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Technological advancement has opened the door to systematic genetics in mammalian cells. Genome‐scale loss‐of‐function screens can assay fitness defects induced by partial gene knockdown, using RNA interference, or complete gene knockout, using new CRISPR techniques. These screens can reveal the basic blueprint required for cellular proliferation. Moreover, comparing healthy to cancerous tissue can uncover genes that are essential only in the tumor; these genes are targets for the development of specific anticancer therapies. Unfortunately, progress in this field has been hampered by off‐target effects of perturbation reagents and poorly quantified error rates in large‐scale screens. To improve the quality of information derived from these screens, and to provide a framework for understanding the capabilities and limitations of CRISPR technology, we derive gold‐standard reference sets of essential and nonessential genes, and provide a Bayesian classifier of gene essentiality that outperforms current methods on both RNAi and CRISPR screens. Our results indicate that CRISPR technology is more sensitive than RNAi and that both techniques have nontrivial false discovery rates that can be mitigated by rigorous analytical methods. Synopsis This study provides a gold‐standard set for essential and nonessential human genes in cancer cell lines. The ‘Daisy model’ for core versus context‐specific essentiality provides a method to evaluate data quality in genome‐scale RNAi and CRISPR screens. Gold‐standard reference sets of human essential and nonessential genes are leveraged to improve analyses of RNAi and CRISPR screens. Characteristics of human essential genes are derived from the cumulative analysis of RNAi screens. The Daisy model of gene essentiality is derived from the difference between core and context‐specific cell line essentials. A computational framework is presented for the prediction of human essential genes from reverse genetic screening data. Graphical Abstract This study provides a gold‐standard set for essential and nonessential human genes in cancer cell lines. The ‘Daisy model’ for core versus context‐specific essentiality provides a method to evaluate data quality in genome‐scale RNAi and CRISPR screens.

The adaptation of CRISPR/SpCas9 technology to mammalian cell lines is transforming the study of human functional genomics. Pooled libraries of CRISPR guide RNAs (gRNAs) targeting human protein-coding genes and encoded in viral vectors have been used to systematically create gene knockouts in a variety of human cancer and immortalized cell lines, in an effort to identify whether these knockouts cause cellular fitness defects. Previous work has shown that CRISPR screens are more sensitive and specific than pooled-library shRNA screens in similar assays, but currently there exists significant variability across CRISPR library designs and experimental protocols. In this study, we reanalyze 17 genome-scale knockout screens in human cell lines from three research groups, using three different genome-scale gRNA libraries. Using the Bayesian Analysis of Gene Essentiality algorithm to identify essential genes, we refine and expand our previously defined set of human core essential genes from 360 to 684 genes. We use this expanded set of reference core essential genes, CEG2, plus empirical data from six CRISPR knockout screens to guide the design of a sequence-optimized gRNA library, the Toronto KnockOut version 3.0 (TKOv3) library. We then demonstrate the high effectiveness of the library relative to reference sets of essential and nonessential genes, as well as other screens using similar approaches. The optimized TKOv3 library, combined with the CEG2 reference set, provide an efficient, highly optimized platform for performing and assessing gene knockout screens in human cell lines.

Graphical Abstract A recent study by Haapaniemi et al (2018) reported that intact p53 signaling hampers CRISPR‐based functional genomic screens. Brown et al report good performance of genome‐scale screens in TP53 wild‐type cells and reiterate best practices for CRISPR screening.

