Pooled genetic perturbation screens with image-based phenotypes

A genome-wide atlas of human cell morphology

A key challenge of the modern genomics era is developing empirical data-driven representations of gene function. Here we present the first unbiased morphology-based genome-wide perturbation atlas in human cells, containing three genome-wide genotype-phenotype maps comprising CRISPR-Cas9-based knockouts of >20,000 genes in >30 million cells. Our optical pooled cell profiling platform (PERISCOPE) combines a destainable high-dimensional phenotyping panel (based on Cell Painting) with optical sequencing of molecular barcodes and a scalable open-source analysis pipeline to facilitate massively parallel screening of pooled perturbation libraries. This perturbation atlas comprises high-dimensional phenotypic profiles of individual cells with sufficient resolution to cluster thousands of human genes, reconstruct known pathways and protein-protein interaction networks, interrogate subcellular processes and identify culture media-specific responses. Using this atlas, we identify the poorly characterized disease-associated TMEM251/LYSET as a Golgi-resident transmembrane protein essential for mannose-6-phosphate-dependent trafficking of lysosomal enzymes. In sum, this perturbation atlas and screening platform represents a rich and accessible resource for connecting genes to cellular functions at scale.

Attention-based deep learning for accurate cell image analysis

High-content analysis (HCA) holds enormous potential for drug discovery and research, but widely used methods can be cumbersome and yield inaccurate results. Noisy and redundant signals in cell images impede accurate deep learning-based image analysis. To address these issues, we introduce X-Profiler, a novel HCA method that combines cellular experiments, image processing, and deep learning modeling. X-Profiler combines the convolutional neural network and Transformer to encode high-content images, effectively filtering out noisy signals and precisely characterizing cell phenotypes. In comparative tests on drug-induced cardiotoxicity, mitochondrial toxicity classification, and compound classification, X-Profiler outperformed both DeepProfiler and CellProfiler, as two highly recognized and representative methods in this field. Our results demonstrate the utility and versatility of X-Profiler, and we anticipate its wide application in HCA for advancing drug development and disease research.

Classification and functional characterization of regulators of intracellular STING trafficking identified by genome-wide optical pooled screening

Stimulator of interferon genes (STING) traffics across intracellular compartments to trigger innate responses. Mutations in factors regulating this process lead to inflammatory disorders. To systematically identify factors involved in STING trafficking, we performed a genome-wide optical pooled screen (OPS). Based on the subcellular localization of STING in 45 million cells, we defined 464 clusters of gene perturbations based on their cellular phenotypes. A secondary, higher-dimensional OPS identified 73 finer clusters. We show that the loss of the gene of unknown function C19orf25, which clustered with USE1, a protein involved in Golgi-to-endoplasmic reticulum (ER) transport, enhances STING signaling. Additionally, HOPS deficiency delayed STING degradation and consequently increased signaling. Similarly, GARP/RIC1-RGP1 loss increased STING signaling by delaying STING Golgi exit. Our findings demonstrate that genome-wide genotype-phenotype maps based on high-content cell imaging outperform other screening approaches and provide a community resource for mining factors that impact STING trafficking and other cellular processes.

How to build the virtual cell with artificial intelligence: Priorities and opportunities

Cells are essential to understanding health and disease, yet traditional models fall short of modeling and simulating their function and behavior. Advances in AI and omics offer groundbreaking opportunities to create an AI virtual cell (AIVC), a multi-scale, multi-modal large-neural-network-based model that can represent and simulate the behavior of molecules, cells, and tissues across diverse states. This Perspective provides a vision on their design and how collaborative efforts to build AIVCs will transform biological research by allowing high-fidelity simulations, accelerating discoveries, and guiding experimental studies, offering new opportunities for understanding cellular functions and fostering interdisciplinary collaborations in open science.

