first studies applying this technology for functional miRNA studies was by Sirotkin and colleagues, who reported an immunocytochemistryand fluorescence-microscopy-based screen to identify miR-NAs regulating proliferation and apoptosis [113]. ...
In the last years miRNAs have increasingly been recognised as potent posttranscriptional regulators of gene expression. Possibly, miRNAs exert their action on virtually any biological process by simultaneous regulation of numerous genes. The importance of miRNA-based regulation in health and disease has inspired research to investigate diverse aspects of miRNA origin, biogenesis, and function. Despite the recent rapid accumulation of experimental data, and the emergence of functional models, the complexity of miRNA-based regulation is still far from being well understood. In particular, we lack comprehensive knowledge as to which cellular processes are regulated by which miRNAs, and, furthermore, how temporal and spatial interactions of miRNAs to their targets occur. Results from large-scale functional analyses have immense potential to address these questions. In this review, we discuss the latest progress in application of high-content and high-throughput functional analysis for the systematic elucidation of the biological roles of miRNAs.
... In fact, only three genes were hits in two of the three screens: BCAR3, LIMK1, and the JNK kinase MAP2K7. The lack of overlap in these RNAi screens supports the notion that such large-scale knockdown experiments require special analysis of the raw data for proper interpretation (Sacher et al, 2008). In our own unpublished experiments using siRNA and shRNA approaches, we have found it much more difficult to knock down protein expression in primary neurons than in cell lines. ...
Development and regeneration of the nervous system requires the precise formation of axons and dendrites. Kinases and phosphatases are pervasive regulators of cellular function and have been implicated in controlling axodendritic development and regeneration. We undertook a gain-of-function analysis to determine the functions of kinases and phosphatases in the regulation of neuron morphology. Over 300 kinases and 124 esterases and phosphatases were studied by high-content analysis of rat hippocampal neurons. Proteins previously implicated in neurite growth, such as ERK1, GSK3, EphA8, FGFR, PI3K, PKC, p38, and PP1a, were confirmed to have effects in our functional assays. We also identified novel positive and negative neurite growth regulators. These include neuronal-developmentally regulated kinases such as the activin receptor, interferon regulatory factor 6 (IRF6) and neural leucine-rich repeat 1 (LRRN1). The protein kinase N2 (PKN2) and choline kinase alpha (CHKA) kinases, and the phosphatases PPEF2 and SMPD1, have little or no established functions in neuronal function, but were sufficient to promote neurite growth. In addition, pathway analysis revealed that members of signaling pathways involved in cancer progression and axis formation enhanced neurite outgrowth, whereas cytokine-related pathways significantly inhibited neurite formation. Molecular Systems Biology 6: 391; published online 27 July 2010; doi:10.1038/msb.2010.52 Subject Categories: functional genomics; neuroscience
... Accurate and automated subcellular object segmentation is essential for a variety of applications. For example, interpreting complex cellular phenotypes is typically dependent on identifying and quantifying various parameters associated with small organelles, setting high requirements for the accuracy of the image analysis [7]. Also the analysis of cellular structures based on 3D images obtained with fluorescence and confocal microscopes requires accurate detection. ...
Several algorithms have been proposed for detecting fluorescently labeled subcellular objects in microscope images. Many of these algorithms have been designed for specific tasks and validated with limited image data. But despite the potential of using extensive comparisons between algorithms to provide useful information to guide method selection and thus more accurate results, relatively few studies have been performed.
To better understand algorithm performance under different conditions, we have carried out a comparative study including eleven spot detection or segmentation algorithms from various application fields. We used microscope images from well plate experiments with a human osteosarcoma cell line and frames from image stacks of yeast cells in different focal planes. These experimentally derived images permit a comparison of method performance in realistic situations where the number of objects varies within image set. We also used simulated microscope images in order to compare the methods and validate them against a ground truth reference result. Our study finds major differences in the performance of different algorithms, in terms of both object counts and segmentation accuracies.
These results suggest that the selection of detection algorithms for image based screens should be done carefully and take into account different conditions, such as the possibility of acquiring empty images or images with very few spots. Our inclusion of methods that have not been used before in this context broadens the set of available detection methods and compares them against the current state-of-the-art methods for subcellular particle detection.
... They allow cell biologists to adopt modern statistical methods in order to find and quantify single-cell phenotypes and reveal new functions of genes. In fact, these tools enable quantitative analyses of large-scale screens at the singlecell level (Pelkmans et al., 2005; Sacher et al., 2008). However, it will be imperative that these tools are easy-to-install and provide a user-friendly experience to cell biologists who are not familiar with programming. ...
CellClassifier is a tool for classifying single-cell phenotypes in microscope images. It includes several unique and user-friendly
features for classification using multiclass support vector machines
Availability: Source code, user manual and SaveObjectSegmentation CellProfiler module available for download at www.cellclassifier.ethz.ch under the GPL license (implemented in Matlab).
Contact: pelkmans{at}imsb.biol.ethz.ch
Supplementary information: Supplementary data are available at Bioinformatics online.
... Genome-scale knockdown studies in Drosophila and human cell lines also demonstrate that a relatively small proportion of knockdowns affect growth phenotypes [8,9]. Several reasons for robustness include signaling modularity, redundancy and feedback loops [2,[10][11][12]. As a result, knockdowns that cause an impaired growth phenotype provide a glimpse to uncommonly sensitive areas of cell signaling. ...
