Single-cell analysis of population context advances RNAi screening at multiple levels

Increasing the Content of High-Content Screening: An Overview

Target-based high-throughput screening (HTS) has recently been critiqued for its relatively poor yield compared to phenotypic screening approaches. One type of phenotypic screening, image-based high-content screening (HCS), has been seen as particularly promising.

In this article, we assess whether HCS is as high content as it can be. We analyze HCS publications and find that although the number of HCS experiments published each year continues to grow steadily, the information content lags behind. We find that a majority of high-content screens published so far (60−80%) made use of only one or two image-based features measured from each sample and disregarded the distribution of those features among each cell population. We discuss several potential explanations, focusing on the hypothesis that data analysis traditions are to blame. This includes practical problems related to managing large and multidimensional HCS data sets as well as the adoption of assay quality statistics from HTS to HCS. Both may have led to the simplification or systematic rejection of assays carrying complex and valuable phenotypic information.

We predict that advanced data analysis methods that enable full multiparametric data to be harvested for entire cell populations will enable HCS to finally reach its potential.

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.

Multiparametric Analysis of Screening Data: Growing Beyond the Single Dimension to Infinity and Beyond

Advances in instrumentation now allow the development of screening assays that are capable of monitoring multiple readouts such as transcript or protein levels, or even multiple parameters derived from images. Such advances in assay technologies highlight the complex nature of biology and disease. Harnessing this complexity requires integration of all the different parameters that can be measured rather than just monitoring a single dimension as is commonly used. Although some of the methods used to combine multiple measurements, such as principal component analysis, are commonly used for microarray analysis, biologists are not yet using many of the tools that have been developed in other fields to address such issues. Visualization of multiparametric data sets is one of the major challenges in this field, and a depiction of the results in a manner that can be readily interpreted is essential. This article describes a number of assay systems being used to generate such data sets en masse, and the methods being applied to their visualization and analysis. We also discuss some of the challenges of applying methods developed in other fields to biology.

Nanoscale distribution of CD20 on B-cell lymphoma tumour cells and its potential role in the clinical efficacy of rituximab

Rituximab is an exciting monoclonal antibody drug approved for treating B-cell lymphomas and its target is the CD20 antigen which is expressed on the surface of B cells. In recent years, the variable efficacies of rituximab among different lymphoma patients have become an important clinical issue and urgently need to be solved for further development of antibodies with enhanced efficacies. In this work, atomic force microscopy (AFM) was used to investigate the nanoscale distribution of CD20 on the surface of tumour B cells from lymphoma patients to examine its potential role in the clinical therapeutic effects of rituximab. By performing ROR1 fluorescence labelling (ROR1 is a specific tumour cell surface marker) on the bone marrow cells prepared from B-cell lymphoma patients, the tumour B cells were recognized, and then AFM tips carrying rituximabs via polyethylene glycol crosslinkers were moved to the tumour cells to probe the specific CD20-rituximab interactions. By applying AFM single-molecule force spectroscopy (SMFS) at the local areas (500×500 nm²) on the surface of tumour B cells, the nanoscale distributions of CD20 on the surface of tumour B cells were mapped, visually showing that CD20 distributed heterogeneously on the cell surface. Bone marrow cell samples from three clinical B-cell lymphoma cases were collected to analyze the binding affinity and nanoscale distribution of CD20 on tumour cells. The experimental results showed that CD20 distribution on tumour cells were to some extent related to the clinical therapeutic outcomes while the CD20-rituximab binding forces did not have distinct effects to the clinical outcomes. These results can provide novel insights in understanding the rituximab's clinical efficacies from the nanoscale distribution of CD20 on the tumour cells at single-cell and single-molecule levels.

