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Sanchez HM, Lapidot T, Shalem O. High-throughput optimized prime editing mediated endogenous protein tagging for pooled imaging of protein localization. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.09.16.613361. [PMID: 39345511 PMCID: PMC11429766 DOI: 10.1101/2024.09.16.613361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 10/01/2024]
Abstract
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.
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2
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Melore SM, Hamilton MC, Reddy TE. HyperCas12a enables highly-multiplexed epigenome editing screens. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.08.602263. [PMID: 39026853 PMCID: PMC11257430 DOI: 10.1101/2024.07.08.602263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
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.
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Affiliation(s)
- Schuyler M. Melore
- University Program in Genetics & Genomics, Duke University, Durham, NC, USA
- Department of Biostatistics & Bioinformatics, Duke University School of Medicine, Durham, NC, USA
- Center for Advanced Genomic Technologies, Duke University, Durham, NC, USA
- Center for Combinatorial Gene Regulation, Duke University, Durham, NC, USA
| | - Marisa C. Hamilton
- University Program in Genetics & Genomics, Duke University, Durham, NC, USA
- Center for Advanced Genomic Technologies, Duke University, Durham, NC, USA
- Center for Combinatorial Gene Regulation, Duke University, Durham, NC, USA
| | - Timothy E. Reddy
- University Program in Genetics & Genomics, Duke University, Durham, NC, USA
- Department of Biostatistics & Bioinformatics, Duke University School of Medicine, Durham, NC, USA
- Center for Advanced Genomic Technologies, Duke University, Durham, NC, USA
- Center for Combinatorial Gene Regulation, Duke University, Durham, NC, USA
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3
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McGee AV, Liu YV, Griffith AL, Szegletes ZM, Wen B, Kraus C, Miller NW, Steger RJ, Escude Velasco B, Bosch JA, Zirin JD, Viswanatha R, Sontheimer EJ, Goodale A, Greene MA, Green TM, Doench JG. Modular vector assembly enables rapid assessment of emerging CRISPR technologies. CELL GENOMICS 2024; 4:100519. [PMID: 38484704 PMCID: PMC10943585 DOI: 10.1016/j.xgen.2024.100519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 12/31/2023] [Accepted: 02/08/2024] [Indexed: 03/19/2024]
Abstract
The diversity of CRISPR systems, coupled with scientific ingenuity, has led to an explosion of applications; however, to test newly described innovations in their model systems, researchers typically embark on cumbersome, one-off cloning projects to generate custom reagents that are optimized for their biological questions. Here, we leverage Golden Gate cloning to create the Fragmid toolkit, a modular set of CRISPR cassettes and delivery technologies, along with a web portal, resulting in a combinatorial platform that enables scalable vector assembly within days. We further demonstrate that multiple CRISPR technologies can be assessed in parallel in a pooled screening format using this resource, enabling the rapid optimization of both novel technologies and cellular models. These results establish Fragmid as a robust system for the rapid design of CRISPR vectors, and we anticipate that this assembly approach will be broadly useful for systematic development, comparison, and dissemination of CRISPR technologies.
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Affiliation(s)
- Abby V McGee
- Genetic Perturbation Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Yanjing V Liu
- Genetic Perturbation Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Audrey L Griffith
- Genetic Perturbation Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Zsofia M Szegletes
- Genetic Perturbation Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Bronte Wen
- Genetic Perturbation Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Carolyn Kraus
- RNA Therapeutics Institute, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA
| | - Nathan W Miller
- Genetic Perturbation Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Ryan J Steger
- Genetic Perturbation Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Berta Escude Velasco
- Genetic Perturbation Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Justin A Bosch
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Jonathan D Zirin
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Raghuvir Viswanatha
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Erik J Sontheimer
- RNA Therapeutics Institute, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA; Program in Molecular Medicine, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA; Li Weibo Institute for Rare Diseases Research, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA
| | - Amy Goodale
- Genetic Perturbation Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Matthew A Greene
- Genetic Perturbation Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Thomas M Green
- Genetic Perturbation Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - John G Doench
- Genetic Perturbation Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
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4
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McGee AV, Liu YV, Griffith AL, Szegletes ZM, Wen B, Kraus C, Miller NW, Steger RJ, Velasco BE, Bosch JA, Zirin JD, Viswanatha R, Sontheimer EJ, Goodale A, Greene MA, Green TM, Doench JG. Modular vector assembly enables rapid assessment of emerging CRISPR technologies. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.25.564061. [PMID: 37961518 PMCID: PMC10634825 DOI: 10.1101/2023.10.25.564061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
The diversity of CRISPR systems, coupled with scientific ingenuity, has led to an explosion of applications; however, to test newly-described innovations in their model systems, researchers typically embark on cumbersome, one-off cloning projects to generate custom reagents that are optimized for their biological questions. Here, we leverage Golden Gate cloning to create the Fragmid toolkit, a modular set of CRISPR cassettes and delivery technologies, along with a web portal, resulting in a combinatorial platform that enables scalable vector assembly within days. We further demonstrate that multiple CRISPR technologies can be assessed in parallel in a pooled screening format using this resource, enabling the rapid optimization of both novel technologies and cellular models. These results establish Fragmid as a robust system for the rapid design of CRISPR vectors, and we anticipate that this assembly approach will be broadly useful for systematic development, comparison, and dissemination of CRISPR technologies.
