1932

Abstract

Assigning functions to genes and learning how to control their expression are part of the foundation of cell biology and therapeutic development. An efficient and unbiased method to accomplish this is genetic screening, which historically required laborious clone generation and phenotyping and is still limited by scale today. The rapid technological progress on modulating gene function with CRISPR-Cas and measuring it in individual cells has now relaxed the major experimental constraints and enabled pooled screening with complex readouts from single cells. Here, we review the principles and practical considerations for pooled single-cell CRISPR screening. We discuss perturbation strategies, experimental model systems, matching the perturbation to the individual cells, reading out cell phenotypes, and data analysis. Our focus is on single-cell RNA sequencing and cell sorting–based readouts, including image-enabled cell sorting. We expect this transformative approach to fuel biomedical research for the next several decades.

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2023-11-27
2024-05-04
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Literature Cited

  1. 1.
    Adamson B, Norman TM, Jost M, Cho MY, Nuñez JK et al. 2016. A multiplexed single-cell CRISPR screening platform enables systematic dissection of the unfolded protein response. Cell 167:1867–82.e21
    [Google Scholar]
  2. 2.
    Adamson B, Norman TM, Jost M, Weissman JS. 2018. Approaches to maximize sgRNA-barcode coupling in Perturb-seq screens. bioRxiv 298349. https://doi.org/10.1101/298349
  3. 3.
    Alerasool N, Segal D, Lee H, Taipale M. 2020. An efficient KRAB domain for CRISPRi applications in human cells. Nat. Methods 17:1093–96
    [Google Scholar]
  4. 4.
    Andrews TS, Kiselev VY, McCarthy D, Hemberg M. 2021. Tutorial: guidelines for the computational analysis of single-cell RNA sequencing data. Nat. Protoc. 16:1–9
    [Google Scholar]
  5. 5.
    Anzalone AV, Koblan LW, Liu DR. 2020. Genome editing with CRISPR-Cas nucleases, base editors, transposases and prime editors. Nat. Biotechnol. 38:824–44
    [Google Scholar]
  6. 6.
    Anzalone AV, Randolph PB, Davis JR, Sousa AA, Koblan LW et al. 2019. Search-and-replace genome editing without double-strand breaks or donor DNA. Nature 576:149–57
    [Google Scholar]
  7. 7.
    Behan FM, Iorio F, Picco G, Goncalves E, Beaver CM et al. 2019. Prioritization of cancer therapeutic targets using CRISPR-Cas9 screens. Nature 568:511–16
    [Google Scholar]
  8. 8.
    Bock C, Datlinger P, Chardon F, Coelho MA, Dong MTB et al. 2022. High-content CRISPR screening. Nat. Rev. Methods Primers 2:8
    [Google Scholar]
  9. 9.
    Bodapati S, Daley TP, Lin X, Zou J, Qi LS. 2020. A benchmark of algorithms for the analysis of pooled CRISPR screens. Genome Biol 21:62
    [Google Scholar]
  10. 10.
    Cao J, Packer JS, Ramani V, Cusanovich DA, Huynh C et al. 2017. Comprehensive single-cell transcriptional profiling of a multicellular organism. Science 357:661–67
    [Google Scholar]
  11. 11.
    Cetin R, Wegner M, Luwisch L, Saud S, Achmedov T et al. 2023. Optimized metrics for orthogonal combinatorial CRISPR screens. Sci. Rep 13:7405
    [Google Scholar]
  12. 12.
    Chen PJ, Liu DR. 2023. Prime editing for precise and highly versatile genome manipulation. Nat. Rev. Genet. 24:161–77
    [Google Scholar]
  13. 13.
    Cheng J, Lin G, Wang T, Wang Y, Guo W et al. 2023. Massively parallel CRISPR-based genetic perturbation screening at single-cell resolution. Adv. Sci. 10:e2204484
    [Google Scholar]
  14. 14.
    Cheng L, Li YC, Qi Q, Xu P, Feng RP et al. 2021. Single-nucleotide-level mapping of DNA regulatory elements that control fetal hemoglobin expression. Nat. Genet. 53:869–80
    [Google Scholar]
  15. 15.
    Cleary B, Cong L, Cheung A, Lander ES, Regev A. 2017. Efficient generation of transcriptomic profiles by random composite measurements. Cell 171:1424–36.e18
    [Google Scholar]
  16. 16.