Identifying proteins controlling key disease signaling pathways

MOTIVATION

Several types of studies, including genome-wide association studies and RNA interference screens, strive to link genes to diseases. Although these approaches have had some success, genetic variants are often only present in a small subset of the population, and screens are noisy with low overlap between experiments in different labs. Neither provides a mechanistic model explaining how identified genes impact the disease of interest or the dynamics of the pathways those genes regulate. Such mechanistic models could be used to accurately predict downstream effects of knocking down pathway members and allow comprehensive exploration of the effects of targeting pairs or higher-order combinations of genes.

RESULTS

We developed methods to model the activation of signaling and dynamic regulatory networks involved in disease progression. Our model, SDREM, integrates static and time series data to link proteins and the pathways they regulate in these networks. SDREM uses prior information about proteins' likelihood of involvement in a disease (e.g. from screens) to improve the quality of the predicted signaling pathways. We used our algorithms to study the human immune response to H1N1 influenza infection. The resulting networks correctly identified many of the known pathways and transcriptional regulators of this disease. Furthermore, they accurately predict RNA interference effects and can be used to infer genetic interactions, greatly improving over other methods suggested for this task. Applying our method to the more pathogenic H5N1 influenza allowed us to identify several strain-specific targets of this infection.

AVAILABILITY

SDREM is available from http://sb.cs.cmu.edu/sdrem.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Predicting functional gene interactions with the hierarchical interaction score

Systems biology aims to unravel the vast network of functional interactions that govern biological systems. To date, the inference of gene interactions from large-scale 'omics data is typically achieved using correlations. We present the hierarchical interaction score (HIS) and show that the HIS outperforms commonly used methods in the inference of functional interactions between genes measured in large-scale experiments, making it a valuable statistic for systems biology.

Cell-based fuzzy metrics enhance high-content screening (HCS) assay robustness

High-content screening (HCS) allows the exploration of complex cellular phenotypes by automated microscopy and is increasingly being adopted for small interfering RNA genomic screening and phenotypic drug discovery. We introduce a series of cell-based evaluation metrics that have been implemented and validated in a mono-parametric HCS for regulators of the membrane trafficking protein caveolin 1 (CAV1) and have also proved useful for the development of a multiparametric phenotypic HCS for regulators of cytoskeletal reorganization. Imaging metrics evaluate imaging quality such as staining and focus, whereas cell biology metrics are fuzzy logic-based evaluators describing complex biological parameters such as sparseness, confluency, and spreading. The evaluation metrics were implemented in a data-mining pipeline, which first filters out cells that do not pass a quality criterion based on imaging metrics and then uses cell biology metrics to stratify cell samples to allow further analysis of homogeneous cell populations. Use of these metrics significantly improved the robustness of the monoparametric assay tested, as revealed by an increase in Z' factor, Kolmogorov-Smirnov distance, and strict standard mean difference. Cell biology evaluation metrics were also implemented in a novel supervised learning classification method that combines them with phenotypic features in a statistical model that exceeded conventional classification methods, thus improving multiparametric phenotypic assay sensitivity.

A proteomic perspective of inbuilt viral protein regulation: pUL46 tegument protein is targeted for degradation by ICP0 during herpes simplex virus type 1 infection

Much like the host cells they infect, viruses must also regulate their life cycles. Herpes simples virus type 1 (HSV-1), a prominent human pathogen, uses a promoter-rich genome in conjunction with multiple viral trans-activating factors. Following entry into host cells, the virion-associated outer tegument proteins pUL46 and pUL47 act to increase expression of viral immediate-early (α) genes, thereby helping initiate the infection life cycle. Because pUL46 has gone largely unstudied, we employed a hybrid mass spectrometry-based approach to determine how pUL46 exerts its functions during early stages of infection. For a spatio-temporal characterization of pUL46, time-lapse microscopy was performed in live cells to define its dynamic localization from 2 to 24 h postinfection. Next, pUL46-containing protein complexes were immunoaffinity purified during infection of human fibroblasts and analyzed by mass spectrometry to investigate virus-virus and virus-host interactions, as well as post-translational modifications. We demonstrated that pUL46 is heavily phosphorylated in at least 23 sites. One phosphorylation site matched the consensus 14-3-3 phospho-binding motif, consistent with our identification of 14-3-3 proteins and host and viral kinases as specific pUL46 interactions. Moreover, we determined that pUL46 specifically interacts with the viral E3 ubiquitin ligase ICP0. We demonstrated that pUL46 is partially degraded in a proteasome-mediated manner during infection, and that the catalytic activity of ICP0 is responsible for this degradation. This is the first evidence of a viral protein being targeted for degradation by another viral protein during HSV-1 infection. Together, these data indicate that pUL46 levels are tightly controlled and important for the temporal regulation of viral gene expression throughout the virus life cycle. The concept of a structural virion protein, pUL46, performing nonstructural roles is likely to reflect a theme common to many viruses, and a better understanding of these functions will be important for developing therapeutics.

