Section14:RNAi Loss-of-Function Screens

From Assay Guidance Wiki

Reagent library: Focused pathway collection (e.g. PI3K pathway), gene family cassettes (e.g. GPCRs or kinases), disease specific library (e.g. cancer), genome-wide library (e.g. human or mouse genome), subsets of the genome (e.g. drugable genome). A variety of vendors offer these reagents as well as services (custom printed plates) e.g. Qiagen, Dharmacon, Ambion, Sigma.

Reagent type: siRNA or shRNA-lentivirus (refer to Appendix for example shRNA virus production protocol). Table 1 shows the comparison of siRNA and shRNA-lentivirus reagents. Table 1. Comparison of siRNA and shRNA-lentivirus.

siRNA	shRNA-lentivirus
Short-term target KD (< 1 week)	Long-term target KD
Minimal library maintenance	Significant library maintenance
Some cell types are not transfected efficiently	Infection is generally more effective than transfection, thus larger repertoire of cells can be used
Dosing to control extent of KD possible	Difficult to control extent of KD, though inducible system possible
Chemical modifications possible	Stable target KD cell line can be generated, whenever possible
More consistent quality of reagent	Titer of shRNA-lentiviral particles can be more variable

Note: The issue of off-target effects is a main concern of this technology. Consistent phenotypic effect by reagent redundancy (two or more silencing reagents (si/shRNAs) for the same gene) is one of the agreed principles in the RNAi community to support target-specific phenotypic effect. To this end, a library containing different sequences of si/shRNAs targeting the same gene can be employed as the screening library. Two such approaches are generally practiced:

Use of more than one si/shRNA per gene simultaneously in the screen and evaluate them for concordant phenotypic effects. This is a more expensive option and can increase the scale of the screen.
Use of pools of si/shRNA (e.g. Dharmacon pools of 4) targeting the same gene to increase the chances of knocking down gene expression followed by deconvolution of the pool by testing individual si/shRNA sequences. While this is a faster and less expensive option, it increases the number of steps and may introduce pooling artifacts due to varying potencies and selectivity of the constituent si/shRNA species.

The choice of these two approaches will be determined by time and cost factors, scale of the anticipated screen, lab capacity and overall strategy to identify and validate hits.</LI> </UL>

Assay Optimization

Optimization needs to be done for each cell line used in the screen. Table 2 lists the important parameters for consideration in RNAi optimization.

Table 2. Important parameters in RNAi-assay optimization.

Parameter	Key Factors
Cell line for screening	transfection efficiency, growth rate, assay sensitivity
Cell growth media	should not interfere with readout or transfection efficiency
[si/shRNA]	concentration must produce effective silencing and limit off-target effects
Plate format	medium evaporation, machine readout, barcode
Negative control si/shRNA	should have no effect on assay readout
Positive control si/shRNA	Should have large measurable effect on assay readout
Transfection reagent	should be effective in introducing RNAi reagent into cells with low toxicity
Transfection reagent diluent	should not interfere with assay readout, or transfection efficiency
Transfection reagent ratio	Toxicity vs. efficiency
Transfection reagent incubation time	enough time to complex RNAi reagent and transfection reagent
Mechanism for addition of transfection reagent	minimize well-to-well, plate-to-plate variability
Complexing time	enough time to complex RNAi reagent and transfection reagent
Cell volume added	well-to-well, plate-to-plate variability
Cell number added	optimize to give greater dynamic range at readout
Mechanism for addition of cells	minimize well-to-well, plate-to-plate variability
Mechanism for addition of readout reagent	minimize well-to-well, plate-to-plate variability
Incubation time for readout reagent	optimized to give greater dynamic range at readout
Readout method	sensitivity, accuracy

A few essential parameters (and their purpose) are worth highlighting:

General guidelines for cell-based assays such as growth media, seeding density, growth rate, incubation time, etc. can be found in Tissue Culture Assays Section in this manual.
Transfection efficiency, see B.2.1 and B.2.2 for details (below are the most important parameters for RNAi optimization, with reverse transfection being the preferred method for screening)
- cell seeding density
- cell exposure time to reagent and time of assay
- choice of transfection reagent and the amount
- siRNA concentration
Determination of knockdown (KD) efficiency along with transfection efficiency should constitute an essential part of assay development and optimization. The extent of KD can be determined by qRT-PCR or bDNA quantification of target transcript level after si/shRNA treatment. Since this is likely to be cell line dependent as well, we feel that testing KD for 'carefully picked' cell line specific positive controls where a phenotypic effect (such as cell killing) is seen only when >70% target KD occurs with 95-100% transfection would be necessary. The use of such controls would also allow us to evaluate both transfection and KD efficiency after a large scale screen to check for performance.
Choice of controls
- Positive controls: cells with gene specific si/shRNAs transfected that will result in a significant change to the assay readout. For example si/shRNAs targeting UBB or PLK1 can be used as positive controls in cell proliferation or apoptosis assays. These controls can be informative in evaluating transfection efficiency and KD efficiency (see note above).
- Negative controls: cells with non-silencing si/shRNAs (NS), also known as non-targeting control (NTC), transfected but without significant effect on the assay readout. Besides, we also recommend including the following as negative controls:
  - Non-transfected (NT) cells: cultured cells only, without transfection/infection
  - Mock-transfected (MT) cells: cells with transfection reagent only, without si/shRNA

siRNA transfection optimization experiments

A convenient way to optimize siRNA transfection efficiency is to use phenotypic assays. Two examples of a 96-well plate layout for transfection optimization are shown below (Figure 3).