    Colic M, Hart T. 2021. Common computational tools for analyzing CRISPR screens. Emerg. Top. Life Sci. 5:779–88
    [Google Scholar]
  17. 17.
    Cossarizza A, Chang H-D, Radbruch A, Acs A, Adam D et al. 2019. Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition). Eur. J. Immunol. 49:1457–973
    [Google Scholar]
  18. 18.
    Datlinger P, Rendeiro AF, Schmidl C, Krausgruber T, Traxler P et al. 2017. Pooled CRISPR screening with single-cell transcriptome readout. Nat. Methods 14:297–301
    [Google Scholar]
  19. 19.
    de Boer CG, Ray JP, Hacohen N, Regev A. 2020. MAUDE: inferring expression changes in sorting-based CRISPR screens. Genome Biol 21:134
    [Google Scholar]
  20. 20.
    Dede M, McLaughlin M, Kim E, Hart T. 2020. Multiplex enCas12a screens detect functional buffering among paralogs otherwise masked in monogenic Cas9 knockout screens. Genome Biol 21:262
    [Google Scholar]
  21. 21.
    Deshpande R, Nelson J, Simpkins SW, Costanzo M, Piotrowski JS et al. 2017. Efficient strategies for screening large-scale genetic interaction networks. bioRxiv 159632. https://doi.org/10.1101/159632
  22. 22.
    Dixit A, Parnas O, Li B, Chen J, Fulco CP et al. 2016. Perturb-seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens. Cell 167:1853–66.e17
    [Google Scholar]
  23. 23.
    Doench JG. 2018. Am I ready for CRISPR? A user's guide to genetic screens. Nat. Rev. Genet. 19:67–80
    [Google Scholar]
  24. 24.
    Doench JG, Fusi N, Sullender M, Hegde M, Vaimberg EW et al. 2016. Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. Nature Biotechnol 34:184–91
    [Google Scholar]
  25. 25.
    Drager NM, Sattler SM, Huang CT, Teter OM, Leng K et al. 2022. A CRISPRi/a platform in human iPSC-derived microglia uncovers regulators of disease states. Nat. Neurosci. 25:1149–62
    [Google Scholar]
  26. 26.
    Erwood S, Bily TMI, Lequyer J, Yan J, Gulati N et al. 2022. Saturation variant interpretation using CRISPR prime editing. Nat. Biotechnol. 40:885–95
    [Google Scholar]
  27. 27.
    Feldman D, Singh A, Garrity AJ, Blainey PC. 2018. Lentiviral co-packaging mitigates the effects of intermolecular recombination and multiple integrations in pooled genetic screens. bioRxiv 262121. https://doi.org/10.1101/262121
    [Crossref]
  28. 28.
    Feldman D, Singh A, Schmid-Burgk JL, Carlson RJ, Mezger A et al. 2019. Optical pooled screens in human cells. Cell 179:787–99.e17
    [Google Scholar]
  29. 29.
    Findlay GM, Boyle EA, Hause RJ, Klein JC, Shendure J. 2014. Saturation editing of genomic regions by multiplex homology-directed repair. Nature 513:120–23
    [Google Scholar]
  30. 30.
    Fleck JS, Jansen SMJ, Wollny D, Zenk F, Seimiya M et al. 2022. Inferring and perturbing cell fate regulomes in human brain organoids. Nature https://doi.org/10.1038/s41586-022-05279-8
    [Crossref] [Google Scholar]
  31. 31.
    Frangieh CJ, Melms JC, Thakore PI, Geiger-Schuller KR, Ho P et al. 2021. Multimodal pooled Perturb-CITE-seq screens in patient models define mechanisms of cancer immune evasion. Nat. Genet. 53:332–41
    [Google Scholar]
  32. 32.
    Funk L, Su KC, Ly J, Feldman D, Singh A et al. 2022. The phenotypic landscape of essential human genes. Cell 185:4634–53.e22
    [Google Scholar]
  33. 33.
    Gasperini M, Hill AJ, McFaline-Figueroa JL, Martin B, Kim S et al. 2019. A genome-wide framework for mapping gene regulation via cellular genetic screens. Cell 176:377–90
    [Google Scholar]
  34. 34.
