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Pooled genetic perturbation screens with image-based phenotypes

  • 1.

    Shalem, O. et al. Genome-scale CRISPR-Cas9 knockout screening in human cells. Science 343, 84–87 (2014).

    CAS 
    PubMed 

    Google Scholar 

  • 2.

    Wang, T., Wei, J. J., Sabatini, D. M. & Lander, E. S. Genetic screens in human cells using the CRISPR–Cas9 system. Science 343, 80–84 (2014).

    CAS 
    PubMed 

    Google Scholar 

  • 3.

    Joung, J. et al. Genome-scale CRISPR–Cas9 knockout and transcriptional activation screening. Nat. Protoc. 12, 828–863 (2017).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 4.

    Parnas, O. et al. A genome-wide CRISPR screen in primary immune cells to dissect regulatory networks. Cell 162, 675–686 (2015).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 5.

    Dixit, A. et al. Perturb-Seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens. Cell 167, 1853–1866.e17 (2016).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 6.

    Adamson, B. et al. A multiplexed single-cell CRISPR screening platform enables systematic dissection of the unfolded protein response. Cell 167, 1867–1882.e21 (2016).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 7.

    Jaitin, D. A. et al. Dissecting immune circuits by linking CRISPR-pooled screens with single-cell RNA-seq. Cell 167, 1883–1896.e15 (2016).

    CAS 
    PubMed 

    Google Scholar 

  • 8.

    Wroblewska, A. et al. Protein barcodes enable high-dimensional single-cell CRISPR screens. Cell 175, 1141–1155.e16 (2018).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 9.

    Rubin, A. J. et al. Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks. Cell 176, 361–376.e17 (2019).

    CAS 
    PubMed 

    Google Scholar 

  • 10.

    Mimitou, E. P. et al. Multiplexed detection of proteins, transcriptomes, clonotypes and CRISPR perturbations in single cells. Nat. Methods 16, 409–412 (2019).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 11.

    Dhainaut, M. et al. Perturb-map enables CRISPR genomics with spatial resolution and identifies regulators of tumor immune composition. Preprint at bioRxiv https://doi.org/10.1101/2021.07.13.451021 (2021).

  • 12.

    Neumann, B. et al. Phenotypic profiling of the human genome by time-lapse microscopy reveals cell division genes. Nature 464, 721–727 (2010).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 13.

    Collinet, C. et al. Systems survey of endocytosis by multiparametric image analysis. Nature 464, 243–249 (2010).

    CAS 
    PubMed 

    Google Scholar 

  • 14.

    Orvedahl, A. et al. Image-based genome-wide siRNA screen identifies selective autophagy factors. Nature 480, 113–117 (2011).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 15.

    Karlas, A. et al. Genome-wide RNAi screen identifies human host factors crucial for influenza virus replication. Nature 463, 818–822 (2010).

    CAS 
    PubMed 

    Google Scholar 

  • 16.

    Mercer, J. et al. RNAi screening reveals proteasome- and Cullin3-dependent stages in vaccinia virus infection. Cell Rep. 2, 1036–1047 (2012).

    CAS 
    PubMed 

    Google Scholar 

  • 17.

    Agaisse, H. et al. Genome-wide RNAi screen for host factors required for intracellular bacterial infection. Science 309, 1248–1251 (2005).

    CAS 
    PubMed 

    Google Scholar 

  • 18.

    Saka, S. K. et al. Immuno-SABER enables highly multiplexed and amplified protein imaging in tissues. Nat. Biotechnol. 37, 1080–1090 (2019).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 19.

    Codeluppi, S. et al. Spatial organization of the somatosensory cortex revealed by osmFISH. Nat. Methods 15, 932–935 (2018).

    CAS 
    PubMed 

    Google Scholar 

  • 20.

    Lin, J.-R., Fallahi-Sichani, M. & Sorger, P. K. Highly multiplexed imaging of single cells using a high-throughput cyclic immunofluorescence method. Nat. Commun. 6, 8390 (2015).

    CAS 
    PubMed 

    Google Scholar 

  • 21.

    Gut, G., Herrmann, M. D. & Pelkmans, L. Multiplexed protein maps link subcellular organization to cellular states. Science 361, (2018).

  • 22.

    Regot, S., Hughey, J. J., Bajar, B. T., Carrasco, S. & Covert, M. W. High-sensitivity measurements of multiple kinase activities in live single cells. Cell 157, 1724–1734 (2014).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 23.

    Hochbaum, D. R. et al. All-optical electrophysiology in mammalian neurons using engineered microbial rhodopsins. Nat. Methods 11, 825–833 (2014).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 24.

    Zhao, Y. et al. An expanded palette of genetically encoded Ca2+ indicators. Science 333, 1888–1891 (2011).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 25.