A major goal of biomedical research is to assign functions to the myriad alternative RNA and protein isoforms. This challenge is particularly relevant to the mammalian nervous system, which produces complex repertoires of alternative splicing events. Here, we describe CHyMErA-seq, a platform that couples systematic deletion of exons to a single cell transcriptomics read-out, and apply this method to investigate a critical program of brain-specific microexons. Perturbation of microexons during neurogenesis reveals convergent roles in the temporal regulation of gene expression programs that direct signaling pathways and morphogenesis. We further observe microexons, including those in the Bin1 , Clasp1 , Gfra1 , Med23 , Ptprf and Ralgapb genes, that are required for the correct timing of autism-linked gene expression. Collectively, we describe a flexible system for isoform-resolution perturbation at a single cell level, together with insights into the roles of microexons in the developmental timing of neurogenesis transcriptomic signatures linked to brain disorders. The functions of the vast majority of brain-expressed spliced isoforms are unknown. Here the authors describe an isoform-resolution perturbation system coupled to a single cell transcriptomics read-out, and through this approach identify neuronal microexons that control autism-linked signatures underlying neuronal maturation and function

Glioblastoma (GBM) remains a formidable challenge in clinical settings due to limited treatments available. The surface protein CD133 marks glioblastoma stem cells (GSCs), cells capable of overcoming therapeutic pressures and correlate with more aggressiveness tumor phenotypes. In this study, we employed a CRISPR-Cas9 functional screen to deconvolute CD133 dynamics in tumors. This led us to establish that SOX2 is a key player in controlling the PROM1 gene, which in turn influences how cells react to stress factors, including those induced by chemoradiation treatment. The discoveries in this study shed light on the complex web of mechanisms that control the survival and resistance of GSCs, offering promising new avenues for targeting and potentially overcoming therapy resistance.

RAS-MAPK signaling mediates processes critical to normal development including cell proliferation, survival, and differentiation. Germline mutation of RAS-MAPK genes lead to the Noonan-spectrum of syndromes. Here, we present a patient affected by a 6p-interstitial microdeletion with unknown underlying molecular etiology. Examination of 6p-interstitial microdeletion cases reveals shared clinical features consistent with Noonan-spectrum disorders including short stature, facial dysmorphia and cardiovascular abnormalities. We find the RAS-responsive element binding protein-1 ( RREB1 ) is the common deleted gene in multiple 6p-interstitial microdeletion cases. Rreb1 hemizygous mice display orbital hypertelorism and cardiac hypertrophy phenocopying the human syndrome. Rreb1 haploinsufficiency leads to sensitization of MAPK signaling. Rreb1 recruits Sin3a and Kdm1a to control H3K4 methylation at MAPK pathway gene promoters. Haploinsufficiency of SIN3A and mutations in KDM1A cause syndromes similar to RREB1 haploinsufficiency suggesting genetic perturbation of the RREB1-SIN3A-KDM1A complex represents a new category of RASopathy-like syndromes arising through epigenetic reprogramming of MAPK pathway genes. Mutations in RAS-MAPK pathway genes are implicated in Noonan-spectrum, yet up to 20% of cases have unknown cause. Here, the authors identify RREB1 underlying a 6p microdeletion RASopathy-like syndrome and show that RREB1, SIN3A and KDM1A form a transcriptional repressive complex to control methylation of MAPK pathway genes.

We present FLEX (Functional evaluation of experimental perturbations), a pipeline that leverages several functional annotation resources to establish reference standards for benchmarking human genome‐wide CRISPR screen data and methods for analyzing them. FLEX provides a quantitative measurement of the functional information captured by a given gene‐pair dataset and a means to explore the diversity of functions captured by the input dataset. We apply FLEX to analyze data from the diverse cell line screens generated by the DepMap project. We identify a predominant mitochondria‐associated signal within co‐essentiality networks derived from these data and explore the basis of this signal. Our analysis and time‐resolved CRISPR screens in a single cell line suggest that the variable phenotypes associated with mitochondria genes across cells may reflect screen dynamics and protein stability effects rather than genetic dependencies. We characterize this functional bias and demonstrate its relevance for interpreting differential hits in any CRISPR screening context. More generally, we demonstrate the utility of the FLEX pipeline for performing robust comparative evaluations of CRISPR screens or methods for processing them. SYNOPSIS FLEX is a method for systematic evaluation and benchmarking of large‐scale genetic datasets that measures both the quantity and the composition of functional signals in gene‐pair data. FLEX allows users to measure the predictive performance of functional networks against several annotation standards. FLEX provides information about the diversity of functional modules captured by the input data. Application of FLEX to co‐essentiality networks derived from DepMap CRISPR screens reveals a major functional bias for mitochondrial complexes. Differential phenotypes for ETC‐related genes in CRISPR screens may reflect differences in the effective sampling time, cell line doubling rate, and protein stability. Graphical Abstract FLEX is a method for systematic evaluation and benchmarking of large‐scale genetic datasets that measures both the quantity and the composition of functional signals in gene‐pair data.