    Gayoso A, Lopez R, Xing G, Boyeau P, Valiollah Pour Amiri V et al. 2022. A Python library for probabilistic analysis of single-cell omics data. Nat. Biotechnol. 40:163–66
    [Google Scholar]
  35. 35.
    Geiger-Schuller K, Eraslan B, Kuksenko O, Dey KK, Jagadeesh KA et al. 2023. Systematically characterizing the roles of E3-ligase family members in inflammatory responses with massively parallel Perturb-seq. bioRxiv 2023.01.23.525198. https://doi.org/10.1101/2023.01.23.525198
  36. 36.
    Gier RA, Budinich KA, Evitt NH, Cao Z, Freilich ES et al. 2020. High-performance CRISPR-Cas12a genome editing for combinatorial genetic screening. Nat. Commun. 11:3455
    [Google Scholar]
  37. 37.
    Guna A, Page KR, Replogle JR, Esantsi TK, Wang ML et al. 2023. A dual sgRNA library design to probe genetic modifiers using genome-wide CRISPRi screens. bioRxiv 2023.01.22.525086. https://doi.org/10.1101/2023.01.22.525086
    [Crossref]
  38. 38.
    Hao Y, Hao S, Andersen-Nissen E, Mauck WM 3rd, Zheng S et al. 2021. Integrated analysis of multimodal single-cell data. Cell 184:3573–87.e29
    [Google Scholar]
  39. 39.
    Hasle N, Cooke A, Srivatsan S, Huang H, Stephany JJ et al. 2020. High-throughput, microscope-based sorting to dissect cellular heterogeneity. Mol. Syst. Biol. 16:e9442
    [Google Scholar]
  40. 40.
    Heumos L, Schaar AC, Lance C, Litinetskaya A, Drost F et al. 2023. Best practices for single-cell analysis across modalities. Nat. Rev. Genet. 24:550–72
    [Google Scholar]
  41. 41.
    Hill AJ, McFaline-Figueroa JL, Starita LM, Gasperini MJ, Matreyek KA et al. 2018. On the design of CRISPR-based single-cell molecular screens. Nat. Methods 15:271–74
    [Google Scholar]
  42. 42.
    Hilton IB, D'Ippolito AM, Vockley CM, Thakore PI, Crawford GE et al. 2015. Epigenome editing by a CRISPR-Cas9-based acetyltransferase activates genes from promoters and enhancers. Nat. Biotechnol. 33:510–17
    [Google Scholar]
  43. 43.
    Horlbeck MA, Gilbert LA, Villalta JE, Adamson B, Pak RA et al. 2016. Compact and highly active next-generation libraries for CRISPR-mediated gene repression and activation. eLife 5:e19760
    [Google Scholar]
  44. 44.
    Hou J, Liang S, Xu C, Wei Y, Wang Y et al. 2022. Single-cell CRISPR immune screens reveal immunological roles of tumor intrinsic factors. NAR Cancer 4:zcac038
    [Google Scholar]
  45. 45.
    Hou Y, Guo H, Cao C, Li X, Hu B et al. 2016. Single-cell triple omics sequencing reveals genetic, epigenetic, and transcriptomic heterogeneity in hepatocellular carcinomas. Cell Res 26:304–19
    [Google Scholar]
  46. 46.
    Huang TP, Newby GA, Liu DR. 2021. Precision genome editing using cytosine and adenine base editors in mammalian cells. Nat. Protoc. 16:1089–128
    [Google Scholar]
  47. 47.
    Hwang B, Lee DS, Tamaki W, Sun Y, Ogorodnikov A et al. 2021. SCITO-seq: single-cell combinatorial indexed cytometry sequencing. Nat. Methods 18:903–11
    [Google Scholar]
  48. 48.
    Ibraheim R, Tai PWL, Mir A, Javeed N, Wang J et al. 2021. Self-inactivating, all-in-one AAV vectors for precision Cas9 genome editing via homology-directed repair in vivo. Nat. Commun. 12:6267
    [Google Scholar]
  49. 49.
    Jaitin DA, Kenigsberg E, Keren-Shaul H, Elefant N, Paul F et al. 2014. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science 343:776–79
    [Google Scholar]
  50. 50.
    Jaitin DA, Weiner A, Yofe I, Lara-Astiaso D, Keren-Shaul H et al. 2016. Dissecting immune circuits by linking CRISPR-pooled screens with single-cell RNA-seq. Cell 167:1883–96.e15
    [Google Scholar]
  51. 51.
