The DOI matches its Crossref record; 60 of the 60 references it cites were found in a registry or an index.
Genome-Scale CRISPR-Mediated Control of Gene Repression and Activation
Open accessAbstract
While the catalog of mammalian transcripts and their expression levels in different cell types and disease states is rapidly expanding, our understanding of transcript function lags behind. We present a robust technology enabling systematic investigation of the cellular consequences of repressing or inducing individual transcripts. We identify rules for specific targeting of transcriptional repressors (CRISPRi), typically achieving 90%-99% knockdown with minimal off-target effects, and activators (CRISPRa) to endogenous genes via endonuclease-deficient Cas9. Together they enable modulation of gene expression over a ∼1,000-fold range. Using these rules, we construct genome-scale CRISPRi and CRISPRa libraries, each of which we validate with two pooled screens. Growth-based screens identify essential genes, tumor suppressors, and regulators of differentiation. Screens for sensitivity to a cholera-diphtheria toxin provide broad insights into the mechanisms of pathogen entry, retrotranslocation and toxicity. Our results establish CRISPRi and CRISPRa as powerful tools that provide rich and complementary information for mapping complex pathways.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Compared with gain-of-function perturbations, a higher proportion of loss-of-function perturbations yield phenotypes in growth and chemical-genetic screens, especially for members of protein complexes (Gilbert et al., 2014; Horlbeck et al., 2016).”
From Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq (2022) · cites this paper as Gilbert et al., 2014
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“For example, CRISPRi (interference)23–25 and CRISPRa (activation)26–30 technologies utilize catalytically inactive Cas9 (dCas9) fused to either a transcriptional repressor (e.g., KRAB, Krüppel associated box) or a transcriptional activator (e.g., VPR, VP64-p65-Rta).”
From Large-scale CRISPR screening in primary human 3D gastric organoids enables comprehensive dissection of gene-drug interactions (2025) · cites this paper as [28]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Alternatives to DNA cleavage-induced editing include using CRISPR-Cas9 for direct chemical sequence alteration (base editing) 25,26 , providing RNA templates for gene alteration (prime editing) 27,28 , and for transcriptional control (CRISPR interference, CRISPRi; CRISPR activation, CRISPRa) 29,30 (Fig. 3).”
From The promise and challenge of therapeutic genome editing (2020) · cites this paper as [30]
Experimental — accuracy not yet measuredIn the citing paper
“Pooling gene perturbations greatly improves scalability, but is not compatible with imaging of complex and dynamic cellular phenotypes.”
From Optical Pooled Screens in Human Cells (2019) · cites this paper as Gilbert et al., 2014
In the citing paper
“Striking examples are familial mutations causing neurodegenerative diseases, such as hereditary motor and sensory neuropathies (Charcot-Marie-Tooth disease), which can be caused by mutations in ubiquitously expressed trafficking factors (RAB7A, DNM2, DYNC1H1), or aminoacyl-tRNA synthetases (AARS, GARS, HARS, MARS)2.”
From Genome-wide CRISPRi/a screens in human neurons link lysosomal failure to ferroptosis (2021) · cites this paper as [2]
In the citing paper
“In addition to gene expression, gene function can also be cell type-specific, as evidenced by the fact that mutations in broadly expressed or housekeeping genes can lead to cell-type specific defects and disease states.”
From Genome-wide CRISPRi/a screens in human neurons link lysosomal failure to ferroptosis (2021) · cites this paper as [2]
In the citing paper
“CRISPRa evaluates gain-of-function phenotypes and can provide complementary insight to CRISPR knockout or CRISPRi-based loss-of-function screens42.”
From CRISPR-based functional genomics for neurological disease (2020) · cites this paper as [42]
In the citing paper
“CRISPRi-based gene knockdown may result in weaker phenotypes than full gene knockout, however it is non-toxic in iPSCs and iPSC-derived cells76, inducible76 and reversible42, thereby enabling time-resolved analysis of gene function76.”
From CRISPR-based functional genomics for neurological disease (2020) · cites this paper as [42]
In the citing paper
“CRISPRi represents an important advance over RNAi, since it lacks the pervasive off-target effects (Gilbert et al., 2014) inherent to RNAi-based screening approaches (Adamson et al., 2012; Jackson et al., 2003; Kaelin, 2012).”
