A Three-Way Combinatorial CRISPR Screen for Analyzing Interactions among Druggable Targets

Synthetic lethal connectivity and graph transformer improve synthetic lethality prediction

Synthetic lethality (SL) has shown great promise for the discovery of novel targets in cancer. CRISPR double-knockout (CDKO) technologies can only screen several hundred genes and their combinations, but not genome-wide. Therefore, good SL prediction models are highly needed for genes and gene pairs selection in CDKO experiments. However, lack of scalable SL properties prevents generalizability of SL interactions to out-of-sample data, thereby hindering modeling efforts. In this paper, we recognize that SL connectivity is a scalable and generalizable SL property. We develop a novel two-step multilayer encoder for individual sample-specific SL prediction model (MLEC-iSL), which predicts SL connectivity first and SL interactions subsequently. MLEC-iSL has three encoders, namely, gene, graph, and transformer encoders. MLEC-iSL achieves high SL prediction performance in K562 (AUPR, 0.73; AUC, 0.72) and Jurkat (AUPR, 0.73; AUC, 0.71) cells, while no existing methods exceed 0.62 AUPR and AUC. The prediction performance of MLEC-iSL is validated in a CDKO experiment in 22Rv1 cells, yielding a 46.8% SL rate among 987 selected gene pairs. The screen also reveals SL dependency between apoptosis and mitosis cell death pathways.

HyperCas12a enables highly-multiplexed epigenome editing screens

Interactions between multiple genes or cis-regulatory elements (CREs) underlie a wide range of biological processes in both health and disease. High-throughput screens using dCas9 fused to epigenome editing domains have allowed researchers to assess the impact of activation or repression of both coding and non-coding genomic regions on a phenotype of interest, but assessment of genetic interactions between those elements has been limited to pairs. Here, we combine a hyper-efficient version of Lachnospiraceae bacterium dCas12a (dHyperLbCas12a) with RNA Polymerase II expression of long CRISPR RNA (crRNA) arrays to enable efficient highly-multiplexed epigenome editing. We demonstrate that this system is compatible with several activation and repression domains, including the P300 histone acetyltransferase domain and SIN3A interacting domain (SID). We also show that the dCas12a platform can perform simultaneous activation and repression using a single crRNA array via co-expression of multiple dCas12a orthologues. Lastly, demonstrate that the dCas12a system is highly effective for high-throughput screens. We use dHyperLbCas12a-KRAB and a ~19,000-member barcoded library of crRNA arrays containing six crRNAs each to dissect the independent and combinatorial contributions of CREs to the dose-dependent control of gene expression at a glucocorticoid-responsive locus. The tools and methods introduced here create new possibilities for highly multiplexed control of gene expression in a wide variety of biological systems.

A high-throughput synthetic biology approach for studying combinatorial chromatin-based transcriptional regulation

The construction of synthetic gene circuits requires the rational combination of multiple regulatory components, but predicting their behavior can be challenging due to poorly understood component interactions and unexpected emergent behaviors. In eukaryotes, chromatin regulators (CRs) are essential regulatory components that orchestrate gene expression. Here, we develop a screening platform to investigate the impact of CR pairs on transcriptional activity in yeast. We construct a combinatorial library consisting of over 1,900 CR pairs and use a high-throughput workflow to characterize the impact of CR co-recruitment on gene expression. We recapitulate known interactions and discover several instances of CR pairs with emergent behaviors. We also demonstrate that supervised machine learning models trained with low-dimensional amino acid embeddings accurately predict the impact of CR co-recruitment on transcriptional activity. This work introduces a scalable platform and machine learning approach that can be used to study how networks of regulatory components impact gene expression.

Engineered CRISPR-Cas12a for higher-order combinatorial chromatin perturbations

Multiplexed genetic perturbations are critical for testing functional interactions among coding or non-coding genetic elements. Compared to double-stranded DNA cutting, repressive chromatin formation using CRISPR interference (CRISPRi) avoids genotoxicity and is more effective for perturbing non-coding regulatory elements in pooled assays. However, current CRISPRi pooled screening approaches are limited to targeting one to three genomic sites per cell. We engineer an Acidaminococcus Cas12a (AsCas12a) variant, multiplexed transcriptional interference AsCas12a (multiAsCas12a), that incorporates R1226A, a mutation that stabilizes the ribonucleoprotein-DNA complex via DNA nicking. The multiAsCas12a-KRAB fusion improves CRISPRi activity over DNase-dead AsCas12a-KRAB fusions, often rescuing the activities of lentivirally delivered CRISPR RNAs (crRNA) that are inactive when used with the latter. multiAsCas12a-KRAB supports CRISPRi using 6-plex crRNA arrays in high-throughput pooled screens. Using multiAsCas12a-KRAB, we discover enhancer elements and dissect the combinatorial function of cis-regulatory elements in human cells. These results instantiate a group testing framework for efficiently surveying numerous combinations of chromatin perturbations for biological discovery and engineering.

