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Collaborative RNAi

About RNAi

I. RNAi Screening: The Basics

II. RNAi Screening: Making the Most of What’s Available through the DF/HCC Collaborative RNAi Core

III. RNAi Screening: What Steps are Involved in Preparing to Screen?

IV. RNAi Screening: Am I ready to screen?

V. RNAi Screening: The Importance of Hit Verification and Validation

VI. RNAi Screening: Bioinformatics Analysis of Verified Hits

 *NEW* If you know the sequence of an RNAi reagent (mouse, human, worm or fly) or the RNAi reagent ID (worm or fly) and want to check the most updated interpretation of on-target and off-target effects, please check out the UP-TORR online tool. Links are:

UP-TORR for human RNAi reagent sequences (sequence required)

UP-TORR for mouse RNAi reagent sequences (sequence required)

UP-TORR for worm RNAi (sequence, PCR primer sequences or reagent ID required)

UP-TORR for fly RNAi (sequence, PCR primer sequences or reagent ID required)

I. RNAi Screening:The Basics

RNA interference (RNAi) is a method that is based on an endogenous cellular response to the presence of long or short double-stranded RNA and is useful to knock down (i.e. reduce but not eliminate) gene function.

The specific RNAi reagent and the method for its delivery to cells is likely to be different for different types of cells, model organisms and assays. But in general, a gene-specific segment of double-stranded RNA is introduced into cells, leading over time to degradation of the endogenous target mRNA. Short segments (~21 bps) are typical for mammalian systems, as longer segments can induce a non-specific interferon response. Longer segments (~500 bps) are typical for model systems that lack an interferon response, such as Drosophila. Similarly, the appropriate delivery systems differ for different systems. Common systems for delivery of the RNAi reagents include viral transduction (shRNAs), transfection or electroporation (shRNAs, siRNAs or dsRNAs) and bathing (dsRNAs).

RNAi can be compared with loss-of-function genetic approaches. When the normal function of a gene is required for a given function, RNAi knockdown may lead to a phenotype detectable in an assay that tests that function, either directly or indirectly. Example assays include activation of a transcriptional reporter; responses to an external stimulus (e.g. stress, drug treatment, pathogen); and sub-cellular localization or morphology of a specific protein or organelle.

Cell-based RNAi screens are typically performed in one of two formats, pooled or arrayed.  Both formats are supported by the collaborative DF/HCC Collaborative RNAi Core.

(1) With a pooled approach, all of the gene-specific reagents are pooled together (or synthesized en masse) and added, at random, to cells. This could be compared to transformation of a cDNA library into bacterial cells. You will know that each cell gets about one gene-specific RNAi reagent. But you will not know which cell got which one.

Pooled Selection. In some cases, the researcher next performs a selection; that is, applies a treatment (e.g. a drug, condition or pathogen) that kills most cells, leaving only cells that have an appropriate RNAi knockdown to survive. That is, when the gene-specific RNAi phenotype is viability under conditions in which most cells would die. Subsequent to the selection, a molecular method is used to figure out which RNAi reagent (and thus, which putative gene target) is responsible for “escape” of the lethal treatment.

Pooled Comparison. In other cases, sub-sets of cells (or different cell types) are treated differently before and/or after being subjected to the RNAi library (creating reference and experimental sets). Subsequently, a molecular method such as micro-array analysis is used to compare the two sample sets. The array detects the abundance of the RNAi reagents (such as via detection of the shRNA sequence itself or a molecular barcode in the construct). The array is used to determine which RNAi reagents (thus, which putative gene targets) are under- and/or over-represented in the experimental set as compared with the reference set.

Advantages: a pooled approach is typically more practical to do in a standard lab space and in a relatively small total volume, thus making it more feasible for some groups and less expensive. Disadvantages: deconvolution of the putative “hits” (positive results) requires specialized or custom microarrays and their analysis.

(2) With an arrayed approach, each unique RNAi reagent (or unique set of reagents targeting a single gene, such as for a small pool of independent siRNAs targeting one gene) occupies a unique well in a microtiter plate, such as a 384-well plate. Experiments are done in 384- or 96-well format. Thus, after the assay, you can easily determine which cells got which specific RNAi reagent by looking up the identity of the reagent in a given well using a database or spreadsheet.

