Genetic Screening: Making better CRISPR libraries

  1. Shiyou Zhu
  2. Wensheng Wei  Is a corresponding author
  1. School of Life Sciences, Peking University, China
  2. Peking University, China

Since the human genome sequence was completed in 2003, genome-wide screening has become a popular method for quickly associating specific genes with their roles in cells. More recently, the CRISPR-Cas9 system has become the dominant tool for genome-editing (Jinek et al., 2012; Cong et al., 2013; Mali et al., 2013) and it has subsequently been adapted to make highly effective genetic screening platforms (Shalem et al., 2014; Zhou et al., 2014).

The CRISPR-Cas9 system is derived from the methods used by certain bacteria to identify and cut up foreign genetic material (Barrangou et al., 2007). To edit the genome, specially designed RNA molecules guide a nuclease enzyme called Cas9 to the location of interest in the DNA sequence; the Cas9 enzyme then cuts the DNA at this position. A mutant form of Cas9 that is unable to cut DNA can also be used to generate libraries of single guide RNAs (sgRNAs) that target regions around transcription start sites in the genome. By allowing researchers to either repress or activate gene expression – techniques that are known as CRISPR interference (CRISPRi) and CRISPR activation (CRISPRa), respectively – these sgRNAs make it possible to carry out powerful genetic screens in mammalian cells (Gilbert et al., 2014; Konermann et al., 2015). Now, in eLife, Jonathan Weissman and colleagues at the University of California, San Francisco – including Max Horlbeck as first author – report that a new algorithm can predict the activity of sgRNAs more accurately than existing algorithms (Horlbeck et al., 2016a).

Many factors affect the ability of sgRNAs to activate or repress genes including the sequence, length and secondary structure of the sgRNA (Doench et al., 2014; Xu et al., 2015). Furthermore, the DNA in mammalian cells (and also in other eukaryotic cells) is packaged inside structures called nucleosomes, which make it difficult for the Cas9 enzyme to access the DNA (Hinz et al., 2015; Horlbeck et al., 2016b; Isaac et al., 2016). This is particularly important for CRISPRi and CRISPRa screens because the mutant Cas9 enzyme must stay bound to the DNA for extended periods of time. Horlbeck et al. therefore optimized the design of their sgRNAs to target DNA regions that were not packaged in nucleosomes and thus were more accessible to mutant Cas9.

To improve the CRISPRi and CRISPRa libraries that they had made previously (Gilbert et al., 2014), Horlbeck et al. analyzed data from 30 CRISPRi screens and 9 CRISPRa screens and defined “activity scores” for every sgRNA relative to the sgRNA with the strongest activity for each gene. They then used this information to make new CRISPRi and CRISPRa libraries that contained the ten most active sgRNAs for each gene.

The new human CRISPRi library was used to screen chronic myeloid leukemia K562 cells to identify genes that are essential for cell growth. Impressively, this library was able to identify about 10% more essential genes compared with the original CRISPRi library (Gilbert et al., 2014). Furthermore, a half-sized version of the new human CRISPRi library (with only the top five sgRNAs per gene) performed similarly to the full-sized version. This is reassuring because smaller libraries are easier to construct and use in screens. Similarly, Horlbeck et al. also demonstrated that the new human CRISPRa library outperformed the original one.

Horlbeck et al. found that, when used with the mutant form of Cas9, none of the CRISPRi libraries had toxic side effects like those observed with other approaches that use the active enzyme (Wang et al., 2015). This makes it possible to effectively identify genes, even if they show only slight differences in expression compared to negative controls.

To summarize, this study established an effective algorithm to predict the activity of sgRNAs based on the location of nucleosomes in the genome. Horlbeck et al. used this algorithm to generate new CRISPRi and CRISPRa libraries with much improved performance in genetic screens in humans and mice. It remains to be seen if the algorithm could be used to optimize other types of CRISPR screens, especially ones that use the normal Cas9 enzyme.


Article and author information

Author details

  1. Shiyou Zhu

    1. Biodynamic Optical Imaging Center, State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing, China
    2. Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program, Peking University, Beijing, China
    Competing interests
    The authors declare that no competing interests exist.
  2. Wensheng Wei

    1. Biodynamic Optical Imaging Center, State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing, China
    2. Beijing Advanced Innovation Center for Genomics, Peking University, Beijing, China
    3. Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
    For correspondence
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8053-2423

Publication history

  1. Version of Record published: November 3, 2016 (version 1)


© 2016, Zhu et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.


  • 2,116
    Page views
  • 440
  • 0

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Shiyou Zhu
  2. Wensheng Wei
Genetic Screening: Making better CRISPR libraries
eLife 5:e21863.

Further reading

    1. Computational and Systems Biology
    2. Genetics and Genomics
    Jayashree Kumar et al.
    Research Article Updated

    Splicing is highly regulated and is modulated by numerous factors. Quantitative predictions for how a mutation will affect precursor mRNA (pre-mRNA) structure and downstream function are particularly challenging. Here, we use a novel chemical probing strategy to visualize endogenous precursor and mature MAPT mRNA structures in cells. We used these data to estimate Boltzmann suboptimal structural ensembles, which were then analyzed to predict consequences of mutations on pre-mRNA structure. Further analysis of recent cryo-EM structures of the spliceosome at different stages of the splicing cycle revealed that the footprint of the Bact complex with pre-mRNA best predicted alternative splicing outcomes for exon 10 inclusion of the alternatively spliced MAPT gene, achieving 74% accuracy. We further developed a β-regression weighting framework that incorporates splice site strength, RNA structure, and exonic/intronic splicing regulatory elements capable of predicting, with 90% accuracy, the effects of 47 known and 6 newly discovered mutations on inclusion of exon 10 of MAPT. This combined experimental and computational framework represents a path forward for accurate prediction of splicing-related disease-causing variants.

    1. Computational and Systems Biology
    Mayank Baranwal et al.
    Research Article

    Predicting the dynamics and functions of microbiomes constructed from the bottom-up is a key challenge in exploiting them to our benefit. Current models based on ecological theory fail to capture complex community behaviors due to higher order interactions, do not scale well with increasing complexity and in considering multiple functions. We develop and apply a long short-term memory (LSTM) framework to advance our understanding of community assembly and health-relevant metabolite production using a synthetic human gut community. A mainstay of recurrent neural networks, the LSTM learns a high dimensional data-driven non-linear dynamical system model. We show that the LSTM model can outperform the widely used generalized Lotka-Volterra model based on ecological theory. We build methods to decipher microbe-microbe and microbe-metabolite interactions from an otherwise black-box model. These methods highlight that Actinobacteria, Firmicutes and Proteobacteria are significant drivers of metabolite production whereas Bacteroides shape community dynamics. We use the LSTM model to navigate a large multidimensional functional landscape to design communities with unique health-relevant metabolite profiles and temporal behaviors. In sum, the accuracy of the LSTM model can be exploited for experimental planning and to guide the design of synthetic microbiomes with target dynamic functions.