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• W2938408085 abstract "•A universal and scalable genetic platform in hPSCs for general use across all lineages•Robust knockout efficiencies translate into high-performance screening at genome scale•Stem cell-specific components of TP53 and OCT4 genetic networks in hPSCs are identified•Validation of PMAIP1 and PAWR function in sensitivity to DNA damage or dissociation Human pluripotent stem cells (hPSCs) generate a variety of disease-relevant cells that can be used to improve the translation of preclinical research. Despite the potential of hPSCs, their use for genetic screening has been limited by technical challenges. We developed a scalable and renewable Cas9 and sgRNA-hPSC library in which loss-of-function mutations can be induced at will. Our inducible mutant hPSC library can be used for multiple genome-wide CRISPR screens in a variety of hPSC-induced cell types. As proof of concept, we performed three screens for regulators of properties fundamental to hPSCs: their ability to self-renew and/or survive (fitness), their inability to survive as single-cell clones, and their capacity to differentiate. We identified the majority of known genes and pathways involved in these processes, as well as a plethora of genes with unidentified roles. This resource will increase the understanding of human development and genetics. This approach will be a powerful tool to identify disease-modifying genes and pathways. Human pluripotent stem cells (hPSCs) generate a variety of disease-relevant cells that can be used to improve the translation of preclinical research. Despite the potential of hPSCs, their use for genetic screening has been limited by technical challenges. We developed a scalable and renewable Cas9 and sgRNA-hPSC library in which loss-of-function mutations can be induced at will. Our inducible mutant hPSC library can be used for multiple genome-wide CRISPR screens in a variety of hPSC-induced cell types. As proof of concept, we performed three screens for regulators of properties fundamental to hPSCs: their ability to self-renew and/or survive (fitness), their inability to survive as single-cell clones, and their capacity to differentiate. We identified the majority of known genes and pathways involved in these processes, as well as a plethora of genes with unidentified roles. This resource will increase the understanding of human development and genetics. This approach will be a powerful tool to identify disease-modifying genes and pathways. Human pluripotent stem cells (hPSCs) can be used to generate a wide variety of disease-relevant cell types and have the potential to improve the translation of preclinical research by enhancing disease models. Despite the huge potential, genetic screening using hPSCs has been limited by their expensive and tedious cell culture requirements (Chen et al., 2011Chen G. Gulbranson D.R. Hou Z. Bolin J.M. Ruotti V. Probasco M.D. Smuga-Otto K. Howden S.E. Diol N.R. Propson N.E. et al.Chemically defined conditions for human iPSC derivation and culture.Nat. Methods. 2011; 8: 424-429Crossref PubMed Scopus (964) Google Scholar) and reduced genetic manipulation efficiencies (Ihry et al., 2018Ihry R.J. Worringer K.A. Salick M.R. Frias E. Ho D. Theriault K. Kommineni S. Chen J. Sondey M. Ye C. et al.p53 inhibits CRISPR-Cas9 engineering in human pluripotent stem cells.Nat. Med. 2018; 24: 939-946Crossref PubMed Scopus (512) Google Scholar). Only a few short hairpin RNA (shRNA) screens have been conducted in hPSCs (Chia et al., 2010Chia N.Y. Chan Y.S. Feng B. Lu X. Orlov Y.L. Moreau D. Kumar P. Yang L. Jiang J. Lau M.S. et al.A genome-wide RNAi screen reveals determinants of human embryonic stem cell identity.Nature. 2010; 468: 316-320Crossref PubMed Scopus (365) Google Scholar, Zhang et al., 2013Zhang Y. Schulz V.P. Reed B.D. Wang Z. Pan X. Mariani J. Euskirchen G. Snyder M.P. Vaccarino F.M. Ivanova N. et al.Functional genomic screen of human stem cell differentiation reveals pathways involved in neurodevelopment and neurodegeneration.Proc. Natl. Acad. Sci. U S A. 2013; 110: 12361-12366Crossref PubMed Scopus (19) Google Scholar), but shRNAs have a high level of off targets and do not cause a complete loss of function, which is difficult to interpret (DasGupta et al., 2005DasGupta R. Kaykas A. Moon R. Perrimon N. Functional genomic analysis of the Wnt-wingless signaling pathway.Science. 