CellClassifier: supervised learning of cellular phenotypes

Summary: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: [email protected] Supplementary information: Supplementary data are available at Bioinformatics onlin

Specific inhibition of diverse pathogens in human cells by synthetic microRNA-like oligonucleotides inferred from RNAi screens

Systematic genetic perturbation screening in human cells remains technically challenging. Typically, large libraries of chemically synthesized siRNA oligonucleotides are used, each designed to degrade a specific cellular mRNA via the RNA interference (RNAi) mechanism. Here, we report on data from three genome-wide siRNA screens, conducted to uncover host factors required for infection of human cells by two bacterial and one viral pathogen. We find that the majority of phenotypic effects of siRNAs are unrelated to the intended “on-target” mechanism, defined by full complementarity of the 21-nt siRNA sequence to a target mRNA. Instead, phenotypes are largely dictated by “off-target” effects resulting from partial complementarity of siRNAs to multiple mRNAs via the “seed” region (i.e., nucleotides 2–8), reminiscent of the way specificity is determined for endogenous microRNAs. Quantitative analysis enabled the prediction of seeds that strongly and specifically block infection, independent of the intended on-target effect. This prediction was confirmed experimentally by designing oligos that do not have any on-target sequence match at all, yet can strongly reproduce the predicted phenotypes. Our results suggest that published RNAi screens have primarily, and unintentionally, screened the sequence space of microRNA seeds instead of the intended on-target space of protein-coding genes. This helps to explain why previously published RNAi screens have exhibited relatively little overlap. Our analysis suggests a possible way of identifying “seed reagents” for controlling phenotypes of interest and establishes a general strategy for extracting valuable untapped information from past and future RNAi screens

Genome-Wide siRNA Screen Identifies Complementary Signaling Pathways Involved in Listeria Infection and Reveals Different Actin Nucleation Mechanisms during Listeria Cell Invasion and Actin Comet Tail Formation

Listeria monocytogenes enters nonphagocytic cells by a receptor-mediated mechanism that is dependent on a clathrin-based molecular machinery and actin rearrangements. Bacterial intra- and intercellular movements are also actin dependent and rely on the actin nucleating Arp2/3 complex, which is activated by host-derived nucleation-promoting factors downstream of the cell receptor Met during entry and by the bacterial nucleation-promoting factor ActA during comet tail formation. By genome-wide small interfering RNA (siRNA) screening for host factors involved in bacterial infection, we identified diverse cellular signaling networks and protein complexes that support or limit these processes. In addition, we could precise previously described molecular pathways involved in Listeria invasion. In particular our results show that the requirements for actin nucleators during Listeria entry and actin comet tail formation are different. Knockdown of several actin nucleators, including SPIRE2, reduced bacterial invasion while not affecting the generation of comet tails. Most interestingly, we observed that in contrast to our expectations, not all of the seven subunits of the Arp2/3 complex are required for Listeria entry into cells or actin tail formation and that the subunit requirements for each of these processes differ, highlighting a previously unsuspected versatility in Arp2/3 complex composition and function. IMPORTANCE: Listeria is a bacterial pathogen that induces its internalization within the cytoplasm of human cells and has been used for decades as a major molecular tool to manipulate cells in order to explore and discover cellular functions. We have inactivated individually, for the first time in epithelial cells, all the genes of the human genome to investigate whether each gene modifies positively or negatively the Listeria infectious process. We identified novel signaling cascades that have never been associated with Listeria infection. We have also revisited the role of the molecular complex Arp2/3 involved in the polymerization of the actin cytoskeleton, which was shown previously to be required for Listeria entry and movement inside host cells, and we demonstrate that contrary to the general dogma, some subunits of the complex are dispensable for both Listeria entry and bacterial movement