From CRISPR Interference-Based Platform for Multimodal Genetic Screens in Human iPSC-Derived Neurons (2019) · cites this paper as Gilbert et al., 2014
In the citing paper
“Second, it is inducible and reversible (Gilbert et al., 2014)”
From CRISPR Interference-Based Platform for Multimodal Genetic Screens in Human iPSC-Derived Neurons (2019) · cites this paper as Gilbert et al., 2014
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources
Huang, D. W., Sherman, B. T., & Lempicki, R. A. (2009) · Nature Protocols · doi:10.1038/nprot.2008.211 · Cited by 35,890
Takahashi, K., & Yamanaka, S. (2006) · Cell · doi:10.1016/j.cell.2006.07.024 · Cited by 26,673
Ultrafast and memory-efficient alignment of short DNA sequences to the human genome.
Langmead, B., Trapnell, C., Pop, M., & Salzberg, S. L. (2009) · Genome Biology · doi:10.1186/gb-2009-10-3-r25 · Cited by 21,849
Huang, D. W., Sherman, B. T., & Lempicki, R. A. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkn923 · Cited by 14,108
Mootha, V. K., Lindgren, C. M., Eriksson, K.-F., Subramanian, A., Sihag, S., Lehar, J., Puigserver, P., Carlsson, E., Ridderstråle, M., Laurila, E., et al. (2003) · Nature Genetics · doi:10.1038/ng1180 · Cited by 10,197
Vogelstein, B., Papadopoulos, N., Velculescu, V. E., Zhou, S., Diaz, L. A., & Kinzler, K. W. (2013) · Science · doi:10.1126/science.1235122 · Cited by 7,863
Genome-scale CRISPR-Cas9 knockout screening in human cells
Shalem, O., Sanjana, N. E., Hartenian, E., Shi, X., Scott, D. A., Mikkelsen, T. S., Heckl, D., Ebert, B. L., Root, D. E., Doench, J. G., et al. (2014) · Science · doi:10.1126/science.1247005 · Cited by 5,234
Landscape of transcription in human cells
Djebali, S., Davis, C. A., Merkel, A., Dobin, A., Lassmann, T., Mortazavi, A., Tanzer, A., Lagarde, J., Lin, W., Schlesinger, F., et al. (2012) · Nature · doi:10.1038/nature11233 · Cited by 5,187
Derrien, T., Johnson, R., Bussotti, G., Andrea Tanzer, A., Djebali, S., Tilgner, H., Guernec, G., Martin, D., Merkel, A., Knowles, D. G., et al. (2012) · Genome Research · doi:10.1101/gr.132159.111 · Cited by 5,135
Repurposing CRISPR as an RNA-Guided Platform for Sequence-Specific Control of Gene Expression
Qi, L. S., Larson, M. H., Gilbert, L. A., Doudna, J. A., Weissman, J. S., Arkin, A. P., & Lim, W. A. (2013) · Cell · doi:10.1016/j.cell.2013.02.022 · Cited by 5,016
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Items 301–400 of 1,008
CRISPR-Cas9 for in vivo Gene Therapy: Promise and Hurdles
Dai, W.-J., Zhu, L., Yan, Z., Xu, Y., Wang, Q., & Lu, X. (2016) · Molecular Therapy — Nucleic Acids · doi:10.1038/mtna.2016.58 · Cited by 148
Editing the Epigenome: Reshaping the Genomic Landscape
Holtzman, L., & Gersbach, C. A. (2018) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-083117-021632 · Cited by 147
The hexosamine biosynthesis pathway is a targetable liability in KRAS/LKB1 mutant lung cancer
Kim, J., Lee, H. M., Cai, F., Ko, B., Yang, C., Lieu, E. L., Muhammad, N., Rhyne, S., Li, K., Haloul, M., et al. (2020) · Nature Metabolism · doi:10.1038/s42255-020-00316-0 · Cited by 143
The physiology and genetics of bacterial responses to antibiotic combinations
Roemhild, R., Bollenbach, T., & Andersson, D. I. (2022) · Nature Reviews Microbiology · doi:10.1038/s41579-022-00700-5 · Cited by 142
Emerging Roles of Non-Coding RNA Transcription
Kaikkonen, M. U., & Adelman, K. (2018) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2018.06.002 · Cited by 146
Stem Cells, Genome Editing, and the Path to Translational Medicine
Soldner, F., & Jaenisch, R. (2018) · Cell · doi:10.1016/j.cell.2018.09.010 · Cited by 144
CRISPR-Cas-mediated transcriptional modulation: The therapeutic promises of CRISPRa and CRISPRi
