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Next Generation siRNAs to Make Your Silencing Roar
- Uses a new algorithm that improves prediction accuracy by >25%
- Up to 100-fold more potent than other siRNAs
- Novel modifications reduce off-target effects by up to 90%
- Proven improvements in consistency and reliability of phenotypic results
- 100% guaranteed to silence—the best guarantee in the industry
Ambion Silencer Select siRNAs incorporate the latest improvements in siRNA design, prediction algorithms for off-target effects, and chemical modifications to yield siRNAs with unrivaled efficacy, potency, and specificity. The result is more successful experiments and cleaner, more consistent phenotypic data.
siRNA Efficacy
New Design Algorithm Predicts Effective siRNAs with 94% Accuracy
Applied Biosystems researchers used a powerful machine-learning method and performance data from thousands of siRNAs to better understand the link between an siRNA’s silencing efficiency and its sequence, target location, and thermodynamic properties. This rigorous analysis yielded a new algorithm that incorporates over 90 sequence and thermodynamic parameters. Stringent testing of siRNAs designed with this algorithm demonstrated its ability to predict siRNAs that can knock down target mRNA levels by 70% or better, 94% of the time. More importantly, 87% of the siRNAs tested silenced their targets by 80% or better, and both sets of experiments were performed at only 5 nM siRNA. This represents a 28% increase in predictive accuracy at the 80% knockdown level over Applied Biosystems' previous-generation siRNA design algorithm.
Applied Biosystems researchers used a powerful machine-learning method and performance data from thousands of siRNAs to better understand the link between an siRNA’s silencing efficiency and its sequence, target location, and thermodynamic properties. This rigorous analysis yielded a new algorithm that incorporates over 90 sequence and thermodynamic parameters. Stringent testing of siRNAs designed with this algorithm demonstrated its ability to predict siRNAs that can knock down target mRNA levels by 70% or better, 94% of the time. More importantly, 87% of the siRNAs tested silenced their targets by 80% or better, and both sets of experiments were performed at only 5 nM siRNA. This represents a 28% increase in predictive accuracy at the 80% knockdown level over Applied Biosystems' previous-generation siRNA design algorithm.
siRNA Efficacy
Up to 100-fold More Potent than Competing siRNA Technologies
Silencer Select siRNAs are so potent that we recommend they be routinely used at low concentrations (i.e., 3–5 nM) in easily transfected cells such as HeLa cells. This represents a 10- to 20-fold lower concentration range than most suppliers currently recommend. Lower siRNA concentrations significantly reduce off-target effects and decrease reagent costs per experiment. Figure 2 provides data from 130 siRNAs to 10 gene targets. The data show that Silencer Select siRNAs are 5- to 100-fold more potent than siRNAs—both modified and unmodified—from other suppliers.
Figure 2. Silencer Select siRNAs Provide Up to 100X Higher Potency Compared to Competing siRNAs. Each data point represents a minimum of 30 siRNAs to 10 gene targets, tested in triplicate: 3 Silencer Select siRNAs to each of the 10 targets (30 siRNAs total); 4 modified siRNAs to each of 10 targets (Supplier D—modified; 40 siRNAs total); 2 sets of 3 unmodified siRNAs to each of 10 targets (Supplier Q—unmodified, 30 siRNAs total). Each siRNA was individually transfected into HeLa cells at the indicated siRNA concentration. After transfection (48 hr), RNA was isolated using the MagMAX™-96 Total RNA Isolation Kit, cDNA was synthesized using the High Capacity cDNA Reverse Transcription Kit, and real-time PCR was performed using TaqMan Gene Expression Assays. Results are expressed as percent of mRNA remaining compared to that of Silencer Negative Control #1 siRNA transfected cells.
Silencer Select siRNAs are so potent that we recommend they be routinely used at low concentrations (i.e., 3–5 nM) in easily transfected cells such as HeLa cells. This represents a 10- to 20-fold lower concentration range than most suppliers currently recommend. Lower siRNA concentrations significantly reduce off-target effects and decrease reagent costs per experiment. Figure 2 provides data from 130 siRNAs to 10 gene targets. The data show that Silencer Select siRNAs are 5- to 100-fold more potent than siRNAs—both modified and unmodified—from other suppliers.