    Lin, M. Z. & Schnitzer, M. J. Genetically encoded indicators of neuronal activity. Nat. Neurosci. 19, 1142–1153 (2016).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 26.

    Nelson, D. E. et al. Oscillations in NF-κB signaling control the dynamics of gene expression. Science 306, 704–708 (2004).

    CAS 
    PubMed 

    Google Scholar 

  • 27.

    Cohen-Saidon, C., Cohen, A. A., Sigal, A., Liron, Y. & Alon, U. Dynamics and variability of ERK2 response to EGF in individual living cells. Mol. Cell 36, 885–893 (2009).

    CAS 
    PubMed 

    Google Scholar 

  • 28.

    Albeck, J. G., Mills, G. B. & Brugge, J. S. Frequency-modulated pulses of ERK activity transmit quantitative proliferation signals. Mol. Cell 49, 249–261 (2013).

    CAS 
    PubMed 

    Google Scholar 

  • 29.

    Lahav, G. et al. Dynamics of the p53–Mdm2 feedback loop in individual cells. Nat. Genet. 36, 147–150 (2004).

    CAS 
    PubMed 

    Google Scholar 

  • 30.

    Biederer, T. & Scheiffele, P. Mixed-culture assays for analyzing neuronal synapse formation. Nat. Protoc. 2, 670–676 (2007).

    CAS 
    PubMed 

    Google Scholar 

  • 31.

    Scheiffele, P., Fan, J., Choih, J., Fetter, R. & Serafini, T. Neuroligin expressed in nonneuronal cells triggers presynaptic development in contacting axons. Cell 101, 657–669 (2000).

    CAS 
    PubMed 

    Google Scholar 

  • 32.

    Varadarajan, N. et al. A high-throughput single-cell analysis of human CD8+ T cell functions reveals discordance for cytokine secretion and cytolysis. J. Clin. Invest. 121, 4322–4331 (2011).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 33.

    Moen, E. et al. Deep learning for cellular image analysis. Nat. Methods 16, 1233–1246 (2019).

    CAS 
    PubMed 

    Google Scholar 

  • 34.

    Grys, B. T. et al. Machine learning and computer vision approaches for phenotypic profiling. J. Cell Biol. 216, 65–71 (2016).

    PubMed 

    Google Scholar 

  • 35.

    Caicedo, J. C. et al. Data-analysis strategies for image-based cell profiling. Nat. Methods 14, 849–863 (2017).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 36.

    Caicedo, J. C., McQuin, C., Goodman, A., Singh, S. & Carpenter, A. E. Weakly supervised learning of single-cell feature embeddings. Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. 2018, 9309–9318 (2018).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 37.

    Kobayashi, H., Cheveralls, K. C., Leonetti, M. D. & Royer, L. A. Self-supervised deep-learning encodes high-resolution features of protein subcellular localization. Preprint at bioRxiv https://doi.org/10.1101/2021.03.29.437595 (2021).

  • 38.

    Strezoska, Ž. et al. High-content analysis screening for cell cycle regulators using arrayed synthetic crRNA libraries. J. Biotechnol. 251, 189–200 (2017).

    CAS 
    PubMed 

    Google Scholar 

  • 39.

    Kim, H. S. et al. Arrayed CRISPR screen with image-based assay reliably uncovers host genes required for coxsackievirus infection. Genome Res 28, 859–868 (2018).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 40.

    de Groot, R., Lüthi, J., Lindsay, H., Holtackers, R. & Pelkmans, L. Large-scale image-based profiling of single-cell phenotypes in arrayed CRISPR–Cas9 gene perturbation screens. Mol. Syst. Biol. 14, e8064 (2018).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 41.

    Michlits, G. et al. CRISPR-UMI: single-cell lineage tracing of pooled CRISPR–Cas9 screens. Nat. Methods 14, 1191–1197 (2017).

    CAS 
    PubMed 

    Google Scholar 

  • 42.

    Schmierer, B. et al. CRISPR/Cas9 screening using unique molecular identifiers. Mol. Syst. Biol. 13, 945 (2017).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 43.

    Ke, R. et al. In situ sequencing for RNA analysis in preserved tissue and cells. Nat. Methods 10, 857–860 (2013).

    CAS 
    PubMed 

    Google Scholar 

  • 44.

    Larsson, C., Grundberg, I., Söderberg, O. & Nilsson, M. In situ detection and genotyping of individual mRNA molecules. Nat. Methods 7, 395–397 (2010).

    CAS 
    PubMed 

    Google Scholar 

  • 45.

    Feldman, D. et al. Optical pooled screens in human cells. Cell 179, 787–799.e17 (2019).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 46.