An arrayed approach is useful for a wide variety of cell-based assays, including plate-reader assays (i.e. total light output, such as from GFP, FITC or luciferase) and low- or high-content image-based assays. Most cells will have the expected normal response, behavior, shape etc.  But some specific reagents will have an abnormal response, behavior, shape etc. (mutant phenotype) that is detected in the assay. For some approaches, such as an imaging approach, multiple read-outs might be detected (i.e. via antibodies or dyes, using multiple fluorescent channels).

Advantages: Arrayed screening arguably opens the door to the widest possible range of cell-based assays that can be performed. You will be able to quickly associate hits with the putative gene target of the unique RNAi reagent that was present in a specific well in which the mutant phenotype was observed. Disadvantages: Typically requires specialized high-throughput equipment for plate processing and assay detection and reagents, media, etc. can be costly.

We recommend also reading the screening guidelines at the ICCB Longwood Screening Facility web site.

II. RNAi Screening: Making the Most of What’s Available through the DF/HCC RNAi Core

Most researchers will probably spend the majority of their time and effort working at or with one of the participating groups. What we emphasize below are those assay pipelines that combine resources from different participating groups and centers. These are just a few examples of how you can design a screen assay pipeline that will take best advantage of the resources available through the DF/HCC Collaborative RNAi Core.

Ex.

Primary Assay

Follow-Up Assays

1

Full-genome arrayed screen in Drosophila cells

Screen homologs of hits in human cells (reducing time and cost compared to full-genome screening in human or mouse cells)

2

Screen a set of candidate genes in a mouse cell line, the same candidates in a human cell line (arrayed format)

Integrate the data, follow up on hits found in both cell types (with the idea these are more likely to be “real” and on-target hits)

3

Large-scale arrayed screen with siRNAs in a human cell type that is transfectable and amenable to large-scale work

Small-scale screens testing the set of verified hits across many cell lines using viral-encoded shRNAs, including into less tractable but more biologically relevant cell-types

4

Full-genome pooled screen with shRNAs in a human cell type (reference and experimental cell sets)

Small-scale screens testing the set of deconvolved hits across many cell lines using shRNAs or siRNAs

5

Concurrent screen of all Drosophila, mouse and human kinases in the appropriate cell lines

Integrate results to minimize the chance of false negative and false positive results, re-test with ‘master’ list in a human cell line of interest

III. RNAi Screening: What Steps are Involved in Preparing to Screen?
The first step in screening is to develop an assay that addresses the biological question of interest. Thoughtful assay design and planning is essential, including use of appropriate controls. For example, if you are interested in changes in the levels of a firefly luciferase reporter, you should control for cell number, such as via ubiquitous expression of Renilla luciferase. Otherwise, RNAi reagents that affect cell viability, the cell cycle, or related processes could lead to false positive results (that is, low signal in 1,000 cells in a well is not the same as low signal because there are only 10 viable cells left in the well). In a best-case scenario, several positive and negative control RNAi reagents will behave as expected in your assay, and you will have a high signal-to-noise ratio.

At a practical level, you should also ensure that the assay:

  • Is feasible in the format you have selected (i.e. pooled or arrayed)
  • Can be performed in the appropriate volume and scale (e.g. cells grow fast and robustly enough to facilitate the assay; volumes can be scaled down)
  • Distinguishes hits of interest from other phenotypes that could complicate interpretation of results (or at least can be reasonably culled from the list of hits via one or more secondary assay)

After assay development the researcher makes a transition to assay optimization. At this stage, you should be working in the same format in which you will do the screen. And ideally, using the same instruments for assay read-out that you will use in the full screen.

You should ensure that the assay:

  • Is robust in the screening format (e.g. still works well in 384-well plates if your initial tests were in 48- or 96-well plates)
  • Is minimal in terms of costs, via optimization and testing of various antibodies, tags, dyes, transfection reagents, media, serum, dilution series, etc.