    Jin X, Simmons SK, Guo A, Shetty AS, Ko M et al. 2020. In vivo Perturb-Seq reveals neuronal and glial abnormalities associated with autism risk genes. Science 370:eaaz6063
    [Google Scholar]
  52. 52.
    Kamimoto K, Stringa B, Hoffmann CM, Jindal K, Solnica-Krezel L, Morris SA. 2023. Dissecting cell identity via network inference and in silico gene perturbation. Nature 614:742–51
    [Google Scholar]
  53. 53.
    Kampmann M. 2018. CRISPRi and CRISPRa screens in mammalian cells for precision biology and medicine. ACS Chem. Biol. 13:406–16
    [Google Scholar]
  54. 54.
    Kanfer G, Sarraf SA, Maman Y, Baldwin H, Dominguez-Martin E et al. 2021. Image-based pooled whole-genome CRISPRi screening for subcellular phenotypes. J. Cell Biol. 220:e202006180
    [Google Scholar]
  55. 55.
    Kearns NA, Pham H, Tabak B, Genga RM, Silverstein NJ et al. 2015. Functional annotation of native enhancers with a Cas9-histone demethylase fusion. Nat. Methods 12:401–3
    [Google Scholar]
  56. 56.
    Klein AM, Mazutis L, Akartuna I, Tallapragada N, Veres A et al. 2015. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161:1187–201
    [Google Scholar]
  57. 57.
    Koeppel J, Peets EM, Weller J, Pallaseni A, Liberante F, Parts L. 2021. Predicting efficiency of writing short sequences into the genome using prime editing. bioRxiv 2021.11.10.468024. https://doi.org/10.1101/2021.11.10.468024
    [Crossref]
  58. 58.
    Li W, Xu H, Xiao T, Cong L, Love MI et al. 2014. MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens. Genome Biol 15:554
    [Google Scholar]
  59. 59.
    Liscovitch-Brauer N, Montalbano A, Deng J, Mendez-Mancilla A, Wessels HH et al. 2021. Profiling the genetic determinants of chromatin accessibility with scalable single-cell CRISPR screens. Nat. Biotechnol. 39:1270–77
    [Google Scholar]
  60. 60.
    Lopez R, Tagasovska N, Ra S, Cho K, Pritchard JK, Regev A. 2022. Learning causal representations of single cells via sparse mechanism shift modeling. arXiv:2211.03553 [q-bio.GN]
  61. 61.
    Love MI, Huber W, Anders S. 2014. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15:550
    [Google Scholar]
  62. 62.
    Macosko EZ, Basu A, Satija R, Nemesh J, Shekhar K et al. 2015. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161:1202–14
    [Google Scholar]
  63. 63.
    Mair B, Aldridge PM, Atwal RS, Philpott D, Zhang M et al. 2019. High-throughput genome-wide phenotypic screening via immunomagnetic cell sorting. Nat. Biomed. Eng. 3:796–805
    [Google Scholar]
  64. 64.
    Mandegar MA, Huebsch N, Frolov EB, Shin E, Truong A et al. 2016. CRISPR interference efficiently induces specific and reversible gene silencing in human iPSCs. Cell Stem Cell 18:541–53
    [Google Scholar]
  65. 65.
    Marshall JL, Doughty BR, Subramanian V, Guckelberger P, Wang Q et al. 2020. HyPR-seq: single-cell quantification of chosen RNAs via hybridization and sequencing of DNA probes. PNAS 117:33404–13
    [Google Scholar]
  66. 66.
    McCarty NS, Graham AE, Studena L, Ledesma-Amaro R. 2020. Multiplexed CRISPR technologies for gene editing and transcriptional regulation. Nat. Commun. 11:1281
    [Google Scholar]
  67. 67.
    Meyers RM, Bryan JG, McFarland JM, Weir BA, Sizemore AE et al. 2017. Computational correction of copy number effect improves specificity of CRISPR-Cas9 essentiality screens in cancer cells. Nat. Genet. 49:1779–84
    [Google Scholar]
  68. 68.
    Michlits G, Hubmann M, Wu SH, Vainorius G, Budusan E et al. 2017. CRISPR-UMI: single-cell lineage tracing of pooled CRISPR-Cas9 screens. Nat. Methods 14:1191–97
    [Google Scholar]
  69. 69.