Figure 3. siRNA optimization plate layout examples. (A) has a wider range of transfection reagent concentrations to judge, given a fixed siRNA concentration (the edge wells are intentionally left blank); while in (B) the concentrations of siRNA and transfection reagent are optimized together. Different cell seeding density can also be tested along with the two factors in different plates. NC-negative control; PC-positive control; TRx.R – transfection reagent. In (B) each of the four blocks (4x4) in the center is a factorial design of siRNA and transfection reagent concentrations

The optimal condition is determined mainly based on two aspects:

the negative controls should be as close as to NT; while the positive controls should be as far as from NT
the Z prime factor (seen section II of QB manual) from the negative control and positive control should be within acceptable range, >0.4

In the following example (Figure 4), an experiment using layout B in Figure 3 was performed with a cell viability readout using one negative control, NS-AS and one positive control, PLK1_7. The condition of 0.05ul transfection reagent and 5nM siRNA is chosen as the best based on the main criteria described above (NS-AS is close to NT PLK1_7 is far from NT and Z’ = 0.66). In this case, other combinations maybe acceptable as well.

Figure 4. siRNA transfection optimization experiment in a cell viability assay, using layout B in Figure 3 with one negative control NS-AS and one positive control PLK1_7 for a cell proliferation assay. The y-axis represents control (i.e. NT (cells only), black bar) normalized values. Various combinations of siRNA concentration (represented by the colors) and transfection reagent concentration (Rmax_xx: reagent “Rmax” with concentration xx) are shown along the X-axis.

shRNA-lentivirus Infection Optimization Guidelines

Optimization of shRNA-lentivirus infection for each cell line is a more involved process. There are various parameters that should be considered when optimizing infection.

Determination of cell seeding density from performing a simple growth curve experiment
Determination of puromycin concentration by performing a 10-point dose response curve, ranging from 0.1mg/ml to 10mg/ml (a typical concentration ranges between 2-5mg/ml)
Time course for puromycin treatment
Effect of protamine sulfate to cells
The amount of virus to be used for maximal infection. A detailed protocol on viral infection can be found at http://www.broad.mit.edu/genome_bio/trc/publicProtocols.html. Furthermore, infectability can be measured for each cell line using a cell count assay:

Infectability = (((No. of infected cells with puromycin) – (No. of non-infected cells with puromycin*))/Total No. of cells without puromycin)*100. *Refers to background (killing) i.e. just cells with puromycin added

Basic Protocols

siRNA protocols

siRNA printing (take 96-well microplate format as an example)
1. Dilute 20 µM siRNA stock in RNase-free buffer provided to a 20X working solution (i.e. 200 µM for a final concentration of 10 µM)
2. Print 5 µl per well by hand using an electronic multichannel pipette or by automated liquid handling robot
3. Printed plates can be stored at -20ºC or -80ºC if needed
siRNA transfection
1. Dilute transfection agent (e.g. RNAiMax from Invitrogen) in serum-free cell culture medium to a 2X working solution
2. Add 45 µl per well to the siRNA-printed plate, mix well and incubate for 30 min at RT
3. Dispense 50 µl cell suspension per well, i.e. final volume of 100 µl.
4. Let the seeded cells settle down for an hour at RT (or as soon as they adhered to the plate) before putting into a 37ºC cell incubator

shRNA-lentivirus Infection Protocol

Viral infection in 96 well microplate format. This step is similar for determination of viral titer.

Seed cells of interest overnight in a total volume of 50µl of growth media
Add 40µl of growth media with 2X of protamine sulfate (16mg/ml) to cells. This volume is dependent of the viral supernatant added in (3).
Add 2-10µl of viral supernatant to mix above. This volume is dependent on the viral titer. The final volume of (2) and (3) is 50µl. Incubate at 37°C overnight.
Add 2X puromycin (4mg/ml) in 100µl of growth media and incubate for 37°C overnight.
Wash off puromycin and replace with normal growth media.
Incubate for 2-4 days depending on the assays.

RNAi Screen Design and Data Analysis

Design of Experiment and Plate Layout

When designing the plates one should consider including a sufficient number of control replicates to help evaluate data quality. The number of wells for each type of control within the plate should be ≥ 4 for 96-well plates (preferably 8 wells); or ≥ 8 for 384-well plates (preferably 16 wells).