Higher-order combinatorial chromatin perturbations by engineered CRISPR-Cas12a for functional genomics

Multiplexed genetic perturbations are critical for testing functional interactions among coding or non-coding genetic elements. Compared to double-stranded DNA cutting, repressive chromatin formation using CRISPR interference (CRISPRi) avoids genotoxicity and is more effective for perturbing non-coding regulatory elements in pooled assays. However, current CRISPRi pooled screening approaches are limited to targeting 1-3 genomic sites per cell. To develop a tool for higher-order ( > 3) combinatorial targeting of genomic sites with CRISPRi in functional genomics screens, we engineered an Acidaminococcus Cas12a variant -- referred to as mul tiplexed transcriptional interference AsCas12a (multiAsCas12a). multiAsCas12a incorporates a key mutation, R1226A, motivated by the hypothesis of nicking-induced stabilization of the ribonucleoprotein:DNA complex for improving CRISPRi activity. multiAsCas12a significantly outperforms prior state-of-the-art Cas12a variants in combinatorial CRISPRi targeting using high-order multiplexed arrays of lentivirally transduced CRISPR RNAs (crRNA), including in high-throughput pooled screens using 6-plex crRNA array libraries. Using multiAsCas12a CRISPRi, we discover new enhancer elements and dissect the combinatorial function of cis-regulatory elements. These results instantiate a group testing framework for efficiently surveying potentially numerous combinations of chromatin perturbations for biological discovery and engineering.

CRISPR screening in hematology research: from bulk to single-cell level

The CRISPR genome editing technology has revolutionized the way gene function is studied. Genome editing can be achieved in single genes or for thousands of genes simultaneously in sensitive genetic screens. While conventional genetic screens are limited to bulk measurements of cell behavior, recent developments in single-cell technologies make it possible to combine CRISPR screening with single-cell profiling. In this way, cell behavior and gene expression can be monitored simultaneously, with the additional possibility of including data on chromatin accessibility and protein levels. Moreover, the availability of various Cas proteins leading to inactivation, activation, or other effects on gene function further broadens the scope of such screens. The integration of single-cell multi-omics approaches with CRISPR screening open the path to high-content information on the impact of genetic perturbations at single-cell resolution. Current limitations in cell throughput and data density need to be taken into consideration, but new technologies are rapidly evolving and are likely to easily overcome these limitations. In this review, we discuss the use of bulk CRISPR screening in hematology research, as well as the emergence of single-cell CRISPR screening and its added value to the field.

Comprehensive synergy mapping links a BAF- and NSL-containing "supercomplex" to the transcriptional silencing of HIV-1

Host repressors mediate HIV latency, but how they interactively silence the virus remains unclear. Here, we develop "reiterative enrichment and authentication of CRISPRi targets for synergies (REACTS)" to probe the genome for synergies between HIV transcription repressors. Using eight known host repressors as queries, we identify 32 synergies involving eleven repressors, including BCL7C, KANSL2, and SIRT2. Overexpression of these three proteins reduces HIV reactivation in Jurkat T cells and in CD4 T cells from people living with HIV on antiretroviral therapy (ART). We show that the BCL7C-containing BAF complex and the KANSL2-containing NSL complex form a "supercomplex" that increases inhibitory histone acetylation of the HIV long-terminal repeat (LTR) and its occupancy by the short variant of the acetyl-lysine reader Brd4. Collectively, we provide a validated platform for defining gene synergies genome wide, and the BAF-NSL "supercomplex" represents a potential target for overcoming HIV rebound after ART cessation.