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Affiliation(s)
- Abby V McGee
- Genetic Perturbation Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Yanjing V Liu
- Genetic Perturbation Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Audrey L Griffith
- Genetic Perturbation Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Zsofia M Szegletes
- Genetic Perturbation Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Bronte Wen
- Genetic Perturbation Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Carolyn Kraus
- RNA Therapeutics Institute, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA
| | - Nathan W Miller
- Genetic Perturbation Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Ryan J Steger
- Genetic Perturbation Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Berta Escude Velasco
- Genetic Perturbation Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Justin A Bosch
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Jonathan D Zirin
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Raghuvir Viswanatha
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Erik J Sontheimer
- RNA Therapeutics Institute, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA
- Program in Molecular Medicine, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA
- Li Weibo Institute for Rare Diseases Research, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA
| | - Amy Goodale
- Genetic Perturbation Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Matthew A Greene
- Genetic Perturbation Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Thomas M Green
- Genetic Perturbation Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - John G Doench
- Genetic Perturbation Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
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5
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Blaeschke F, Chen YY, Apathy R, Daniel B, Chen AY, Chen PA, Sandor K, Zhang W, Li Z, Mowery CT, Yamamoto TN, Nyberg WA, To A, Yu R, Bueno R, Kim MC, Schmidt R, Goodman DB, Feuchtinger T, Eyquem J, Jimmie Ye C, Carnevale J, Satpathy AT, Shifrut E, Roth TL, Marson A. Modular pooled discovery of synthetic knockin sequences to program durable cell therapies. Cell 2023; 186:4216-4234.e33. [PMID: 37714135 PMCID: PMC10508323 DOI: 10.1016/j.cell.2023.08.013] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Revised: 04/22/2023] [Accepted: 08/15/2023] [Indexed: 09/17/2023]
Abstract
Chronic stimulation can cause T cell dysfunction and limit the efficacy of cellular immunotherapies. Improved methods are required to compare large numbers of synthetic knockin (KI) sequences to reprogram cell functions. Here, we developed modular pooled KI screening (ModPoKI), an adaptable platform for modular construction of DNA KI libraries using barcoded multicistronic adaptors. We built two ModPoKI libraries of 100 transcription factors (TFs) and 129 natural and synthetic surface receptors (SRs). Over 30 ModPoKI screens across human TCR- and CAR-T cells in diverse conditions identified a transcription factor AP4 (TFAP4) construct that enhanced fitness of chronically stimulated CAR-T cells and anti-cancer function in vitro and in vivo. ModPoKI's modularity allowed us to generate an ∼10,000-member library of TF combinations. Non-viral KI of a combined BATF-TFAP4 polycistronic construct enhanced fitness. Overexpressed BATF and TFAP4 co-occupy and regulate key gene targets to reprogram T cell function. ModPoKI facilitates the discovery of complex gene constructs to program cellular functions.
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Affiliation(s)
- Franziska Blaeschke
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA 94158, USA; Department of Medicine, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Yan Yi Chen
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA 94158, USA; Department of Medicine, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Ryan Apathy
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA 94158, USA; Department of Medicine, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Bence Daniel
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA 94158, USA; Department of Pathology, Stanford University, Stanford, CA 94305, USA; Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA 94305, USA
| | - Andy Y Chen
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA 94158, USA; Department of Pathology, Stanford University, Stanford, CA 94305, USA; Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Peixin Amy Chen
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA 94158, USA; Department of Medicine, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Katalin Sandor
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA 94158, USA; Department of Pathology, Stanford University, Stanford, CA 94305, USA
| | - Wenxi Zhang
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA 94158, USA; Department of Pathology, Stanford University, Stanford, CA 94305, USA
| | - Zhongmei Li
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA 94158, USA; Department of Medicine, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Cody T Mowery
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA 94158, USA; Department of Medicine, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Tori N Yamamoto
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA 94158, USA; Department of Medicine, University of California, San Francisco, San Francisco, CA 94143, USA
| | - William A Nyberg
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA 94158, USA; Department of Medicine, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Angela To
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA 94158, USA; Department of Medicine, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Ruby Yu
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA 94158, USA; Department of Medicine, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Raymund Bueno
- Institute for Human Genetics (IHG), University of California, San Francisco, San Francisco, CA 94143, USA
| | - Min Cheol Kim
- Institute for Human Genetics (IHG), University of California, San Francisco, San Francisco, CA 94143, USA
| | - Ralf Schmidt
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA 94158, USA; Department of Medicine, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Daniel B Goodman
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA 94158, USA; Department of Medicine, University of California, San Francisco, San Francisco, CA 94143, USA; Diabetes Center, University of California, San Francisco, San Francisco, CA 94143, USA; Parker Institute for Cancer Immunotherapy, University of California, San Francisco, San Francisco, CA 94129, USA
| | - Tobias Feuchtinger
- Department of Pediatric Hematology, Oncology and Stem Cell Transplantation, Dr. von Hauner Children's Hospital, University Hospital, LMU Munich, Munich 80337, Germany; German Cancer Consortium (DKTK), Partner Site Munich, Munich 80336, Germany; National Center for Infection Research (DZIF), Munich 81377, Germany
| | - Justin Eyquem
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA 94158, USA; Department of Medicine, University of California, San Francisco, San Francisco, CA 94143, USA; Institute for Human Genetics (IHG), University of California, San Francisco, San Francisco, CA 94143, USA; Parker Institute for Cancer Immunotherapy, University of California, San Francisco, San Francisco, CA 94129, USA; Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA 94143, USA; UCSF Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Chun Jimmie Ye
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA 94158, USA; Department of Medicine, University of California, San Francisco, San Francisco, CA 94143, USA; Institute for Human Genetics (IHG), University of California, San Francisco, San Francisco, CA 94143, USA; Parker Institute for Cancer Immunotherapy, University of California, San Francisco, San Francisco, CA 94129, USA; Institute for Computational Health Sciences, University of California, San Francisco, San Francisco, CA 94143, USA; Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA 94143, USA; Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94143, USA; Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Julia Carnevale
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA 94158, USA; Department of Medicine, University of California, San Francisco, San Francisco, CA 94143, USA; Parker Institute for Cancer Immunotherapy, University of California, San Francisco, San Francisco, CA 94129, USA; UCSF Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Ansuman T Satpathy
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA 94158, USA; Department of Pathology, Stanford University, Stanford, CA 94305, USA; Parker Institute for Cancer Immunotherapy, University of California, San Francisco, San Francisco, CA 94129, USA; Program in Immunology, Stanford University, Stanford, CA 94305, USA
| | - Eric Shifrut
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA 94158, USA; Department of Medicine, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Theodore L Roth
- Department of Pathology, Stanford University, Stanford, CA 94305, USA.