NEMix: single-cell nested effects models for probabilistic pathway stimulation

Nested effects models have been used successfully for learning subcellular networks from high-dimensional perturbation effects that result from RNA interference (RNAi) experiments. Here, we further develop the basic nested effects model using high-content single-cell imaging data from RNAi screens of cultured cells infected with human rhinovirus. RNAi screens with single-cell readouts are becoming increasingly common, and they often reveal high cell-to-cell variation. As a consequence of this cellular heterogeneity, knock-downs result in variable effects among cells and lead to weak average phenotypes on the cell population level. To address this confounding factor in network inference, we explicitly model the stimulation status of a signaling pathway in individual cells. We extend the framework of nested effects models to probabilistic combinatorial knock-downs and propose NEMix, a nested effects mixture model that accounts for unobserved pathway activation. We analyzed the identifiability of NEMix and developed a parameter inference scheme based on the Expectation Maximization algorithm. In an extensive simulation study, we show that NEMix improves learning of pathway structures over classical NEMs significantly in the presence of hidden pathway stimulation. We applied our model to single-cell imaging data from RNAi screens monitoring human rhinovirus infection, where limited infection efficiency of the assay results in uncertain pathway stimulation. Using a subset of genes with known interactions, we show that the inferred NEMix network has high accuracy and outperforms the classical nested effects model without hidden pathway activity. NEMix is implemented as part of the R/Bioconductor package 'nem' and available at www.cbg.ethz.ch/software/NEMix

Autophagy Proteins Promote Repair of Endosomal Membranes Damaged by the Salmonella Type Three Secretion System 1

Salmonella Typhimurium (S.Tm) is an enteropathogen requiring multiple virulence factors, including two type three secretion systems (T1 and T2). T1 triggers epithelium invasion in which the bacteria are taken up into endosomes that mature into Salmonella-containing vacuoles (SCV) and trigger T2 induction upon acidification. Mechanisms controlling endosome membrane integrity or pathogen egress into the cytosol are incompletely understood. We screened for host factors affecting invasion and SCV maturation and identified a role for autophagy in sealing endosomal membranes damaged by T1 during host cell invasion. S.Tm-infected autophagy-deficient (atg5(-/-)) cells exhibit reduced SCV dye retention and lower T2 expression but no effects on steps preceding SCV maturation. However, in the absence of T1, autophagy is dispensable for T2 induction. These findings establish a role of autophagy at early stages of S.Tm infection and suggest that autophagy-mediated membrane repair might be generally important for invasive pathogens and endosomal membrane function

Multi-scale Gaussian representation and outline-learning based cell image segmentation

BACKGROUND: High-throughput genome-wide screening to study gene-specific functions, e.g. for drug discovery, demands fast automated image analysis methods to assist in unraveling the full potential of such studies. Image segmentation is typically at the forefront of such analysis as the performance of the subsequent steps, for example, cell classification, cell tracking etc., often relies on the results of segmentation. METHODS: We present a cell cytoplasm segmentation framework which first separates cell cytoplasm from image background using novel approach of image enhancement and coefficient of variation of multi-scale Gaussian scale-space representation. A novel outline-learning based classification method is developed using regularized logistic regression with embedded feature selection which classifies image pixels as outline/non-outline to give cytoplasm outlines. Refinement of the detected outlines to separate cells from each other is performed in a post-processing step where the nuclei segmentation is used as contextual information. RESULTS AND CONCLUSIONS: We evaluate the proposed segmentation methodology using two challenging test cases, presenting images with completely different characteristics, with cells of varying size, shape, texture and degrees of overlap. The feature selection and classification framework for outline detection produces very simple sparse models which use only a small subset of the large, generic feature set, that is, only 7 and 5 features for the two cases. Quantitative comparison of the results for the two test cases against state-of-the-art methods show that our methodology outperforms them with an increase of 4-9% in segmentation accuracy with maximum accuracy of 93%. Finally, the results obtained for diverse datasets demonstrate that our framework not only produces accurate segmentation but also generalizes well to different segmentation tasks

Inferred MAPK networks on HRV infection data.

<p>Best networks of the 5 top scoring siRNAs from the MAPK pathway for HRV infection for the different compared methods are displayed. (A) shows the known KEGG pathway. (B) is the inferred NEM and (C) the sc-NEM. (D) left shows the known network with the most likely attachment of the hidden variable <i>Z</i> (blue) and (E) is the inferred NEMix. For all networks their performance is summarized in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004078#pcbi.1004078.t001" target="_blank">Table 1</a>. Subfigure (F) summarizes robustness of the MAPK network inference. For the inferred MAPK signaling networks on the HRV infection data, we assessed robustness of the accuracy for edge recovery. Box-plots display the result of 50 bootstrap samples for the three compared methods, on the 5 gene (<i>n</i> = 5) and 8 gene (<i>n</i> = 8) network.</p