How to Build the Virtual Cell with Artificial Intelligence: Priorities and Opportunities

The cell is arguably the most fundamental unit of life and is central to understanding biology. Accurate modeling of cells is important for this understanding as well as for determining the root causes of disease. Recent advances in artificial intelligence (AI), combined with the ability to generate large-scale experimental data, present novel opportunities to model cells. Here we propose a vision of leveraging advances in AI to construct virtual cells, high-fidelity simulations of cells and cellular systems under different conditions that are directly learned from biological data across measurements and scales. We discuss desired capabilities of such AI Virtual Cells, including generating universal representations of biological entities across scales, and facilitating interpretable in silico experiments to predict and understand their behavior using Virtual Instruments. We further address the challenges, opportunities and requirements to realize this vision including data needs, evaluation strategies, and community standards and engagement to ensure biological accuracy and broad utility. We envision a future where AI Virtual Cells help identify new drug targets, predict cellular responses to perturbations, as well as scale hypothesis exploration. With open science collaborations across the biomedical ecosystem that includes academia, philanthropy, and the biopharma and AI industries, a comprehensive predictive understanding of cell mechanisms and interactions has come into reach.

Multiplexed, image-based pooled screens in primary cells and tissues with PerturbView

Optical pooled screening (OPS) is a scalable method for linking image-based phenotypes with cellular perturbations. However, it has thus far been restricted to relatively low-plex phenotypic readouts in cancer cell lines in culture due to limitations associated with in situ sequencing of perturbation barcodes. Here, we develop PerturbView, an OPS technology that leverages in vitro transcription to amplify barcodes before in situ sequencing, enabling screens with highly multiplexed phenotypic readouts across diverse systems, including primary cells and tissues. We demonstrate PerturbView in induced pluripotent stem cell-derived neurons, primary immune cells and tumor tissue sections from animal models. In a screen of immune signaling pathways in primary bone marrow-derived macrophages, PerturbView uncovered both known and novel regulators of NF-κB signaling. Furthermore, we combine PerturbView with spatial transcriptomics in tissue sections from a mouse xenograft model, paving the way to in situ screens with rich optical and transcriptomic phenotypes. PerturbView broadens the scope of OPS to a wide range of models and applications.

Mapping multimodal phenotypes to perturbations in cells and tissue with CRISPRmap

Unlike sequencing-based methods, which require cell lysis, optical pooled genetic screens enable investigation of spatial phenotypes, including cell morphology, protein subcellular localization, cell-cell interactions and tissue organization, in response to targeted CRISPR perturbations. Here we report a multimodal optical pooled CRISPR screening method, which we call CRISPRmap. CRISPRmap combines in situ CRISPR guide-identifying barcode readout with multiplexed immunofluorescence and RNA detection. Barcodes are detected and read out through combinatorial hybridization of DNA oligos, enhancing barcode detection efficiency. CRISPRmap enables in situ barcode readout in cell types and contexts that were elusive to conventional optical pooled screening, including cultured primary cells, embryonic stem cells, induced pluripotent stem cells, derived neurons and in vivo cells in a tissue context. We conducted a screen in a breast cancer cell line of the effects of DNA damage repair gene variants on cellular responses to commonly used cancer therapies, and we show that optical phenotyping pinpoints likely pathogenic patient-derived mutations that were previously classified as variants of unknown clinical significance.

High-throughput optimized prime editing mediated endogenous protein tagging for pooled imaging of protein localization

The subcellular organization of proteins carries important information on cellular state and gene function, yet currently there are no technologies that enable accurate measurement of subcellular protein localizations at scale. Here we develop an approach for pooled endogenous protein tagging using prime editing, which coupled with an optical readout and sequencing, provides a snapshot of proteome organization in a manner akin to perturbation-based CRISPR screens. We constructed a pooled library of 17,280 pegRNAs designed to exhaustively tag 60 endogenous proteins spanning diverse localization patterns and explore a large space of genomic and pegRNA design parameters. Pooled measurements of tagging efficiency uncovered both genomic and pegRNA features associated with increased efficiency, including epigenetic states and interactions with transcription. We integrate pegRNA features into a computational model with predictive value for tagging efficiency to constrain the design space of pegRNAs for large-scale peptide knock-in. Lastly, we show that combining in-situ pegRNA sequencing with high-throughput deep learning image analysis, enables exploration of subcellular protein localization patterns for many proteins in parallel following a single pooled lentiviral transduction, setting the stage for scalable studies of proteome dynamics across cell types and environmental perturbations.