The de novo synthesis of fatty acids has emerged as a therapeutic target for various diseases, including cancer. Because cancer cells are intrinsically buffered to combat metabolic stress, it is important to understand how cells may adapt to the loss of de novo fatty acid biosynthesis. Here, we use pooled genome-wide CRISPR screens to systematically map genetic interactions (GIs) in human HAP1 cells carrying a loss-of-function mutation in fatty acid synthase ( FASN ), whose product catalyses the formation of long-chain fatty acids. FASN -mutant cells show a strong dependence on lipid uptake that is reflected in negative GIs with genes involved in the LDL receptor pathway, vesicle trafficking and protein glycosylation. Further support for these functional relationships is derived from additional GI screens in query cell lines deficient in other genes involved in lipid metabolism, including LDLR , SREBF1 , SREBF2 and ACACA . Our GI profiles also identify a potential role for the previously uncharacterized gene C12orf49 (which we call LUR1 ) in regulation of exogenous lipid uptake through modulation of SREBF2 signalling in response to lipid starvation. Overall, our data highlight the genetic determinants underlying the cellular adaptation associated with loss of de novo fatty acid synthesis and demonstrate the power of systematic GI mapping for uncovering metabolic buffering mechanisms in human cells. Aregger et al. provide an approach to study genetic interactions in mammalian cells and describe genetic interaction maps that characterize genes involved in lipid metabolism. They identify the role of C12orf49 , a previously uncharacterized gene, in regulating lipid uptake in human cells.

Signaling pathways transmit information through protein interaction networks that are dynamically regulated by complex extracellular cues. We developed LUMIER (for luminescence-based mammalian interactome mapping), an automated high-throughput technology, to map protein-protein interaction networks systematically in mammalian cells and applied it to the transforming growth factor-{szligbeta} (TGF{szligbeta}) pathway. Analysis using self-organizing maps and k-means clustering identified links of the TGF{szligbeta} pathway to the p21-activated kinase (PAK) network, to the polarity complex, and to Occludin, a structural component of tight junctions. We show that Occludin regulates TGF{szligbeta} type I receptor localization for efficient TGF{szligbeta}-dependent dissolution of tight junctions during epithelial-to-mesenchymal transitions.

Single-cell RNA-sequencing (scRNA-seq) offers functional insight into complex biology, allowing for the interrogation of cellular populations and gene expression programs at single-cell resolution. Here, we introduce scPipeline, a single-cell data analysis toolbox that builds on existing methods and offers modular workflows for multi-level cellular annotation and user-friendly analysis reports. Advances to scRNA-seq annotation include: (i) co-dependency index (CDI)-based differential expression, (ii) cluster resolution optimization using a marker-specificity criterion, (iii) marker-based cell-type annotation with Miko scoring, and (iv) gene program discovery using scale-free shared nearest neighbor network (SSN) analysis. Both unsupervised and supervised procedures were validated using a diverse collection of scRNA-seq datasets and illustrative examples of cellular transcriptomic annotation of developmental and immunological scRNA-seq atlases are provided herein. Overall, scPipeline offers a flexible computational framework for in-depth scRNA-seq analysis. scPipeline is a single-cell data analysis toolbox that builds on existing methods and offers modular workflows for multi-level cellular annotation and user-friendly analysis reports.