Bendixen, L., Jensen, T. I., & Bak, R. O. (2023) · Molecular Therapy · doi:10.1016/j.ymthe.2023.03.024 · Cited by 150
Gene Regulatory Elements, Major Drivers of Human Disease
Chatterjee, S., & Ahituv, N. (2017) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-091416-035537 · Cited by 144
The chemistry of Cas9 and its CRISPR colleagues
Chen, J. S., & Doudna, J. A. (2017) · Nature Reviews Chemistry · doi:10.1038/s41570-017-0078 · Cited by 143
Zhou, W., Brown, W., Bardhan, A., Delaney, M., Ilk, A. S., Rauen, R. R., Kahn, S. I., Tsang, M., & Deiters, A. (2020) · Angewandte Chemie International Edition · doi:10.1002/anie.201914575 · Cited by 141
DNA stretching induces Cas9 off-target activity
Newton, M. D., Taylor, B., Driessen, R. P. C., Roos, L., Cvetešić, N., Allyjaun, S., Lenhard, B., Cuomo, M. E., & Rueda, D. (2019) · Nature Structural & Molecular Biology · doi:10.1038/s41594-019-0188-z · Cited by 142
CRISPRi-based radiation modifier screen identifies long non-coding RNA therapeutic targets in glioma
Liu, S. J., Malatesta, M., Lien, B. V., Saha, P., Thombare, S. S., Hong, S. J., Pedraza, L., Koontz, M., Seo, K., Horlbeck, M. A., et al. (2020) · Genome biology · doi:10.1186/s13059-020-01995-4 · Cited by 144
Self-citationInference of single-cell phylogenies from lineage tracing data using Cassiopeia
Jones, M. G., Khodaverdian, A., Quinn, J. J., Chan, M. M., Hussmann, J. A., Wang, R., Xu, C. A., Weissman, J. S., & Yosef, N. (2020) · Genome biology · doi:10.1186/s13059-020-02000-8 · Cited by 145
Self-citationPalmer, A. C., Chidley, C., & Sorger, P. K. (2018) · eLife · doi:10.7554/elife.50036 · Cited by 136
CRISPR screens in physiologic medium reveal conditionally essential genes in human cells
Rossiter, N. J., Huggler, K. S., Adelmann, C. H., Keys, H. R., Soens, R. W., Sabatini, D. M., & Cantor, J. R. (2021) · Cell Metabolism · doi:10.1016/j.cmet.2021.02.005 · Cited by 138
Long-lasting analgesia via targeted in situ repression of Na V 1.7 in mice
Moreno, A. M., Alemán, F., Catroli, G. F., Hunt, M. A., Hu, M. Y., Dailamy, A., Pla, A., Woller, S. A., Palmer, N. D., Parekh, U., et al. (2021) · Science Translational Medicine · doi:10.1126/scitranslmed.aay9056 · Cited by 143
In Vivo Transcriptional Activation Using CRISPR/Cas9 in Drosophila
Lin, S., Ewen‐Campen, B., Ni, X., Housden, B. E., & Perrimon, N. (2015) · Genetics · doi:10.1534/genetics.115.181065 · Cited by 136
An Outlook on Global Regulatory Landscape for Genome-Edited Crops
Ahmad, A., Munawar, N., Khan, Z., Qumsani, A. T., Khan, S. H., Jamil, A., Ashraf, S., Ghouri, M. Z., Aslam, S., Mubarik, M. S., et al. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms222111753 · Cited by 140
RNA‐based medicine: from molecular mechanisms to therapy
Sparmann, A., & Vogel, J. (2023) · The EMBO Journal · doi:10.15252/embj.2023114760 · Cited by 151
RNAi/CRISPR Screens: from a Pool to a Valid Hit
Schuster, A. M., Erasimus, H., Fritah, S., Nazarov, P. V., Dyck, E. V., Niclou, S. P., & Golebiewska, A. (2018) · Trends in biotechnology · doi:10.1016/j.tibtech.2018.08.002 · Cited by 140
Massively parallel kinetic profiling of natural and engineered CRISPR nucleases
Jones, S. K., Hawkins, J. A., Johnson, N. V., Jung, C., Hu, K., Rybarski, J. R., Chen, J. S., Doudna, J. A., Press, W. H., & Finkelstein, I. J. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0646-5 · Cited by 131
Specificity of RNAi, LNA and CRISPRi as loss-of-function methods in transcriptional analysis
Stojic, L., Lun, A. T. L., Mangei, J., Mascalchi, P., Quarantotti, V., Barr, A. R., Bakal, C., Marioni, J. C., Gergely, F. V., & Odom, D. T. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky437 · Cited by 132
LncGata6 maintains stemness of intestinal stem cells and promotes intestinal tumorigenesis
