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Genome-scale CRISPR-Cas9 viability screens performed in cancer cell lines provide a systematic approach to identify cancer dependencies and new therapeutic targets. As multiple large-scale screens become available, a formal assessment of the reproducibility of these experiments becomes necessary. We analyze data from recently published pan-cancer CRISPR-Cas9 screens performed at the Broad and Sanger Institutes. Despite significant differences in experimental protocols and reagents, we find that the screen results are highly concordant across multiple metrics with both common and specific dependencies jointly identified across the two studies. Furthermore, robust biomarkers of gene dependency found in one data set are recovered in the other. Through further analysis and replication experiments at each institute, we show that batch effects are driven principally by two key experimental parameters: the reagent library and the assay length. These results indicate that the Broad and Sanger CRISPR-Cas9 viability screens yield robust and reproducible findings. Integrating independent large-scale pharmacogenomic screens can enable unprecedented characterization of genetic vulnerabilities in cancers. Here, the authors show that the two largest independent CRISPR-Cas9 gene-dependency screens are concordant, paving the way for joint analysis of the data sets.

The eukaryotic translation initiation factor 2α (eIF2α) kinase GCN2 is activated by amino acid starvation to elicit a rectifying physiological program known as the Integrated Stress Response (ISR). A role for uncharged tRNAs as activating ligands of yeast GCN2 is supported experimentally. However, mouse GCN2 activation has recently been observed in circumstances associated with ribosome stalling with no global increase in uncharged tRNAs. We report on a mammalian CHO cell-based CRISPR-Cas9 mutagenesis screen for genes that contribute to ISR activation by amino acid starvation. Disruption of genes encoding components of the ribosome P-stalk, uL10 and P1, selectively attenuated GCN2-mediated ISR activation by amino acid starvation or interference with tRNA charging without affecting the endoplasmic reticulum unfolded protein stress-induced ISR, mediated by the related eIF2α kinase PERK. Wildtype ribosomes isolated from CHO cells, but not those with P-stalk lesions, stimulated GCN2-dependent eIF2α phosphorylation in vitro. These observations support a model whereby lack of a cognate charged tRNA exposes a latent capacity of the ribosome P-stalk to activate GCN2 in cells and help explain the emerging link between ribosome stalling and ISR activation. Often thought of as “workhorse” molecules, proteins take part in almost every structure and activity in a living cell. They are constructed from smaller building blocks called amino acids by molecular machines called ribosomes. Each cell needs a constant supply of amino acids to make new proteins. If cells are running low on amino acids, they can change their internal biochemistry to use amino acids more economically. GCN2 is one protein that helps activate these biochemical changes, but it was unclear how a shortage of amino acids could activate GCN2. Earlier in 2019, researchers reported that, in a test tube at least, isolated ribosomes could themselves activate GCN2. They also identified a part of the ribosome called the P-stalk as playing an important role in the interaction. Now, Harding et al. – who include some of the researchers involved in the earlier study – explore the activation of GCN2 further, but this time based on experiments with mammalian cells. First, a genetic screen was conducted to identify genes that if mutated specifically prevented the activation of GCN2 in cells that were starved of amino acids. This screen identified a few genes, several of which are involved in creating the P-stalk of the ribosome. By isolating the mutant ribosomes from these cells and studying them in the laboratory, Harding et al. then showed that these ribosomes are unable to activate GCN2. These findings confirm that the P-stalk of the ribosome plays an essential role in activating GCN2 in response to a shortage of amino acids. They shed light on a fundamental biological system, and further work will undoubtedly seek to uncover the details of the process by which GCN2 is activated.