| | - Alexander Marson
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA 94158, USA; Department of Medicine, University of California, San Francisco, San Francisco, CA 94143, USA; Institute for Human Genetics (IHG), University of California, San Francisco, San Francisco, CA 94143, USA; Diabetes Center, University of California, San Francisco, San Francisco, CA 94143, USA; Parker Institute for Cancer Immunotherapy, University of California, San Francisco, San Francisco, CA 94129, USA; Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA 94143, USA; Innovative Genomics Institute, University of California Berkeley, Berkeley, CA 94720, USA; UCSF Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA 94158, USA.
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Park BS, Jeon H, Chi SG, Kim T. Efficient prioritization of CRISPR screen hits by accounting for targeting efficiency of guide RNA. BMC Biol 2023; 21:45. [PMID: 36829149 PMCID: PMC9960226 DOI: 10.1186/s12915-023-01536-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 02/03/2023] [Indexed: 02/26/2023] Open
Abstract
BACKGROUND CRISPR-based screens are revolutionizing drug discovery as tools to identify genes whose ablation induces a phenotype of interest. For instance, CRISPR-Cas9 screening has been successfully used to identify novel therapeutic targets in cancer where disruption of genes leads to decreased viability of malignant cells. However, low-activity guide RNAs may give rise to variable changes in phenotype, preventing easy identification of hits and leading to false negative results. Therefore, correcting the effects of bias due to differences in guide RNA efficiency in CRISPR screening data can improve the efficiency of prioritizing hits for further validation. Here, we developed an approach to identify hits from negative CRISPR screens by correcting the fold changes (FC) in gRNA frequency by the actual, observed frequency of indel mutations generated by gRNA. RESULTS Each gRNA was coupled with the "reporter sequence" that can be targeted by the same gRNA so that the frequency of mutations in the reporter sequence can be used as a proxy for the endogenous target gene. The measured gRNA activity was used to correct the FC. We identified indel generation efficiency as the dominant factor contributing significant bias to screening results, and our method significantly removed such bias and was better at identifying essential genes when compared to conventional fold change analysis. We successfully applied our gRNA activity data to previously published gRNA screening data, and identified novel genes whose ablation could synergize with vemurafenib in the A375 melanoma cell line. Our method identified nicotinamide N-methyltransferase, lactate dehydrogenase B, and polypyrimidine tract-binding protein 1 as synergistic targets whose ablation sensitized A375 cells to vemurafenib. CONCLUSIONS We identified the variations in target cleavage efficiency, even in optimized sgRNA libraries, that pose a strong bias in phenotype and developed an analysis method that corrects phenotype score by the measured differences in the targeting efficiency among sgRNAs. Collectively, we expect that our new analysis method will more accurately identify genes that confer the phenotype of interest.
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Affiliation(s)
- Byung-Sun Park
- grid.35541.360000000121053345Medicinal Materials Research Center, Korea Institute of Science and Technology, 5 Hwarangro-14-Gil, SeongbukGu, Seoul, 02792 Republic of Korea ,grid.222754.40000 0001 0840 2678Department of Biological Sciences, Korea University, 145 AnamRo, SeongbukGu, Seoul, 02841 Republic of Korea
| | - Heeju Jeon
- grid.35541.360000000121053345Medicinal Materials Research Center, Korea Institute of Science and Technology, 5 Hwarangro-14-Gil, SeongbukGu, Seoul, 02792 Republic of Korea ,grid.222754.40000 0001 0840 2678Department of Biological Sciences, Korea University, 145 AnamRo, SeongbukGu, Seoul, 02841 Republic of Korea
| | - Sung-Gil Chi
- grid.222754.40000 0001 0840 2678Department of Biological Sciences, Korea University, 145 AnamRo, SeongbukGu, Seoul, 02841 Republic of Korea
| | - Tackhoon Kim
- Medicinal Materials Research Center, Korea Institute of Science and Technology, 5 Hwarangro-14-Gil, SeongbukGu, Seoul, 02792, Republic of Korea. .,Department of Biological Sciences, Korea University, 145 AnamRo, SeongbukGu, Seoul, 02841, Republic of Korea. .,Division of Bio-Medical Science and Technology, Korea University of Science and Technology, 217 GajeongRo YuseongGu, Daejeon, 34113, Republic of Korea.
