Navigation Links
Recommendations for Successful siRNA Library Screens

By performing several RNAi screens with siRNA libraries, Ambion scientists identified five procedures that significantly improved siRNA library screen results.

Minimize cytotoxicity
Reduce siRNA concentration
Use individual, validated siRNAs, not siRNA pools
Correct for cell number
Note cell type differences

Here, we share our recommendations for achieving RNAi screening success. These guidelines will give your data more validity.

(1) Minimize Cytotoxicity

Because screening experiments involve monitoring a cellular phenotype, it is important to maintain cell health throughout the experiment. Long-term exposure to transfection reagents can reduce cell viability. Replacing the cell medium in wells approximately 24 hours post-transfection improves cell viability without reducing target gene expression. The optimal time for replacing medium varies by cell type and depends on cell sensitivity to transfection reagent and the rate at which siRNAs are taken up. Optimize these conditions by transfecting cells with Silencer GAPDH and Negative Control siRNAs; replacing medium at 4, 8, 24, and 32 hours; and measuring cell viability and GAPDH mRNA or protein for all samples at 48 to 72 hours post-transfection.

(2) R educe siRNA Concentration

Although many siRNA transfections are still performed with 100 nM siRNA, published results indicate that transfecting lower siRNA concentrations can reduce nonspecific effects exhibited by some siRNAs [1]. Silencer siRNAs reduce target mRNA levels when transfected at significantly lower concentrations (Figure 1). To confirm that phenotypes induced by 100 nM siRNA were apparent at lower siRNA concentrations, cells were transfected with multiple concentrations of five different siRNAs found to block caspase 3 activation (a marker for apoptosis) in a phenotypic screen. As predicted by mRNA studies, there was essentially no difference in the phenotype induced by the Silencer Pre-designed siRNAs even when very low siRNA concentrations (10 nM) were used for transfection (Figure 2).

Figure 1. siRNA Concentration vs. mRNA Knockdown. HeLa cells were transfected with 1-100 nM of six different Silencer Pre-designed siRNAs (Ambion). siRNAs transfected at 1-3 nM concentrations often elicited effective silencing .

Figure 2. Low siRNA Concentrations are Effective in Functional Assays. HeLa cells were transfected with siRNA to nine different targets. One day post-transfection, cells were treated with etoposide to induce apoptosis. Three days post-transfection, cells were lysed and monitored for caspase 3 activity. Results for each well are graphed relative to the average caspase 3 activity from a negative control transfected s ample.

(3) Use Individual, Validated siRNAs, Not siRNA Pools

siRNA design algorithms have essentially eliminated the need to use pools of low potency siRNAs. To address whether siRNA pools benefit screening experiments, (sets of) three siRNAs targeting each of 59 kinases were each transfected into HeLa cells sequentially, one by one, and as a pool (Figure 3).

Figure 3. siRNA Pools vs. Single siRNAs. The expression levels of 59 kinases were reduced in individual HeLa cell populations in 96 well plates by transfecting, in triplicate, three single siRNAs (Panel A; #1, #2, #3) as well as a pool of siRNAs (Panel B; #1+#2+#3) targeting each kinase. After assaying cell number with Alamar Blue (Biosource) caspase 3 assays were performed as described for Figure 2. Yellow arrows indicate results that were confirmed targets (i.e. inhibiting kinase activity via siRNA inhibits activation of the apoptosis pathway as measured by caspase 3 activity) by both methods. Red arrows indicate false negative results from pooled transfections, and green arrows indicate false positive results from pooled transfections. The graph (Panel C) shows representative examples of genes identified as positive results, false negative from pooled assays, false positive from pooled assays, and negative results. The yellow line indicates the 70% threshold used to determine "hits."

The transfected cells were monitored for their ability to activate caspase 3 following induction of apoptosis. "Hits" were defined as cells that had at least 30% less caspase 3 activity than negative c ontrol transfected cells. Interestingly, the hits identified by the pools of siRNAs only partially overlapped with the hits identified by the three individually transfected siRNAs (Figure 3A).