A systems biology interpretation of genome-scale RNA interference (RNAi) experiments is complicated by scope, experimental variability and network signaling robustness. Over representation approaches (ORA), such as the Hypergeometric or z-score, are an established statistical framework used to associate RNA interference effectors to biologically annotated gene sets or pathways. These methods, however, do not directly take advantage of our growing understanding of the interactome. Furthermore, these methods can miss partial pathway activation and may be biased by protein complexes. Here we present a novel ORA, protein interaction permutation analysis (PIPA), that takes advantage of canonical pathways and established protein interactions to identify pathways enriched for protein interactions connecting RNAi hits.
We use PIPA to analyze genome-scale siRNA cell growth screens performed in HeLa and TOV cell lines. First we show that interacting gene pair siRNA hits are more reproducible than single gene hits. Using protein interactions, PIPA identifies enriched pathways not found using the standard Hypergeometric analysis including the FAK cytoskeletal remodeling pathway. Different branches of the FAK pathway are distinctly essential in HeLa versus TOV cell lines while other portions are uneffected by siRNA perturbations. Enriched hits belong to protein interactions associated with cell cycle regulation, anti-apoptosis, and signal transduction.
PIPA provides an analytical framework to interpret siRNA screen data by merging biologically annotated gene sets with the human interactome. As a result we identify pathways and signaling hypotheses that are statistically enriched to effect cell growth in human cell lines. This method provides a complementary approach to standard gene set enrichment that utilizes the additional knowledge of specific interactions within biological gene sets.
Robotics and automated fluorescence microscopes have promoted high-content cell-based screenings: fluorescent probes targeting DNA or other major components are used to image hundreds of thousands of cells under many different conditions. Cell-based assays have proven to be efficient at discovering first-in-class therapeutic drugs, i.e. drugs
acting on a new target. They allow to detect promising molecules and to profile them, by associating functional annotations to them, like their molecular target or mechanism of action (MOA). I studied heterogeneity of cell responses at different levels and how this phenotypic heterogeneity can be leveraged to better profile drugs. The first level is about
studying heterogeneity between patients. We showed that using different patient-derived cell lines increases the chance of predicting the correct molecular target of the tested drug. The second level corresponds to the diversity of cell responses within the same cell line under the same treatment. Appropriate clustering approaches can be used to unravel
this complexity and group cells into subpopulations. The proportions of each subpopulation per treatment allow to predict the correct MOA. The third level looks at how the cell subpopulations are spatially organized. I found that neighboring
cells influence each others, and display a similar phenotype more frequently than expected at random. These results assessed across a hundred of treatments, show that even genetically identical cells are not all alike and independent, but create spatial heterogeneity via cell lineage and interaction. Using spatial information as well as phenotypic heterogeneity with graph kernel methods improves the MOA classification under some conditions. Alongside, as spatial analysis could be applied on any cell microscopy image, I developed a Python analysis package, PySpacell, to study spatial randomness
from quantitative and qualitative cell markers.
PRDM14 is a crucial regulator of mouse primordial germ cells (mPGC), epigenetic reprogramming and pluripotency, but its role in the evolutionarily divergent regulatory network of human PGCs (hPGCs) remains unclear. Besides, a previous knockdown study indicated that PRDM14 might be dispensable for human germ cell fate. Here, we decided to use inducible degrons for a more rapid and comprehensive PRDM14 depletion. We show that PRDM14 loss results in significantly reduced specification efficiency and an aberrant transcriptome of human PGC-like cells (hPGCLCs) obtained in vitro from human embryonic stem cells (hESCs). Chromatin immunoprecipitation and transcriptomic analyses suggest that PRDM14 cooperates with TFAP2C and BLIMP1 to upregulate germ cell and pluripotency genes, while repressing WNT signalling and somatic markers. Notably, PRDM14 targets are not conserved between mouse and human, emphasising the divergent molecular mechanisms of PGC specification. The effectiveness of degrons for acute protein depletion is widely applicable in various developmental contexts.
Quantitative analysis of high throughput fluorescence microscopic image is a powerful tool to study dynamic processes in living cells. Many subcellular objects of interest appear as diffraction-limited spots in the image. The limitations of imaging conditions often lead to fluorescence microscopic images inhomogeneous and lower signal-to-noise ratio (SNR), making manual analysis a very challenging task. Designing the automatic subcellular spot detection method is a prerequisite for high throughput fluorescence microscopic image processing. This review presented a detailed overview of recent advances in the key techniques of spot detection method, including noise reduction, signal enhancement and signal thresholding. The bottlenecks and common difficulties of the algorithm design were discussed on the basis of summarizing advantages and disadvantages of the exiting spot detection methods. At the same time, the prospect of the related research was discussed.
Using RNAi interference (RNAi), it is possible to study the effect of specific gene knockdowns in mammalian cells. In this protocol we present the automated preparation of "ready to transfect" multiwell plates and cell arrays, on which cells can be grown which are then reversely transfected with one type of siRNA in every individual well or spot. Additionally, different microscope types for screening approaches are compared and considerations about the information workflow are made.
Introduction Dissection of Physiological and Pathological Processes with Genetic Screens Large-Scale RNAi-Based Screens in Mammalian Cells Design and Practical Implementation of a High-Throughput RNAi Screens Rnai Screen Validation Rnai Screens-Examples Limitations of RNAi Screening Summary and Outlook References
ABSTRACT: A multi-parametric genetic screening approach sheds light on integrated control of the endocytic pathway in mammalian cells.