Improved Combinatorial Assembly and Barcode Sequencing for Gene-Sized DNA Constructs

Synergistic and supportive interactions among genes can be incorporated in engineering biology to enhance and stabilize the performance of biological systems, but combinatorial numerical explosion challenges the analysis of multigene interactions. The incorporation of DNA barcodes to mark genes coupled with next-generation sequencing offers a solution to this challenge. We describe improvements for a key method in this space, CombiGEM, to broaden its application to assembling typical gene-sized DNA fragments and to reduce the cost of sequencing for prevalent small-scale projects. The expanded reach of the method beyond currently targeted small RNA genes promotes the discovery and incorporation of gene synergy in natural and engineered processes such as biocontainment, the production of desired compounds, and previously uncharacterized fundamental biological mechanisms.

Parallel engineering and activity profiling of a base editor system

Selecting the most suitable existing base editors and engineering new variants for installing specific base conversions with maximal efficiency and minimal undesired edits are pivotal for precise genome editing applications. Here, we present a platform for creating and analyzing a library of engineered base editor variants to enable head-to-head evaluation of their editing performance at scale. Our comprehensive comparison provides quantitative measures on each variant's editing efficiency, purity, motif preference, and bias in generating single and multiple base conversions, while uncovering undesired higher indel generation rate and noncanonical base conversion for some of the existing base editors. In addition to engineering the base editor protein, we further applied this platform to investigate a hitherto underexplored engineering route and created guide RNA scaffold variants that augment the editor's base-editing activity. With the unknown performance and compatibility of the growing number of engineered parts including deaminase, CRISPR-Cas enzyme, and guide RNA scaffold variants for assembling the expanding collection of base editor systems, our platform addresses the unmet need for an unbiased, scalable method to benchmark their editing outcomes and accelerate the engineering of next-generation precise genome editors.

Multi-view graph convolutional network for cancer cell-specific synthetic lethality prediction

Synthetic lethal (SL) genetic interactions have been regarded as a promising focus for investigating potential targeted therapeutics to tackle cancer. However, the costly investment of time and labor associated with wet-lab experimental screenings to discover potential SL relationships motivates the development of computational methods. Although graph neural network (GNN) models have performed well in the prediction of SL gene pairs, existing GNN-based models are not designed for predicting cancer cell-specific SL interactions that are more relevant to experimental validation in vitro. Besides, neither have existing methods fully utilized diverse graph representations of biological features to improve prediction performance. In this work, we propose MVGCN-iSL, a novel multi-view graph convolutional network (GCN) model to predict cancer cell-specific SL gene pairs, by incorporating five biological graph features and multi-omics data. Max pooling operation is applied to integrate five graph-specific representations obtained from GCN models. Afterwards, a deep neural network (DNN) model serves as the prediction module to predict the SL interactions in individual cancer cells (iSL). Extensive experiments have validated the model's successful integration of the multiple graph features and state-of-the-art performance in the prediction of potential SL gene pairs as well as generalization ability to novel genes.

Synthetic lethal gene pairs: Experimental approaches and predictive models

Synthetic lethality (SL) refers to a genetic interaction in which the simultaneous perturbation of two genes leads to cell or organism death, whereas viability is maintained when only one of the pair is altered. The experimental exploration of these pairs and predictive modeling in computational biology contribute to our understanding of cancer biology and the development of cancer therapies. We extensively reviewed experimental technologies, public data sources, and predictive models in the study of synthetic lethal gene pairs and herein detail biological assumptions, experimental data, statistical models, and computational schemes of various predictive models, speculate regarding their influence on individual sample- and population-based synthetic lethal interactions, discuss the pros and cons of existing SL data and models, and highlight potential research directions in SL discovery.

Pooled genetic screens with image-based profiling

Spatial structure in biology, spanning molecular, organellular, cellular, tissue, and organismal scales, is encoded through a combination of genetic and epigenetic factors in individual cells. Microscopy remains the most direct approach to exploring the intricate spatial complexity defining biological systems and the structured dynamic responses of these systems to perturbations. Genetic screens with deep single-cell profiling via image features or gene expression programs have the capacity to show how biological systems work in detail by cataloging many cellular phenotypes with one experimental assay. Microscopy-based cellular profiling provides information complementary to next-generation sequencing (NGS) profiling and has only recently become compatible with large-scale genetic screens. Optical screening now offers the scale needed for systematic characterization and is poised for further scale-up. We discuss how these methodologies, together with emerging technologies for genetic perturbation and microscopy-based multiplexed molecular phenotyping, are powering new approaches to reveal genotype-phenotype relationships.