Toward a foundation model of causal cell and tissue biology with a Perturbation Cell and Tissue Atlas

Comprehensively charting the biologically causal circuits that govern the phenotypic space of human cells has often been viewed as an insurmountable challenge. However, in the last decade, a suite of interleaved experimental and computational technologies has arisen that is making this fundamental goal increasingly tractable. Pooled CRISPR-based perturbation screens with high-content molecular and/or image-based readouts are now enabling researchers to probe, map, and decipher genetically causal circuits at increasing scale. This scale is now eminently suitable for the deployment of artificial intelligence and machine learning (AI/ML) to both direct further experiments and to predict or generate information that was not-and sometimes cannot-be gathered experimentally. By combining and iterating those through experiments that are designed for inference, we now envision a Perturbation Cell Atlas as a generative causal foundation model to unify human cell biology.

Applications of single-cell technologies in drug discovery for tumor treatment

Single-cell technologies have been known as advanced and powerful tools to study tumor biological systems at the single-cell resolution and are playing increasingly critical roles in multiple stages of drug discovery and development. Specifically, single-cell technologies can promote the discovery of drug targets, help high-throughput screening at single-cell level, and contribute to pharmacokinetic studies of anti-tumor drugs. Emerging single-cell analysis technologies have been developed to further integrating multidimensional single-cell molecular features, expanding the scale of single-cell data, profiling phenotypic impact of genes in single cell, and providing full-length coverage single-cell sequencing. In this review, we systematically summarized the applications of single-cell technologies in various sections of drug discovery for tumor treatment, including target identification, high-throughput drug screening, and pharmacokinetic evaluation and highlighted emerging single-cell technologies in providing in-depth understanding of tumor biology. Single-cell-technology-based drug discovery is expected to further optimize therapeutic strategies and improve clinical outcomes of tumor patients.

Accessible high-speed image-activated cell sorting

Over the past six decades, fluorescence-activated cell sorting (FACS) has become an essential technology for basic and clinical research by enabling the isolation of cells of interest in high throughput. Recent technological advancements have started a new era of flow cytometry. By combining the spatial resolution of microscopy with high-speed cell sorting, new instruments allow cell sorting based on simple image-derived parameters or sophisticated image analysis algorithms, thereby greatly expanding the scope of applications. In this review, we discuss the systems that are commercially available or have been described in enough methodological and engineering detail to allow their replication. We summarize their strengths and limitations and highlight applications that have the potential to transform various fields in basic life science research and clinical settings.

How CRISPR Is Revolutionizing the Generation of New Models for Cancer Research

Cancers arise through acquisition of mutations in genes that regulate core biological processes like cell proliferation and cell death. Decades of cancer research have led to the identification of genes and mutations causally involved in disease development and evolution, yet defining their precise function across different cancer types and how they influence therapy responses has been challenging. Mouse models have helped define the in vivo function of cancer-associated alterations, and genome-editing approaches using CRISPR have dramatically accelerated the pace at which these models are developed and studied. Here, we highlight how CRISPR technologies have impacted the development and use of mouse models for cancer research and discuss the many ways in which these rapidly evolving platforms will continue to transform our understanding of this disease.

Classification and functional characterization of regulators of intracellular STING trafficking identified by genome-wide optical pooled screening

STING is an innate immune sensor that traffics across many cellular compartments to carry out its function of detecting cyclic di-nucleotides and triggering defense processes. Mutations in factors that regulate this process are often linked to STING-dependent human inflammatory disorders. To systematically identify factors involved in STING trafficking, we performed a genome-wide optical pooled screen and examined the impact of genetic perturbations on intracellular STING localization. Based on subcellular imaging of STING protein and trafficking markers in 45 million cells perturbed with sgRNAs, we defined 464 clusters of gene perturbations with similar cellular phenotypes. A higher-dimensional focused optical pooled screen on 262 perturbed genes which assayed 11 imaging channels identified 73 finer phenotypic clusters. In a cluster containing USE1, a protein that mediates Golgi to ER transport, we found a gene of unknown function, C19orf25. Consistent with the known role of USE1, loss of C19orf25 enhanced STING signaling. Other clusters contained subunits of the HOPS, GARP and RIC1-RGP1 complexes. We show that HOPS deficiency delayed STING degradation and consequently increased signaling. Similarly, GARP/RIC1-RGP1 loss increased STING signaling by delaying STING exit from the Golgi. Our findings demonstrate that genome-wide genotype-phenotype maps based on high-content cell imaging outperform other screening approaches, and provide a community resource for mining for factors that impact STING trafficking as well as other cellular processes observable in our dataset.