    Mimitou EP, Cheng A, Montalbano A, Hao S, Stoeckius M et al. 2019. Multiplexed detection of proteins, transcriptomes, clonotypes and CRISPR perturbations in single cells. Nat. Methods 16:409–12
    [Google Scholar]
  70. 70.
    Najm FJ, Strand C, Donovan KF, Hegde M, Sanson KR et al. 2018. Orthologous CRISPR-Cas9 enzymes for combinatorial genetic screens. Nat. Biotechnol. 36:179–89
    [Google Scholar]
  71. 71.
    Panganiban RA, Park HR, Sun M, Shumyatcher M, Himes BE, Lu Q. 2019. Genome-wide CRISPR screen identifies suppressors of endoplasmic reticulum stress-induced apoptosis. PNAS 116:13384–93
    [Google Scholar]
  72. 72.
    Peets EM, Crepaldi L, Zhou Y, Allen F, Elmentaite R et al. 2019. Minimized double guide RNA libraries enable scale-limited CRISPR/Cas9 screens. bioRxiv 859652. https://doi.org/10.1101/859652
  73. 73.
    Peidli S, Green TD, Shen C, Gross T, Min J et al. 2023. scPerturb: harmonized single-cell perturbation data. bioRxiv 2022.08.20.504663. https://www.biorxiv.org/content/10.1101/2022.08.20.504663v3
  74. 74.
    Perez AR, Sala L, Perez RK, Vidigal JA. 2021. CSC software corrects off-target mediated gRNA depletion in CRISPR-Cas9 essentiality screens. Nat. Commun. 12:6461
    [Google Scholar]
  75. 75.
    Pierce SE, Granja JM, Greenleaf WJ. 2021. High-throughput single-cell chromatin accessibility CRISPR screens enable unbiased identification of regulatory networks in cancer. Nat. Commun. 12:2969
    [Google Scholar]
  76. 76.
    Replogle JM, Norman TM, Xu A, Hussmann JA, Chen J et al. 2020. Combinatorial single-cell CRISPR screens by direct guide RNA capture and targeted sequencing. Nat. Biotechnol. 38:954–61
    [Google Scholar]
  77. 77.
    Replogle JM, Saunders RA, Pogson AN, Hussmann JA, Lenail A et al. 2022. Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq. Cell 185:2559–75.e28
    [Google Scholar]
  78. 78.
    Rodriguez-Meira A, Buck G, Clark SA, Povinelli BJ, Alcolea V et al. 2019. Unravelling intratumoral heterogeneity through high-sensitivity single-cell mutational analysis and parallel RNA sequencing. Mol. Cell 73:1292–305.e8
    [Google Scholar]
  79. 79.
    Rosenberg AB, Roco CM, Muscat RA, Kuchina A, Sample P et al. 2018. Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science 360:176–82
    [Google Scholar]
  80. 80.
    Roth TL, Li PJ, Blaeschke F, Nies JF, Apathy R et al. 2020. Pooled knockin targeting for genome engineering of cellular immunotherapies. Cell 181:728–44.e21
    [Google Scholar]
  81. 81.
    Rubin AJ, Parker KR, Satpathy AT, Qi Y, Wu B et al. 2019. Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks. Cell 176:361–76.e17
    [Google Scholar]
  82. 82.
    Sanson KR, Hanna RE, Hegde M, Donovan KF, Strand C et al. 2018. Optimized libraries for CRISPR-Cas9 genetic screens with multiple modalities. Nat. Commun. 9:5416
    [Google Scholar]
  83. 83.
    Schmidt R, Steinhart Z, Layeghi M, Freimer JW, Bueno R et al. 2022. CRISPR activation and interference screens decode stimulation responses in primary human T cells. Science 375:eabj4008
    [Google Scholar]
  84. 84.
    Schraivogel D, Gschwind AR, Milbank JH, Leonce DR, Jakob P et al. 2020. Targeted Perturb-seq enables genome-scale genetic screens in single cells. Nat. Methods 17:629–35
    [Google Scholar]
  85. 85.
    Schraivogel D, Kuhn TM, Rauscher B, Rodríguez-Martínez M, Paulsen M et al. 2022. High-speed fluorescence image–enabled cell sorting. Science 375:315–20
    [Google Scholar]
  86. 86.