2005; 308: 826-833Crossref PubMed Scopus (288) Google Scholar, Echeverri et al., 2006Echeverri C.J. Beachy P.A. Baum B. Boutros M. Buchholz F. Chanda S.K. Downward J. Ellenberg J. Fraser A.G. Hacohen N. et al.Minimizing the risk of reporting false positives in large-scale RNAi screens.Nat. Methods. 2006; 3: 777-779Crossref PubMed Scopus (366) Google Scholar, Kampmann et al., 2015Kampmann M. Horlbeck M.A. Chen Y. Tsai J.C. Bassik M.C. Gilbert L.A. Villalta J.E. Kwon S.C. Chang H. Kim V.N. Weissman J.S. Next-generation libraries for robust RNA interference-based genome-wide screens.Proc. Natl. Acad. Sci. U S A. 2015; 112: E3384-E3391Crossref PubMed Scopus (68) Google Scholar, McDonald et al., 2017McDonald 3rd, E.R. de Weck A. Schlabach M.R. Billy E. Mavrakis K.J. Hoffman G.R. Belur D. Castelletti D. Frias E. Gampa K. et al.Project DRIVE: a compendium of cancer dependencies and synthetic lethal relationships uncovered by large-scale, deep RNAi screening.Cell. 2017; 170: 577-592.e10Abstract Full Text Full Text PDF PubMed Scopus (348) Google Scholar). Currently, the CRISPR/Cas9 system is the genetic screening tool of choice because it can efficiently cause loss-of-function alleles (Jinek et al., 2012Jinek M. Chylinski K. Fonfara I. Hauer M. Doudna J.A. Charpentier E. A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity.Science. 2012; 337: 816-821Crossref PubMed Scopus (9424) Google Scholar, Cong et al., 2013Cong L. Ran F.A. Cox D. Lin S. Barretto R. Habib N. Hsu P.D. Wu X. Jiang W. Marraffini L.A. Zhang F. Multiplex genome engineering using CRISPR/Cas systems.Science. 2013; 339: 819-823Crossref PubMed Scopus (10063) Google Scholar, Mali et al., 2013Mali P. Yang L. Esvelt K.M. Aach J. Guell M. DiCarlo J.E. Norville J.E. Church G.M. RNA-guided human genome engineering via Cas9.Science. 2013; 339: 823-826Crossref PubMed Scopus (6463) Google Scholar). Hundreds of genome-scale pooled CRISPR screens have been performed in immortalized human cell lines (Hart et al., 2015Hart T. Chandrashekhar M. Aregger M. Durocher D. Angers S. Moffat J. Crispr H. Hart T. Chandrashekhar M. Aregger M. et al.High-resolution CRISPR screens reveal fitness genes and genotype-specific cancer liabilities.Cell. 2015; 163: 1515-1526Abstract Full Text Full Text PDF PubMed Scopus (868) Google Scholar, Wang et al., 2015Wang T. Birsoy K. Hughes N.W. Krupczak K.M. Post Y. Wei J.J. Lander E.S. Sabatini D.M. Identification and characterization of essential genes in the human genome.Science. 2015; 350: 1096-1101Crossref PubMed Scopus (954) Google Scholar, Meyers et al., 2017Meyers R.M. Bryan J.G. McFarland J.M. Weir B.A. Sizemore A.E. Xu H. Dharia N.V. Montgomery P.G. Cowley G.S. Pantel S. et al.Computational correction of copy number effect improves specificity of CRISPR-Cas9 essentiality screens in cancer cells.Nat. Genet. 2017; 49: 1779-1784Crossref PubMed Scopus (818) Google Scholar). However, in hPSCs the CRISPR/Cas9 system has been used primarily for small-scale genome engineering (Merkle et al., 2015Merkle F.T. Neuhausser W.M. Santos D. Valen E. Gagnon J.A. Maas K. Sandoe J. Schier A.F. Eggan K. Efficient CRISPR-Cas9-mediated generation of knockin human pluripotent stem cells lacking undesired mutations at the targeted locus.Cell Rep. 2015; 11: 875-883Abstract Full Text Full Text PDF PubMed Scopus (107) Google Scholar). In genetically intact hPSCs, the only genome-scale CRISPR screen to date used methods developed for cancer cells, suffered from technical issues, had poor performance, and identified few developmentally relevant genes (Hart et al., 2014Hart T. Brown K.R. Sircoulomb F. Rottapel R. Moffat J. Measuring error rates in genomic perturbation screens: gold standards for human functional genomics.Mol. Syst. Biol. 2014; 10: 733Crossref PubMed Scopus (187) Google Scholar, Shalem et al., 2014Shalem O. Sanjana N.E. Hartenian E. Shi X. Scott D.A. Mikkelson T. Heckl D. Ebert B.L. Root D.E. Doench J.G. Zhang F. Genome-scale CRISPR-Cas9 knockout screening in human cells.Science. 