Single-cell image-based genetic screens systematically identify regulators of Ebola virus subcellular infection dynamics

Ebola virus (EBOV) is a high-consequence filovirus that gives rise to frequent epidemics with high case fatality rates and few therapeutic options. Here, we applied image-based screening of a genome-wide CRISPR library to systematically identify host cell regulators of Ebola virus infection in 39,085,093 million single cells. Measuring viral RNA and protein levels together with their localization in cells identified over 998 related host factors and provided detailed information about the role of each gene across the virus replication cycle. We trained a deep learning model on single-cell images to associate each host factor with predicted replication steps, and confirmed the predicted relationship for select host factors. Among the findings, we showed that the mitochondrial complex III subunit UQCRB is a post-entry regulator of Ebola virus RNA replication, and demonstrated that UQCRB inhibition with a small molecule reduced overall Ebola virus infection with an IC50 of 5 μM. Using a random forest model, we also identified perturbations that reduced infection by disrupting the equilibrium between viral RNA and protein. One such protein, STRAP, is a spliceosome-associated factor that was found to be closely associated with VP35, a viral protein required for RNA processing. Loss of STRAP expression resulted in a reduction in full-length viral genome production and subsequent production of non-infectious virus particles. Overall, the data produced in this genome-wide high-content single-cell screen and secondary screens in additional cell lines and related filoviruses (MARV and SUDV) revealed new insights about the role of host factors in virus replication and potential new targets for therapeutic intervention.

CROPseq-multi: a versatile solution for multiplexed perturbation and decoding in pooled CRISPR screens

Forward genetic screens seek to dissect complex biological systems by systematically perturbing genetic elements and observing the resulting phenotypes. While standard screening methodologies introduce individual perturbations, multiplexing perturbations improves the performance of single-target screens and enables combinatorial screens for the study of genetic interactions. Current tools for multiplexing perturbations are incompatible with pooled screening methodologies that require mRNA-embedded barcodes, including some microscopy and single cell sequencing approaches. Here, we report the development of CROPseq-multi, a CROPseq1-inspired lentiviral system to multiplex Streptococcus pyogenes (Sp) Cas9-based perturbations with mRNA-embedded barcodes. CROPseq-multi has equivalent per-guide activity to CROPseq and low lentiviral recombination frequencies. CROPseq-multi is compatible with enrichment screening methodologies and optical pooled screens, and is extensible to screens with single-cell sequencing readouts. For optical pooled screens, an optimized and multiplexed in situ detection protocol improves barcode detection efficiency 10-fold, enables detection of recombination events, and increases decoding efficiency 3-fold relative to CROPseq. CROPseq-multi is a widely applicable multiplexing solution for diverse SpCas9-based genetic screening approaches.

Simultaneous CRISPR screening and spatial transcriptomics reveals intracellular, intercellular, and functional transcriptional circuits

Pooled optical screens have enabled the study of cellular interactions, morphology, or dynamics at massive scale, but have not yet leveraged the power of highly-plexed single-cell resolved transcriptomic readouts to inform molecular pathways. Here, we present Perturb-FISH, which bridges these approaches by combining imaging spatial transcriptomics with parallel optical detection of in situ amplified guide RNAs. We show that Perturb-FISH recovers intracellular effects that are consistent with Perturb-seq results in a screen of lipopolysaccharide response in cultured monocytes, and uncover new intercellular and density-dependent regulation of the innate immune response. We further pair Perturb-FISH with a functional readout in a screen of autism spectrum disorder risk genes, showing common calcium activity phenotypes in induced pluripotent stem cell derived astrocytes and their associated genetic interactions and dysregulated molecular pathways. Perturb-FISH is thus a generally applicable method for studying the genetic and molecular associations of spatial and functional biology at single-cell resolution.

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.

High throughput microscopy and single cell phenotypic image-based analysis in toxicology and drug discovery

Measuring single cell responses to the universe of chemicals (drugs, natural products, environmental toxicants etc.) is of paramount importance to human health as phenotypic variability in sensing stimuli is a hallmark of biology that is considered during high throughput screening. One of the ways to approach this problem is via high throughput, microscopy-based assays coupled with multi-dimensional single cell analysis methods. Here, we will summarize some of the efforts in this vast and growing field, focusing on phenotypic screens (e.g., Cell Painting), single cell analytics and quality control, with particular attention to environmental toxicology and drug screening. We will discuss advantages and limitations of high throughput assays with various end points and levels of complexity.