IV. RNAi Screening: Am I ready to screen?

Once you have developed and optimized an assay, you are probably ready to screen. Most centers will have a set of criteria that must be fulfilled before a screen is initiated. In addition, most groups will start you off with a test plate and then set of pilot plates, before you start processing a large number of plates.

Please be aware that the hands-on involvement of a researcher from your lab is essential to the process and helps to ensure success. No one will be able to perform the screen better, or more quickly and accurately gauge how thing are going, than those folks who designed and will later analyze the results of the screen.

V. RNAi Screening: The Importance of Hit Verification and Validation

For the purposes of this text, we will define hit verification as verification of the RNAi results from a primary screen (such as using a different RNAi reagent directed against the same gene), and hit validation as successful testing of the subset of verified hits using one or more independent assay (such as a genetic, biochemical, or proteomics approach). Note that the results of statistical and bioinformatics analyses of your primary screen dataset (see below) will also inform your interpretation of  screen results.

Hit Verification
One reason that hit verification is necessary that in any high-throuhgput approach, there is a significant risk of false positive and false negative results. Verification can limit the chance of including a false positive in your analyzed dataset. Another reason that hit verification is important is the complication of off-target effects (OTEs), in which one or more genes other than the intended target gene is affected by the RNAi reagent.

In general, the same groups that help facilitate RNAi screening can also provide reagents and help with hit verification. For example, after an arrayed siRNA screen in which each well contained a pool of, say, four siRNAs directed against the same gene target, positive hits are typically repeated with each individual siRNA contained in the primary screening pool. A positive result with just one of the pooled siRNAs suggests the likelihood that the primary result represents an OTE. By contrast, positive results with more than one reagent suggest, but do not guarantee, a gene-specific result. However, as we are still learning about RNAi reagent design, things are not always as cut-and-dried as that suggests, such that validation (see below) is critically important. The participating screening centers can provide reagents for hit verification. You can also utilize reagents from more than one center (e.g. follow up an shRNA screen with siRNAs, or vice-versa).

Hit Validation
Even after hit verification, it is still very important to demonstrate that the result you observe with RNAi in a particular cell type(s) is biologically relevant. In general, hit validation is designed and executed in your own lab, rather than in conjunction with a screening center. The appropriate validation assay(s) will be dependent upon your screening project, your biological goals, and the scale at which you wish to validate results. For example, you may want to go deep into understanding one or a few genes on the list of hits, such that sub-cellular localization, co-IP and/or genetic analysis is an appropriate validation. Alternatively, you may want to gain a system-wide or pathway-wide understanding of the results, such as by comparing the full set of hits with results from another high-throughput technique, e.g. micro-array or RNAseq analysis, or a proteomics technique like yeast two-hybrid analysis or co-IP followed by mass spec.

VI. RNAi Screening: Bioinformatics Analysis of Verified Hits

Regardless of your strategy, planning follow-up often benefits from bioinformatics analysis. You might start by asking some of the following questions: 

  • Are there GO terms that are over-represented in the list of verified hits?
  • Did expected or unexpected genes, pathways, and/or functions show up in the list of verified hits?
  • How do these results compare with what is already known about the gene hits in terms of expression, function, sub-cellular localization and/or protein-protein interaction?
  • How do my results compare with the results of previous, related RNAi screens in the same or a different organism?
  • What can be learned from what is known about homologs of the gene hits in other organisms?
  • Can I detect trends in the data (e.g. do genes with weak, moderate and strong phenotypes group into meaningful categories)?

Commercial software packages such as Microsoft Excel, Spotfire, or Pipeline Pilot; custom databases such as those put together in FileMaker Pro, Microscoft Access, or another database platform; and non-commercial resources such as NIH DAVID or the Boutros group's cellHTS may also be of use for various analyses. In addition, most of the centers have databases and on-line software tools that can help with statistical analysis, viewing analyzed data (such as as a 'heat map'), and provide additional analysis and data management solutions.

For assays that generate image data, image analysis will also be critically important to your screen. Please see the "services" section for information about image analysis support and software available from the participating centers.