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7
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Shi H, Doench JG, Chi H. CRISPR screens for functional interrogation of immunity. Nat Rev Immunol 2022:10.1038/s41577-022-00802-4. [DOI: 10.1038/s41577-022-00802-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/04/2022] [Indexed: 12/13/2022]
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8
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Braun CJ, Adames AC, Saur D, Rad R. Tutorial: design and execution of CRISPR in vivo screens. Nat Protoc 2022; 17:1903-1925. [PMID: 35840661 DOI: 10.1038/s41596-022-00700-y] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 03/22/2022] [Indexed: 11/09/2022]
Abstract
Here we provide a detailed tutorial on CRISPR in vivo screening. Using the mouse as the model organism, we introduce a range of CRISPR tools and applications, delineate general considerations for 'transplantation-based' or 'direct in vivo' screening design, and provide details on technical execution, sequencing readouts, computational analyses and data interpretation. In vivo screens face unique pitfalls and limitations, such as delivery issues or library bottlenecking, which must be counteracted to avoid screening failure or flawed conclusions. A broad variety of in vivo phenotypes can be interrogated such as organ development, hematopoietic lineage decision and evolutionary licensing in oncogenesis. We describe experimental strategies to address various biological questions and provide an outlook on emerging CRISPR applications, such as genetic interaction screening. These technological advances create potent new opportunities to dissect the molecular underpinnings of complex organismal phenotypes.
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Affiliation(s)
- Christian J Braun
- Department of Pediatrics, Dr. von Hauner Children's Hospital, University Hospital, LMU Munich, Munich, Germany. .,Institute of Molecular Oncology and Functional Genomics, School of Medicine, Technical University of Munich, Munich, Germany. .,Hopp Children's Cancer Center Heidelberg (KiTZ), German Cancer Research Center (DKFZ), Heidelberg, Germany.
| | - Andrés Carbonell Adames
- Department of Pediatrics, Dr. von Hauner Children's Hospital, University Hospital, LMU Munich, Munich, Germany
| | - Dieter Saur
- Institute of Experimental Cancer Therapy, Technical University of Munich, Munich, Germany.,Center for Translational Cancer Research (TranslaTUM), School of Medicine, Technical University of Munich, Munich, Germany.,Department of Medicine II, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany.,German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Roland Rad
- Institute of Molecular Oncology and Functional Genomics, School of Medicine, Technical University of Munich, Munich, Germany. .,Center for Translational Cancer Research (TranslaTUM), School of Medicine, Technical University of Munich, Munich, Germany. .,Department of Medicine II, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany. .,German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany.
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9
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Aregger M, Xing K, Gonatopoulos-Pournatzis T. Application of CHyMErA Cas9-Cas12a combinatorial genome-editing platform for genetic interaction mapping and gene fragment deletion screening. Nat Protoc 2021; 16:4722-4765. [PMID: 34508260 DOI: 10.1038/s41596-021-00595-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2020] [Accepted: 06/17/2021] [Indexed: 02/08/2023]
Abstract
CRISPR-based forward genetic screening represents a powerful approach for the systematic characterization of gene function. Recent efforts have been directed toward establishing CRISPR-based tools for the programmable delivery of combinatorial genetic perturbations, most of which are mediated by a single nuclease and the expression of structurally identical guide backbones from two promoters. In contrast, we have developed CHyMErA (Cas hybrid for multiplexed editing and screening applications), which is based on the co-expression of Cas9 and Cas12a nucleases in conjunction with a hybrid guide RNA (hgRNA) engineered by the fusion of Cas9 and Cas12a guides and expressed from a single U6 promoter. CHyMErA is suitable for the high-throughput deletion of genetic segments including the excision of individual exons. Furthermore, CHyMErA enables the concomitant targeting of two or more genes and can thus be used for the systematic mapping of genetic interactions in mammalian cells. CHyMErA can also be applied for the perturbation of paralogous gene pairs, thereby allowing the capturing of phenotypic roles that would otherwise be masked because of genetic redundancy. Here, we provide instructions for the cloning of hgRNA screening libraries and individual hgRNA constructs and offer guidelines for designing and performing combinatorial pooled genetic screens using CHyMErA. Starting with the generation of Cas9- and Cas12a-expressing cell lines, CHyMErA screening can be implemented within 15-20 weeks.
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Affiliation(s)
- Michael Aregger
- RNA Biology Laboratory, National Cancer Institute, National Institutes of Health, Frederick, MD, USA.
| | - Kun Xing
- RNA Biology Laboratory, National Cancer Institute, National Institutes of Health, Frederick, MD, USA
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10
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DeWeirdt PC, Sanson KR, Sangree AK, Hegde M, Hanna RE, Feeley MN, Griffith AL, Teng T, Borys SM, Strand C, Joung JK, Kleinstiver BP, Pan X, Huang A, Doench JG. Optimization of AsCas12a for combinatorial genetic screens in human cells. Nat Biotechnol 2021; 39:94-104. [PMID: 32661438 PMCID: PMC7854777 DOI: 10.1038/s41587-020-0600-6] [Citation(s) in RCA: 89] [Impact Index Per Article: 29.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2019] [Accepted: 06/09/2020] [Indexed: 02/07/2023]
Abstract
Cas12a RNA-guided endonucleases are promising tools for multiplexed genetic perturbations because they can process multiple guide RNAs expressed as a single transcript, and subsequently cleave target DNA. However, their widespread adoption has lagged behind Cas9-based strategies due to low activity and the lack of a well-validated pooled screening toolkit. In the present study, we describe the optimization of enhanced Cas12a from Acidaminococcus (enAsCas12a) for pooled, combinatorial genetic screens in human cells. By assaying the activity of thousands of guides, we refine on-target design rules and develop a comprehensive set of off-target rules to predict and exclude promiscuous guides. We also identify 38 direct repeat variants that can substitute for the wild-type sequence. We validate our optimized AsCas12a toolkit by screening for synthetic lethalities in OVCAR8 and A375 cancer cells, discovering an interaction between MARCH5 and WSB2. Finally, we show that enAsCas12a delivers similar performance to Cas9 in genome-wide dropout screens but at greatly reduced library size, which will facilitate screens in challenging models.