2014; 343: 84-87Crossref PubMed Scopus (3124) Google Scholar). We addressed these technical issues by systematically tailoring the CRISPR/Cas9 system for hPSCs (Ihry et al., 2018Ihry R.J. Worringer K.A. Salick M.R. Frias E. Ho D. Theriault K. Kommineni S. Chen J. Sondey M. Ye C. et al.p53 inhibits CRISPR-Cas9 engineering in human pluripotent stem cells.Nat. Med. 2018; 24: 939-946Crossref PubMed Scopus (512) Google Scholar). We developed a doxycycline (dox)-inducible Cas9 (iCas9) hPSC line and stably infected it with a genome-scale single guide RNA (sgRNA) library. We banked and expanded the CRISPR-infected hPSC library in the absence of editing (−dox), which enabled us to generate a renewable stem cell pool with stable but inactive sgRNAs. This allowed us to conduct multiple independent screens with the same cell library. In the first screen, we identified genes that suppress or enhance hPSC fitness over long-term culture. Although previous screens have generated gold-standard gene lists of core-essential genes that reduce cell survival when mutated, little is known about the mutations that enhance survival and proliferation. Unlike core-essential genes, these enhancing mutations appear to be cell type specific, and no consistent lists exist for this type of gene (Hart et al., 2014Hart T. Brown K.R. Sircoulomb F. Rottapel R. Moffat J. Measuring error rates in genomic perturbation screens: gold standards for human functional genomics.Mol. Syst. Biol. 2014; 10: 733Crossref PubMed Scopus (187) Google Scholar). In hPSCs, karyotypic analysis has detected recurrent copy number variations (CNVs) that confer a growth advantage (Amps et al., 2011Amps K. Andrews P.W. Anyfantis G. Armstrong L. Avery S. Baharvand H. Baker J. Baker D. Munoz M.B. Beil S. et al.International Stem Cell InitiativeScreening ethnically diverse human embryonic stem cells identifies a chromosome 20 minimal amplicon conferring growth advantage.Nat. Biotechnol. 2011; 29: 1132-1144Crossref PubMed Scopus (394) Google Scholar, Laurent et al., 2011Laurent L.C. Ulitsky I. Slavin I. Tran H. Schork A. Morey R. Lynch C. Harness J.V. Lee S. Barrero M.J. et al.Dynamic changes in the copy number of pluripotency and cell proliferation genes in human ESCs and iPSCs during reprogramming and time in culture.Cell Stem Cell. 2011; 8: 106-118Abstract Full Text Full Text PDF PubMed Scopus (696) Google Scholar); however, these studies lack gene level resolution. Recently, next-generation sequencing of hundreds of hPSCs identified the recurrence of dominant-negative TP53 mutations that can expand within a population of hPSCs (Merkle et al., 2017Merkle F.T. Ghosh S. Kamitaki N. Mitchell J. Avior Y. Mello C. Kashin S. Mekhoubad S. Ilic D. Charlton M. et al.Human pluripotent stem cells recurrently acquire and expand dominant negative P53 mutations.Nature. 2017; 545: 229-233Crossref PubMed Scopus (293) Google Scholar). We mined our data for gene knockouts that enriched in culture and identified many genes, including components of the TP53 pathway and other known tumor suppressors. We validated the strongest hit, PMAIP1/NOXA, which appears to be a stem cell-specific gene conferring sensitivity to DNA damage downstream of TP53. In the second screen, we identified genes required for single-cell cloning. hPSCs have a poor survival rate after dissociation to single cells, which is detrimental for genome engineering. Multiple groups have extensively characterized death induced by single-cell cloning and have demonstrated the process is similar to but distinct from anoikis and is triggered by a ROCK/myosin/actin pathway (Chen et al., 2010Chen G. Hou Z. Gulbranson D.R. Thomson J.A. Actin-myosin contractility is responsible for the reduced viability of dissociated human embryonic stem cells.Cell Stem Cell. 2010; 7: 240-248Abstract Full Text Full Text PDF PubMed Scopus (218) Google Scholar, Ohgushi et al., 2010Ohgushi M. Matsumura M. Eiraku M. Murakami K. Aramaki T. Nishiyama A. Muguruma K. Nakano T. Suga H. Ueno M. et al.Molecular pathway and cell state responsible for dissociation-induced apoptosis in human pluripotent stem cells.Cell Stem Cell. 2010; 7: 225-239Abstract Full Text Full Text PDF PubMed Scopus (325) Google Scholar). To prevent death, hPSCs are passaged as clumps or treated with ROCK inhibitors (Watanabe et al., 2007Watanabe K. Ueno M. Kamiya D. Nishiyama A. Matsumura M. Wataya T. Takahashi J.B. Nishikawa S. Nishikawa S. Muguruma K. Sasai Y. A ROCK inhibitor permits survival of dissociated human embryonic stem cells.Nat. Biotechnol. 