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Fitness effects of CRISPR/Cas9-targeting of long noncoding RNA genes

Nature Biotechnology volume 38, pages 573–576 (2020)Cite this article

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Matters Arising to this article was published on 24 February 2020

The Original Article was published on 05 November 2018

arising from Liu et al. Nature Biotechnology https://doi.org/10.1038/nbt.4283 (2018)

Long noncoding RNAs (lncRNAs) are a large class of transcripts that do not encode proteins, and while some lncRNAs are known to have important functions in mammalian cells, the function of the large majority of lncRNAs remains to be explored in depth. Thus, large-scale and unbiased approaches to screen for functional lncRNA loci are critical for the advancement of this field. Moreover, as any single approach will likely be affected by both false positives and false negatives, there is clear value in having a multitude of orthogonal strategies for exploring lncRNA function. Liu et al.1 recently have presented an approach for systematically investigating lncRNA function by targeting the CRISPR/Cas9 nuclease to lncRNA splice acceptor/donor sites. Although the approach is clearly a valuable addition to the arsenal of strategies used to characterize lncRNA function, there are some important caveats as well. Specifically, the authors identified 469 lncRNA loci that confer fitness defects in three cell lines when their splice sites were targeted in this manner. To validate lncRNA hits and their overall approach, they used paired Cas9 targeting to remove lncRNA exons. We find evidence that a substantial fraction of the hits in these screens (at least 30–39% in each cell line) are likely to be false positives due to either nuclease activity in copy number-amplified regions or overlap with protein-coding genes. Furthermore, the validation method chosen by the authors was not sufficiently orthogonal to identify such false positives.

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Fig. 1: LncRNA CRISPR screen hits inside copy-number-amplified regions or overlapping protein-coding genes.
Fig. 2: Top lncRNA screen hits in HeLa cells are located in the HPV18 integration locus.

Data availability

All analysis was performed on previously published datasets. Sources and accession numbers are tabulated in the Supplementary Methods.

Code availability

All code used for analysis, figure generation and preparation for visualization in genome browsers is included as a Jupyter Notebook in Supplementary File 1. Software dependencies and versions are listed in the Supplementary Methods.

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Acknowledgements

The authors were supported by the University of California San Francisco Medical Scientist Training Program (M.A.H. and S.J.L.); NIH grant nos. F30NS092319-01 (S.J.L.), 1R01NS0091544 (D.A.L.) and R35CA209919 (H.Y.C.) and the Howard Hughes Medical Institute (M.A.H., H.Y.C. and J.S.W.).

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Author notes

  1. Max A. Horlbeck

    Present address: Boston Children’s Hospital, Boston, MA, USA

Authors and Affiliations

  1. Department of Cellular & Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA

    Max A. Horlbeck & Jonathan S. Weissman

  2. Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, CA, USA

    Max A. Horlbeck & Jonathan S. Weissman

  3. California Institute for Quantitative Biomedical Research, University of California, San Francisco, San Francisco, CA, USA

    Max A. Horlbeck & Jonathan S. Weissman

  4. Department of Neurological Surgery, University of California, San Francisco, CA, USA

    S. John Liu & Daniel A. Lim

  5. Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, CA, USA

    S. John Liu & Daniel A. Lim

  6. Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA, USA

    Howard Y. Chang

  7. Department of Dermatology, Stanford University, Stanford, CA, USA

    Howard Y. Chang

  8. Department of Genetics, Stanford University, Stanford, CA, USA

    Howard Y. Chang

  9. Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA

    Howard Y. Chang

  10. San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA

    Daniel A. Lim

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Contributions

M.A.H. performed the analysis. S.J.L. contributed to CCAT1 analysis. M.A.H., S.J.L., H.Y.C., D.A.L. and J.S.W. wrote the manuscript and provided critical feedback.

Corresponding author

Correspondence to Jonathan S. Weissman.

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Competing interests

M.A.H. and J.S.W. have filed a patent application related to CRISPR interference screening (15/326,428).

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Supplementary information

Supplementary Methods and Figs. 1 and 2.

Method description and two figures

Reporting Summary

Supplementary File 1

Jupyter notebook containing all analysis scripts. Notebook is provided in HTML and Jupyter notebook (.ipynb) formats.

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Horlbeck, M.A., Liu, S.J., Chang, H.Y. et al. Fitness effects of CRISPR/Cas9-targeting of long noncoding RNA genes. Nat Biotechnol 38, 573–576 (2020). https://doi.org/10.1038/s41587-020-0428-0

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