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CRISPRi-seq for genome-wide fitness quantification in bacteria

Abstract

CRISPR interference (CRISPRi) is a powerful tool to link essential and nonessential genes to specific phenotypes and to explore their functions. Here we describe a protocol for CRISPRi screenings to assess genome-wide gene fitness in a single sequencing step (CRISPRi-seq). We demonstrate the use of the protocol in Streptococcus pneumoniae, an important human pathogen; however, the protocol can easily be adapted for use in other organisms. The protocol includes a pipeline for single-guide RNA library design, workflows for pooled CRISPRi library construction, growth assays and sequencing steps, a read analysis tool (2FAST2Q) and instructions for fitness quantification. We describe how to make an IPTG-inducible system with small libraries that are easy to handle and cost-effective and overcome bottleneck issues, which can be a problem when using similar, transposon mutagenesis-based methods. Ultimately, the procedure yields a fitness score per single-guide RNA target for any given growth condition. A genome-wide screening can be finished in 1 week with a constructed library. Data analysis and follow-up confirmation experiments can be completed in another 2–3 weeks.

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Fig. 1: Growth curves of individual CRISPRi strains indicate essentiality of sgRNA targets.
Fig. 2: Overview of the CRISPRi-seq workflow.
Fig. 3: Construction of the pooled CRISPRi library.
Fig. 4: Workflow of the experimental CRISPRi-seq screening steps.
Fig. 5: Example outcomes of differential sgRNA enrichment analyses.

Data availability

Source data are provided with this paper.

Code availability

Source code for the sgRNA design and evaluation pipelines, as well as for the naïve power analysis and corresponding data, can be found at https://github.com/veeninglab/CRISPRi-seq. 2FAST2Q code, files and executables can be found at https://github.com/veeninglab/2FAST2Q, https://pypi.org/project/fast2q/ or https://doi.org/10.5281/zenodo.5079789. (Off-)target binding site tables from Liu et al.11 can be found through our website: https://www.veeninglab.com/crispri-seq.

References

  1. Cain, A. K. et al. A decade of advances in transposon-insertion sequencing. Nat. Rev. Genet. 21, 526–540 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Qi, L. S. et al. Repurposing CRISPR as an RNA-guided platform for sequence-specific control of gene expression. Cell 152, 1173–1183 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Bikard, D. et al. Programmable repression and activation of bacterial gene expression using an engineered CRISPR-Cas system. Nucleic Acids Res. 41, 7429–7437 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Lee, H. H. et al. Functional genomics of the rapidly replicating bacterium Vibrio natriegens by CRISPRi. Nat. Microbiol. 4, 1105–1113 (2019).

    CAS  PubMed  Google Scholar 

  5. Liu, X. et al. High‐throughput CRISPRi phenotyping identifies new essential genes in Streptococcus pneumoniae. Mol. Syst. Biol. 13, 931 (2017).

    PubMed  PubMed Central  Google Scholar 

  6. Wang, T. et al. Pooled CRISPR interference screening enables genome-scale functional genomics study in bacteria with superior performance. Nat. Commun. 9, 2475 (2018).

    PubMed  PubMed Central  Google Scholar 

  7. Peters, J. M. et al. A comprehensive, CRISPR-based functional analysis of essential genes in bacteria. Cell 165, 1493–1506 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Zhao, C., Shu, X. & Sun, B. Construction of a gene knockdown system based on catalytically inactive (“dead”) Cas9 (dCas9) in Staphylococcus aureus. Appl. Environ. Microbiol. 83, e00291–17 (2017).

    PubMed  PubMed Central  Google Scholar 

  9. Larson, M. H. et al. CRISPR interference (CRISPRi) for sequence-specific control of gene expression. Nat. Protoc. 8, 2180–2196 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. de Wet, T. J., Winkler, K. R., Mhlanga, M., Mizrahi, V. & Warner, D. F. Arrayed CRISPRi and quantitative imaging describe the morphotypic landscape of essential mycobacterial genes. eLife 9, e60083 (2020).