2007; 25: 681-686Crossref PubMed Scopus (1568) Google Scholar). By subjecting our hPSC mutant library to single-cell dissociation without ROCK inhibitors, we selected for mutations that survive single-cell cloning. sgRNAs for the ROCK and myosin pathways were enriched in the surviving clones. The most enriched gene was the pro-apoptotic regulator PAWR (Burikhanov et al., 2009Burikhanov R. Zhao Y. Goswami A. Qiu S. Schwarze S.R. Rangnekar V.M. The tumor suppressor Par-4 activates an extrinsic pathway for apoptosis.Cell. 2009; 138: 377-388Abstract Full Text Full Text PDF PubMed Scopus (181) Google Scholar). Validation studies confirmed a role for PAWR as a component of the actin-cytoskeleton that induces membrane blebbing and cell death caused by single-cell cloning. The additional genes identified here will further our understanding about the sensitivity of hPSCs to single-cell cloning. In the final screen, we used a fluorescence-activated cell sorting (FACS)-based OCT4 assay to identify regulators of pluripotency and differentiation. Pluripotency is a defining feature of hPSCs, and it allows them to differentiate into all three germ layers. OCT4/POU5F1, NANOG, and SOX2 are critical transcription factors that maintain pluripotency in vivo and in vitro (Nichols et al., 1998Nichols J. Zevnik B. Anastassiadis K. Niwa H. Klewe-Nebenius D. Chambers I. Schöler H. Smith A. Formation of pluripotent stem cells in the mammalian embryo depends on the POU transcription factor Oct4.Cell. 1998; 95: 379-391Abstract Full Text Full Text PDF PubMed Scopus (2683) Google Scholar, Chambers et al., 2007Chambers I. Silva J. Colby D. Nichols J. Nijmeijer B. Robertson M. Vrana J. Jones K. Grotewold L. Smith A. Nanog safeguards pluripotency and mediates germline development.Nature. 2007; 450: 1230-1234Crossref PubMed Scopus (1161) Google Scholar, Masui et al., 2007Masui S. Nakatake Y. Toyooka Y. Shimosato D. Yagi R. Takahashi K. Okochi H. Okuda A. Matoba R. Sharov A.A. et al.Pluripotency governed by Sox2 via regulation of Oct3/4 expression in mouse embryonic stem cells.Nat. Cell Biol. 2007; 9: 625-635Crossref PubMed Scopus (892) Google Scholar). OCT4 and SOX2 overexpression is commonly used to reprogram somatic cells toward the pluripotent state (Takahashi and Yamanaka, 2006Takahashi K. Yamanaka S. Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors.Cell. 2006; 126: 663-676Abstract Full Text Full Text PDF PubMed Scopus (18970) Google Scholar, Takahashi et al., 2007Takahashi K. Tanabe K. Ohnuki M. Narita M. Ichisaka T. Tomoda K. Yamanaka S. Induction of pluripotent stem cells from adult human fibroblasts by defined factors.Cell. 2007; 131: 861-872Abstract Full Text Full Text PDF PubMed Scopus (15051) Google Scholar, Yu et al., 2007Yu J. Vodyanik M.A. Smuga-Otto K. Antosiewicz-Bourget J. Frane J.L. Tian S. Nie J. Jonsdottir G.A. Ruotti V. Stewart R. et al.Induced pluripotent stem cell lines derived from human somatic cells.Science. 2007; 318: 1917-1920Crossref PubMed Scopus (8184) Google Scholar). By isolating mutant cells with high or low OCT4 protein expression, we identified many genes involved in maintaining the pluripotent state, along with genes involved with induction of differentiation. By using an iCas9 hPSC line stably infected with a genome-scale lentiCRISPR library, we were able to bank a CRISPR-hPSC library that was renewable and enabled a large number of independent screens with the same starting library. This allows direct comparison between screens and reduces screen to screen variability. We rigorously tested the system and identified genes important for fitness, single-cell cloning, and pluripotency of hPSCs. Herein we provide a resource with detailed methods and all available data including many genes that are involved in hPSC biology. This resource will serve as a parts list of genes that are functionally important for the human stem cell state. These lists of genes provide unique insights into the genetic regulation of human development that could only be identified in normal diploid cells that are not transformed or cancerous. Furthermore, the gene sets