Signaling events are frequently described in textbooks as linear cascades. However, in reality, input cues are processed by dynamic and context-specific networks, which are assembled from numerous signaling molecules. Diseases, such as cancer, are typically associated with multiple genomic alterations that likely change the structure and dynamics of cellular signaling networks. To assess the impact of such genomic alterations on the structure of signaling networks and on the ability of cells to accurately translate environmental cues into phenotypic changes, we argue studies must be conducted on a network level. Advances in technologies and computational approaches for data integration have permitted network studies of signaling events in both cancer and normal cells. Here we will review recent advances and how they have impacted our view on signaling networks with a specific angle on signal processing in cancer.
Single-cell heterogeneity in cell populations arises from a combination of intrinsic and extrinsic factors. This heterogeneity has been measured for gene transcription, phosphorylation, cell morphology and drug perturbations, and used to explain various aspects of cellular physiology. In all cases, however, the causes of heterogeneity were not studied. Here we analyse, for the first time, the heterogeneous patterns of related cellular activities, namely virus infection, endocytosis and membrane lipid composition in adherent human cells. We reveal correlations with specific cellular states that are defined by the population context of a cell, and we derive probabilistic models that can explain and predict most cellular heterogeneity of these activities, solely on the basis of each cell's population context. We find that accounting for population-determined heterogeneity is essential for interpreting differences between the activity levels of cell populations. Finally, we reveal that synergy between two molecular components, focal adhesion kinase and the sphingolipid GM1, enhances the population-determined pattern of simian virus 40 (SV40) infection. Our findings provide an explanation for the origin of heterogeneity patterns of cellular activities in adherent cell populations.
Quantitative analytical approaches for discovering new compound mechanisms are required for summarizing high-throughput, image-based drug screening data. Here we present a multivariate method for classifying untreated and treated human cancer cells based on approximately 300 single-cell phenotypic measurements. This classification provides a score, measuring the magnitude of the drug effect, and a vector, indicating the simultaneous phenotypic changes induced by the drug. These two quantities were used to characterize compound activities and identify dose-dependent multiphasic responses. A systematic survey of profiles extracted from a 100-compound compendium of image data revealed that only 10-15% of the original features were required to detect a compound effect. We report the most informative image features for each compound and fluorescence marker set using a method that will be useful for determining minimal collections of readouts for drug screens. Our approach provides human-interpretable profiles and automatic determination of on- and off-target effects.
We present a method for high-throughput cytological profiling by microscopy. Our system provides quantitative multidimensional measures of individual cell states over wide ranges of perturbations. We profile dose-dependent phenotypic effects of drugs in human cell culture with a titration-invariant similarity score (TISS). This method successfully categorized blinded drugs and suggested targets for drugs of uncertain mechanism. Multivariate single-cell analysis is a starting point for identifying relationships among drug effects at a systems level and a step toward phenotypic profiling at the single-cell level. Our methods will be useful for discovering the mechanism and predicting the toxicity of new drugs.
Experimental murine genetic models of complex human disease show great potential for understanding human disease pathogenesis. To reduce the time required for analysis of such models from many months down to milliseconds, a computational method for predicting chromosomal regions regulating phenotypic traits and a murine database of single nucleotide polymorphisms were developed. After entry of phenotypic information obtained from inbred mouse strains, the phenotypic and genotypic information is analyzed in silico to predict the chromosomal regions regulating the phenotypic trait.
The nematode Caenorhabditis elegans was chosen as a model genetic organism because its attributes, chiefly its hermaphroditic lifestyle and rapid generation time, make it suitable for the isolation and characterization of genetic mutants. The most important challenge for the geneticist is to design a genetic screen that will identify mutations that specifically disrupt the biological process of interest. Since 1974, when Sydney Brenner published his pioneering genetic screen, researchers have developed increasingly powerful methods for identifying genes and genetic pathways in C. elegans.
By integrating functional genomic and proteomic mapping approaches, biological hypotheses should be formulated with increasing levels of confidence. For example, yeast interactome and transcriptome data can be correlated in biologically meaningful ways. Here, we combine interactome mapping data generated for a multicellular organism with data from both large-scale phenotypic analysis ("phenome mapping") and transcriptome profiling. First, we generated a two-hybrid interactome map of the Caenorhabditis elegans germline by using 600 transcripts enriched in this tissue. We compared this map to a phenome map of the germline obtained by RNA interference (RNAi) and to a transcriptome map obtained by clustering worm genes across 553 expression profiling experiments. In this dataset, we find that essential proteins have a tendency to interact with each other, that pairs of genes encoding interacting proteins tend to exhibit similar expression profiles, and that, for approximately 24% of germline interactions, both partners show overlapping embryonic lethal or high incidence of males RNAi phenotypes and similar expression profiles. We propose that these interactions are most likely to be relevant to germline biology. Similar integration of interactome, phenome, and transcriptome data should be possible for other biological processes in the nematode and for other organisms, including humans.
RNA interference is thought to require near-identity between the
small interfering RNA (siRNA) and its cognate mRNA. Here, we used gene
expression profiling to characterize the specificity of gene silencing by
siRNAs in cultured human cells. Transcript profiles revealed siRNA-specific
rather than target-specific signatures, including direct silencing of
nontargeted genes containing as few as eleven contiguous nucleotides of
identity to the siRNA. These results demonstrate that siRNAs may cross-react
with targets of limited sequence similarity.