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Affiliation(s)
- Peter C DeWeirdt
- Genetic Perturbation Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kendall R Sanson
- Genetic Perturbation Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Annabel K Sangree
- Genetic Perturbation Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Mudra Hegde
- Genetic Perturbation Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ruth E Hanna
- Genetic Perturbation Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Marissa N Feeley
- Genetic Perturbation Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Audrey L Griffith
- Genetic Perturbation Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Teng Teng
- Tango Therapeutics, Cambridge, MA, USA
| | - Samantha M Borys
- Genetic Perturbation Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Christine Strand
- Genetic Perturbation Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - J Keith Joung
- Molecular Pathology Unit, Massachusetts General Hospital, Charlestown, MA, USA
- Center for Cancer Research, Massachusetts General Hospital, Charlestown, MA, USA
- Center for Computational and Integrative Biology, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Pathology, Harvard Medical School, Boston, MA, USA
| | - Benjamin P Kleinstiver
- Department of Pathology, Harvard Medical School, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Pathology, Massachusetts General Hospital, Boston, MA, USA
| | | | | | - John G Doench
- Genetic Perturbation Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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11
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Resolving Neurodevelopmental and Vision Disorders Using Organoid Single-Cell Multi-omics. Neuron 2020; 107:1000-1013. [PMID: 32970995 DOI: 10.1016/j.neuron.2020.09.001] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Revised: 08/26/2020] [Accepted: 08/31/2020] [Indexed: 12/20/2022]
Abstract
Human organoid models of the central nervous system, including the neural retina, are providing unprecedented opportunities to explore human neurodevelopment and neurodegeneration in controlled culture environments. In this Perspective, we discuss how the single-cell multi-omic toolkit has been used to identify features and limitations of brain and retina organoids and how these tools can be deployed to study congenital brain malformations and vision disorders in organoids. We also address how to improve brain and retina organoid protocols to revolutionize in vitro disease modeling.
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12
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Gier RA, Budinich KA, Evitt NH, Cao Z, Freilich ES, Chen Q, Qi J, Lan Y, Kohli RM, Shi J. High-performance CRISPR-Cas12a genome editing for combinatorial genetic screening. Nat Commun 2020; 11:3455. [PMID: 32661245 PMCID: PMC7359328 DOI: 10.1038/s41467-020-17209-1] [Citation(s) in RCA: 67] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Accepted: 06/15/2020] [Indexed: 12/26/2022] Open
Abstract
CRISPR-based genetic screening has revolutionized cancer drug target discovery, yet reliable, multiplex gene editing to reveal synergies between gene targets remains a major challenge. Here, we present a simple and robust CRISPR-Cas12a-based approach for combinatorial genetic screening in cancer cells. By engineering the CRISPR-AsCas12a system with key modifications to the Cas protein and its CRISPR RNA (crRNA), we can achieve high efficiency combinatorial genetic screening. We demonstrate the performance of our optimized AsCas12a (opAsCas12a) through double knockout screening against epigenetic regulators. This screen reveals synthetic sick interactions between Brd9&Jmjd6, Kat6a&Jmjd6, and Brpf1&Jmjd6 in leukemia cells.
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Affiliation(s)
- Rodrigo A Gier
- Department of Cancer Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Epigenetics Institute, Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Abramson Family Cancer Research Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Krista A Budinich
- Department of Cancer Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Epigenetics Institute, Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Abramson Family Cancer Research Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Niklaus H Evitt
- Department of Cancer Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Epigenetics Institute, Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Abramson Family Cancer Research Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Zhendong Cao
- Department of Cancer Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Epigenetics Institute, Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Abramson Family Cancer Research Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Elizabeth S Freilich
- Department of Cancer Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Epigenetics Institute, Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Abramson Family Cancer Research Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Qingzhou Chen
- Department of Cancer Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Epigenetics Institute, Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Abramson Family Cancer Research Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Jun Qi
- Department of Cancer Biology, Dana-Farber Cancer Institute, Department of Medicine, Harvard Medical School, Boston, MA, 02215, USA
| | - Yemin Lan
- Epigenetics Institute, Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Rahul M Kohli
- Epigenetics Institute, Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Junwei Shi
- Department of Cancer Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Epigenetics Institute, Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Abramson Family Cancer Research Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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13
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Roth TL, Li PJ, Blaeschke F, Nies JF, Apathy R, Mowery C, Yu R, Nguyen MLT, Lee Y, Truong A, Hiatt J, Wu D, Nguyen DN, Goodman D, Bluestone JA, Ye CJ, Roybal K, Shifrut E, Marson A. Pooled Knockin Targeting for Genome Engineering of Cellular Immunotherapies. Cell 2020; 181:728-744.e21. [PMID: 32302591 PMCID: PMC7219528 DOI: 10.1016/j.cell.2020.03.039] [Citation(s) in RCA: 125] [Impact Index Per Article: 31.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 01/13/2020] [Accepted: 03/18/2020] [Indexed: 12/12/2022]
Abstract
Adoptive transfer of genetically modified immune cells holds great promise for cancer immunotherapy. CRISPR knockin targeting can improve cell therapies, but more high-throughput methods are needed to test which knockin gene constructs most potently enhance primary cell functions in vivo. We developed a widely adaptable technology to barcode and track targeted integrations of large non-viral DNA templates and applied it to perform pooled knockin screens in primary human T cells. Pooled knockin of dozens of unique barcoded templates into the T cell receptor (TCR)-locus revealed gene constructs that enhanced fitness in vitro and in vivo. We further developed pooled knockin sequencing (PoKI-seq), combining single-cell transcriptome analysis and pooled knockin screening to measure cell abundance and cell state ex vivo and in vivo. This platform nominated a novel transforming growth factor β (TGF-β) R2-41BB chimeric receptor that improved solid tumor clearance. Pooled knockin screening enables parallelized re-writing of endogenous genetic sequences to accelerate discovery of knockin programs for cell therapies.