Zhu, P., Wu, J., Wang, Y., Zhu, X., Lu, T., Liu, B., He, L., Ye, B., Wang, S., Meng, S., et al. (2018) · Nature Cell Biology · doi:10.1038/s41556-018-0194-0 · Cited by 130
Impeding Transcription of Expanded Microsatellite Repeats by Deactivated Cas9
Pinto, B. S., Saxena, T., Oliveira, R., Méndez‐Gómez, H. R., Cleary, J. D., Denes, L. T., McConnell, O. L., Arboleda, J. D., Xia, G., Swanson, M. S., et al. (2017) · Molecular Cell · doi:10.1016/j.molcel.2017.09.033 · Cited by 130
Crossing enhanced and high fidelity SpCas9 nucleases to optimize specificity and cleavage
Kulcsár, P. I., Tálas, A., Huszár, K., Ligeti, Z., Tóth, E., Weinhardt, N., Fodor, E., & Welker, E. (2017) · Genome biology · doi:10.1186/s13059-017-1318-8 · Cited by 134
Hawkins, J. S., Silvis, M. R., Koo, B., Peters, J. M., Osadnik, H., Jost, M., Hearne, C. C., Weissman, J. S., Todor, H., & Gross, C. A. (2020) · Cell Systems · doi:10.1016/j.cels.2020.09.009 · Cited by 126
Self-citationWidespread Influence of 3′-End Structures on Mammalian mRNA Processing and Stability
Wu, X., & Bartel, D. P. (2017) · Cell · doi:10.1016/j.cell.2017.04.036 · Cited by 131
Single-Cell Heterogeneity Analysis and CRISPR Screen Identify Key β-Cell-Specific Disease Genes
Fang, Z., Weng, C., Li, H., Tao, R., Mai, W., Liu, X., Lu, L., Lai, S., Duan, Q., Álvarez‐González, C. A., et al. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.02.043 · Cited by 126
Refactoring transcription factors for metabolic engineering
Chen, D., Wu, Y., Lv, X., Li, J., Liu, Y., Du, G., Chen, J., & Liu, L. (2022) · Biotechnology Advances · doi:10.1016/j.biotechadv.2022.107935 · Cited by 127
Fourth generation biofuel from genetically modified algal biomass: Challenges and future directions.
Shokravi, H., Shokravi, Z., Heidarrezaei, M., Ong, H. C., Rahimian Koloor, S. S., Petrů, M., Lau, W. J., & Ismail, A. F. (2021) · Chemosphere · doi:10.1016/j.chemosphere.2021.131535 · Cited by 127
Efficient delivery of nuclease proteins for genome editing in human stem cells and primary cells
Liu, J., Gaj, T., Yang, Y., Wang, N., Shui, S., Kim, S., Kanchiswamy, C. N., Kim, J., & Barbas, C. F. (2015) · Nature Protocols · doi:10.1038/nprot.2015.117 · Cited by 129
Engineering Synthetic Gene Circuits in Living Cells with CRISPR Technology
Jusiak, B., Cleto, S., Pérez‐Piñera, P., & Lu, T. K. (2016) · Trends in biotechnology · doi:10.1016/j.tibtech.2015.12.014 · Cited by 132
Zhang, X., Choi, P. S., Francis, J. M., Gao, G. F., Campbell, J. D., Ramachandran, A., Mitsuishi, Y., Ha, G., Shih, J., Vázquez, F., et al. (2017) · Cancer Discovery · doi:10.1158/2159-8290.cd-17-0532 · Cited by 126
CRISPR/Cas9 gene-editing: Research technologies, clinical applications and ethical considerations
Memi, F., Ntokou, A., & Papangeli, I. (2018) · Seminars in Perinatology · doi:10.1053/j.semperi.2018.09.003 · Cited by 134
Hultquist, J. F., Hiatt, J. B., Schumann, K., McGregor, M., Roth, T. L., Haas, P., Doudna, J. A., Marson, A., & Krogan, N. J. (2018) · Nature Protocols · doi:10.1038/s41596-018-0069-7 · Cited by 124
Dead Cas Systems: Types, Principles, and Applications
Brezgin, S., Kostyusheva, A., Kostyushev, D. S., & Chulanov, V. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20236041 · Cited by 135
Polstein, L. R., Pérez‐Piñera, P., Koçak, D. D., Vockley, C. M., Bledsoe, P. J., Song, L., Safi, A., Crawford, G. E., Reddy, T. E., & Gersbach, C. A. (2015) · Genome Research · doi:10.1101/gr.179044.114 · Cited by 132
CRISPR/dCas-mediated transcriptional and epigenetic regulation in plants
Pan, C., Sretenovic, S., & Qi, Y. (2021) · Current Opinion in Plant Biology · doi:10.1016/j.pbi.2020.101980 · Cited by 127