Simian Virus 40 (SV40) has been shown to enter host cells by caveolar endocytosis followed by transport via caveosomes to the endoplasmic reticulum (ER). Using a caveolin-1 (cav-1)-deficient cell line (human hepatoma 7) and embryonic fibroblasts from a cav-1 knockout mouse, we found that in the absence of caveolae, but also in wild-type embryonic fibroblasts, the virus exploits an alternative, cav-1-independent pathway. Internalization was rapid (t1/2 = 20 min) and cholesterol and tyrosine kinase dependent but independent of clathrin, dynamin II, and ARF6. The viruses were internalized in small, tight-fitting vesicles and transported to membrane-bounded, pH-neutral organelles similar to caveosomes but devoid of cav-1 and -2. The viruses were next transferred by microtubule-dependent vesicular transport to the ER, a step that was required for infectivity. Our results revealed the existence of a virus-activated endocytic pathway from the plasma membrane to the ER that involves neither clathrin nor caveolae and that can be activated also in the presence of cav-1.
The internalization of various cargo proteins and lipids from the mammalian cell surface occurs through the clathrin and lipid-raft endocytic pathways. Protein-lipid and protein-protein interactions control the targeting of signalling molecules and their partners to various specialized membrane compartments in these pathways. This functions to control the activity of signalling cascades and the termination of signalling events, and therefore has a key role in defining how a cell responds to its environment.
A key challenge of functional genomics today is to generate well-annotated data sets that can be interpreted across different platforms and technologies. Large-scale functional genomics data often fail to connect to standard experimental approaches of gene characterization in individual laboratories. Furthermore, a lack of universal annotation standards for phenotypic data sets makes it difficult to compare different screening approaches. Here we address this problem in a screen designed to identify all genes required for the first two rounds of cell division in the Caenorhabditis elegans embryo. We used RNA-mediated interference to target 98% of all genes predicted in the C. elegans genome in combination with differential interference contrast time-lapse microscopy. Through systematic annotation of the resulting movies, we developed a phenotypic profiling system, which shows high correlation with cellular processes and biochemical pathways, thus enabling us to predict new functions for previously uncharacterized genes.
Endocytosis is a key cellular process, encompassing different entry routes and endocytic compartments. To what extent endocytosis is subjected to high-order regulation by the cellular signalling machinery remains unclear. Using high-throughput RNA interference and automated image analysis, we explored the function of human kinases in two principal types of endocytosis: clathrin- and caveolae/raft-mediated endocytosis. We monitored this through infection of vesicular stomatitis virus, simian virus 40 and transferrin trafficking, and also through cell proliferation and apoptosis assays. Here we show that a high number of kinases are involved in endocytosis, and that each endocytic route is regulated by a specific kinase subset. Notably, one group of kinases exerted opposite effects on the two endocytic routes, suggesting coordinate regulation. Our analysis demonstrates that signalling functions such as those controlling cell adhesion, growth and proliferation, are built into the machinery of endocytosis to a much higher degree than previously recognized.
Guidance receptors detect extracellular cues and instruct migrating cells how to orient in space. Border cells perform a directional invasive migration during Drosophila oogenesis and use two receptor tyrosine kinases (RTKs), EGFR and PVR (PDGF/VEGF Receptor), to read guidance cues. We find that spatial localization of RTK signaling within these migrating cells is actively controlled. Border cells lacking Cbl, an RTK-associated E3 ubiquitin ligase, have delocalized guidance signaling, resulting in severe migration defects. Absence of Sprint, a receptor-recruited, Ras-activated Rab5 guanine exchange factor, gives related defects. In contrast, increasing the level of RTK signaling by receptor overexpression or removing Hrs and thereby decreasing RTK degradation does not perturb migration. Cbl and Sprint both regulate early steps of RTK endocytosis. Thus, a physiological role of RTK endocytosis is to ensure localized intracellular response to guidance cues by stimulating spatial restriction of signaling.
Growth of normal cells is anchorage dependent because signalling through multiple pathways including Erk, phosphatidylinositol-3-OH kinase (PI(3)K) and Rac requires integrin-mediated cell adhesion. Components of these pathways localize to low-density, cholesterol-rich domains in the plasma membrane named 'lipid rafts' or 'cholesterol-enriched membrane microdomains' (CEMM). We previously reported that integrin-mediated adhesion regulates CEMM transport such that cell detachment from the extracellular matrix triggers CEMM internalization and clearance from the plasma membrane. We now report that this internalization is mediated by dynamin-2 and caveolin-1. Internalization requires phosphorylation of caveolin-1 on Tyr 14. A shift in localization of phospho-caveolin-1 from focal adhesions to caveolae induces CEMM internalization upon cell detachment, which mediates inhibition of Erk, PI(3)K and Rac. These data define a novel molecular mechanism for growth and tumour suppression by caveolin-1.
Certain types of cellular differentiation are probabilistic and transient. In such systems individual cells can switch to an alternative state and, after some time, switch back again. In Bacillus subtilis, competence is an example of such a transiently differentiated state associated with the capability for DNA uptake from the environment. Individual genes and proteins underlying differentiation into the competent state have been identified, but it has been unclear how these genes interact dynamically in individual cells to control both spontaneous entry into competence and return to vegetative growth. Here we show that this behaviour can be understood in terms of excitability in the underlying genetic circuit. Using quantitative fluorescence time-lapse microscopy, we directly observed the activities of multiple circuit components simultaneously in individual cells, and analysed the resulting data in terms of a mathematical model. We find that an excitable core module containing positive and negative feedback loops can explain both entry into, and exit from, the competent state. We further tested this model by analysing initiation in sister cells, and by re-engineering the gene circuit to specifically block exit. Excitable dynamics driven by noise naturally generate stochastic and transient responses, thereby providing an ideal mechanism for competence regulation.