Flavivirus-Host Interaction Landscape Visualized through Genome-Wide CRISPR Screens

Flaviviruses comprise several important human pathogens which cause significant morbidity and mortality worldwide. Like any other virus, they are obligate intracellular parasites. Therefore, studying the host cellular factors that promote or restrict their replication and pathogenesis becomes vital. Since inhibiting the host dependency factors or activating the host restriction factors can suppress the viral replication and propagation in the cell, identifying them reveals potential targets for antiviral therapeutics. Clustered regularly interspaced short palindromic repeats (CRISPR) technology has provided an effective means of producing customizable genetic modifications and performing forward genetic screens in a broad spectrum of cell types and organisms. The ease, rapidity, and high reproducibility of CRISPR technology have made it an excellent tool for carrying out genome-wide screens to identify and characterize viral host dependency factors systematically. Here, we review the insights from various Genome-wide CRISPR screens that have advanced our understanding of Flavivirus-Host interactions.

Multiplexed high-throughput localized electroporation workflow with deep learning-based analysis for cell engineering

Manipulation of cells for applications such as biomanufacturing and cell-based therapeutics involves introducing biomolecular cargoes into cells. However, successful delivery is a function of multiple experimental factors requiring several rounds of optimization. Here, we present a high-throughput multiwell-format localized electroporation device (LEPD) assisted by deep learning image analysis that enables quick optimization of experimental factors for efficient delivery. We showcase the versatility of the LEPD platform by successfully delivering biomolecules into different types of adherent and suspension cells. We also demonstrate multicargo delivery with tight dosage distribution and precise ratiometric control. Furthermore, we used the platform to achieve functional gene knockdown in human induced pluripotent stem cells and used the deep learning framework to analyze protein expression along with changes in cell morphology. Overall, we present a workflow that enables combinatorial experiments and rapid analysis for the optimization of intracellular delivery protocols required for genetic manipulation.

Covering the Combinatorial Design Space of Multiplex CRISPR/Cas Experiments in Plants

Over the past years, CRISPR/Cas-mediated genome editing has revolutionized plant genetic studies and crop breeding. Specifically, due to its ability to simultaneously target multiple genes, the multiplex CRISPR/Cas system has emerged as a powerful technology for functional analysis of genetic pathways. As such, it holds great potential for application in plant systems to discover genetic interactions and to improve polygenic agronomic traits in crop breeding. However, optimal experimental design regarding coverage of the combinatorial design space in multiplex CRISPR/Cas screens remains largely unexplored. To contribute to well-informed experimental design of such screens in plants, we first establish a representation of the design space at different stages of a multiplex CRISPR/Cas experiment. We provide two independent computational approaches yielding insights into the plant library size guaranteeing full coverage of all relevant multiplex combinations of gene knockouts in a specific multiplex CRISPR/Cas screen. These frameworks take into account several design parameters (e.g., the number of target genes, the number of gRNAs designed per gene, and the number of elements in the combinatorial array) and efficiencies at subsequent stages of a multiplex CRISPR/Cas experiment (e.g., the distribution of gRNA/Cas delivery, gRNA-specific mutation efficiency, and knockout efficiency). With this work, we intend to raise awareness about the limitations regarding the number of target genes and order of genetic interaction that can be realistically analyzed in multiplex CRISPR/Cas experiments with a given number of plants. Finally, we establish guidelines for designing multiplex CRISPR/Cas experiments with an optimal coverage of the combinatorial design space at minimal plant library size.

CRISPR screening in cancer stem cells

Cancer stem cells (CSCs) are a subpopulation of tumor cells with self-renewal ability. Increasing evidence points to the critical roles of CSCs in tumorigenesis, metastasis, therapy resistance, and cancer relapse. As such, the elimination of CSCs improves cancer treatment outcomes. However, challenges remain due to limited understanding of the molecular mechanisms governing self-renewal and survival of CSCs. Clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9 screening has been increasingly used to identify genetic determinants in cancers. In this primer, we discuss the progress made and emerging opportunities of coupling advanced CRISPR screening systems with CSC models to reveal the understudied vulnerabilities of CSCs.

CRISPR in cancer biology and therapy

Over the past decade, CRISPR has become as much a verb as it is an acronym, transforming biomedical research and providing entirely new approaches for dissecting all facets of cell biology. In cancer research, CRISPR and related tools have offered a window into previously intractable problems in our understanding of cancer genetics, the noncoding genome and tumour heterogeneity, and provided new insights into therapeutic vulnerabilities. Here, we review the progress made in the development of CRISPR systems as a tool to study cancer, and the emerging adaptation of these technologies to improve diagnosis and treatment.