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Affiliation(s)
- Theodore L Roth
- Medical Scientist Training Program, University of California, San Francisco, San Francisco, CA, USA; Biomedical Sciences Graduate Program, University of California, San Francisco, San Francisco, CA, USA; Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA, USA; Diabetes Center, University of California, San Francisco, San Francisco, CA, USA; Innovative Genomics Institute, University of California, Berkeley, Berkeley, CA, USA.
| | - P Jonathan Li
- Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA, USA; Diabetes Center, University of California, San Francisco, San Francisco, CA, USA; Innovative Genomics Institute, University of California, Berkeley, Berkeley, CA, USA
| | - Franziska Blaeschke
- Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA, USA; Diabetes Center, University of California, San Francisco, San Francisco, CA, USA; Innovative Genomics Institute, University of California, Berkeley, Berkeley, CA, USA
| | - Jasper F Nies
- Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA, USA; Diabetes Center, University of California, San Francisco, San Francisco, CA, USA; Innovative Genomics Institute, University of California, Berkeley, Berkeley, CA, USA
| | - Ryan Apathy
- Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA, USA; Diabetes Center, University of California, San Francisco, San Francisco, CA, USA; Innovative Genomics Institute, University of California, Berkeley, Berkeley, CA, USA
| | - Cody Mowery
- Medical Scientist Training Program, University of California, San Francisco, San Francisco, CA, USA; Biomedical Sciences Graduate Program, University of California, San Francisco, San Francisco, CA, USA; Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA, USA; Diabetes Center, University of California, San Francisco, San Francisco, CA, USA; Innovative Genomics Institute, University of California, Berkeley, Berkeley, CA, USA
| | - Ruby Yu
- Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA, USA; Diabetes Center, University of California, San Francisco, San Francisco, CA, USA; Innovative Genomics Institute, University of California, Berkeley, Berkeley, CA, USA
| | - Michelle L T Nguyen
- Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA, USA; Diabetes Center, University of California, San Francisco, San Francisco, CA, USA; Innovative Genomics Institute, University of California, Berkeley, Berkeley, CA, USA
| | - Youjin Lee
- Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA, USA; Diabetes Center, University of California, San Francisco, San Francisco, CA, USA; Innovative Genomics Institute, University of California, Berkeley, Berkeley, CA, USA
| | - Anna Truong
- Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA, USA; Diabetes Center, University of California, San Francisco, San Francisco, CA, USA; Innovative Genomics Institute, University of California, Berkeley, Berkeley, CA, USA
| | - Joseph Hiatt
- Medical Scientist Training Program, University of California, San Francisco, San Francisco, CA, USA; Biomedical Sciences Graduate Program, University of California, San Francisco, San Francisco, CA, USA; Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA, USA; Diabetes Center, University of California, San Francisco, San Francisco, CA, USA; Innovative Genomics Institute, University of California, Berkeley, Berkeley, CA, USA
| | - David Wu
- Medical Scientist Training Program, University of California, San Francisco, San Francisco, CA, USA; Biomedical Sciences Graduate Program, University of California, San Francisco, San Francisco, CA, USA
| | - David N Nguyen
- Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA, USA; Diabetes Center, University of California, San Francisco, San Francisco, CA, USA; Innovative Genomics Institute, University of California, Berkeley, Berkeley, CA, USA; Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Daniel Goodman
- Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA, USA; Diabetes Center, University of California, San Francisco, San Francisco, CA, USA; Innovative Genomics Institute, University of California, Berkeley, Berkeley, CA, USA
| | - Jeffrey A Bluestone
- Diabetes Center, University of California, San Francisco, San Francisco, CA, USA; Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA; Sean N. Parker Autoimmune Research Laboratory, University of California, San Francisco, San Francisco, CA, USA
| | - Chun Jimmie Ye
- Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA; Chan Zuckerberg Biohub, San Francisco, CA, USA; Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA; Division of Rheumatology, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA; Institute of Computational Health Sciences, University of California, San Francisco, San Francisco, CA, USA; Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - Kole Roybal
- Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA, USA; Chan Zuckerberg Biohub, San Francisco, CA, USA; Sean N. Parker Autoimmune Research Laboratory, University of California, San Francisco, San Francisco, CA, USA; UCSF Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
| | - Eric Shifrut
- Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA, USA; Diabetes Center, University of California, San Francisco, San Francisco, CA, USA; Innovative Genomics Institute, University of California, Berkeley, Berkeley, CA, USA
| | - Alexander Marson
- Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA, USA; Diabetes Center, University of California, San Francisco, San Francisco, CA, USA; Innovative Genomics Institute, University of California, Berkeley, Berkeley, CA, USA; Department of Medicine, University of California, San Francisco, San Francisco, CA, USA; Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA; Chan Zuckerberg Biohub, San Francisco, CA, USA; UCSF Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA.