A genome-wide atlas of human cell morphology

A key challenge of the modern genomics era is developing data-driven representations of gene function. Here, we present the first unbiased morphology-based genome-wide perturbation atlas in human cells, containing three genome-scale genotype-phenotype maps comprising >20,000 single-gene CRISPR-Cas9-based knockout experiments in >30 million cells. Our optical pooled cell profiling approach (PERISCOPE) combines a de-stainable high-dimensional phenotyping panel (based on Cell Painting1,2) with optical sequencing of molecular barcodes and a scalable open-source analysis pipeline to facilitate massively parallel screening of pooled perturbation libraries. This approach provides high-dimensional phenotypic profiles of individual cells, while simultaneously enabling interrogation of subcellular processes. Our atlas reconstructs known pathways and protein-protein interaction networks, identifies culture media-specific responses to gene knockout, and clusters thousands of human genes by phenotypic similarity. Using this atlas, we identify the poorly-characterized disease-associated transmembrane protein TMEM251/LYSET as a Golgi-resident protein essential for mannose-6-phosphate-dependent trafficking of lysosomal enzymes, showing the power of these representations. In sum, our atlas and screening technology represent a rich and accessible resource for connecting genes to cellular functions at scale.

Functional genomics in stem cell models: considerations and applications

Protocols to differentiate human pluripotent stem cells have advanced in terms of cell type specificity and tissue-level complexity over the past 2 decades, which has facilitated human disease modeling in the most relevant cell types. The ability to generate induced PSCs (iPSCs) from patients further enables the study of disease mutations in an appropriate cellular context to reveal the mechanisms that underlie disease etiology and progression. As iPSC-derived disease models have improved in robustness and scale, they have also been adopted more widely for use in drug screens to discover new therapies and therapeutic targets. Advancement in genome editing technologies, in particular the discovery of CRISPR-Cas9, has further allowed for rapid development of iPSCs containing disease-causing mutations. CRISPR-Cas9 technologies have now evolved beyond creating single gene edits, aided by the fusion of inhibitory (CRISPRi) or activation (CRISPRa) domains to a catalytically dead Cas9 protein, enabling inhibition or activation of endogenous gene loci. These tools have been used in CRISPR knockout, CRISPRi, or CRISPRa screens to identify genetic modifiers that synergize or antagonize with disease mutations in a systematic and unbiased manner, resulting in identification of disease mechanisms and discovery of new therapeutic targets to accelerate drug discovery research. However, many technical challenges remain when applying large-scale functional genomics approaches to differentiated PSC populations. Here we review current technologies in the field of iPSC disease modeling and CRISPR-based functional genomics screens and practical considerations for implementation across a range of modalities, applications, and disease areas, as well as explore CRISPR screens that have been performed in iPSC models to-date and the insights and therapies these screens have produced.

Pooled tagging and hydrophobic targeting of endogenous proteins for unbiased mapping of unfolded protein responses

System-level understanding of proteome organization and function requires methods for direct visualization and manipulation of proteins at scale. We developed an approach enabled by high-throughput gene tagging for the generation and analysis of complex cell pools with endogenously tagged proteins. Proteins are tagged with HaloTag to enable visualization or direct perturbation. Fluorescent labeling followed by in situ sequencing and deep learning-based image analysis identifies the localization pattern of each tag, providing a bird's-eye-view of cellular organization. Next, we use a hydrophobic HaloTag ligand to misfold tagged proteins, inducing spatially restricted proteotoxic stress that is read out by single cell RNA sequencing. By integrating optical and perturbation data, we map compartment-specific responses to protein misfolding, revealing inter-compartment organization and direct crosstalk, and assigning proteostasis functions to uncharacterized genes. Altogether, we present a powerful and efficient method for large-scale studies of proteome dynamics, function, and homeostasis.