Elling, R., Chan, J., & Fitzgerald, K. A. (2016) · European Journal of Immunology · doi:10.1002/eji.201444558 · Cited by 123
Characterization of noncoding regulatory DNA in the human genome
Elkon, R., & Agami, R. (2017) · Nature Biotechnology · doi:10.1038/nbt.3863 · Cited by 132
CRISPR/Cas9 for Cancer Therapy: Hopes and Challenges
Martínez-Lage, M., Puig-Serra, P., Menéndez, P. R., Torres-Ruiz, R., & Rodríguez, S. (2018) · Biomedicines · doi:10.3390/biomedicines6040105 · Cited by 128
Promoter-interacting expression quantitative trait loci are enriched for functional genetic variants
Chandra, V., Bhattacharyya, S., Schmiedel, B. J., Madrigal, A., Gonzalez-Colin, C., Fotsing, S., Crinklaw, A., Seumois, G., Mohammadi, P., Kronenberg, M., et al. (2021) · Nature Genetics · doi:10.1038/s41588-020-00745-3 · Cited by 122
Genome editing for plant research and crop improvement
Zhan, X., Lu, Y., Zhu, J., & Botella, J. R. (2020) · Journal of Integrative Plant Biology · doi:10.1111/jipb.13063 · Cited by 121
Imaging-based pooled CRISPR screening reveals regulators of lncRNA localization
Wang, C., Lu, T., Emanuel, G., Babcock, H. P., & Zhuang, X. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1903808116 · Cited by 111
Applications of CRISPR-Cas Enzymes in Cancer Therapeutics and Detection
Huang, C., Lee, K.-C., & Doudna, J. A. (2018) · Trends in cancer · doi:10.1016/j.trecan.2018.05.006 · Cited by 119
Harnessing CRISPR/Cas systems for programmable transcriptional and post-transcriptional regulation
Mahas, A., Neal Stewart, C., & Mahfouz, M. M. (2018) · Biotechnology Advances · doi:10.1016/j.biotechadv.2017.11.008 · Cited by 110
Ablation of cDC2 development by triple mutations within the Zeb2 enhancer
Liu, T., Kim, S. K., Desai, P., Kim, D., Huang, X., Ferris, S. T., Wu, R., Ou, F., Egawa, T., Dyken, S. J. V., et al. (2022) · Nature · doi:10.1038/s41586-022-04866-z · Cited by 124
CRISPR/Cas9-mediated noncoding RNA editing in human cancers
Yang, J., Meng, X., Pan, J., Jiang, N., Zhou, C., Wu, Z., & Gong, Z. (2017) · RNA Biology · doi:10.1080/15476286.2017.1391443 · Cited by 122
Chow, R. D.-W., & Chen, S. (2018) · Trends in cancer · doi:10.1016/j.trecan.2018.03.002 · Cited by 122
Precision Control of CRISPR-Cas9 Using Small Molecules and Light
Gangopadhyay, S. A., Cox, K. J., Manna, D., Lim, D., Maji, B., Zhou, Q., & Choudhary, A. (2019) · Biochemistry · doi:10.1021/acs.biochem.8b01202 · Cited by 119
A new age in functional genomics using CRISPR/Cas9 in arrayed library screening
Agrotis, A., & Ketteler, R. (2015) · Frontiers in Genetics · doi:10.3389/fgene.2015.00300 · Cited by 116
Nested epistasis enhancer networks for robust genome regulation
Lin, X., Liu, Y., Liu, S., Zhu, X., Wu, L., Zhu, Y., Zhao, D., Xu, X., Chemparathy, A., Wang, H., et al. (2022) · Science · doi:10.1126/science.abk3512 · Cited by 112
Self-citationCRISPR/Cas9-mediated genome engineering of CHO cell factories: application and perspectives
Lee, J. S., Grav, L. M., Lewis, N. E., & Faustrup Kildegaard, H. (2015) · Biotechnology Journal · doi:10.1002/biot.201500082 · Cited by 117
Single-cell epigenomic variability reveals functional cancer heterogeneity
Litzenburger, U. M., Buenrostro, J. D., Wu, B., Shen, Y., Sheffield, N. C., Kathiria, A., Greenleaf, W. J., & Chang, H. Y. (2017) · Genome Biology · doi:10.1186/s13059-016-1133-7 · Cited by 115
Ultrasound-activated particles as CRISPR/Cas9 delivery system for androgenic alopecia therapy
Ryu, J., Won, E.-J., Lee, H. A. R., Kim, J. H., Hui, E., Kim, H.-P., & Yoon, T. (2019) · Biomaterials · doi:10.1016/j.biomaterials.2019.119736 · Cited by 118
Complementary information derived from CRISPR Cas9 mediated gene deletion and suppression