and methods will increase our systematic knowledge of hPSC biology and will enable additional large-scale CRISPR screens in stem cells and their somatic derivatives. We set out to develop a high-throughput CRISPR/Cas9 platform for hPSCs that would enable successive rounds of screening from a stable library of lentiCRISPR-infected hPSCs (Figures 1A and S1). Generating a genome-scale lentiCRISPR hPSC library would enable both the rigorous testing of CRISPR screen performance and the identification of cell type-specific regulators of the pluripotent state. In our previous work, we developed an all-in-one iCas9 transgene that was inactive in the absence of dox (Ihry et al., 2018Ihry R.J. Worringer K.A. Salick M.R. Frias E. Ho D. Theriault K. Kommineni S. Chen J. Sondey M. Ye C. et al.p53 inhibits CRISPR-Cas9 engineering in human pluripotent stem cells.Nat. Med. 2018; 24: 939-946Crossref PubMed Scopus (512) Google Scholar). The tight control over Cas9 expression allowed us to transduce cells with lentiviruses expressing sgRNAs (lentiCRISPRs) in the absence of dox without causing on-target indels. We tested if it was possible to bank a genome-scale lentiCRISPR-infected cell library (∼5 sgRNAs per gene, 91,725 total sgRNAs) prior to Cas9 mutagenesis (−dox). After one freeze-thaw cycle, next-generation sequencing (NGS) analysis revealed no bottlenecking of the library demonstrating the feasibility of banking a large lentiCRISPR hPSC library for repeated screens (Figure 1B). Next, we performed a fitness screen to evaluate the global performance of the system (Figure 1C). We benchmarked the performance of the screen by using annotated lists of core-essential genes. Core-essential genes are required for the survival of all cells; the corresponding CRISPR knockout causes the sgRNAs to be depleted (Hart et al., 2014Hart T. Brown K.R. Sircoulomb F. Rottapel R. Moffat J. Measuring error rates in genomic perturbation screens: gold standards for human functional genomics.Mol. Syst. Biol. 2014; 10: 733Crossref PubMed Scopus (187) Google Scholar, Hart et al., 2015Hart T. Chandrashekhar M. Aregger M. Durocher D. Angers S. Moffat J. Crispr H. Hart T. Chandrashekhar M. Aregger M. et al.High-resolution CRISPR screens reveal fitness genes and genotype-specific cancer liabilities.Cell. 2015; 163: 1515-1526Abstract Full Text Full Text PDF PubMed Scopus (868) Google Scholar). Genome-scale CRISPR screening in hPSCs has been challenging (Hart et al., 2014Hart T. Brown K.R. Sircoulomb F. Rottapel R. Moffat J. Measuring error rates in genomic perturbation screens: gold standards for human functional genomics.Mol. Syst. Biol. 2014; 10: 733Crossref PubMed Scopus (187) Google Scholar, Shalem et al., 2014Shalem O. Sanjana N.E. Hartenian E. Shi X. Scott D.A. Mikkelson T. Heckl D. Ebert B.L. Root D.E. Doench J.G. Zhang F. Genome-scale CRISPR-Cas9 knockout screening in human cells.Science. 2014; 343: 84-87Crossref PubMed Scopus (3124) Google Scholar). hPSCs have a strong DNA damage response (DDR), and Cas9-induced double-strand breaks (DSBs) cause a significant cell loss (Ihry et al., 2018Ihry R.J. Worringer K.A. Salick M.R. Frias E. Ho D. Theriault K. Kommineni S. Chen J. Sondey M. Ye C. et al.p53 inhibits CRISPR-Cas9 engineering in human pluripotent stem cells.Nat. Med. 2018; 24: 939-946Crossref PubMed Scopus (512) Google Scholar). Failure to account for Cas9-induced cell loss is problematic for pooled screening, because it is critical to maintain representation of each sgRNA-barcoded cell. Our previous work demonstrated a range of Cas9-induced cells loss between 3- and 10-fold across many sgRNAs (Ihry et al., 2018Ihry R.J. Worringer K.A. Salick M.R. Frias E. Ho D. Theriault K. Kommineni S. Chen J. Sondey M. Ye C. et al.p53 inhibits CRISPR-Cas9 engineering in human pluripotent stem cells.Nat. Med. 2018; 24: 939-946Crossref PubMed Scopus (512) Google Scholar). To reduce the bottlenecking of the sgRNA library caused by Cas9-induced toxicity, we started the screen in hPSCs at an average of 1,200 cells per sgRNA (a total of 110 million infected cells). By doing this we maintained about 4-fold more cells than a typical cancer screen (Hart et al., 2015Hart T. Chandrashekhar M. Aregger M. Durocher D. Angers S. Moffat J. Crispr H. Hart T. Chandrashekhar