The synthetic multivulva (synMuv) genes negatively regulate Ras-mediated vulval induction in the nematode Caenorhabditis elegans. The synMuv genes define three classes, A, B, and C, such that double mutants carrying mutations in genes of any two classes are multivulva. The class B synMuv genes include lin-35, a homolog of the retinoblastoma (Rb) tumor suppressor gene, as well as homologs of genes that function with Rb in transcriptional regulation. We screened for additional synMuv mutations using a strategy different from that of previous synMuv genetic screens. Some of the mutations we recovered affect new synMuv genes. We present criteria for assigning synMuv mutations into different genetic classes. We also describe the molecular characterization of the class B synMuv gene lin-65.
The Caenorhabditis elegans genome contains three rac-like genes, ced-10, mig-2, and rac-2. We report that ced-10, mig-2 and rac-2 act redundantly in axon pathfinding: inactivating one gene had little effect, but inactivating two or more genes perturbed both axon outgrowth and guidance. mig-2 and ced-10 also have redundant functions in some cell migrations. By contrast, ced-10 is uniquely required for cell-corpse phagocytosis, and mig-2 and rac-2 have only subtle roles in this process. Rac activators are also used differentially. The UNC-73 Trio Rac GTP exchange factor affected all Rac pathways in axon pathfinding and cell migration but did not affect cell-corpse phagocytosis. CED-5 DOCK180, which acts with CED-10 Rac in cell-corpse phagocytosis, acted with MIG-2 but not CED-10 in axon pathfinding. Thus, distinct regulatory proteins modulate Rac activation and function in different developmental processes.
We previously identified Caenorhabditis elegans mutants in which certain of the six vulval precursor cells adopt fates normally expressed by other vulval precursor cells. These mutants define genes that appear to function in the response to an intercellular signal that induces vulval development. The multivulva (Muv) phenotype of one such mutant, CB1322, results from an interaction between two unlinked mutations, lin-8(n111) II and lin-9(n112) III. In this paper, we identify 18 new mutations, which are alleles of eight genes, that interact with either lin-8(n111) or lin-9(n112) to generate a Muv phenotype. None of these 20 mutations alone causes any vulval cell lineage defects. The "silent Muv" mutations fall into two classes; hermaphrodites carrying a mutation of each class are Muv, while hermaphrodites carrying two mutations of the same class have a wild-type vulval phenotype. Our results indicate that the Muv phenotype of these mutants results from defects in two functionally-redundant pathways, thereby demonstrating that redundancy can occur at the level of gene pathways as well as at the level of gene families.
Acrucial aspect of Caenorhabditis elegans vulval development is induction of the vulval precursor cells by the anchor cell of the developing uterus. Precisely three of the six vulval precursor cells are induced by the anchor cell to generate vulval cells. As discussed in this chapter, this induction uses an epidermal growth factor (EGF)-receptor signaling pathway involving the C elegans EGF-receptor homolog LET-23. There are a number of other cell-signaling events during vulval development including at least three uses of WNT, and the LIN-12 (Notch-type receptor) pathway. The LET-23 pathway is also used for a reciprocal induction by which a subset of the vulval cells signal back to the developing uterus. Because vulval induction is relatively easy to observe, it can be used to study EGF-receptor signaling in vivo.
Setting of the learning problem consistency of learning processes bounds on the rate of convergence of learning processes controlling the generalization ability of learning processes constructing learning algorithms what is important in learning theory?.
We previously identified Caenorhabditis elegans mutants in which certain of the six vulval precursor cells adopt fates normally expressed by other vulval precursor cells. These mutants define genes that appear to function in the response to an intercellular signal that induces vulval development. The multivulva (Muv) phenotype of one such mutant, CB1322, results from an interaction between two unlinked mutations, lin-8(n111) II and lin-9(n112) III. In this paper, we identify 18 new mutations, which are alleles of eight genes, that interact with either lin-8(n111) or lin-9(n112) to generate a Muv phenotype. None of these 20 mutations alone causes any vulval cell lineage defects. The "silent Muv" mutations fall into two classes; hermaphrodites carrying a mutation of each class are Muv, while hermaphrodites carrying two mutations of the same class have a wild-type vulval phenotype. Our results indicate that the Muv phenotype of these mutants results from defects in two functionally-redundant pathways, thereby demonstrating that redundancy can occur at the level of gene pathways as well as at the level of gene families.
This is a revised edition of the well-accepted book by Suzuki. The present edition is broad in scope and has approached the problem of use as a text in interesting but by now well-tried methods: it summarizes the main point of the section as a "message" and provides problems at the end of the chapter. Separate solutions and instructor's manuals are available. There is also a set of color slides designed to assist in instruction.In general, the presentation is clear and useful. Unfortunately, it is not consistent. Many sections are complex or unclear. For example, the discussion of dosage compensation in mammals is incomplete. There is a lengthy description of Liane Russell's experiments, but only casual comment on Mary Lyon's experiments. This is deplorable; the Lyon experiments clarified the interpretation of events, and her name is associated with the lyonization of the X chromosome. Further, no mention is made
There is increasing evidence for the wide-spread existence of functionally redundant genetic pathways in developmental processes. However, both their significance and manner of evolution are still matters of debate. I will argue here that redundancy of gene actions may, in fact, be a necessary requirement for the development and evolution of complex life forms. One can view development as a process that transmits information from the egg to the adult organism. Transmission of information is, however, always an error-prone process, which can only be safeguarded by including redundancies in the message. Molecular examples for well analysed redundant processes indicate that redundancies may best be understood within a conceptual framework of overlaps between different gene functions.
Abramoff, M.D., Magelhaes, P.J., Ram, S.J. "Image Processing with ImageJ". Biophotonics International, volume 11, issue 7, pp. 36-42, 2004.