Technological Convergence: Highlighting the Power of CRISPR Single-Cell Perturbation Toolkit for Functional Interrogation of Enhancers

Higher eukaryotic enhancers, as a major class of regulatory elements, play a crucial role in the regulation of gene expression. Over the last decade, the development of sequencing technologies has flooded researchers with transcriptome-phenotype data alongside emerging candidate regulatory elements. Since most methods can only provide hints about enhancer function, there have been attempts to develop experimental and computational approaches that can bridge the gap in the causal relationship between regulatory regions and phenotypes. The coupling of two state-of-the-art technologies, also referred to as crisprQTL, has emerged as a promising high-throughput toolkit for addressing this question. This review provides an overview of the importance of studying enhancers, the core molecular foundation of crisprQTL, and recent studies utilizing crisprQTL to interrogate enhancer-phenotype correlations. Additionally, we discuss computational methods currently employed for crisprQTL data analysis. We conclude by pointing out common challenges, making recommendations, and looking at future prospects, with the aim of providing researchers with an overview of crisprQTL as an important toolkit for studying enhancers.

Unbiased single-cell morphology with self-supervised vision transformers

Accurately quantifying cellular morphology at scale could substantially empower existing single-cell approaches. However, measuring cell morphology remains an active field of research, which has inspired multiple computer vision algorithms over the years. Here, we show that DINO, a vision-transformer based, self-supervised algorithm, has a remarkable ability for learning rich representations of cellular morphology without manual annotations or any other type of supervision. We evaluate DINO on a wide variety of tasks across three publicly available imaging datasets of diverse specifications and biological focus. We find that DINO encodes meaningful features of cellular morphology at multiple scales, from subcellular and single-cell resolution, to multi-cellular and aggregated experimental groups. Importantly, DINO successfully uncovers a hierarchy of biological and technical factors of variation in imaging datasets. The results show that DINO can support the study of unknown biological variation, including single-cell heterogeneity and relationships between samples, making it an excellent tool for image-based biological discovery.

Large-scale F0 CRISPR screens in vivo using MIC-Drop

The zebrafish is a powerful model system for studying animal development, for modeling genetic diseases, and for large-scale in vivo functional genetics. Because of its ease of use and its high efficiency in targeted gene perturbation, CRISPR-Cas9 has recently gained prominence as the tool of choice for genetic manipulation in zebrafish. However, scaling up the technique for high-throughput in vivo functional genetics has been a challenge. We recently developed a method, Multiplexed Intermixed CRISPR Droplets (MIC-Drop), that makes large-scale CRISPR screening in zebrafish possible. Here, we outline the step-by-step protocol for performing functional genetic screens in zebrafish by using MIC-Drop. MIC-Drop uses multiplexed single-guide RNAs to generate biallelic mutations in injected zebrafish embryos, allowing genetic screens to be performed in F0 animals. Combining microfluidics and DNA barcoding enables simultaneous targeting of tens to hundreds of genes from a single injection needle, while also enabling retrospective and rapid identification of the genotype responsible for an observed phenotype. The primary target audiences for MIC-Drop are developmental biologists, zebrafish geneticists, and researchers interested in performing in vivo functional genetic screens in a vertebrate model system. MIC-Drop will also prove useful in the hands of chemical biologists seeking to identify targets of small molecules that cause phenotypic changes in zebrafish. By using MIC-Drop, a typical screen of 100 genes can be conducted within 2-3 weeks by a single user.

A genome-wide optical pooled screen reveals regulators of cellular antiviral responses

The infection of mammalian cells by viruses and innate immune responses to infection are spatiotemporally organized processes. Cytosolic RNA sensors trigger nuclear translocation of the transcription factor interferon regulatory factor 3 (IRF3) and consequent induction of host immune responses to RNA viruses. Previous genetic screens for factors involved in viral sensing did not resolve changes in the subcellular localization of host or viral proteins. Here, we increased the throughput of our optical pooled screening technology by over fourfold. This allowed us to carry out a genome-wide CRISPR knockout screen using high-resolution multiparameter imaging of cellular responses to Sendai virus infection coupled with in situ cDNA sequencing by synthesis (SBS) to identify 80,408 single guide RNAs (sgRNAs) in 10,366,390 cells-over an order of magnitude more genomic perturbations than demonstrated previously using an in situ SBS readout. By ranking perturbations using human-designed and deep learning image feature scores, we identified regulators of IRF3 translocation, Sendai virus localization, and peroxisomal biogenesis. Among the hits, we found that ATP13A1, an ER-localized P5A-type ATPase, is essential for viral sensing and is required for targeting of mitochondrial antiviral signaling protein (MAVS) to mitochondrial membranes where MAVS must be localized for effective signaling through retinoic acid-inducible gene I (RIG-I). The ability to carry out genome-wide pooled screens with complex high-resolution image-based phenotyping dramatically expands the scope of functional genomics approaches.