    Funk, L. et al. The phenotypic landscape of essential human genes. Preprint at bioRxiv https://doi.org/10.1101/2021.11.28.470116 (2021).

  • 47.

    Lawson, M. J. et al. In situ genotyping of a pooled strain library after characterizing complex phenotypes. Mol. Syst. Biol. 13, 947 (2017).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 48.

    Camsund, D. et al. Time-resolved imaging-based CRISPRi screening. Nat. Methods 17, 86–92 (2020).

    CAS 
    PubMed 

    Google Scholar 

  • 49.

    Emanuel, G., Moffitt, J. R. & Zhuang, X. High-throughput, image-based screening of pooled genetic-variant libraries. Nat. Methods 14, 1159–1162 (2017).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 50.

    Wang, C., Lu, T., Emanuel, G., Babcock, H. P. & Zhuang, X. Imaging-based pooled CRISPR screening reveals regulators of lncRNA localization. Proc. Natl Acad. Sci. USA 116, 10842–10851 (2019).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 51.

    Datlinger, P. et al. Pooled CRISPR screening with single-cell transcriptome readout. Nat. Methods 14, 297–301 (2017).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 52.

    Hasle, N. et al. High-throughput, microscope-based sorting to dissect cellular heterogeneity. Mol. Syst. Biol. 16, e9442 (2020).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 53.

    Yan, X. et al. High-content imaging-based pooled CRISPR screens in mammalian cells. J. Cell Biol. 220, (2021).

  • 54.

    Lee, J. et al. Versatile phenotype-activated cell sorting. Sci. Adv. 6, eabb7438 (2020).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 55.

    Kanfer, G. et al. Image-based pooled whole-genome CRISPRi screening for subcellular phenotypes. J. Cell Biol. 220, (2021).

  • 56.

    Wheeler, E. C. et al. Pooled CRISPR screens with imaging on microraft arrays reveals stress granule-regulatory factors. Nat. Methods 17, 636–642 (2020).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 57.

    Stringer, C., Wang, T., Michaelos, M. & Pachitariu, M. Cellpose: a generalist algorithm for cellular segmentation. Nat. Methods 18, 100–106 (2021).

    CAS 
    PubMed 

    Google Scholar 

  • 58.

    McQuin, C. et al. CellProfiler 3.0: next-generation image processing for biology. PLOS Biol. 16, e2005970 (2018).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 59.

    Reicher, A., Koren, A. & Kubicek, S. Pooled protein tagging, cellular imaging, and in situ sequencing for monitoring drug action in real time. Genome Res. 30, 1846–1855 (2020).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 60.

    Feldman, D., Singh, A., Garrity, A. J. & Blainey, P. C. Lentiviral co-packaging mitigates the effects of intermolecular recombination and multiple integrations in pooled genetic screens. Preprint at bioRxiv https://doi.org/10.1101/262121 (2018).

  • 61.

    Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 62.

    Edelstein, A. D. et al. Advanced methods of microscope control using μManager software. J. Biol. Methods 1, e10 (2014).

    PubMed 

    Google Scholar 

  • 63.

    Schmid-Burgk, J. L. et al. Highly parallel profiling of Cas9 variant specificity. Mol. Cell 78, 794–800.e8 (2020).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 64.

    Feldman, D. & Funk, L. Pooled genetic perturbation screens with image-based phenotypes, OpticalPooledScreens. Zenodo https://doi.org/10.5281/zenodo.5002684 (2021).

  • 65.

    Kosuri, S. et al. Scalable gene synthesis by selective amplification of DNA pools from high-fidelity microchips. Nat. Biotechnol. 28, 1295–1299 (2010).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 66.

    Doench, J. G. et al. Rational design of highly active sgRNAs for CRISPR-Cas9–mediated gene inactivation. Nat. Biotechnol. 32, 1262–1267 (2014).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 67.

    Kutner, R. H., Zhang, X.-Y. & Reiser, J. Production, concentration and titration of pseudotyped HIV-1-based lentiviral vectors. Nat. Protoc. 4, 495–505 (2009).

    CAS 
    PubMed 

    Google Scholar 

  • 68.

    Köster, J. & Rahmann, S. Snakemake—a scalable bioinformatics workflow engine. Bioinformatics 28, 2520–2522 (2012).

    PubMed 

    Google Scholar 

  • 69.

    Williams, E. et al. Image Data Resource: a bioimage data integration and publication platform. Nat. Methods 14, 775–781 (2017).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 70.

    Luo, B. et al. Highly parallel identification of essential genes in cancer cells. Proc. Natl Acad. Sci. USA 105, 20380–20385 (2008).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 71.

    Li, W. et al. MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens. Genome Biol. 15, 554 (2014).

    PubMed 
    PubMed Central 

    Google Scholar 

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