M. Aregger M. et al.High-resolution CRISPR screens reveal fitness genes and genotype-specific cancer liabilities.Cell. 2015; 163: 1515-1526Abstract Full Text Full Text PDF PubMed Scopus (868) Google Scholar). During the fitness screen, DNA was sampled before and after dox exposure at days 0, 8, 14, and 18 (Figure S1). To provide a qualitative measurement of screen performance, we plotted the p values calculated by the redundant small interfering RNA (siRNA) activity (RSA) test against Q1 based Z scores for a set of core-essential and non-essential genes (König et al., 2007König R. Chiang C.Y. Tu B.P. Yan S.F. DeJesus P.D. Romero A. Bergauer T. Orth A. Krueger U. Zhou Y. Chanda S.K. A probability-based approach for the analysis of large-scale RNAi screens.Nat. Methods. 2007; 4: 847-849Crossref PubMed Scopus (253) Google Scholar, Hart et al., 2014Hart T. Brown K.R. Sircoulomb F. Rottapel R. Moffat J. Measuring error rates in genomic perturbation screens: gold standards for human functional genomics.Mol. Syst. Biol. 2014; 10: 733Crossref PubMed Scopus (187) Google Scholar). Before dox treatment the non-essential and core-essential genes are randomly distributed within a tight cluster (Figure 1D). After 18 days of Cas9 treatment, the distribution spreads, and the essential genes significantly drop out while the non-essential genes remain constant (Figure 1D; Tables S1 and S2). To quantify performance, we used the Bayesian analysis of gene essentiality (BAGEL) algorithm, which calculates a Bayes factor for each gene by determining the probability that the observed fold change for a given gene matches that of known essential genes (Hart and Moffat, 2016Hart T. Moffat J. BAGEL: a computational framework for identifying essential genes from pooled library screens.BMC Bioinformatics. 2016; 17: 164Crossref PubMed Scopus (123) Google Scholar).This generates a ranked list of Bayes factors for each gene, which can then be used to quantify screen performance by precision versus recall analysis. In a high-performance screen, essential genes have high Bayes factor scores, and the precision versus recall curve gradually drops off as analysis of the ranked list is completed. In contrast, a poor-performing screen has a precision versus recall curve that rapidly drops off, indicating many false positives (non-essential genes) with high Bayes factor scores. The sample without dox exposure (untreated) has a randomly ranked Bayes factor list with non-essential and essential genes interspersed and exhibits a poor precision versus recall curve (Figure 1E). In the day 18 Cas9 (+dox) treated samples, essential genes and non-essential genes segregate from each other and generates a high-performing precision versus recall curve that gradually drops off (Figure 1E; Table S3). After 18 days of Cas9 exposure, we identified 770 fitness genes at a 5% false discovery rate on the basis of the precision calculation (Figure 1F; Table S4). Comparing the set of 770 hPSC fitness genes with 1,580 core-essential genes from cancer lines revealed an overlap of 405 genes (Figure 1G) (Hart et al., 2015Hart T. Chandrashekhar M. Aregger M. Durocher D. Angers S. Moffat J. Crispr H. Hart T. Chandrashekhar M. Aregger M. et al.High-resolution CRISPR screens reveal fitness genes and genotype-specific cancer liabilities.Cell. 2015; 163: 1515-1526Abstract Full Text Full Text PDF PubMed Scopus (868) Google Scholar). The remaining 365 specifically dropped out in hPSCs. Both the core and hPSC-specific essential genes are abundantly expressed in hPSCs, further supporting that they are required to maintain hPSCs in culture (Figure 1H). Our fitness screen in hPSCs correctly identifies the dropout of core-essential genes with accuracy that is on par with CRISPR screens conducted in cancer cell lines. This demonstrates that cancer cells and stem cells share a common set of core-essential genes that can be used to benchmark performance. By accounting for cell loss caused by Cas9 activity, we limited the effects of Cas9-induced toxicity that have thwarted previous attempts at genome-scale screening in hPSCs (Hart et al., 2014Hart T. Brown K.R. Sircoulomb F. Rottapel R. Moffat J. Measuring error rates in genomic perturbation screens: gold standards for human functional genomics.Mol. Syst. Biol. 2014; 10: 733Crossref PubMed Scopus (187) Google Scholar, Shalem et al., 2014Shalem O. Sanjana N.E. Hartenian E. Shi X. Scott D.A. Mikkelson T. Heckl D. Ebert B.L. Root D.E. Doench J.G. Zhang F. Genome-scale CRISPR-Cas9 knockout screening in human cells.Science. 