After programmed cell death, a cell corpse is engulfed and quickly degraded by a neighboring cell. For degradation to occur, engulfing cells must recognize, phagocytose and digest the corpses of dying cells. Previously, three genes were known to be involved in eliminating cell corpses in the nematode Caenorhabditis elegans: ced-1, ced-2 and nuc-1. We have identified five new genes that play a role in this process: ced-5, ced-6, ced-7, ced-8 and ced-10. Electron microscopic studies reveal that mutations in each of these genes prevent engulfment, indicating that these genes are needed either for the recognition of corpses by other cells or for the initiation of phagocytosis. Based upon our study of double mutants, these genes can be divided into two sets. Animals with mutations in only one of these sets of genes have relatively few unengulfed cell corpses. By contrast, animals with mutations in both sets of genes have many unengulfed corpses. These observations suggest that these two sets of genes are involved in distinct and partially redundant processes that act in the engulfment of cell corpses.
We previously identified Caenorhabditis elegans mutants in which certain of the six vulval precursor cells adopt fates normally expressed by other vulval precursor cells. These mutants define genes that appear to function in the response to an intercellular signal that induces vulval development. The multivulva (Muv) phenotype of one such mutant, CB1322, results from an interaction between two unlinked mutations, lin-8(n111) II and lin-9(n112) III. In this paper, we identify 18 new mutations, which are alleles of eight genes, that interact with either lin-8(n111) or lin-9(n112) to generate a Muv phenotype. None of these 20 mutations alone causes any vulval cell lineage defects. The "silent Muv" mutations fall into two classes; hermaphrodites carrying a mutation of each class are Muv, while hermaphrodites carrying two mutations of the same class have a wild-type vulval phenotype. Our results indicate that the Muv phenotype of these mutants results from defects in two functionally-redundant pathways, thereby demonstrating that redundancy can occur at the level of gene pathways as well as at the level of gene families.
Partial functional redundancy among genes is frequently observed in a wide range of organisms and processes, but the selective value of such redundancy is not immediately apparent. Any fully redundant function should be evolutionarily unstable: unless selection acts to maintain the redundancy it will tend to be lost by mutational drift. I discuss four possible mechanisms by which selection might act to maintain genetic redundancy.
The neural receptor tyrosine phosphatases DPTP69D,
DPTP99A and DLAR are involved in motor axon guidance
in the Drosophila embryo. Here we analyze the requirements
for these three phosphatases in growth cone guidance decisions along the ISN and SNb motor pathways.
Any one of the three suffices for the progression of ISN
pioneer growth cones beyond their first intermediate target
in the dorsal muscle field. DLAR or DPTP69D can facilitate
outgrowth beyond a second intermediate target, and
DLAR is uniquely required for formation of a normal
terminal arbor. A different pattern of partial redundancy
among the three phosphatases is observed for the SNb
pathway. Any one of the three suffices to allow SNb axons
to leave the common ISN pathway at the exit junction.
When DLAR is not expressed, however, SNb axons
sometimes bypass their ventrolateral muscle targets after
leaving the common pathway, instead growing out as a
separate bundle adjacent to the ISN. This abnormal
guidance decision can be completely suppressed by also
removing DPTP99A, suggesting that DLAR turns off or
counteracts a DPTP99A signal that favors the bypass axon
trajectory. Our results show that the relationships among
the tyrosine phosphatases are complex and dependent on
cellular context. At growth cone choice points along one
nerve, two phosphatases cooperate, while along another
nerve these same phosphatases can act in opposition to one
another.
The Caenorhabditis elegans mpk-1 gene encodes a MAP kinase protein that plays an important role in Ras-mediated induction of vulval cell fates. We show that mutations that eliminate mpk-1 activity result in a highly penetrant, vulvaless phenotype. A double mutant containing a gain-of-function mpk-1 mutation and a gain-of-function mek mutation (MEK phosphorylates and activates MPK-1) exhibits a multivulva phenotype. These results suggest that mpk-1 may transduce most or all of the anchor cell signal. Epistasis analysis suggests that mpk-1 acts downstream of mek-2 (encodes a MEK homolog) and upstream of lin-1 (encodes an Ets transcription factor) in the anchor cell signaling pathway. Finally, mpk-1 may act together with let-60 ras in multiple developmental processes, as mpk-1 mutants exhibit nearly the same range of developmental phenotypes as let-60 ras mutants.
Engulfment of apoptotic cells in Caenorhabditis elegans is controlled by two partially redundant pathways. Mutations in genes in one of these pathways, defined by the genes ced-2, ced-5 and ced-10, result in defects both in the engulfment of dying cells and in the migrations of the two distal tip cells of the developing gonad. Here we find that ced-2 and ced-10 encode proteins similar to the human adaptor protein CrkII and the human GTPase Rac, respectively. Together with the previous observation that ced-5 encodes a protein similar to human DOCK180, our findings define a signalling pathway that controls phagocytosis and cell migration. We provide evidence that CED-2 and CED-10 function in engulfing rather than dying cells to control the phagocytosis of cell corpses, that CED-2 and CED-5 physically interact, and that ced-10 probably functions downstream of ced-2 and ced-5. We propose that CED-2/CrkII and CED-5/DOCK180 function to activate CED-10/Rac in a GTPase signalling pathway that controls the polarized extension of cell surfaces.