    PubMed  PubMed Central  Google Scholar 

  11. Liu, X. et al. Exploration of bacterial bottlenecks and Streptococcus pneumoniae pathogenesis by CRISPRi-Seq. Cell Host Microbe 29, 107–120.e6 (2021).

    CAS  PubMed  Google Scholar 

  12. de Wet, T. J., Gobe, I., Mhlanga, M. M. & Warner, D. F. CRISPRi-Seq for the identification and characterisation of essential mycobacterial genes and transcriptional units. Preprint at bioRxiv https://doi.org/10.1101/358275 (2018).

  13. Beuter, D. et al. Selective enrichment of slow-growing bacteria in a metabolism-wide CRISPRi library with a TIMER protein. ACS Synth. Biol. 7, 2775–2782 (2018).

    CAS  PubMed  Google Scholar 

  14. Yao, L. et al. Pooled CRISPRi screening of the cyanobacterium Synechocystis sp PCC 6803 for enhanced industrial phenotypes. Nat. Commun. 11, 1666 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Calvo-Villamañán, A. et al. On-target activity predictions enable improved CRISPR–dCas9 screens in bacteria. Nucleic Acids Res 48, e64 (2020).

    PubMed  PubMed Central  Google Scholar 

  16. Rousset, F. et al. Genome-wide CRISPR-dCas9 screens in E. coli identify essential genes and phage host factors. PLOS Genet 14, e1007749 (2018).

    PubMed  PubMed Central  Google Scholar 

  17. Vigouroux, A. & Bikard, D. CRISPR tools to control gene expression in bacteria. Microbiol. Mol. Biol. Rev. 84, e00077–19 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Rousset, F. & Bikard, D. CRISPR screens in the era of microbiomes. Curr. Opin. Microbiol. 57, 70–77 (2020).

    CAS  PubMed  Google Scholar 

  19. Slager, J., Aprianto, R. & Veening, J.-W. Deep genome annotation of the opportunistic human pathogen Streptococcus pneumoniae D39. Nucleic Acids Res. 46, 9971–9989 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Donati, S. et al. Multi-omics analysis of CRISPRi-knockdowns identifies mechanisms that buffer decreases of enzymes in E. coli metabolism. Cell Syst. 12, 56–67.e6 (2021).

    CAS  PubMed  Google Scholar 

  21. Hawkins, J. S. et al. Mismatch-CRISPRi reveals the co-varying expression–fitness relationships of essential genes in Escherichia coli and Bacillus subtilis. Cell Syst. 11, 523–535.e9 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Jiang, W., Oikonomou, P. & Tavazoie, S. Comprehensive genome-wide perturbations via CRISPR adaptation reveal complex genetics of antibiotic sensitivity. Cell 180, 1002–1017.e31 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Bosch, B. et al. Genome-wide gene expression tuning reveals diverse vulnerabilities of M. tuberculosis. Cell 184, 4579–4592.e24 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Momen-Roknabadi, A., Oikonomou, P., Zegans, M. & Tavazoie, S. An inducible CRISPR interference library for genetic interrogation of Saccharomyces cerevisiae biology. Commun. Biol. 3, 723 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Du, D. et al. Genetic interaction mapping in mammalian cells using CRISPR interference. Nat. Methods 14, 577–580 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Rock, J. M. et al. Programmable transcriptional repression in mycobacteria using an orthogonal CRISPR interference platform. Nat. Microbiol. 2, 16274 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Guzzo, M., Castro, L. K., Reisch, C. R., Guo, M. S. & Laub, M. T. A CRISPR interference system for efficient and rapid gene knockdown in Caulobacter crescentus. MBio 11, e02415–e02419 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. van Opijnen, T., Bodi, K. L. & Camilli, A. Tn-seq: high-throughput parallel sequencing for fitness and genetic interaction studies in microorganisms. Nat. Methods 6, 767–772 (2009).