2014; 343: 84-87Crossref PubMed Scopus (3124) Google Scholar). This demonstrates that it is possible to conduct an effective genome-scale CRISPR screen in hPSCs using the methods described here. Curated lists of genes that enhance fitness during a CRISPR screen do not exist, making it difficult to benchmark the enrichment results (Hart et al., 2015Hart T. Chandrashekhar M. Aregger M. Durocher D. Angers S. Moffat J. Crispr H. Hart T. Chandrashekhar M. Aregger M. et al.High-resolution CRISPR screens reveal fitness genes and genotype-specific cancer liabilities.Cell. 2015; 163: 1515-1526Abstract Full Text Full Text PDF PubMed Scopus (868) Google Scholar). By comparing the top ∼1,000 depleted (RSA-down < −2.25; Table S2) and enriched (RSA-up < −2.25; Table S5) genes, we observed that 31.8% (301 of 946) of the enriched genes were located on the X and Y chromosomes (H1-human embryonic stem cells [hESCs] XY; Figure S3). In contrast, the depleted genes were evenly distributed across all chromosomes. It became apparent that allosome-targeting sgRNAs were behaving similarly to non-targeting controls, which enrich during a CRISPR screen in hPSCs (Ihry et al., 2018Ihry R.J. Worringer K.A. Salick M.R. Frias E. Ho D. Theriault K. Kommineni S. Chen J. Sondey M. Ye C. et al.p53 inhibits CRISPR-Cas9 engineering in human pluripotent stem cells.Nat. Med. 2018; 24: 939-946Crossref PubMed Scopus (512) Google Scholar). We observed that sgRNAs causing a single DSB on the X chromosome are less toxic relative to sgRNAs inducing two DSBs at the MAPT locus despite being able to efficiently induce indels (Figure S2). sgRNAs targeting genomic amplifications in cancer cell lines exhibit a strong depletion irrespective of the gene targets (Aguirre et al., 2016Aguirre A.J. Meyers R.M. Weir B.A. Vazquez F. Zhang C.-Z. Ben-David U. Cook A. Ha G. Harrington W.F. Doshi M.B. et al.Genomic copy number dictates a gene-independent cell response to CRISPR-Cas9 targeting.Cancer Discov. 2016; 6: 914-929Crossref PubMed Scopus (323) Google Scholar, Munoz et al., 2016Munoz D.M. Cassiani P.J. Li L. Billy E. Korn J.M. Jones M.D. Golji J. Ruddy D.A. Yu K. McAllister G. et al.CRISPR screens provide a comprehensive assessment of cancer vulnerabilities but generate false-positive hits for highly amplified genomic regions.Cancer Discov. 2016; 6: 900-913Crossref PubMed Scopus (221) Google Scholar, Meyers et al., 2017Meyers R.M. Bryan J.G. McFarland J.M. Weir B.A. Sizemore A.E. Xu H. Dharia N.V. Montgomery P.G. Cowley G.S. Pantel S. et al.Computational correction of copy number effect improves specificity of CRISPR-Cas9 essentiality screens in cancer cells.Nat. Genet. 2017; 49: 1779-1784Crossref PubMed Scopus (818) Google Scholar). Unlike cancer cell lines, H1-hESCs with a normal diploid karyotype are very sensitive to DNA damage, making the difference between one and two DSBs significant. After recognizing that the enrichment of sgRNAs on the X and Y chromosomes was related to DSB sensitivity and copy number differences in male H1-hESCs, we focused on autosomal genes. In the remaining list of 645 autosomal genes that were enriched (RSA-up −2.25), we identified 41 tumor suppressor genes (Zhao et al., 2016Zhao M. Kim P. Mitra R. Zhao J. Zhao Z. TSGene 2.0: an updated literature-based knowledgebase for tumor suppressor genes.Nucleic Acids Res. 2016; 44: D1023-D1031Crossref PubMed Scopus (210) Google Scholar). The second most enriched gene was TP53, which confirms the selective pressure imposed by Cas9-induced DSBs in hPSCs during a CRISPR screen (Figure 2A). Consistent with this TP53 mutants are able to suppress cell loss induced by Cas9 activity (Ihry et al., 2018Ihry R.J. Worringer K.A. Salick M.R. Frias E. Ho D. Theriault K. Kommineni S. Chen J. Sondey M. Ye C. et al.p53 inhibits CRISPR-Cas9 engineering in human pluripotent stem cells.Nat. Med. 2018; 24: 939-946Crossref PubMed Scopus (512) Google Scholar). Throughout the 18-day screen, the representation of sgRNAs" @default.