The Caenorhabditis elegans genome contains three rac-like genes, ced-10, mig-2, and rac-2. We report that ced-10, mig-2 and rac-2 act redundantly in axon pathfinding: inactivating one gene had little effect, but inactivating two or more genes perturbed both axon outgrowth and guidance. mig-2 and ced-10 also have redundant functions in some cell migrations. By contrast, ced-10 is uniquely required for cell-corpse phagocytosis, and mig-2 and rac-2 have only subtle roles in this process. Rac activators are also used differentially. The UNC-73 Trio Rac GTP exchange factor affected all Rac pathways in axon pathfinding and cell migration but did not affect cell-corpse phagocytosis. CED-5 DOCK180, which acts with CED-10 Rac in cell-corpse phagocytosis, acted with MIG-2 but not CED-10 in axon pathfinding. Thus, distinct regulatory proteins modulate Rac activation and function in different developmental processes.
In Caenorhabditis elegans, Ras/ERK and Wnt/beta-catenin signaling pathways cooperate to induce P12 and vulval cell fates in a Hox-dependent manner. Here we describe eor-1 and eor-2, two new positively acting nuclear components of the Ras and Wnt pathways. eor-1 and eor-2 act downstream or in parallel to ERK and function redundantly with the Mediator complex gene sur-2 and the functionally related gene lin-25, such that removal of both eor-1/eor-2 and sur-2/lin-25 mimics the removal of a main Ras pathway component. Furthermore, the eor-1 and eor-2 mutant backgrounds reveal an essential role for the Elk1-related gene lin-1. eor-1 and eor-2 also act downstream or in parallel to pry-1 Axin and therefore act at the convergence of the Ras and Wnt pathways. eor-1 encodes the ortholog of human PLZF, a BTB/zinc-finger transcription factor that is fused to RARalpha in acute promyelocytic leukemia. eor-2 encodes a novel protein. EOR-1/PLZF and EOR-2 appear to function closely together and cooperate with Hox genes to promote the expression of Ras- and Wnt-responsive genes. Further studies of eor-1 and eor-2 may provide insight into the roles of PLZF in normal development and leukemogenesis.
Recently, a set of 766 genes that are enriched in the ovary as compared to the soma was identified by microarray analysis [1]. Here, we report a functional analysis of 98% of these genes by RNA interference (RNAi). Over half the genes tested showed at least one detectable phenotype, most commonly embryonic lethality, consistent with the expectation that ovary transcripts would be enriched for genes that are essential in basic cellular and developmental processes. We find that essential genes are more likely to be conserved and to be highly expressed in the ovary. We extend previous observations and find that fewer than the expected number of ovary-expressed essential genes are present on the X chromosome. We characterized early embryonic defects for 161 genes and used time-lapse microscopy to systematically describe the defects for each gene in terms of 47 RNAi-associated phenotypes. In this paper, we discuss the use of these data to group genes into "phenoclusters"; in the accompanying paper, we use these data as one component in the integration of different types of large-scale functional analyses. We find that phenoclusters correlate well with sequence-based functional predictions and thus may be useful in predicting functions of uncharacterized genes.
We all know that gene expression occurs within cells, yet we do not think of expression in terms of its fundamental unit -- a single cell. Instead, we understand the expression of genes in terms of a cell population as all of our information comes from samples containing millions of cells. From a complex mixture of cells, we attempt to infer the probable state of an average cell in the population. In truth, what we obtain is an averaged cell, a contrivance for representing biological knowledge beyond the limits of detection. We never know the variation among the members of the population that our methods average into a mean. Recent technological advances allow the precise measurement of single-cell transcriptional states to study this variability more rigorously. How genes are expressed in the population is strikingly different to what we have assumed from extrapolating to an average cell. Does the average cell actually exist? As we discuss, it is becoming increasingly clear that it doesn't.
Viruses have long served as tools in molecular and cellular biology to study a variety of complex cellular processes. Currently, there is a revived interest in virus entry into animal cells because it is evident that incoming viruses make use of numerous endocytic pathways that are otherwise difficult to study. Besides the classical clathrin-mediated uptake route, viruses use caveolae-mediated endocytosis, lipid-raft-mediated endocytic pathways, and macropinocytosis. Some of these are subject to regulation, involve novel endocytic organelles, and some of them connect organelles that were previously not known to communicate by membrane traffic.
Rac1 is a GTP-binding molecule involved in a wide range of cellular processes. Using digital image analysis, agonist-induced translocation of green fluorescent protein (GFP) Rac1 to the cellular membrane can be estimated quantitatively for individual cells.
A fully automatic image analysis method for cell segmentation, feature extraction, and classification of cells according to their activation, i.e., GFP-Rac1 translocation and ruffle formation at stimuli, is described. Based on training data produced by visual annotation of four image series, a statistical classifier was created.
The results of the automatic classification were compared with results from visual inspection of the same time sequences. The automatic classification differed from the visual classification at about the same level as visual classifications performed by two different skilled professionals differed from each other. Classification of a second image set, consisting of seven image series with different concentrations of agonist, showed that the classifier could detect an increased proportion of activated cells at increased agonist concentration.
Intracellular activities, such as ruffle formation, can be quantified by fully automatic image analysis, with an accuracy comparable to that achieved by visual inspection. This analysis can be done at a speed of hundreds of cells per second and without the subjectivity introduced by manual judgments.