Rosenbluh, J., Xu, H., Harrington, W., Gill, S., Wang, X., Vázquez, F., Root, D. E., Tsherniak, A., & Hahn, W. C. (2017) · Nature Communications · doi:10.1038/ncomms15403 · Cited by 111
Gao, J., Jiang, L., & Lian, J. (2021) · Synthetic and Systems Biotechnology · doi:10.1016/j.synbio.2021.04.005 · Cited by 114
Precision cancer mouse models through genome editing with CRISPR-Cas9
Mou, H., Kennedy, Z., Anderson, D. G., Yin, H., & Xue, W. (2015) · Genome Medicine · doi:10.1186/s13073-015-0178-7 · Cited by 116
CRISPR-Based Gene Therapies: From Preclinical to Clinical Treatments
Laurent, M., Geoffroy, M., Pavani, G., & Guiraud, S. (2024) · Cells · doi:10.3390/cells13100800 · Cited by 126
Shao, H., Li, X., Moses, M. A., Gilbert, L. A., Kalyanaraman, C., Young, Z. T., Chernova, M., Journey, S. N., Weissman, J. S., Hann, B. C., et al. (2018) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.8b00583 · Cited by 109
Self-citationSherkatghanad, Z., Abdar, M., Charlier, J., & Makarenkov, V. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad131 · Cited by 123
Convergence of coronary artery disease genes onto endothelial cell programs
Schnitzler, G. R., Kang, H. Y., Fang, S., Angom, R. S., Lee-Kim, V. S., Ma, X. R., Zhou, R., Zeng, T., Guo, K., Taylor, M. S., et al. (2024) · Nature · doi:10.1038/s41586-024-07022-x · Cited by 108
An, Ö., Dall’Olio, G. M., Mourikis, T. P., & Ciccarelli, F. D. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv1123 · Cited by 106
CRISPR guide RNA design for research applications
Mohr, S. E., Hu, Y., Ewen‐Campen, B., Housden, B. E., Viswanatha, R., & Perrimon, N. (2016) · FEBS Journal · doi:10.1111/febs.13777 · Cited by 112
Editing the genome of hiPSC with CRISPR/Cas9: disease models
Bassett, A. R. (2017) · Mammalian Genome · doi:10.1007/s00335-017-9684-9 · Cited by 125
Berry, S. M., Dean, C., & Howard, M. (2017) · Cell Systems · doi:10.1016/j.cels.2017.02.013 · Cited by 112
Prediction of CRISPR sgRNA Activity Using a Deep Convolutional Neural Network
Xue, L., Tang, B., Chen, W., & Luo, J. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00368 · Cited by 113
Galamb, O., Barták, B. K., Kalmár, A., Nagy, Z. B., Szigeti, K. A., Tulassay, Z., Igaz, P., & Molnar, B. A. (2019) · World Journal of Gastroenterology · doi:10.3748/wjg.v25.i34.5026 · Cited by 113
CRISPR-mediated Activation of Latent HIV-1 Expression
Limsirichai, P., Gaj, T., & Schaffer, D. V. (2015) · Molecular Therapy · doi:10.1038/mt.2015.213 · Cited by 112
CRISPR-based genomic tools for the manipulation of genetically intractable microorganisms
Shapiro, R. S., Chavez, A., & Collins, J. J. (2018) · Nature Reviews Microbiology · doi:10.1038/s41579-018-0002-7 · Cited by 107
A glance at genome editing with CRISPR–Cas9 technology
Barman, A., Deb, B., & Chakraborty, S. (2019) · Current Genetics · doi:10.1007/s00294-019-01040-3 · Cited by 107
Mouse Genome Engineering via CRISPR-Cas9 for Study of Immune Function
Pelletier, S. W., Gingras, S., & Green, D. R. (2015) · Immunity · doi:10.1016/j.immuni.2015.01.004 · Cited by 108
Genome editing: the road of CRISPR/Cas9 from bench to clinic
Eid, A., & Mahfouz, M. M. (2016) · Experimental & Molecular Medicine · doi:10.1038/emm.2016.111 · Cited by 111
Multiplexed CRISPR/Cas9 Genome Editing and Gene Regulation Using Csy4 in Saccharomyces cerevisiae
Ferreira, R., Skrekas, C., Nielsen, J., & David, F. (2017) · ACS Synthetic Biology · doi:10.1021/acssynbio.7b00259 · Cited by 108
CRISPR/Cas9 library screening for drug target discovery
Kurata, M., Yamamoto, K., Moriarity, B. S., Kitagawa, M., & Largaespada, D. A. (2017) · Journal of Human Genetics · doi:10.1038/s10038-017-0376-9 · Cited by 108
Liu, R., Yeh, Y.-H. J., Varabyou, A., Collora, J. A., Sherrill-Mix, S. A., Talbot, C. C., Mehta, S. S., Albrecht, K., Hao, H., Zhang, H., et al. (2020) · Science Translational Medicine · doi:10.1126/scitranslmed.aaz0802 · Cited by 105