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• W2938408085 title "Genome-Scale CRISPR Screens Identify Human Pluripotency-Specific Genes" @default.
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• W2938408085 cites W1973260880 @default.
• W2938408085 cites W1974365957 @default.
• W2938408085 cites W1980426094 @default.
• W2938408085 cites W1981088384 @default.
• W2938408085 cites W1992950313 @default.
• W2938408085 cites W1999574084 @default.
• W2938408085 cites W2003171404 @default.
• W2938408085 cites W2008376982 @default.
• W2938408085 cites W2011125717 @default.
• W2938408085 cites W2011177881 @default.
• W2938408085 cites W2024235347 @default.
• W2938408085 cites W2025842404 @default.
• W2938408085 cites W2028835848 @default.
• W2938408085 cites W2029604570 @default.
• W2938408085 cites W2032903538 @default.
• W2938408085 cites W2034569328 @default.
• W2938408085 cites W2035415099 @default.
• W2938408085 cites W2036454804 @default.
• W2938408085 cites W2045435533 @default.
• W2938408085 cites W2049358079 @default.
• W2938408085 cites W2053274595 @default.
• W2938408085 cites W2059958613 @default.
• W2938408085 cites W2064815984 @default.
• W2938408085 cites W2074744546 @default.
• W2938408085 cites W2079933639 @default.
• W2938408085 cites W2094116748 @default.
• W2938408085 cites W2096381270 @default.
• W2938408085 cites W2098997846 @default.
• W2938408085 cites W2121665844 @default.
• W2938408085 cites W2125987139 @default.
• W2938408085 cites W2126432370 @default.
• W2938408085 cites W2128323764 @default.
• W2938408085 cites W2131395804 @default.
• W2938408085 cites W2134526812 @default.
• W2938408085 cites W2138033616 @default.
• W2938408085 cites W2138164217 @default.
• W2938408085 cites W2138977668 @default.
• W2938408085 cites W2142046859 @default.
• W2938408085 cites W2153004457 @default.
• W2938408085 cites W2156656429 @default.
• W2938408085 cites W2160051211 @default.
• W2938408085 cites W2160816805 @default.
• W2938408085 cites W2161028863 @default.
• W2938408085 cites W2162603028 @default.
• W2938408085 cites W2163404991 @default.
• W2938408085 cites W2169456326 @default.
• W2938408085 cites W2170551349 @default.
• W2938408085 cites W2177605184 @default.
• W2938408085 cites W2179438025 @default.
• W2938408085 cites W2180291937 @default.
• W2938408085 cites W2286983239 @default.
• W2938408085 cites W2338215681 @default.
• W2938408085 cites W2411238967 @default.
• W2938408085 cites W2414684615 @default.
• W2938408085 cites W2461558445 @default.
• W2938408085 cites W2503636044 @default.
• W2938408085 cites W2558723698 @default.
• W2938408085 cites W2595380755 @default.
• W2938408085 cites W2608500349 @default.
• W2938408085 cites W2735132087 @default.
• W2938408085 cites W2739511077 @default.
• W2938408085 cites W2796523908 @default.
• W2938408085 cites W2807198924 @default.
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• W2938408085 doi "https://doi.org/10.1016/j.celrep.2019.03.043" @default.
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