Note: MT rather than NT controls may be convenient from a practical standpoint. However, lack of cytotoxicity by lipo alone should be established during assay development. As for the sample si/shRNAs under testing, in high throughput LOF RNAi screens (for example, the genome library screens), the number of replicates is usually limited by the cost of the experiments. We recommend duplicate runs as a minimum requirement. For low or medium throughput experiments, 3 technical replicates per run and at least two biological runs (different days) would be a minimum number of replicates.

Figure5. Experimental plate layout example shown in 384-well format.

Quality Control

Uniformity: Uniformity within-plate or from plate to plate is also a key factor to check for quality control. Heat maps are recommended to visualize each screen plate as they help to identify geometric effects due to experimental errors or systematic problems. Section II of this manual has guidelines on within-plate variation evaluation. Given the steps involved and the cell-based nature of RNAi experiments, CV (coefficient of variation) <30% of controls is acceptable.

Plate-plate variation: Scatter plots of common control wells across plates help to evaluate plate-plate variation.

Figure 6. Scatter plots from two exemplar screens. Plots A and B show plate to plate variability as assessed by controls (different colors for different controls). It can be seen that the signals (Y-axis) for each kind of control are similar from plate to plate when analyzing all plates (X-axis) indicating low plate-plate variation while in plot B there is dramatic change from plate to plate, which could be corrected by some appropriate normalization method if the variation is consistent for all kinds of controls, or one needs to consider dropping some plate/wells with inconsistent variation.

To evaluate the reproducibility between replicated plates (or within plate replicates), one can use Lin’s Concordance Correlation Coefficient (Lin’s CCC, Ref 9, 10), Bland-Altman test (Ref 2) and Pearson correlation coefficients. Please consult your statistician for the most applicable method.

Signal window: This is described by Z prime (Z’) factor or Signal Window (SW). Usually it is acceptable if Z’>0.4 or SW>2. In HTS, a scatter plot of Z prime factors for every plate versus the plate index will reveal the plate-plate differences and may help to troubleshoot any existing problems.

Transfection / Infection Efficiency: The ratios between the negative control and positive control describe the efficiency of the transfection or infection from a biological point of view. For example, in a cell viability assay, the ratio of the potent positive control versus the negative controls should ideally be <5%, which can be interpreted as high (> 95%) transfection/infection efficiency. While there is no theoretically defined threshold value, often these ratios will depend on the type of assays and potency of controls.

For shRNA, infection efficiency is assessed during infection for assays based on comparison of infected wells (puromycin treated) vs non-infected wells (without puromycin treatment). Choosing a small number of representative wells for examination under a microscope may also help.

LOF RNAi Assay Hit Selection

Data should be normalized by control-based; median-based; Z score; B score (for geometric effects seen in plates; Ref 4). Please consult your statistician for most suitable method. Hit selection in LOF assays is usually done by comparing with a negative control to generate fold change values for a specified cutoff with numerical and/or biological significance (e.g. > 60% loss of cell viability). For low or medium throughput screening with large number of replicates, statistical tests (such as two sample t-test) can be used with appropriate multiple testing correction (for example Tukey’s, or Dunnett) if necessary. Since fold-change do not account for variation, the hit list can be filtered first by adjusted p-values then ranked by the corresponding fold-change values. Alternatively, in the case of large scale screens especially HTS, population-based methods, such as 2 standard deviations away from the mean (or trimmed mean), can be and have been used to rank hits.

By designing screens with multiple si/shRNA sequences per target, target specificity can be ascertained by concordant phenotypic effects for si/shRNAs reagents that correspond to the same gene. Also, subsequent follow-up experiments (see B.5) will be necessary.

Follow-up assays

The detailed follow up plan for hits identified in a screen would depend on the nature of the investigation and the goal(s) of the study. That said, a few general steps are described below.

Validate KD of target gene expression by QPCR: To confirm RNAi-mediated effect, it is recommended that the hits be tested in by RT/QPCR or b-DNA assay to measure target gene knockdown. This would be essential in ascribing phenotypic observations to target gene suppression. In most cases KD experiments will follow screens, but at times QPCR or bDNA assays can be run in parallel where hits can be simultaneously scored for positive phenotypic effect and confirmed KD. Although the effects are likely to be message and/or reagent dependent, as a general suggestion, knockdown measured over 24h or 48h should suffice. Best practices for carrying out QPCR experiments can be found elsewhere in the current or future versions of the QB manual.
Validate target protein expression by Western blotting: siRNAs that knockdown target RNA and protein are likely to be most informative in characterizing targets of interest. Although not always possible, we recommend performing Westerns on high confidence hits from a screen to eliminate any false positives. Other variations on the theme exist such as ELISA, immunocytochemistry, etc.
Test additional siRNAs for the target of interest
Test additional cell lines
Secondary assays: This is recommended to eliminate assay artifacts and characterize target biology in more detail. Therefore, the exact nature of the assay may differ as a function of target pathway, biological process and disease biology. Validated high-content assays (Acumen, Cellomics, GE InCell Analyzer) maybe particularly useful in this regard. These are described elsewhere in the QB manual.