High-throughput functional variant screens via in vivo production of single-stranded DNA
Schubert, M. G., Goodman, D. B., Wannier, T. M., Kaur, D., Farzadfard, F., Lu, T. K., Shipman, S. L., & Church, G. M. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2018181118 · Cited by 108
Applications of CRISPR/Cas9 in the Synthesis of Secondary Metabolites in Filamentous Fungi
Jiang, C., Lv, G., Tu, Y., Cheng, X., Duan, Y., Zeng, B., & He, B. (2021) · Frontiers in Microbiology · doi:10.3389/fmicb.2021.638096 · Cited by 111
dCas9: A Versatile Tool for Epigenome Editing
Brocken, D. J. W., Tark‐Dame, M., & Dame, R. T. (2017) · Current Issues in Molecular Biology · doi:10.21775/cimb.026.015 · Cited by 113
CRISPR-based therapeutics: current challenges and future applications
Modell, A. E., Lim, D., Nguyen, T. M., Sreekanth, V., & Choudhary, A. (2021) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2021.10.012 · Cited by 114
Ligand-binding domains of nuclear receptors facilitate tight control of split CRISPR activity
Nguyen, D., Miyaoka, Y., Gilbert, L. A., Mayerl, S. J., Lee, B. H., Weissman, J. S., Conklin, B. R., & Wells, J. A. (2016) · Nature Communications · doi:10.1038/ncomms12009 · Cited by 107
Self-citationEmerging Gene Therapies for Genetic Hearing Loss
Ahmed, H., Shubina-Oleinik, O., & Holt, J. R. (2017) · Journal of the Association for Research in Otolaryngology · doi:10.1007/s10162-017-0634-8 · Cited by 105
Fontana, J. T., Dong, C., Kiattisewee, C., Chavali, V. P., Tickman, B. I., Carothers, J. M., & Zalatan, J. G. (2020) · Nature Communications · doi:10.1038/s41467-020-15454-y · Cited by 108
Isolating live cell clones from barcoded populations using CRISPRa-inducible reporters
Umkehrer, C., Holstein, F., Formenti, L., Jude, J., Froussios, K., Neumann, T., Cronin, S. M., Haas, L., Lipp, J., Burkard, T. R., et al. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0614-0 · Cited by 103
Genome Editing Weds CRISPR: What Is in It for Phytoremediation?
Basharat, Z., Novo, L. A. B., & Yasmin, A. (2018) · Plants · doi:10.3390/plants7030051 · Cited by 104
Modeling Disease In Vivo With CRISPR/Cas9
Dow, L. E. (2015) · Trends in Molecular Medicine · doi:10.1016/j.molmed.2015.07.006 · Cited by 111
Resources for the design of CRISPR gene editing experiments
Graham, D. B., & Root, D. E. (2015) · Genome biology · doi:10.1186/s13059-015-0823-x · Cited by 108
CRISPR technologies for stem cell engineering and regenerative medicine
Hsu, M., Chang, Y., Truong, V. A., Lai, P., Nguyen, T. K. N., & Hu, Y. (2019) · Biotechnology Advances · doi:10.1016/j.biotechadv.2019.107447 · Cited by 106
Photoswitchable gRNAs for Spatiotemporally Controlled CRISPR-Cas-Based Genomic Regulation
Moroz, E., Satyapertiwi, D., Ramel, M., Høgset, H., Sunyovszki, I., Liu, Z., Wojciechowski, J. P., Zhang, Y., Grigsby, C. L., Brito, L., et al. (2020) · ACS Central Science · doi:10.1021/acscentsci.9b01093 · Cited by 104
Enabling functional genomics with genome engineeringPreprint
Hilton, I. B., & Gersbach, C. A. (2015) · Genome Research · doi:10.1101/gr.190124.115 · Cited by 106
CRISPR/Cas9 for Human Genome Engineering and Disease Research
Xiong, X., Chen, M., Lim, W. A., Zhao, D., & Qi, L. S. (2016) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-083115-022258 · Cited by 111
Self-citationDifferent Effects of sgRNA Length on CRISPR-mediated Gene Knockout Efficiency
Zhang, J., Li, X., Neises, A., Chen, W., Hu, L., Ji, G., Yu, J.-Y., Xu, J., Yuan, W., Cheng, T., et al. (2016) · Scientific Reports · doi:10.1038/srep28566 · Cited by 104
STARR-seq — Principles and applications