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14
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Gonatopoulos-Pournatzis T, Aregger M, Brown KR, Farhangmehr S, Braunschweig U, Ward HN, Ha KCH, Weiss A, Billmann M, Durbic T, Myers CL, Blencowe BJ, Moffat J. Genetic interaction mapping and exon-resolution functional genomics with a hybrid Cas9-Cas12a platform. Nat Biotechnol 2020; 38:638-648. [PMID: 32249828 DOI: 10.1038/s41587-020-0437-z] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2019] [Accepted: 01/27/2020] [Indexed: 12/11/2022]
Abstract
Systematic mapping of genetic interactions (GIs) and interrogation of the functions of sizable genomic segments in mammalian cells represent important goals of biomedical research. To advance these goals, we present a CRISPR (clustered regularly interspaced short palindromic repeats)-based screening system for combinatorial genetic manipulation that employs coexpression of CRISPR-associated nucleases 9 and 12a (Cas9 and Cas12a) and machine-learning-optimized libraries of hybrid Cas9-Cas12a guide RNAs. This system, named Cas Hybrid for Multiplexed Editing and screening Applications (CHyMErA), outperforms genetic screens using Cas9 or Cas12a editing alone. Application of CHyMErA to the ablation of mammalian paralog gene pairs reveals extensive GIs and uncovers phenotypes normally masked by functional redundancy. Application of CHyMErA in a chemogenetic interaction screen identifies genes that impact cell growth in response to mTOR pathway inhibition. Moreover, by systematically targeting thousands of alternative splicing events, CHyMErA identifies exons underlying human cell line fitness. CHyMErA thus represents an effective screening approach for GI mapping and the functional analysis of sizable genomic regions, such as alternative exons.
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Affiliation(s)
| | - Michael Aregger
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
| | - Kevin R Brown
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
| | - Shaghayegh Farhangmehr
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | | | - Henry N Ward
- Bioinformatics and Computational Biology Graduate Program, University of Minnesota, Minneapolis, MN, USA
| | - Kevin C H Ha
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
| | - Alexander Weiss
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
| | - Maximilian Billmann
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, USA
| | - Tanja Durbic
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
| | - Chad L Myers
- Bioinformatics and Computational Biology Graduate Program, University of Minnesota, Minneapolis, MN, USA.,Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, USA
| | - Benjamin J Blencowe
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada. .,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.
| | - Jason Moffat
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada. .,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada. .,Institute for Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada.
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15
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Piper AM, Batovska J, Cogan NOI, Weiss J, Cunningham JP, Rodoni BC, Blacket MJ. Prospects and challenges of implementing DNA metabarcoding for high-throughput insect surveillance. Gigascience 2019; 8:giz092. [PMID: 31363753 PMCID: PMC6667344 DOI: 10.1093/gigascience/giz092] [Citation(s) in RCA: 82] [Impact Index Per Article: 16.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Revised: 06/25/2019] [Accepted: 07/09/2019] [Indexed: 12/21/2022] Open
Abstract
Trap-based surveillance strategies are widely used for monitoring of invasive insect species, aiming to detect newly arrived exotic taxa as well as track the population levels of established or endemic pests. Where these surveillance traps have low specificity and capture non-target endemic species in excess of the target pests, the need for extensive specimen sorting and identification creates a major diagnostic bottleneck. While the recent development of standardized molecular diagnostics has partly alleviated this requirement, the single specimen per reaction nature of these methods does not readily scale to the sheer number of insects trapped in surveillance programmes. Consequently, target lists are often restricted to a few high-priority pests, allowing unanticipated species to avoid detection and potentially establish populations. DNA metabarcoding has recently emerged as a method for conducting simultaneous, multi-species identification of complex mixed communities and may lend itself ideally to rapid diagnostics of bulk insect trap samples. Moreover, the high-throughput nature of recent sequencing platforms could enable the multiplexing of hundreds of diverse trap samples on a single flow cell, thereby providing the means to dramatically scale up insect surveillance in terms of both the quantity of traps that can be processed concurrently and number of pest species that can be targeted. In this review of the metabarcoding literature, we explore how DNA metabarcoding could be tailored to the detection of invasive insects in a surveillance context and highlight the unique technical and regulatory challenges that must be considered when implementing high-throughput sequencing technologies into sensitive diagnostic applications.
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Affiliation(s)
- Alexander M Piper
- Agriculture Victoria Research, AgriBio Centre, 5 Ring Road, Bundoora 3083, VIC, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora 3083, VIC, Australia
| | - Jana Batovska
- Agriculture Victoria Research, AgriBio Centre, 5 Ring Road, Bundoora 3083, VIC, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora 3083, VIC, Australia
| | - Noel O I Cogan
- Agriculture Victoria Research, AgriBio Centre, 5 Ring Road, Bundoora 3083, VIC, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora 3083, VIC, Australia
| | - John Weiss
- Agriculture Victoria Research, AgriBio Centre, 5 Ring Road, Bundoora 3083, VIC, Australia
| | - John Paul Cunningham
- Agriculture Victoria Research, AgriBio Centre, 5 Ring Road, Bundoora 3083, VIC, Australia
| | - Brendan C Rodoni
- Agriculture Victoria Research, AgriBio Centre, 5 Ring Road, Bundoora 3083, VIC, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora 3083, VIC, Australia
| | - Mark J Blacket
- Agriculture Victoria Research, AgriBio Centre, 5 Ring Road, Bundoora 3083, VIC, Australia
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16
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Gohl DM, Magli A, Garbe J, Becker A, Johnson DM, Anderson S, Auch B, Billstein B, Froehling E, McDevitt SL, Beckman KB. Measuring sequencer size bias using REcount: a novel method for highly accurate Illumina sequencing-based quantification. Genome Biol 2019; 20:85. [PMID: 31036053 PMCID: PMC6489363 DOI: 10.1186/s13059-019-1691-6] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2019] [Accepted: 04/09/2019] [Indexed: 01/15/2023] Open
Abstract
Quantification of DNA sequence tags from engineered constructs such as plasmids, transposons, or other transgenes underlies many functional genomics measurements. Typically, such measurements rely on PCR followed by next-generation sequencing. However, PCR amplification can introduce significant quantitative error. We describe REcount, a novel PCR-free direct counting method. Comparing measurements of defined plasmid pools to droplet digital PCR data demonstrates that REcount is highly accurate and reproducible. We use REcount to provide new insights into clustering biases due to molecule length across different Illumina sequencers and illustrate the impacts on interpretation of next-generation sequencing data and the economics of data generation.