In rodents, a druglike small molecule named ISRIB enhances cognition and reverses cognitive deficits after traumatic brain injury. ISRIB activates a protein complex called eIF2B that is required for the synthesis of new proteins. Tsai et al. report the visualization of eIF2B bound to ISRIB at near-atomic resolution by cryo–electron microscopy. Biochemical studies revealed that ISRIB is a “molecular staple” that promotes assembly of the fully active form of eIF2B. Zyryanova et al. report similar structures together with information on the binding of ISRIB analogs and their effects on protein translation. Science , this issue p. eaaq0939 , p. 1533 Cryo–electron microscopy and chemogenetic selection define an ISRIB binding pocket in the core of the eIF2B decamer. The integrated stress response (ISR) is a conserved translational and transcriptional program affecting metabolism, memory, and immunity. The ISR is mediated by stress-induced phosphorylation of eukaryotic translation initiation factor 2α (eIF2α) that attenuates the guanine nucleotide exchange factor eIF2B. A chemical inhibitor of the ISR, ISRIB, reverses the attenuation of eIF2B by phosphorylated eIF2α, protecting mice from neurodegeneration and traumatic brain injury. We describe a 4.1-angstrom-resolution cryo–electron microscopy structure of human eIF2B with an ISRIB molecule bound at the interface between the β and δ regulatory subunits. Mutagenesis of residues lining this pocket altered the hierarchical cellular response to ISRIB analogs in vivo and ISRIB binding in vitro. Our findings point to a site in eIF2B that can be exploited by ISRIB to regulate translation.

Technological advances in genomics and imaging have led to an explosion of molecular and cellular profiling data from large numbers of samples. This rapid increase in biological data dimension and acquisition rate is challenging conventional analysis strategies. Modern machine learning methods, such as deep learning, promise to leverage very large data sets for finding hidden structure within them, and for making accurate predictions. In this review, we discuss applications of this new breed of analysis approaches in regulatory genomics and cellular imaging. We provide background of what deep learning is, and the settings in which it can be successfully applied to derive biological insights. In addition to presenting specific applications and providing tips for practical use, we also highlight possible pitfalls and limitations to guide computational biologists when and how to make the most use of this new technology. Graphical Abstract Deep learning, a class of modern machine learning methods, has become a go‐to approach for analysing large‐scale high‐dimensional data. This review discusses its applications in biology, focusing on regulatory genomics and cellular imaging, and gives guidelines for practitioners.

High-throughput microscopy of many single cells generates high-dimensional data that are far from straightforward to analyze. One important problem is automatically detecting the cellular compartment where a fluorescently-tagged protein resides, a task relatively simple for an experienced human, but difficult to automate on a computer. Here, we train an 11-layer neural network on data from mapping thousands of yeast proteins, achieving per cell localization classification accuracy of 91%, and per protein accuracy of 99% on held-out images. We confirm that low-level network features correspond to basic image characteristics, while deeper layers separate localization classes. Using this network as a feature calculator, we train standard classifiers that assign proteins to previously unseen compartments after observing only a small number of training examples. Our results are the most accurate subcellular localization classifications to date, and demonstrate the usefulness of deep learning for high-throughput microscopy.

The emergence of microbial antibiotic resistance is a global health threat. In clinical settings, the key to controlling spread of resistant strains is accurate and rapid detection. As traditional culture-based methods are time consuming, genetic approaches have recently been developed for this task. The detection of antibiotic resistance is typically made by measuring a few known determinants previously identified from genome sequencing, and thus requires the prior knowledge of its biological mechanisms. To overcome this limitation, we employed machine learning models to predict resistance to 11 compounds across four classes of antibiotics from existing and novel whole genome sequences of 1936 E. coli strains. We considered a range of methods, and examined population structure, isolation year, gene content, and polymorphism information as predictors. Gradient boosted decision trees consistently outperformed alternative models with an average accuracy of 0.91 on held-out data (range 0.81-0.97). While the best models most frequently employed gene content, an average accuracy score of 0.79 could be obtained using population structure information alone. Single nucleotide variation data were less useful, and significantly improved prediction only for two antibiotics, including ciprofloxacin. These results demonstrate that antibiotic resistance in E. coli can be accurately predicted from whole genome sequences without a priori knowledge of mechanisms, and that both genomic and epidemiological data can be informative. This paves way to integrating machine learning approaches into diagnostic tools in the clinic.