The removal of apoptotic cells is essential for the physiological well being of the organism. In Caenorhabditis elegans, two conserved, partially redundant genetic pathways regulate this process. In the first pathway, the proteins CED-2, CED-5 and CED-12 (mammalian homologues CrkII, Dock180 and ELMO, respectively) function to activate CED-10 (Rac1). In the second group, the candidate receptor CED-1 (CD91/LRP/SREC) probably recognizes an unknown ligand on the apoptotic cell and signals via its cytoplasmic tail to the adaptor protein CED-6 (hCED-6/GULP), whereas CED-7 (ABCA1) is thought to play a role in membrane dynamics. Molecular understanding of how the second pathway promotes engulfment of the apoptotic cell is lacking. Here, we show that CED-1, CED-6 and CED-7 are required for actin reorganization around the apoptotic cell corpse, and that CED-1 and CED-6 colocalize with each other and with actin around the dead cell. Furthermore, we find that the CED-10(Rac) GTPase acts genetically downstream of these proteins to mediate corpse removal, functionally linking the two engulfment pathways and identifying the CED-1, -6 and -7 signalling module as upstream regulators of Rac activation.
In this paper, we describe a new bioimage informatics system developed for high content screening (HCS) applications with the goal to extract and analyze phenotypic features of hundreds of thousands of mitotic cells simultaneously. The system introduces the algorithm of multi-phenotypic mitotic analysis (MMA) and integrates that with algorithms of correlation analysis and compound clustering used in gene microarray studies. The HCS-MMA system combines different phenotypic information of cellular images obtained from three-channel acquisitions to distinguish and label individual cells at various phases of mitosis. The proposed system can also be used to extract and count the number of cells in each phase in cell-based assay experiments and archive the extracted data into a structured database for more sophisticated statistical and data analysis. To recognize different mitotic phases, binary patterns are set up based on a known biological mitotic spindle model to characterize cellular morphology of actin, microtubules, and DNA. To illustrate its utility, the HCS-MMA system has been applied to screen the quantitative response of 320 different drug compounds in suppressing Monastrol. The results are validated and evaluated by comparing the performance of HCS-MMA with visual analysis, as well as clustering of the drug compounds under evaluation.
Organisms in fluctuating environments must constantly adapt their behavior to survive. In clonal populations, this may be achieved through sensing followed by response or through the generation of diversity by stochastic phenotype switching. Here we show that stochastic switching can be favored over sensing when the environment changes infrequently. The optimal switching rates then mimic the statistics of environmental changes. We derive a relation between the long-term growth rate of the organism and the information available about its fluctuating environment.
In recent years, it has been unambiguously shown that caveolae and lipid rafts can internalize cargo upon stimulation by multivalent ligands, demonstrated by the infectious entry routes of certain non-enveloped viruses that bind integrins or glycosphingolipids. We currently understand little about the membrane trafficking principles of this endocytic route, but it is clear that we cannot use paradigms from classical membrane traffic. Recent evidence indicates that caveolae- and lipid raft-mediated endocytosis plays important roles in cell adhesion and anchorage-dependent cell growth, but the underlying mechanisms are not known. In this review, I will introduce new models based on current research that aims at identifying the core machinery, regulatory components and design principles of this endocytic route in order to understand its role in cellular physiology. Again, viruses are proving to be excellent tools to reach that goal.
Cell-cell signaling coordinates proliferation of metazoan tissues during development, and its alteration can induce malignant transformation. Endocytosis regulates signaling by controlling the levels and activity of transmembrane receptors, both prior to and following ligand engagement. Here, we identify Vps25, a component of the ESCRT machinery that regulates endocytic sorting of signaling receptors, as an unconventional type of Drosophila tumor suppressor. vps25 mutant cells undergo autonomous neoplastic-like transformation, but they also stimulate nonautonomous cell proliferation. Endocytic trafficking defects in vps25 cells cause endosomal accumulation of the signaling receptor Notch and enhanced Notch signaling. Increased Notch activity leads to ectopic production of the mitogenic JAK-STAT pathway ligand Unpaired, which is secreted from mutant cells to induce overproliferation of the surrounding epithelium. Our data show that defects in endocytic sorting can both transform cells and, through heterotypic signaling, alter the behavior of neighboring wild-type tissue.
Intracellular protein transport is a key factor in epithelial cell polarity. Here we report that mutations in two core components of the vesicle trafficking machinery - a syntaxin and a Rab protein - cause an expansion of the apical membrane domain of Drosophila melanogaster epithelia; this polarity defect is coupled with overproliferation to form neoplastic tumours. Surprisingly, these proteins are associated with the endocytic, and not the exocytic, pathway. The syntaxin Avalanche (Avl) localizes to early endosomes, and loss of avl results in the cellular accumulation of specific membrane proteins, including the Notch signalling receptor and the polarity determinant Crumbs (Crb). Protein accumulation results from a failure in endosomal entry and progression towards lysosomal degradation; these and other avl phenotypes are also detected in Rab5 null mutant cells. Overexpression of Crb alone is sufficient to induce overproliferation of wild-type imaginal tissue, suggesting that polarity alterations in avl and Rab5 mutants directly contribute to tumour formation. Our findings reveal a critical and specific role for endocytic traffic in the control of both apico-basal polarity and cell proliferation.
Technological advances in mammalian systems are providing new tools to identify the molecular components of signalling pathways. Foremost among these tools is the ability to knock down gene function through the use of RNA interference (RNAi). The fact that RNAi can be scaled up for use in high-throughput techniques has motivated the creation of genome-wide RNAi reagents. We are now at the brink of being able to harness the power of RNAi for large-scale functional discovery in mammalian cells.
Detailed information about the replication cycle of viruses and their interactions with host organisms is required to develop strategies to stop them. Cell biology studies, live-cell imaging, and systems biology have started to illuminate the multiple and subtly different pathways that animal viruses use to enter host cells. These insights are revolutionizing our understanding of endocytosis and the movement of vesicles within cells. In addition, such insights reveal new targets for attacking viruses before they can usurp the host-cell machinery for replication.