Inferring signalling networks from images

The mapping of signalling networks is one of biology's most important goals. However, given their size, complexity and dynamic nature, obtaining comprehensive descriptions of these networks has proven extremely challenging. A fast and cost-effective means to infer connectivity between genes on a systems-level is by quantifying the similarity between high-dimensional cellular phenotypes following systematic gene depletion. This review describes the methodology used to map signalling networks using data generated in the context of RNAi screens.

Expanding the Diversity of Imaging-Based RNAi Screen Applications Using Cell Spot Microarrays

Over the past decade, great strides have been made in identifying gene aberrations and deregulated pathways that are associated with specific disease states. These association studies guide experimental studies aimed at identifying the aberrant genes and networks that cause the disease states. This requires functional manipulation of these genes and networks in laboratory models of normal and diseased cells. One approach is to assess molecular and biological responses to high-throughput RNA interference (RNAi)-induced gene knockdown. These responses can be revealed by immunofluorescent staining for a molecular or cellular process of interest and quantified using fluorescence image analysis. These applications are typically performed in multiwell format, but are limited by high reagent costs and long plate processing times. These limitations can be mitigated by analyzing cells grown in cell spot microarray (CSMA) format. CSMAs are produced by growing cells on small (~200 μm diameter) spots with each spot carrying an siRNA with transfection reagent. The spacing between spots is only a few hundred micrometers, thus thousands of cell spots can be arranged on a single cell culture surface. These high-density cell cultures can be immunofluorescently stained with minimal reagent consumption and analyzed quickly using automated fluorescence microscopy platforms. This review covers basic aspects of imaging-based CSMA technology, describes a wide range of immunofluorescence assays that have already been implemented successfully for CSMA screening and suggests future directions for advanced RNAi screening experiments.

Systems-level analysis of host-pathogen interaction using RNA interference

Hand-in-hand with the availability of full genome sequences for eukaryotic model organisms and humans the demand for analysis of gene function on a system level has grown. In a process called RNA interference (RNAi) specific mRNA species can be degraded by introduction of double-stranded small interfering RNAs (siRNAs) that are complementary to the targeted transcript sequence. This enables the selective impairment of gene function. During the past decade RNAi has been exploited in many different eukaryotic cell types and model organisms. Large-scale and eventually genome-wide RNAi screens ablating gene functions in a systematic manner have delivered an overwhelming amount of data on the requirement of distinct gene products for major cellular pathways. A large part of the RNAi field is dedicated to disease states such as cancer or infection with the prospect of discovering pathways suitable for new therapeutic interventions. Here some of the major steps in the development of the RNAi technology will be outlined and exemplified with a focus on the progress made in the field of mammalian host-pathogen interactions.

Live cell imaging of viral entry

Viral entry encompasses the initial steps of infection starting from virion host cell attachment to viral genome release. Given the dynamic interactions between the virus and the host, many questions related to viral entry can be directly addressed by live cell imaging. Recent advances in fluorescent labeling of viral and cellular components, fluorescence microscopy with high sensitivity and spatiotemporal resolution, and image analysis enabled studies of a broad spectrum across many viral entry steps, including virus-receptor interactions, internalization, intracellular transport, genomic release, nuclear transport, and cell-to-cell transmission. Collectively, these live cell imaging studies have not only enriched our understandings of the viral entry mechanisms, but also provided novel insights into basic cellular biology processes.