    Schraivogel D, Steinmetz LM. 2023. Cell sorters see things more clearly now. Mol. Syst. Biol. 19:e11254
    [Google Scholar]
  87. 87.
    Schumann K, Raju SS, Lauber M, Kolb S, Shifrut E et al. 2020. Functional CRISPR dissection of gene networks controlling human regulatory T cell identity. Nat. Immunol. 21:1456–66
    [Google Scholar]
  88. 88.
    Shifrut E, Carnevale J, Tobin V, Roth TL, Woo JM et al. 2018. Genome-wide CRISPR screens in primary human T cells reveal key regulators of immune function. Cell 175:1958–71.e15
    [Google Scholar]
  89. 89.
    Smits AH, Ziebell F, Joberty G, Zinn N, Mueller WF et al. 2019. Biological plasticity rescues target activity in CRISPR knock outs. Nat. Methods 16:1087–93
    [Google Scholar]
  90. 90.
    Srivatsan SR, McFaline-Figueroa JL, Ramani V, Saunders L, Cao JY et al. 2020. Massively multiplex chemical transcriptomics at single-cell resolution. Science 367:45–51
    [Google Scholar]
  91. 91.
    Stoeckius M. 2017. Large-scale simultaneous measurement of epitopes and transcriptomes in single cells. Nat. Methods 14:865–68
    [Google Scholar]
  92. 92.
    Stuart T, Satija R. 2019. Integrative single-cell analysis. Nat. Rev. Genet. 20:257–72
    [Google Scholar]
  93. 93.
    Subramanian A, Narayan R, Corsello SM, Peck DD, Natoli TE et al. 2017. A next generation connectivity map: L1000 platform and the first 1,000,000 profiles. Cell 171:1437–52.e17
    [Google Scholar]
  94. 94.
    Sun N, Petiwala S, Wang R, Lu C, Hu M et al. 2019. Development of drug-inducible CRISPR-Cas9 systems for large-scale functional screening. BMC Genom 20:225
    [Google Scholar]
  95. 95.
    Svensson V, Vento-Tormo R, Teichmann SA. 2018. Exponential scaling of single-cell RNA-seq in the past decade. Nat. Protoc. 13:599–604
    [Google Scholar]
  96. 96.
    Tian R, Abarientos A, Hong J, Hashemi SH, Yan R et al. 2021. Genome-wide CRISPRi/a screens in human neurons link lysosomal failure to ferroptosis. Nat. Neurosci. 24:1020–34
    [Google Scholar]
  97. 97.
    Tian R, Gachechiladze MA, Ludwig CH, Laurie MT, Hong JY et al. 2019. CRISPR interference-based platform for multimodal genetic screens in human iPSC-derived neurons. Neuron 104:239–55.e12
    [Google Scholar]
  98. 98.
    Ting PY, Parker AE, Lee JS, Trussell C, Sharif O et al. 2018. Guide Swap enables genome-scale pooled CRISPR-Cas9 screening in human primary cells. Nat. Methods 15:941–46
    [Google Scholar]
  99. 99.
    Tran V, Papalexi E, Schroeder S, Kim G, Sapre A et al. 2022. High sensitivity single cell RNA sequencing with split pool barcoding. bioRxiv 2022.08.27.505512. https://doi.org/10.1101/2022.08.27.505512
  100. 100.
    Truong DJ, Kuhner K, Kuhn R, Werfel S, Engelhardt S et al. 2015. Development of an intein-mediated split-Cas9 system for gene therapy. Nucleic Acids Res. 43:6450–58
    [Google Scholar]
  101. 101.
    Tsherniak A, Vazquez F, Montgomery PG, Weir BA, Kryukov G et al. 2017. Defining a cancer dependency map. Cell 170:564–76.e16
    [Google Scholar]
  102. 102.
    Ursu O, Neal JT, Shea E, Thakore PI, Jerby-Arnon L et al. 2022. Massively parallel phenotyping of coding variants in cancer with Perturb-seq. Nat. Biotechnol. 40:896–905
    [Google Scholar]
  103. 103.
    Usluer S, Hallast P, Crepaldi L, Zhou Y, Urgo K et al. 2022. Optimised whole-genome CRISPR interference screens identify ARID1A-dependent growth regulators in human induced pluripotent stem cells. Stem Cell Rep 18:51061–74
    [Google Scholar]
  104. 104.