    PubMed  PubMed Central  Google Scholar 

  29. Hutchison, C. A. et al. Polar effects of transposon insertion into a minimal bacterial genome. J. Bacteriol. 201, e00185–19 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. van Opijnen, T., Lazinski, D. W. & Camilli, A. Genome‐wide fitness and genetic interactions determined by Tn‐seq, a high‐throughput massively parallel sequencing method for microorganisms. Curr. Protoc. Mol. Biol. 106, 7.16.1–7.16.24 (2014).

    Google Scholar 

  31. van Opijnen, T. & Camilli, A. Transposon insertion sequencing: a new tool for systems-level analysis of microorganisms. Nat. Rev. Microbiol. 11, 435–442 (2013).

    PubMed  Google Scholar 

  32. Yasir, M. et al. TraDIS-Xpress: a high-resolution whole-genome assay identifies novel mechanisms of triclosan action and resistance. Genome Res 30, 239–249 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Vigouroux, A., Oldewurtel, E., Cui, L., Bikard, D. & Teeffelen, S. Tuning dCas9’s ability to block transcription enables robust, noiseless knockdown of bacterial genes. Mol. Syst. Biol. 14, e7899 (2018).

    PubMed  PubMed Central  Google Scholar 

  34. Cui, L. et al. A CRISPRi screen in E. coli reveals sequence-specific toxicity of dCas9. Nat. Commun. 9, 1912 (2018).

    PubMed  PubMed Central  Google Scholar 

  35. 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 

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

    CAS  PubMed  Google Scholar 

  37. Shiver, A. L., Culver, R., Deutschbauer, A. M. & Huang, K. C. Rapid ordering of barcoded transposon insertion libraries of anaerobic bacteria. Nat. Protoc. 16, 3049–3071 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Baym, M., Shaket, L., Anzai, I. A., Adesina, O. & Barstow, B. Rapid construction of a whole-genome transposon insertion collection for Shewanella oneidensis by Knockout Sudoku. Nat. Commun. 7, 13270 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Zhu, L. J., Holmes, B. R., Aronin, N. & Brodsky, M. H. CRISPRseek: a Bioconductor package to identify target-specific guide RNAs for CRISPR-Cas9 genome-editing systems. PLoS One 9, e108424 (2014).

    PubMed  PubMed Central  Google Scholar 

  40. Drost, H.-G. & Paszkowski, J. Biomartr: genomic data retrieval with R. Bioinformatics 33, 1216–1217 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Blin, K., Pedersen, L. E., Weber, T. & Lee, S. Y. CRISPy-web: an online resource to design sgRNAs for CRISPR applications. Synth. Syst. Biotechnol. 1, 118–121 (2016).

    PubMed  PubMed Central  Google Scholar 

  42. Doench, J. G. et al. Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. Nat. Biotechnol. 34, 184–191 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Sanson, K. R. et al. Optimized libraries for CRISPR-Cas9 genetic screens with multiple modalities. Nat. Commun. 9, 5416 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. van Gestel, J., Hawkins, J. S., Todor, H. & Gross, C. A. Computational pipeline for designing guide RNAs for mismatch-CRISPRi. STAR Protoc. 2, 100521 (2021).

    PubMed  PubMed Central  Google Scholar 

  45. Spoto, M., Guan, C., Fleming, E. & Oh, J. A universal, genomewide GuideFinder for CRISPR/Cas9 targeting in microbial genomes. mSphere 5, e00086–20 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Keller, L. E., Rueff, A.-S., Kurushima, J. & Veening, J.-W. Three new integration vectors and fluorescent proteins for use in the opportunistic human pathogen Streptococcus pneumoniae. Genes 10, 394 (2019).

    CAS  PubMed Central  Google Scholar 

  47. Sorg, R. A., Kuipers, O. P. & Veening, J.-W. Gene expression platform for synthetic biology in the human pathogen Streptococcus pneumoniae. ACS Synth. Biol. 4, 228–239 (2015).