Since our definition of a validated hit is a gene for which two siRNAs yield the same phenotype in a screen, we were able to use the results of the screen to determine if the pool results could be validated with the individual siRNA results. Indeed, only 60% of the siRNA pool hits could be validated with the results of the individual siRNAs. Non-validated hits were considered false positives. The pools gave a false positive rate of 40%.

Of greater concern was that six of the twelve genes for which two or more single siRNAs yielded a hit, failed to register as hits in the siRNA pool screen. The 50% false negative rate observed for pools indicates that screening with siRNA pools can result in lost opportunities for target gene identification. A sampling of confirmed positives, false positives, false negatives, and confirmed negatives is shown in Figure 3B.

We have measured the false positive and false negative rates associated with screening using a single siRNA for each gene target or pools of three siRNAs. The siRNA pool versus individual siRNA screen analysis was repeated using nine different phenotypic assays including cell proliferation, cell morphology, and cytoskeleton formation assays. The pools consistently produced false positive rates of greater than 50% and false nega tive rates that exceed 40% (data not shown). As seen in Figure 4, the false positive and false negative rates associated with using a single siRNA per target gene (#1, #2, #3) are lower than when siRNA were pooled. To minimize the number of False Negative (missed) genes and the number of False Positive (not confirmed by other specific siRNAs) genes, researchers should analyze the combined results from separate transfections of three different gene-specific siRNAs (#1+#2+#3). For larger screening experiments where this may not be feasible, results from single siRNA transfections of one (#1, #2, #3) or two (#1+#2, #1+#3, #2+#3) gene-specific siRNA still reduced the number of missed or False Positive genes compared to transfection of pools of siRNAs.

Figure 4. Screening With Individual siRNAs Results in Fewer False Positives and False Negatives Than Screening With Pooled siRNAs . Inhibition of 11 of 59 kinase genes via transfection of siRNAs inhibited etoposide-activated apoptosis (monitored by caspase 3 activity, see Figure 3). By definition, repression of Confirmed Positive genes resulted in low caspase 3 activity in assays from at least two gene-specific siRNAs. To examine the accuracy of screening methods for identifying important genes, we analyzed the results of transfection experiments involving either a mixture of three siRNAs that target the same gene (siRNA pool) or one of the three siRNAs (#1, #2, #3).

(4) Correct for Cell Number

Inhibition of genes critical for cell division or cell survival can greatly affect cell number. For example, using a series of kinase-specific siRNAs for preliminary work, we noted that targeting specific genes caused an approximately four-fold difference in cell number three days post-transfection (Figure 5). While this difference is not critical for microscopic assays and assays that measure phenotypes for individual cells, it is essential that results be normalized to cell number when using ELISAs, enzymatic activity assays, and other assays that measure total signal in a well.

Figure 5. Cell Number Differences in Cells Transfected with Distinct siRNAs. Panel A . HeLa cells (5,000 per well in 96-well plates) were transfected using siPORT NeoFX (Ambion) in triplicate with three different siRNAs targeting each of 59 different kinase-specific siRNAs. Three days post-transfection, cells were fixed and counted using the ArrayScan V (Cellomics). The average number of cells per view field for wells transfected with the various gene-specific siRNAs were plotted. An approximately four-fold variability in cell number in wells transfected with different siRNAs suggests that results from well-based assays (e.g. ELISA, enzymatic activity assay, reporter signal measurements) must be normalized by the number of cells in each well. Panel B. HeLa cells were transfected as described in Panel A. Caspase 3 activity was monitored as described in Figure 3. Green arrows note genes that fell below the selection threshold irrespective of whether normalization was used; yellow arrows note genes that fell below only when normalized by cell number; and red notes the gene that shifted above the selection threshold when normalized to cell number.