A proteomics protocol to identify stimulation-induced binding partners dependent on a specific gene in mammalian cells

Some protein-protein interactions are induced by different kinds of stimulation and are dependent on specific genes. To identify these interaction partners, we present a protocol which utilizes affinity purification of Flag-tagged protein complexes followed by mass-spectrometry-based proteomics to compare stimulation-induced interactomes between wild-type and CRISPR-Cas9-mediated knockout cells. The candidates of interest are identified using bioinformatic analyses and verified by biochemical approaches. This protocol is highly versatile and applies to a variety of cells and different types of stimulation.

For complete details on the use and execution of this protocol, please refer to (Zhu et al., 2021).

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Protocol for identifying stimulation-dependent protein-protein interaction

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Protocol for identifying a specific gene-dependent interactome

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The protocol applies to a variety of cells and different types of stimulation

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Bioinformatic and biochemical analyses can help to exclude non-specific interactions

Facilitating Machine Learning-Guided Protein Engineering with Smart Library Design and Massively Parallel Assays

Protein design plays an important role in recent medical advances from antibody therapy to vaccine design. Typically, exhaustive mutational screens or directed evolution experiments are used for the identification of the best design or for improvements to the wild-type variant. Even with a high-throughput screening on pooled libraries and Next-Generation Sequencing to boost the scale of read-outs, surveying all the variants with combinatorial mutations for their empirical fitness scores is still of magnitudes beyond the capacity of existing experimental settings. To tackle this challenge, in-silico approaches using machine learning to predict the fitness of novel variants based on a subset of empirical measurements are now employed. These machine learning models turn out to be useful in many cases, with the premise that the experimentally determined fitness scores and the amino-acid descriptors of the models are informative. The machine learning models can guide the search for the highest fitness variants, resolve complex epistatic relationships, and highlight bio-physical rules for protein folding. Using machine learning-guided approaches, researchers can build more focused libraries, thus relieving themselves from labor-intensive screens and fast-tracking the optimization process. Here, we describe the current advances in massive-scale variant screens, and how machine learning and mutagenesis strategies can be integrated to accelerate protein engineering. More specifically, we examine strategies to make screens more economical, informative, and effective in discovery of useful variants.

CRISPR Screening: Molecular Tools for Studying Virus-Host Interactions

CRISPR/Cas is a powerful tool for studying the role of genes in viral infections. The invention of CRISPR screening technologies has made it possible to untangle complex interactions between the host and viral agents. Moreover, whole-genome and pathway-specific CRISPR screens have facilitated identification of novel drug candidates for treating viral infections. In this review, we highlight recent developments in the fields of CRISPR/Cas with a focus on the use of CRISPR screens for studying viral infections and identifying new candidate genes to aid development of antivirals.

A combinatorial CRISPR-Cas9 screen identifies ifenprodil as an adjunct to sorafenib for liver cancer treatment

Systematic testing of existing drugs and their combinations is an attractive strategy to exploit approved drugs for repurposing and identify the best actionable treatment options. To expedite the search among many possible drug combinations, we designed a combinatorial CRISPR-Cas9 screen to inhibit druggable targets. Co-blockade of the N-methyl-D-aspartate receptor (NMDAR) with targets of first-line kinase inhibitors reduced hepatocellular carcinoma (HCC) cell growth. Clinically, HCC patients with low NMDAR1 expression showed better survival. The clinically approved NMDAR antagonist ifenprodil synergized with sorafenib to induce the unfolded protein response, trigger cell cycle arrest, downregulate genes associated with WNT signaling and stemness, and reduce self-renewal ability of HCC cells. In multiple HCC patient-derived organoids and human tumor xenograft models, the drug combination, but neither single drug alone, markedly reduced tumor-initiating cancer cell frequency. Since ifenprodil has an established safety history for its use as a vasodilator in humans, our findings support the repurposing of this drug as an adjunct for HCC treatment to improve clinical outcome and reduce tumor recurrence. These results also validate an approach for readily discovering actionable combinations for cancer therapy.