    Vinceti A, Perron U, Trastulla L, Iorio F. 2022. Reduced gene templates for supervised analysis of scale-limited CRISPR-Cas9 fitness screens. Cell Rep 40:111145
    [Google Scholar]
  105. 105.
    Walton RT, Singh A, Blainey PC. 2022. Pooled genetic screens with image-based profiling. Mol. Syst. Biol. 18:e10768
    [Google Scholar]
  106. 106.
    Wang C, Lu T, Emanuel G, Babcock HP, Zhuang X. 2019. Imaging-based pooled CRISPR screening reveals regulators of lncRNA localization. PNAS 116:10842–851
    [Google Scholar]
  107. 107.
    Wessels HH, Mendez-Mancilla A, Hao Y, Papalexi E, Mauck WM 3rd et al. 2023. Efficient combinatorial targeting of RNA transcripts in single cells with Cas13 RNA Perturb-seq. Nat. Methods 20:86–94
    [Google Scholar]
  108. 108.
    Wolf FA, Angerer P, Theis FJ. 2018. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol 19:15
    [Google Scholar]
  109. 109.
    Wroblewska A, Dhainaut M, Ben-Zvi B, Rose SA, Park ES et al. 2018. Protein barcodes enable high-dimensional single-cell CRISPR screens. Cell 175:1141–55.e16
    [Google Scholar]
  110. 110.
    Xie S, Duan J, Li B, Zhou P, Hon GC. 2017. Multiplexed engineering and analysis of combinatorial enhancer activity in single cells. Mol. Cell 66:285–99.e5
    [Google Scholar]
  111. 111.
    Xu X, Chemparathy A, Zeng L, Kempton HR, Shang S et al. 2021. Engineered miniature CRISPR-Cas system for mammalian genome regulation and editing. Mol. Cell 81:4333–45.e4
    [Google Scholar]
  112. 112.
    Yamano S, Dai J, Moursi AM. 2010. Comparison of transfection efficiency of nonviral gene transfer reagents. Mol. Biotechnol. 46:287–300
    [Google Scholar]
  113. 113.
    Yan X, Stuurman N, Ribeiro SA, Tanenbaum ME, Horlbeck MA et al. 2021. High-content imaging-based pooled CRISPR screens in mammalian cells. J. Cell Biol. 220:e202008158
    [Google Scholar]
  114. 114.
    Yang L, Zhu Y, Yu H, Cheng X, Chen S et al. 2020. scMAGeCK links genotypes with multiple phenotypes in single-cell CRISPR screens. Genome Biol 21:19
    [Google Scholar]
  115. 115.
    Yao D, Binan L, Bezney J, Simonton B, Freedman J et al. 2023. Compressed Perturb-seq: highly efficient screens for regulatory circuits using random composite perturbations. bioRxiv 2023.01.23.525200. https://doi.org/10.1101/2023.01.23.525200
  116. 116.
    Yeo NC, Chavez A, Lance-Byrne A, Chan Y, Menn D et al. 2018. An enhanced CRISPR repressor for targeted mammalian gene regulation. Nat. Methods 15:611–16
    [Google Scholar]
  117. 117.
    Yu L, Wang X, Mu Q, Tam SST, Loi DSC et al. 2023. scONE-seq: A single-cell multi-omics method enables simultaneous dissection of phenotype and genotype heterogeneity from frozen tumors. Sci. Adv. 9:eabp8901
    [Google Scholar]
  118. 118.
    Zappia L, Theis FJ. 2021. Over 1000 tools reveal trends in the single-cell RNA-seq analysis landscape. Genome Biol 22:301
    [Google Scholar]
  119. 119.
    Zetsche B, Volz SE, Zhang F. 2015. A split-Cas9 architecture for inducible genome editing and transcription modulation. Nat. Biotechnol. 33:139–42
    [Google Scholar]
  120. 120.
    Zhang H, Li T, Sun Y, Yang H. 2021. Perfecting targeting in CRISPR. Annu. Rev. Genet. 55:453–77
    [Google Scholar]
  121. 121.
    Zheng GXY, Terry JM, Belgrader P, Ryvkin P, Bent ZW et al. 2017. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8:14049
    [Google Scholar]
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