(5) Note Cell Type Differences

Cell type can affect siRNA screening results, as illustrated in the following example. A screen of siRNAs transfected into HeLa cells revealed three genes whose reduction resulted in a cell proliferation defect. siRNAs targeting these three genes as well as nine other genes were re-transfected into HeLa cells as well as into HepG2 cells. The cell proliferation assay confirmed that reducing the expression of the three genes reduced cell proliferation in HeLa cells (Figure 6A). However, siRNAs targeting only one of the three genes reduced cell proliferation in HepG2cells. Instead, two other genes appear to be involved in cell proliferation in HepG2 cells (Figure 6). These results point out the fundamental differences between cell types. Different cells express different genes and feature different cellular pathways. Depending on the assay being used, it is likely that different cell types will reveal different hits. This points to the importance of selecting appropriate cells for the biological function being studied and highlights the opportunity to identify many interesting target genes by using multiple cell types in screening experiments.

Figure 6. Response to Specific siRNA-mediated Gene Inhibition Varies with Cell Type. HeLa (A) and HepG2 (B) cells were transfected in triplicate with three siRNAs targeting 13 different genes or a scrambled siRNA negative control. Cells were counted three days after transfection. Although MAPK13 (brown) inhibition decreases cell proliferation in both cell types, CDK7 (red) and GAPDH (blue) inhibition preferentially decreases cell proliferation in HeLa cells, and MAPK12 (yellow) preferentially decreases cell proliferation in HepG 2 cells.

back to top



Page: All 1 2 3 4 5 6 7 8

Related biology technology :

1. Keys to Successful Densitometry
2. Successful PCR amplification and subcloning of a GC-rich DNA fragment
3. General Considerations for Successful Transfection Experiments
4. Designing a Successful qRT-PCR Experiment
5. Precursor miRNAs for Successful miRNA Functional Studies
6. Setting up Successful siRNA Library Screens
7. Successful stabilization of gene expression profiles
8. Custom and library siRNA for efficient gene silencing
9. Custom and library siRNA for efficient gene silencing
10. Cancer siRNA Oligo Set Version 1.0
11. Library siRNA
Post Your Comments:

(Date:3/22/2017)... 22, 2017   VWR (NASDAQ: ... product and service solutions to laboratory and ... acquired EPL Archives, Inc., an international biorepository ... entire regulated product research, development and commercialization ... and ancillary services. EPL Archives is widely ...
(Date:3/22/2017)... ... March 21, 2017 , ... Proper glycosylation is critical for ... increase and/or decrease in antibody-dependent cellular cytotoxicity or complement-dependent cytotoxicity, there is a ... , To meet this demand, the team at SCIEX has developed a ...
(Date:3/22/2017)... NY (PRWEB) , ... March 21, 2017 , ... The ... Summit (CMO Summit) to be held on May 10-11, 2017, at the Colonnade ... the country specifically for Chief Medical Officer peer-to-peer learning, benchmarking and support. , “The ...
(Date:3/22/2017)... ... March 22, 2017 , ... The Society for Immunotherapy of Cancer ... in the Administration’s recently published fiscal year 2018 budget request. , This ... (NIH) by $5.8 billion or roughly 20% of its total budget. If applied proportionally ...
Breaking Biology Technology:
(Date:3/7/2017)... Brandwatch , the leading social intelligence company, today announces that it ... insights to support its reporting, help direct future campaigns, and share ... charity will be using Brandwatch Analytics social listening and analytics technology ... the topics and issues that are a priority for its supporters. ... "Until recently we,ve ...
(Date:3/2/2017)... , March 2, 2017 Who risk to ... Download the full report: ... FINGERPRINT SENSOR FIELD? Fingerprint sensors using capacitive technology ... fingerprint sensor vendor Idex forecasts an increase of 360% ... devices and of the fingerprint sensor market between 2014 ...
(Date:2/28/2017)... BARCELONA , Spanien, 27. Februar 2017 /PRNewswire/ ... durch Iris-Scan, wird seine erstklassige biometrische Lösung ... Snapdragon™ 835 mit X16 LTE auf dem ... 2. März) am Qualcomm-Stand in Halle 3, ... 835-Prozessor beinhaltet die Sicherheitsplattform Qualcomm Haven™ – ...
Breaking Biology News(10 mins):