Muerdter, F., Boryń, Ł. M., & Arnold, C. D. (2015) · Genomics · doi:10.1016/j.ygeno.2015.06.001 · Cited by 109
Versatile in vivo regulation of tumor phenotypes by dCas9-mediated transcriptional perturbation
Braun, C. J., Bruno, P. M., Horlbeck, M. A., Gilbert, L. A., Weissman, J. S., & Hemann, M. T. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1600582113 · Cited by 104
Self-citationIntegration of CRISPR/Cas9 with artificial intelligence for improved cancer therapeutics
Bhat, A. A., Nisar, S., Mukherjee, S., Saha, N., Yarravarapu, N., Lone, S. N., Masoodi, T., Chauhan, R., Maacha, S., Bagga, P., et al. (2022) · Journal of Translational Medicine · doi:10.1186/s12967-022-03765-1 · Cited by 114
Himeda, C. L., Jones, T. I., & Jones, P. L. (2015) · Molecular Therapy · doi:10.1038/mt.2015.200 · Cited by 102
Gene therapy for visual loss: Opportunities and concerns
Lee, J. H., Wang, J.-H., Chen, J., Li, F., Edwards, T. L., Hewitt, A. W., & Liu, G. (2018) · Progress in Retinal and Eye Research · doi:10.1016/j.preteyeres.2018.08.003 · Cited by 103
Lindtner, S., Catta-Preta, R., Tian, H., Su-Feher, L., Price, J. D., Dickel, D. E., Greiner, V. J., Silberberg, S. N., McKinsey, G. L., McManus, M. T., et al. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.07.022 · Cited by 102
Suzuki, Y., Onuma, H., Sato, R., Sato, Y., Hashiba, A., Maeki, M., Tokeshi, M., Kayesh, M. E. H., Kohara, M., Tsukiyama–Kohara, K., et al. (2020) · Journal of Controlled Release · doi:10.1016/j.jconrel.2020.12.013 · Cited by 102
Viswanatha, R., Li, Z., Hu, Y., & Perrimon, N. (2018) · eLife · doi:10.7554/elife.36333 · Cited by 79
Metadata
- Authors
- Gilbert, L. A., Horlbeck, M. A., Adamson, B., Villalta, J. E., Chen, Y., Whitehead, E. H., Guimaraes, C., Panning, B., Ploegh, H. L., Bassik, M. C., Qi, L. S., Kampmann, M., & Weissman, J. S.
- Affiliations
- QB3 (US)
- University of California, San Francisco (US)
- Whitehead Institute for Biomedical Research (US)
- Corresponding author
- Kampmann, Martin
- Weissman, Jonathan S.
- ORCID
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2014-10
- Volume
- 159
- Issue
- 3
- Pages
- 647-661
- ISSN
- 0092-8674 (ISSN-L)
- PMC
- PMC4253859
- OpenAlex
- W2152505144
- Cited by
- 2,728
- References
- 60
- Citations by year
- 2026: 190 · 2025: 211 · 2024: 265 · 2023: 212 · 2022: 261 · 2021: 285 · 2020: 285 · 2019: 255 · 2018: 221 · 2017: 214 · 2016: 182 · 2015: 125 · 2014: 14
- FWCI
- 73.17
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cell (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-nd)
- eScholarship (California Digital Library) (submittedVersion)
- PMC
- Funders
- Howard Hughes Medical Institute
- National Institutes of Health (DP5OD017887, 1dp2hd084069-01)
- National Institute on Aging (P01AG010770)
- National Institute on Drug Abuse (R01DA036858)
- National Cancer Institute (K99CA181494, U01CA168370)
- National Institute of General Medical Sciences (T32GM008284, P50GM081879, R01GM100341, P50GM102706, R01GM100518)
- National Institute of Biomedical Imaging and Bioengineering (T32EB009383)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › CRISPR and Genetic Engineering
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chromatin Remodeling and Cancer
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Interference and Gene Delivery
- Keywords
- CRISPR interference, CRISPR activation, transcriptional repression, transcriptional activation, gene essentiality, tumor suppressor, cholera toxin, diphtheria toxin, pathogen entry, retrotranslocation, off-target effects
- MeSH terms
- Cell Line, Cholera Toxin, Diphtheria Toxin, Genetic Techniques, Humans, Transcription, Genetic, Genome, Human, CRISPR-Cas Systems
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review