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shERWOOD UltramiR Pooled Screening Libraries

Pooled Screening Overview

Effective pooled screening is dependent on potent shRNA and equimolar representation

Pooled shRNA Screening Single Copy Knockdown
Figure 1. Efficient single copy knockdown.  Graph of expression level measured by western blot analysis showing representative knockdown in cell lines expressing shRNA from a single integration in the genome.
Pooled shRNA screening requires shRNA capable of knocking down its intended target while expressed from a single integration in the genome.  Early generation
shRNA libraries include a high number of shRNA constructs unable to achieve this possibly due to their designs being based on siRNA design rules rather than shRNA
(which go through processing in the microRNA biogenesis pathway and have different requirements for optimal processing and function).  In addition, shRNA pools created viachip-based method are known to contain a high number of mutations which limits theirfunctionality1,2.  The shERWOOD algorithm efficiently selects designs that are potent at single copy (figure 1.) making them uniquely suited for pooled screening.

Learn more about the Sensor Screen underlying the shERWOOD algorithm.


Pooled shRNA Library Histogram
Figure 2. Representative histogram for pooled shRNA library representation showing that >95% of constructs are within 5-fold of the mean.

Importance of equimolar distribution in a pooled shRNA screening library

Changes in the relative distribution of the shRNA in a pool indicate a selective advantage or disadvantage during the screening process and are detected using next-generation sequencing (NGS).  To ensure that artificial changes in population are not introduced, pools should be constructed to minimize variation in representation across hairpins. For example, false negative results can occur when an shRNA is under represented in the population and lost by chance due to low representation during transduction, insufficient cell plating during passaging, or insufficient sample size. 

transOMIC pooled shRNA libraries start with sequence-verified arrayed shRNA resulting in better normalization of shRNA representation in pools. Typical pooled libraries have a relatively narrow range of shRNA distribution across the pool. >95%  of the shRNA are within a 5-fold of the mean (Figure 2).


Pooled RNAi screening:  The figure above depicts the concept and workflow forpooled selection screening.  Positive (survival) screens select for genes that provide resistance to a selection pressure when knocked down.  Negative (dropout) screens highlight genes that provide resistance or sensitize to a selection pressure when knocked down.

In pooled shRNA screening, hundreds to thousands of hairpins are combined (pooled) and interrogated simultaneously in a multiplex assay
without the need for robotics or liquid handling.

The schematic to the right depicts this process. 
  1. Transduction: Cells are transduced at a low MOI ensuring a single shRNA is expressed in each cell.

  2. Screen: The screen employs a selection process that is specific to the researcher’s assay.  There are two underlying strategies
    for selection: Negative selection (dropout) screens include an untreated control for comparison to allow the detection of shRNA that
    provided resistance or sensitized the cells to the selective reagent
    Positive selection (enrichment) screens only detect surviving cells
    and do not require an untreated control.

  3. Analysis: Under selection, resistant cells increase in the population and sensitized cells decrease in the population.  These changes in
    representation can be detected by sequence analysis (either Sanger sequencing or Next Generation Sequencing).

Schematic of pooled screening strategies.  (Adapted from  "A primer on using pooled shRNA libraries for functional genomic screens", Hu G and Luo J.  Acta Biochim Biophys Sin. 2012 Feb;44(2):103-12)

Pooled screening assays

The underlying workflow of all pooled shRNA screens begins by transducing cells with a heterogeneous pool of viral particles followed by an assay that enriches specific cells based on a phenotypic change.  Many variations have been successfully used in vivo or in vitro. However, most fall into three categories:  viability screen, reporter screen or behavioral screen1,2

Viability Screen:
In viability screens, the pool is most often used in combination  with a selective agent (e.g. drug treatment, exposure to pathogens).  An shRNA's impact on cell health is measured by changes in its representation in population.  A negative impact on cell viability will decrease an shRNA's representation while a positive impact on cell viability will increase representation3,4,5

Reporter Screens:
Reporters, such as fluorescent markers, can be used to indicate changes in transcription or can be fused to proteins to measure stability or localization.  Cells can be sorted for high or low levels of expression and hairpins from each group can be analyzed for their enrichment or depletion8

Behavioral Screens:
Cell behavior can also be assessed in a pooled screening assay.  For example, colony formation on soft agar can be used to select for cells with anchorage independent growth which can be an indicator of oncogenic transformation9,10

In addition, one of the advantages of shRNA over siRNA is the ability to perform these screens in vivo as well as in vitro.  Viability screens have been successfully adapted using xenographs to identify tumor suppressor genes as well as oncogenes5,6,7

This range of application in addition to the accessibility of shRNA pools provides researchers with a powerful option for screening.

  1.  Sims D. et al.  (2011) High-throughput RNA interference screening using pooled shRNA libraries and next generation sequencing. Genome Biology, 12(10):R104
  2. Hu G and Luo J. (2012) A primer on using pooled shRNA libraries for functional genomic screens.  Acta Biochim Biophys Sin. Feb;44(2):103-12
  3. Westerman B. et al. (2011) A genome-wide RNAi screen in mouse embryonic stem cells identifies Mp1 as a key mediator of differentiation. J Exp Med. Dec 19; 208(13): 2675–2689
  4. Schlabach M. et al. (2008) Cancer Proliferation Gene Discovery Through Functional Genomics Science 1 February 2008: Vol. 319 no. 5863 pp. 620-624
  5. Luo, J. et al.  (2009) A Genome-wide RNAi Screen Identifies Multiple Synthetic Lethal Interactions with the Ras Oncogene. Cell Volume 137, Issue 5, p835–848, 29 May
  6. Wuestefeld, T. et al. (2013) A Direct In Vivo RNAi Screen Identifies MKK4 as a Key Regulator of Liver Regeneration.  Cell Apr 11; 153(2): 389–401
  7. Possemato, R. et al. (2011) Functional genomics reveal that the serine synthesis pathway is essential in breast cancer. Nature Aug 18; 476(7360): 346–350
  8. Zender, L. et al. (2008) An Oncogenomics-based in vivo RNAi screen identifies tumor suppressors in liver cancer. Cell. Nov 28; 135(5): 852–864
  9. Gazin , C. et al. (2007) An Elaborate Pathway Required for Ras-Mediated Epigenetic Silencing. Nature, Oct 25; 449(7165): 1073-1077
  10. Smolen G. et al. (2010) A genome-wide RNAi screen identifies multiple RSK-dependent regulators of cell migration. Genes Dev ;24:v2654-2665.
  11. Westbrook T. et al. (2005) A genetic screen for candidate tumor suppressors identifies REST. Cell. 121(6): 837–848.
  12. Agilent, Appl. Note. 2008.  Agilent's Microarray Platform: How High-Fidelity DNA Synthesis Maximizes the Dynamic Range of Gene Expression Measurements (ID 5989-9159EN).
  13. Unpublished data. 2014.  Analysis of sequence fidelity in shRNA library production.  transOMIC technologies