Gene expression measurements are influenced by a wide range of factors, such as the state of the cell, experimental conditions and variants in the sequence of regulatory regions. To understand the effect of a variable of interest, such as the genotype of a locus, it is important to account for variation that is due to confounding causes. Here, we present VBQTL, a probabilistic approach for mapping expression quantitative trait loci (eQTLs) that jointly models contributions from genotype as well as known and hidden confounding factors. VBQTL is implemented within an efficient and flexible inference framework, making it fast and tractable on large-scale problems. We compare the performance of VBQTL with alternative methods for dealing with confounding variability on eQTL mapping datasets from simulations, yeast, mouse, and human. Employing Bayesian complexity control and joint modelling is shown to result in more precise estimates of the contribution of different confounding factors resulting in additional associations to measured transcript levels compared to alternative approaches. We present a threefold larger collection of cis eQTLs than previously found in a whole-genome eQTL scan of an outbred human population. Altogether, 27% of the tested probes show a significant genetic association in cis, and we validate that the additional eQTLs are likely to be real by replicating them in different sets of individuals. Our method is the next step in the analysis of high-dimensional phenotype data, and its application has revealed insights into genetic regulation of gene expression by demonstrating more abundant cis-acting eQTLs in human than previously shown. Our software is freely available online at http://www.sanger.ac.uk/resources/software/peer/.

Analysis of more than a billion repair outcomes yields an algorithm that predicts Cas9-induced mutations. The DNA mutation produced by cellular repair of a CRISPR–Cas9-generated double-strand break determines its phenotypic effect. It is known that the mutational outcomes are not random, but depend on DNA sequence at the targeted location. Here we systematically study the influence of flanking DNA sequence on repair outcome by measuring the edits generated by >40,000 guide RNAs (gRNAs) in synthetic constructs. We performed the experiments in a range of genetic backgrounds and using alternative CRISPR–Cas9 reagents. In total, we gathered data for >10 9 mutational outcomes. The majority of reproducible mutations are insertions of a single base, short deletions or longer microhomology-mediated deletions. Each gRNA has an individual cell-line-dependent bias toward particular outcomes. We uncover sequence determinants of the mutations produced and use these to derive a predictor of Cas9 editing outcomes. Improved understanding of sequence repair will allow better design of gene editing experiments.

Colony-based screens that quantify the fitness of clonal populations on solid agar plates are perhaps the most important source of genome-scale functional information in microorganisms. The images of ordered arrays of mutants produced by such experiments can be difficult to process because of laboratory-specific plate features, morphed colonies, plate edges, noise, and other artifacts. Most of the tools developed to address this problem are optimized to handle a single setup and do not work out of the box in other settings. We present gitter, an image analysis tool for robust and accurate processing of images from colony-based screens. gitter works by first finding the grid of colonies from a preprocessed image and then locating the bounds of each colony separately. We show that gitter produces comparable colony sizes to other tools in simple cases but outperforms them by being able to handle a wider variety of screens and more accurately quantify colony sizes from difficult images. gitter is freely available as an R package from http://cran.r-project.org/web/packages/gitter under the LGPL. Tutorials and demos can be found at http://omarwagih.github.io/gitter

Background Genome-scale CRISPR interference (CRISPRi) has been used in human cell lines; however, the features of effective guide RNAs (gRNAs) in different organisms have not been well characterized. Here, we define rules that determine gRNA effectiveness for transcriptional repression in Saccharomyces cerevisiae . Results We create an inducible single plasmid CRISPRi system for gene repression in yeast, and use it to analyze fitness effects of gRNAs under 18 small molecule treatments. Our approach correctly identifies previously described chemical-genetic interactions, as well as a new mechanism of suppressing fluconazole toxicity by repression of the ERG25 gene. Assessment of multiple target loci across treatments using gRNA libraries allows us to determine generalizable features associated with gRNA efficacy. Guides that target regions with low nucleosome occupancy and high chromatin accessibility are clearly more effective. We also find that the best region to target gRNAs is between the transcription start site (TSS) and 200 bp upstream of the TSS. Finally, unlike nuclease-proficient Cas9 in human cells, the specificity of truncated gRNAs (18 nt of complementarity to the target) is not clearly superior to full-length gRNAs (20 nt of complementarity), as truncated gRNAs are generally less potent against both mismatched and perfectly matched targets. Conclusions Our results establish a powerful functional and chemical genomics screening method and provide guidelines for designing effective gRNAs, which consider chromatin state and position relative to the target gene TSS. These findings will enable effective library design and genome-wide programmable gene repression in many genetic backgrounds.