Deciphering pathogenicity of variants of uncertain significance with CRISPR-edited iPSCs

Genetic variants play an important role in conferring risk for cardiovascular diseases (CVDs). With the rapid development of next-generation sequencing (NGS), thousands of genetic variants associated with CVDs have been identified by genome-wide association studies (GWAS), but the function of more than 40% of genetic variants is still unknown. This gap of knowledge is a barrier to the clinical application of the genetic information. However, determining the pathogenicity of a variant of uncertain significance (VUS) is challenging due to the lack of suitable model systems and accessible technologies. By combining clustered regularly interspaced short palindromic repeats (CRISPR) and human induced pluripotent stem cells (iPSCs), unprecedented advances are now possible in determining the pathogenicity of VUS in CVDs. Here, we summarize recent progress and new strategies in deciphering pathogenic variants for CVDs using CRISPR-edited human iPSCs.

Functional genomics approaches to improve pre-clinical drug screening and biomarker discovery

Advances in sequencing technology have enabled the genomic and transcriptomic characterization of human malignancies with unprecedented detail. However, this wealth of information has been slow to translate into clinically meaningful outcomes. Different models to study human cancers have been established and extensively characterized. Using these models, functional genomic screens and pre-clinical drug screening platforms have identified genetic dependencies that can be exploited with drug therapy. These genetic dependencies can also be used as biomarkers to predict response to treatment. For many cancers, the identification of such biomarkers remains elusive. In this review, we discuss the development and characterization of models used to study human cancers, RNA interference and CRISPR screens to identify genetic dependencies, large-scale pharmacogenomics studies and drug screening approaches to improve pre-clinical drug screening and biomarker discovery.

Neuronal Cell-type Engineering by Transcriptional Activation

Gene activation with the CRISPR-Cas system has great implications in studying gene function, controlling cellular behavior, and modulating disease progression. In this review, we survey recent studies on targeted gene activation and multiplexed screening for inducing neuronal differentiation using CRISPR-Cas transcriptional activation (CRISPRa) and open reading frame (ORF) expression. Critical technical parameters of CRISPRa and ORF-based strategies for neuronal programming are presented and discussed. In addition, recent progress on in vivo applications of CRISPRa to the nervous system are highlighted. Overall, CRISPRa represents a valuable addition to the experimental toolbox for neuronal cell-type programming.

Systematic Prediction of Antifungal Drug Synergy by Chemogenomic Screening in Saccharomyces cerevisiae

Since the earliest days of using natural remedies, combining therapies for disease treatment has been standard practice. Combination treatments exhibit synergistic effects, broadly defined as a greater-than-additive effect of two or more therapeutic agents. Clinicians often use their experience and expertise to tailor such combinations to maximize the therapeutic effect. Although understanding and predicting biophysical underpinnings of synergy have benefitted from high-throughput screening and computational studies, one challenge is how to best design and analyze the results of synergy studies, especially because the number of possible combinations to test quickly becomes unmanageable. Nevertheless, the benefits of such studies are clear-by combining multiple drugs in the treatment of infectious disease and cancer, for instance, one can lessen host toxicity and simultaneously reduce the likelihood of resistance to treatment. This study introduces a new approach to characterize drug synergy, in which we extend the widely validated chemogenomic HIP-HOP assay to drug combinations; this assay involves parallel screening of comprehensive collections of barcoded deletion mutants. We identify a class of "combination-specific sensitive strains" that introduces mechanisms for the synergies we observe and further suggest focused follow-up studies.

CRISPR Screens in Toxicology Research: An Overview

The use of genome editing tools is expanding our understanding of various human diseases by providing insight into gene-disease interactions. Despite the recognized role of toxicants in the development of human health issues and conditions, there is currently limited characterization of their mechanisms of action, and the application of CRISPR-based genome editing to the study of toxicants could help in the identification of novel gene-environment interactions. CRISPR-based functional screens enable identification of cellular mechanisms fundamental for response and susceptibility to a given toxicant. The aim of this review is to inform future directions in the application of CRISPR technologies in toxicological studies. We review and compare different types of CRISPR-based methods including pooled, anchored, combinatorial, and perturb-sequencing screens in vitro, in addition to pooled screenings in model organisms. © 2021 Wiley Periodicals LLC.

Extensible combinatorial CRISPR screening in mammalian cells

The CRISPR-Cas system coupled with Combinatorial Genetics En Masse (CombiGEM) enables systematic analysis of high-order genetic perturbations that are important for understanding biological processes and discovering therapeutic target combinations. Here, we present detailed steps and technical considerations for building multiplexed guide RNA libraries and carrying out a combinatorial CRISPR screen in mammalian cells. We also present an analytical pipeline, CombiPIPE, for mapping two- and three-way genetic interactions. For complete details on the use and execution of this protocol, please refer to Zhou et al. (2020).