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Affiliation(s)
- Daryl M. Gohl
- University of Minnesota Genomics Center, Minneapolis, MN 55455 USA
- Department of Genetics, Cell Biology, and Development, University of Minnesota, Minneapolis, MN 55455 USA
| | - Alessandro Magli
- Department of Medicine, University of Minnesota, Minneapolis, MN 55455 USA
- Stem Cell Institute, University of Minnesota, Minneapolis, MN 55455 USA
| | - John Garbe
- University of Minnesota Genomics Center, Minneapolis, MN 55455 USA
| | - Aaron Becker
- University of Minnesota Genomics Center, Minneapolis, MN 55455 USA
| | | | - Shea Anderson
- University of Minnesota Genomics Center, Minneapolis, MN 55455 USA
| | - Benjamin Auch
- University of Minnesota Genomics Center, Minneapolis, MN 55455 USA
| | - Bradley Billstein
- University of Minnesota Genomics Center, Minneapolis, MN 55455 USA
- Present Address: Illumina, Inc, San Diego, CA 92122 USA
| | - Elyse Froehling
- University of Minnesota Genomics Center, Minneapolis, MN 55455 USA
| | - Shana L. McDevitt
- Vincent J. Coates Genomics Sequencing Laboratory, University of California, Berkeley, CA 94720 USA
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17
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Boettcher M, Covarrubias S, Biton A, Blau J, Wang H, Zaitlen N, McManus MT. Tracing cellular heterogeneity in pooled genetic screens via multi-level barcoding. BMC Genomics 2019; 20:107. [PMID: 30727954 PMCID: PMC6364396 DOI: 10.1186/s12864-019-5480-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2018] [Accepted: 01/24/2019] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND While pooled loss- and gain-of-function CRISPR screening approaches have become increasingly popular to systematically investigate mammalian gene function, the large majority of them have thus far not investigated the influence of cellular heterogeneity on screen results. Instead most screens are analyzed by averaging the abundance of perturbed cells from a bulk population of cells. RESULTS Here we developed multi-level barcoded sgRNA libraries to trace multiple clonal Cas9 cell lines exposed to the same environment. The first level of barcoding allows monitoring growth kinetics and treatment responses of multiplexed clonal cell lines under identical conditions while the second level enables in-sample replication and tracing of sub-clonal lineages of cells expressing the same sgRNA. CONCLUSION Using our approach, we illustrate how heterogeneity in growth kinetics and treatment response of clonal cell lines impairs the results of pooled genetic screens.
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Affiliation(s)
- Michael Boettcher
- Department of Microbiology and Immunology, UCSF Diabetes Center, University of California, San Francisco, San Francisco, CA 94143 USA
| | - Sergio Covarrubias
- Department of Microbiology and Immunology, UCSF Diabetes Center, University of California, San Francisco, San Francisco, CA 94143 USA
| | - Anne Biton
- Department of Medicine, Lung Biology Center, University of California, San Francisco, San Francisco, 94143 CA USA
- Institut Pasteur, Hub Bioinformatique et Biostatistique, Centre de Bioinformatique, Biostatistique et Biologie Intégrative (C3BI, USR 3756 Institut Pasteur et CNRS), Paris, France
| | - James Blau
- Department of Microbiology and Immunology, UCSF Diabetes Center, University of California, San Francisco, San Francisco, CA 94143 USA
| | - Haopeng Wang
- Departments of Medicine and of Microbiology & Immunology, the Rosalind Russell-Ephraim P. Engleman Medical Research Center for Arthritis, and the Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, CA 94143 USA
| | - Noah Zaitlen
- Department of Medicine, Lung Biology Center, University of California, San Francisco, San Francisco, 94143 CA USA
| | - Michael T. McManus
- Department of Microbiology and Immunology, UCSF Diabetes Center, University of California, San Francisco, San Francisco, CA 94143 USA
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18
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Lau CH. Applications of CRISPR-Cas in Bioengineering, Biotechnology, and Translational Research. CRISPR J 2018; 1:379-404. [PMID: 31021245 DOI: 10.1089/crispr.2018.0026] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
CRISPR technology is rapidly evolving, and the scope of CRISPR applications is constantly expanding. CRISPR was originally employed for genome editing. Its application was then extended to epigenome editing, karyotype engineering, chromatin imaging, transcriptome, and metabolic pathway engineering. Now, CRISPR technology is being harnessed for genetic circuits engineering, cell signaling sensing, cellular events recording, lineage information reconstruction, gene drive, DNA genotyping, miRNA quantification, in vivo cloning, site-directed mutagenesis, genomic diversification, and proteomic analysis in situ. It has also been implemented in the translational research of human diseases such as cancer immunotherapy, antiviral therapy, bacteriophage therapy, cancer diagnosis, pathogen screening, microbiota remodeling, stem-cell reprogramming, immunogenomic engineering, vaccine development, and antibody production. This review aims to summarize the key concepts of these CRISPR applications in order to capture the current state of play in this fast-moving field. The key mechanisms, strategies, and design principles for each technological advance are also highlighted.
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Affiliation(s)
- Cia-Hin Lau
- Department of Biomedical Engineering, City University of Hong Kong , Hong Kong, SAR, China
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