Massively Parallel CRISPR-Based Genetic Perturbation Screening at Single-Cell Resolution

The clustered regularly interspaced short palindromic repeats (CRISPR)-based genetic screening has been demonstrated as a powerful approach for unbiased functional genomics research. Single-cell CRISPR screening (scCRISPR) techniques, which result from the combination of single-cell toolkits and CRISPR screening, allow dissecting regulatory networks in complex biological systems at unprecedented resolution. These methods allow cells to be perturbed en masse using a pooled CRISPR library, followed by high-content phenotyping. This is technically accomplished by annotating cells with sgRNA-specific barcodes or directly detectable sgRNAs. According to the integration of distinct single-cell technologies, these methods principally fall into four categories: scCRISPR with RNA-seq, scCRISPR with ATAC-seq, scCRISPR with proteome probing, and imaging-based scCRISPR. scCRISPR has deciphered genotype-phenotype relationships, genetic regulations, tumor biological issues, and neuropathological mechanisms. This review provides insight into the technical breakthrough of scCRISPR by systematically summarizing the advancements of various scCRISPR methodologies and analyzing their merits and limitations. In addition, an application-oriented approach guide is offered to meet researchers' individualized demands.

The phenotypic landscape of essential human genes

Understanding the basis for cellular growth, proliferation, and function requires determining the roles of essential genes in diverse cellular processes, including visualizing their contributions to cellular organization and morphology. Here, we combined pooled CRISPR-Cas9-based functional screening of 5,072 fitness-conferring genes in human HeLa cells with microscopy-based imaging of DNA, the DNA damage response, actin, and microtubules. Analysis of >31 million individual cells identified measurable phenotypes for >90% of gene knockouts, implicating gene targets in specific cellular processes. Clustering of phenotypic similarities based on hundreds of quantitative parameters further revealed co-functional genes across diverse cellular activities, providing predictions for gene functions and associations. By conducting pooled live-cell screening of ∼450,000 cell division events for 239 genes, we additionally identified diverse genes with functional contributions to chromosome segregation. Our work establishes a resource detailing the consequences of disrupting core cellular processes that represents the functional landscape of essential human genes.

High-content CRISPR screening in tumor immunology

CRISPR screening is a powerful tool that links specific genetic alterations to corresponding phenotypes, thus allowing for high-throughput identification of novel gene functions. Pooled CRISPR screens have enabled discovery of innate and adaptive immune response regulators in the setting of viral infection and cancer. Emerging methods couple pooled CRISPR screens with parallel high-content readouts at the transcriptomic, epigenetic, proteomic, and optical levels. These approaches are illuminating cancer immune evasion mechanisms as well as nominating novel targets that augment T cell activation, increase T cell infiltration into tumors, and promote enhanced T cell cytotoxicity. This review details recent methodological advances in high-content CRISPR screens and highlights the impact this technology is having on tumor immunology.

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.

Pooled image-base screening of mitochondria with microraft isolation distinguishes pathogenic mitofusin 2 mutations

Most human genetic variation is classified as variants of uncertain significance. While advances in genome editing have allowed innovation in pooled screening platforms, many screens deal with relatively simple readouts (viability, fluorescence) and cannot identify the complex cellular phenotypes that underlie most human diseases. In this paper, we present a generalizable functional genomics platform that combines high-content imaging, machine learning, and microraft isolation in a method termed "Raft-Seq". We highlight the efficacy of our platform by showing its ability to distinguish pathogenic point mutations of the mitochondrial regulator Mitofusin 2, even when the cellular phenotype is subtle. We also show that our platform achieves its efficacy using multiple cellular features, which can be configured on-the-fly. Raft-Seq enables a way to perform pooled screening on sets of mutations in biologically relevant cells, with the ability to physically capture any cell with a perturbed phenotype and expand it clonally, directly from the primary screen.