    CAS  PubMed  Google Scholar 

  48. Read, A., Gao, S., Batchelor, E. & Luo, J. Flexible CRISPR library construction using parallel oligonucleotide retrieval. Nucleic Acids Res. 45, e101 (2017).

    PubMed  PubMed Central  Google Scholar 

  49. 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 

  50. Bokulich, N. A. et al. Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing. Nat. Methods 10, 57–59 (2013).

    CAS  PubMed  Google Scholar 

  51. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    PubMed  PubMed Central  Google Scholar 

  52. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    CAS  PubMed  Google Scholar 

  53. Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

    PubMed  PubMed Central  Google Scholar 

  54. 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 

  55. Winter, J. et al. CRISPRAnalyzeR: interactive analysis, annotation and documentation of pooled CRISPR screens. Preprint at bioRxiv https://doi.org/10.1101/109967 (2017).

  56. Whatmore, A. M., Barcus, V. A. & Dowson, C. G. Genetic diversity of the streptococcal competence (com) gene locus. J. Bacteriol. 181, 3144–3154 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Zhu, A., Ibrahim, J. G. & Love, M. I. Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences. Bioinformatics 35, 2084–2092 (2019).

    CAS  PubMed  Google Scholar 

  58. Pozzi, G. et al. Competence for genetic transformation in encapsulated strains of Streptococcus pneumoniae: two allelic variants of the peptide pheromone. J. Bacteriol. 178, 6087–6090 (1996).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Kurushima, J. et al. Unbiased homeologous recombination during pneumococcal transformation allows for multiple chromosomal integration events. eLife 9, e58771 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Sorg, R. A. et al. Collective resistance in microbial communities by intracellular antibiotic deactivation. PLOS Biol. 14, e2000631 (2016).

    PubMed  PubMed Central  Google Scholar 

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Acknowledgements

Work in the Veening lab is supported by the Swiss National Science Foundation (SNSF) (project grants 31003A_172861 and 310030_192517), SNSF JPIAMR grant (40AR40_185533), SNSF NCCR ‘AntiResist’ (51NF40_180541) and ERC consolidator grant 771534-PneumoCaTChER.

Author information

Authors and Affiliations

Authors

Contributions

X.L. developed the experimental procedures. V.d.B. developed the computational/analysis procedures. A.M.B. developed 2FAST2Q and the Python code for the computational pipelines. J.W.V. conceptualized this study. V.d.B. and X.L. wrote the original manuscript draft with input from A.M.B. V.d.B., X.L., A.M.B. and J.W.V. revised and edited to obtain the final manuscript.

Corresponding author

Correspondence to Jan-Willem Veening.

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The authors declare no competing interests.

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Peer review information Nature Protocols thanks the anonymous reviewers for their contribution to the peer review of this work.

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Related links

Key references using this protocol

Liu, X. et al. Cell Host Microbe 29, 107–120.e6 (2021): https://doi.org/10.1016/j.chom.2020.10.001

Liu, X. et al. Mol. Syst. Biol. 13, 931 (2017): https://doi.org/10.15252/msb.20167449

Key data used in this protocol

Liu, X. et al. Cell Host Microbe 29, 107–120.e6 (2021): https://doi.org/10.1016/j.chom.2020.10.001

Supplementary information

Supplementary Information

Supplementary Figs. 1–3 and Supplementary Methods.

Supplementary Software

Custom Illumina MiniSeq sequencing recipes with 54 dark cycles for both Mid and High Output Kits

Source data

Source Data Fig. 1

Statistical source data.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 5

Statistical source data.

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de Bakker, V., Liu, X., Bravo, A.M. et al. CRISPRi-seq for genome-wide fitness quantification in bacteria. Nat Protoc 17, 252–281 (2022). https://doi.org/10.1038/s41596-021-00639-6

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