Stochastic profiling of transcriptional regulatory heterogeneities in tissues, tumors and cultured cells

Single-cell variations in gene and protein expression are important during development and disease. Such cell-to-cell heterogeneities can be directly inspected one cell at a time, but global methods are usually not sensitive enough to work with the starting material of a single cell. Here we provide a detailed protocol for stochastic profiling, a method that infers single-cell regulatory heterogeneities by repeatedly sampling small collections of cells selected at random. Repeated stochastic sampling is performed by laser-capture microdissection or limiting dilution, followed by careful exponential cDNA amplification, hybridization to microarrays and statistical analysis. Stochastic profiling surveys the transcriptome for programs that are heterogeneously regulated among cellular subpopulations in their native tissue context. The protocol is readily optimized for specific biological applications and takes about 1 week to complete.

Computational analysis of virus-host interactomes

High-throughput methods for screening of physical and functional interactions now provide the means to study virus-host interactions on a genome scale. The limited coverage of these methods and the large size and uncertain quality of the identified interaction sets, however, require sophisticated computational approaches to obtain novel insights and hypotheses on virus infection processes from these interactions. Here, we describe the central steps of bioinformatics methods applied most commonly for this task and highlight important aspects that need to be considered and potential pitfalls that should be avoided.

Endocytosis of integrin-binding human picornaviruses

Picornaviruses that infect humans form one of the largest virus groups with almost three hundred virus types. They include significant enteroviral pathogens such as rhino-, polio-, echo-, and coxsackieviruses and human parechoviruses that cause wide range of disease symptoms. Despite the economic importance of picornaviruses, there are no antivirals. More than ten cellular receptors are known to participate in picornavirus infection, but experimental evidence of their role in cellular infection has been shown for only about twenty picornavirus types. Three enterovirus types and one parechovirus have experimentally been shown to bind and use integrin receptors in cellular infection. These include coxsackievirus A9 (CV-A9), echovirus 9, and human parechovirus 1 that are among the most common and epidemic human picornaviruses and bind to αV-integrins via RGD motif that resides on virus capsid. In contrast, echovirus 1 (E-1) has no RGD and uses integrin α2β1 as cellular receptor. Endocytosis of CV-A9 has recently been shown to occur via a novel Arf6- and dynamin-dependent pathways, while, contrary to collagen binding, E-1 binds inactive β1 integrin and enters via macropinocytosis. In this paper, we review what is known about receptors and endocytosis of integrin-binding human picornaviruses.

Shedding light on filovirus infection with high-content imaging

Microscopy has been instrumental in the discovery and characterization of microorganisms. Major advances in high-throughput fluorescence microscopy and automated, high-content image analysis tools are paving the way to the systematic and quantitative study of the molecular properties of cellular systems, both at the population and at the single-cell level. High-Content Imaging (HCI) has been used to characterize host-virus interactions in genome-wide reverse genetic screens and to identify novel cellular factors implicated in the binding, entry, replication and egress of several pathogenic viruses. Here we present an overview of the most significant applications of HCI in the context of the cell biology of filovirus infection. HCI assays have been recently implemented to quantitatively study filoviruses in cell culture, employing either infectious viruses in a BSL-4 environment or surrogate genetic systems in a BSL-2 environment. These assays are becoming instrumental for small molecule and siRNA screens aimed at the discovery of both cellular therapeutic targets and of compounds with anti-viral properties. We discuss the current practical constraints limiting the implementation of high-throughput biology in a BSL-4 environment, and propose possible solutions to safely perform high-content, high-throughput filovirus infection assays. Finally, we discuss possible novel applications of HCI in the context of filovirus research with particular emphasis on the identification of possible cellular biomarkers of virus infection.