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CRISPR knockout screening outperforms shRNA and CRISPRi in identifying essential genes

Nature Biotechnology volume 34, pages 631–633 (2016)Cite this article

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Abstract

High-throughput genetic screens have become essential tools for studying a wide variety of biological processes. Here we experimentally compare systems based on clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR-associated protein 9 (Cas9) or its transcriptionally repressive variant, CRISPR-interference (CRISPRi), with a traditional short hairpin RNA (shRNA)-based system for performing lethality screens. We find that the CRISPR technology performed best, with low noise, minimal off-target effects and consistent activity across reagents.

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Figure 1: Screen results of RT-112 cells.
Figure 2: Across cell-line screen performance.

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Acknowledgements

We thank all members of the Bernards and Beijersbergen groups and G. Bounova for useful discussions, and the Netherlands Cancer Institute genomics core facility for next-generation sequencing. The doxycycline-inducible KRAB-dCas9 expression system, consisting of pHR-TRE3G-KRAB-dCas9-P2A-mCherry and pHR-Tet3G, were kind gifts of Luke Gilbert, the Jonathan Weissman laboratories, UCSF. We thank G. Verhaegh, Nijmegen Institute for Molecular Sciences, the Netherlands, and M. Knowles, Leeds Institute of Cancer Studies and Pathology, UK for the cell lines they kindly provided.

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Authors and Affiliations

  1. Division of Molecular Carcinogenesis, Cancer Systems Biology Centre and Cancer Genomics Centre Netherlands, The Netherlands Cancer Institute, Amsterdam, the Netherlands

    Bastiaan Evers, Katarzyna Jastrzebski, Jeroen P M Heijmans, Wipawadee Grernrum, Roderick L Beijersbergen & Rene Bernards

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  1. Bastiaan Evers
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  2. Katarzyna Jastrzebski
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  3. Jeroen P M Heijmans
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  4. Wipawadee Grernrum
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  5. Roderick L Beijersbergen
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  6. Rene Bernards
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Contributions

Experiments were designed by B.E., K.J., R.L.B. and R.B., carried out by B.E., K.J., J.P.M.H. and W.G., and analyzed by B.E. The paper was written by B.E., R.L.B. and R.B. This work was supported by the Cancer Genomics Netherlands consortium.

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Correspondence to Rene Bernards.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Screening technology overview

The shRNA screens were performed in the pLKO.1 puro backbone (Sigma-Aldrich), the CRISPR gRNAs were cloned in a modified version of Lenti-CRISPRv2.0 (see Online Materials and Methods). The CRISPRi system consisted of a Doxycycline inducible KRAB-dCas9 fusion construct co-expressing mCherry, a constitutive tet activator and a plasmid encoding the gRNAs driven from a constitutive U6 promoter.

Supplementary Figure 2 Read-count correlations RT112 shRNA screen

Supplementary Figure 3 Read-count correlations RT112 CRISPR screen

Supplementary Figure 4 Read-count correlations RT112 CRISPRi screen

Supplementary Figure 5 Fraction of functional constructs per gene

Ratios of functional gRNAs to all gRNAs targeting every gene were determined for each screening technology.

Supplementary Figure 6 Comparison of CRISPRi screen with previously published data

Performance of gRNAs that were identical in the libraries used in this publication and the previously published paper by Gilbert et al.7 was plotted as -10Log P-values compared to the performance of the other gRNAs present in the library (A). The growth phenotype upon transduction in K562 cells as reported in this paper was compared to 2Log fold depletion of corresponding gRNAs used in this publication (B).

Supplementary Figure 7 gRNA positions across transcript variants for the CRISPRi library

For every gene, transcripts were plotted from -50 to +300 around the TSS (vertical line) and aligned where possible. gRNAs were plotted yellow when q-values were >0.1 and blue when ≤0.1. For RPL36, two transcripts that were wrongly mapped in hg19 are plotted in gray.

Supplementary Figure 8 Screen results of UM-UC-3 cells

Relative fold-change of averaged normalized read-counts of t=1 vs. t=0 for the shRNA (A) and CRISPR (B) technologies plotted as a function of their initial averaged normalized read-counts at t=0. Orange dots represent constructs targeting essential genes, blue dots represent constructs targeting non-essential genes. Data was averaged over three biological replicate screens.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–8 (PDF 3140 kb)

Supplementary Table 1

Data of shRNA screen (XLSX 123 kb)

Supplementary Table 2

Data of CRISPR screen (XLSX 240 kb)

Supplementary Table 3

Data of CRISPRi screen (XLSX 119 kb)

Supplementary Table 4

Primers (XLSX 10 kb)

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Evers, B., Jastrzebski, K., Heijmans, J. et al. CRISPR knockout screening outperforms shRNA and CRISPRi in identifying essential genes. Nat Biotechnol 34, 631–633 (2016). https://doi.org/10.1038/nbt.3536

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