The DOI matches its Crossref record; 88 of the 88 references it cites were found in a registry or an index.
CRISPRi and CRISPRa Screens in Mammalian Cells for Precision Biology and Medicine
Open accessAbstract
Next-generation DNA sequencing technologies have led to a massive accumulation of genomic and transcriptomic data from patients and healthy individuals. The major challenge ahead is to understand the functional significance of the elements of the human genome and transcriptome, and implications for diagnosis and treatment. Genetic screens in mammalian cells are a powerful approach to systematically elucidating gene function in health and disease states. In particular, recently developed CRISPR/Cas9-based screening approaches have enormous potential to uncover mechanisms and therapeutic strategies for human diseases. The focus of this review is the use of CRISPR interference (CRISPRi) and CRISPR activation (CRISPRa) for genetic screens in mammalian cells. We introduce the underlying technology and present different types of CRISPRi/a screens, including those based on cell survival/proliferation, sensitivity to drugs or toxins, fluorescent reporters, and single-cell transcriptomes. Combinatorial screens, in which large numbers of gene pairs are targeted to construct genetic interaction maps, reveal pathway relationships and protein complexes. We compare and contrast CRISPRi and CRISPRa with alternative technologies, including RNA interference (RNAi) and CRISPR nuclease-based screens. Finally, we highlight challenges and opportunities ahead.
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.
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
1 retracted
Unexpected mutations after CRISPR–Cas9 editing in vivo
Schaefer, K. A., Wu, W.-H., Colgan, D. F., Tsang, S. H., Bassuk, A. G., & Mahajan, V. B. (2017) · Nature Methods · doi:10.1038/nmeth.4293 · Cited by 286
RetractedA Programmable Dual-RNA–Guided DNA Endonuclease in Adaptive Bacterial Immunity
Jinek, M., Chylinski, K., Fonfara, I., Hauer, M., Doudna, J. A., & Charpentier, E. (2012) · Science · doi:10.1126/science.1225829 · Cited by 17,116
Multiplex Genome Engineering Using CRISPR/Cas Systems
Cong, L., Ran, F. A., Cox, D., Lin, S., Barretto, R., Habib, N., Hsu, P. D., Wu, X., Jiang, W., Marraffini, L. A., et al. (2013) · Science · doi:10.1126/science.1231143 · Cited by 15,339
RNA-Guided Human Genome Engineering via Cas9
Mali, P., Yang, L., Esvelt, K. M., Aach, J., Guell, M., DiCarlo, J. E., Norville, J. E., & Church, G. M. (2013) · Science · doi:10.1126/science.1232033 · Cited by 9,210
CRISPR Provides Acquired Resistance Against Viruses in Prokaryotes
Barrangou, R., Fremaux, C., Deveau, H., Richards, M., Boyaval, P., Moineau, S., Romero, D. A., & Horvath, P. (2007) · Science · doi:10.1126/science.1138140 · Cited by 5,947
Programmable editing of a target base in genomic DNA without double-stranded DNA cleavage
Komor, A. C., Kim, Y. B., Packer, M. S., Zuris, J. A., & Liu, D. R. (2016) · Nature · doi:10.1038/nature17946 · Cited by 5,437
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
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
CRISPR-Mediated Modular RNA-Guided Regulation of Transcription in Eukaryotes
Gilbert, L. A., Larson, M. H., Morsut, L., Liu, Z., Brar, G. A., Torres, S. E., Stern-Ginossar, N., Brandman, O., Whitehead, E. H., Doudna, J. A., et al. (2013) · Cell · doi:10.1016/j.cell.2013.06.044 · Cited by 3,696
Defining a Cancer Dependency Map
Tsherniak, A., Vazquez, F., Montgomery, P. G., Weir, B. A., Kryukov, G., Cowley, G. S., Gill, S., Harrington, W. F., Pantel, S., Krill-Burger, J. M., et al. (2017) · Cell · doi:10.1016/j.cell.2017.06.010 · Cited by 3,276
Cited by
CRISPR-Cas guides the future of genetic engineering
Knott, G. J., & Doudna, J. A. (2018) · Science · doi:10.1126/science.aat5011 · Cited by 1,410
CRISPR technology: A decade of genome editing is only the beginning
Wang, J. Y., & Doudna, J. A. (2023) · Science · doi:10.1126/science.add8643 · Cited by 1,049
CRISPR Interference-Based Platform for Multimodal Genetic Screens in Human iPSC-Derived Neurons
Tian, R., Gachechiladze, M. A., Ludwig, C. H., Laurie, M. T., Hong, J. Y., Nathaniel, D., Prabhu, A. V., Fernandopulle, M. S., Patel, R., Abshari, M., et al. (2019) · Neuron · doi:10.1016/j.neuron.2019.07.014 · Cited by 543
Self-citationSynthetic lethality as an engine for cancer drug target discovery
Huang, A., Garraway, L. A., Ashworth, A., & Weber, B. (2020) · Nature Reviews Drug Discovery · doi:10.1038/s41573-019-0046-z · Cited by 469
Synthetic lethal therapies for cancer: what’s next after PARP inhibitors?
Ashworth, A., & Lord, C. J. (2018) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-018-0055-6 · Cited by 420
Metadata
- Authors
- Kampmann, M.
- Venue
- ACS Chemical Biology
- Publisher
- American Chemical Society (ACS)
- Published
- 2017-10-24
- Volume
- 13
- Issue
- 2
- Pages
- 406-416
- ISSN
- 1554-8929 (ISSN-L)
- 1554-8937
- OpenAlex
- W2765809583
- Cited by
- 373
- References
- 88
- FWCI
- 7.57
- Type
- journal article
- Language
- en
- Open access
- green
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: CRISPRi and CRISPRa Screens in Mammalian Cells for Precision Biology and Medicine. https://app.cochat.ai/papers/10.1021/acschembio.7b00657
- The paper
- Kampmann, M. (2018). CRISPRi and CRISPRa Screens in Mammalian Cells for Precision Biology and Medicine. ACS Chemical Biology. https://doi.org/10.1021/acschembio.7b00657
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