Figure 2. Silencer Select siRNAs Provide Up to 100X Higher Potency Compared to Competing siRNAs. Each data point represents a minimum of 30 siRNAs to 10 gene targets, tested in triplicate: 3 Silencer Select siRNAs to each of the 10 targets (30 siRNAs total); 4 modified siRNAs to each of 10 targets (Supplier D—modified; 40 siRNAs total); 2 sets of 3 unmodified siRNAs to each of 10 targets (Supplier Q—unmodified, 30 siRNAs total). Each siRNA was individually transfected into HeLa cells at the indicated siRNA concentration. After transfection (48 hr), RNA was isolated using the MagMAX™-96 Total RNA Isolation Kit, cDNA was synthesized using the High Capacity cDNA Reverse Transcription Kit, and real-time PCR was performed using TaqMan Gene Expression Assays. Results are expressed as percent of mRNA remaining compared to that of Silencer Negative Control #1 siRNA transfected cells.
Improved Efficacy and Potency Increase Consistency of Phenotypic Results
With the improved potency of
Silencer Select siRNAs, maximum knockdown levels are reached more consistently, leading to a higher percentage of targets for which the threshold required to see a biological effect is reached. Figure 3 shows a compilation of data from three suppliers' siRNAs (n= 126) to seven gene targets. 92% of the
Silencer Select siRNAs provided the expected phenotype, compared with only 57–72% of the other siRNAs.
Figure 3. Silencer Select siRNAs Elicit Expected Phenotype at a Higher Rate than Other siRNAs. siRNAs to seven gene targets with well understood RNAi-induced phenotypes were transfected into cultured cells at 3 nM final concentration. Forty-eight hours later, phenotypes were measured by the appropriate cell-based assay: for siRNAs targeting BUB1B, AURKB, WEE1, and PLK1, multi-parametric cellular growth/apoptosis assay in U-2 OS human osteosarcoma cells; for siRNAs targeting HMGCR, LDLR, and FDFT1, an LDL uptake assay in Huh7 human hepatoma cells. Each bar represents the percentage of siRNAs that elicited the expected, silenced phenotype.
Figure 3. Silencer Select siRNAs Elicit Expected Phenotype at a Higher Rate than Other siRNAs. siRNAs to seven gene targets with well understood RNAi-induced phenotypes were transfected into cultured cells at 3 nM final concentration. Forty-eight hours later, phenotypes were measured by the appropriate cell-based assay: for siRNAs targeting BUB1B, AURKB, WEE1, and PLK1, multi-parametric cellular growth/apoptosis assay in U-2 OS human osteosarcoma cells; for siRNAs targeting HMGCR, LDLR, and FDFT1, an LDL uptake assay in Huh7 human hepatoma cells. Each bar represents the percentage of siRNAs that elicited the expected, silenced phenotype.
siRNA Specificity
Five-step Bioinformatic Filtering Process Improves Specificity
Each potential siRNA sequence generated by the new siRNA design algorithm must pass a rigorous bioinformatic filtering process. The sequences of each siRNA strand are compared to all other transcripts from the genome of interest. siRNAs with too many matches to transcripts other than their intended target are eliminated. The siRNA sequences are then filtered to remove siRNAs that are predicted to have strong off-target apoptotic phenotypes. This process uses the Silencer Select Toxicity Classifier, a new algorithm designed to eliminate sequences predicted to elicit a toxic response. The bioinformatic filtering process also involves scanning siRNA sequences for known antiviral response motifs and naturally occurring miRNA seed regions. These potentially problematic siRNA sequences are not used. Finally, siRNA sequences are ranked according to the frequency of potential siRNA seed region matches to the 3' untranslated region (UTR) of off-target transcripts. Only those siRNAs that have the highest rankings from the siRNA design process, pass all specificity filters, and have the fewest predicted 3' UTR–siRNA seed region matches to off-target transcripts qualify as Silencer Select Pre-designed siRNAs.
Modifications Reduce Off-target Effects by Up to 90% and Enhance Guide Strand Bias
Although siRNA design and bioinformatic filters can greatly improve siRNA specificity, further improvements can be made with carefully selected chemical modifications. Figure 4A demonstrates that the modifications used in Silencer Select siRNAs enhance the guide strand bias (preferential incorporation of siRNA guide strand over passenger strand into the RNA-induced silencing complex, RISC). Figure 4B shows that enhanced guide strand bias strongly correlates with enhanced knockdown. The Silencer Select modifications resulted in dramatic improvements in siRNA specificity, measured both by gene expression arrays and in cell-based assays.
Figure 4. Silencer Select siRNA Modifications Enhance Guide Strand Bias, Which Correlates with Knockdown. Luciferase reporter gene constructs with siRNA targets cloned in either the sense (guide strand target) or antisense (passenger strand target) orientation were co-transfected with the corresponding siRNA and a β-galactosidase-encoding control vector. Luciferase and β-galactosidase assays were performed 72 hr later, and knockdown for each strand was calculated relative to negative control siRNA-transfected cells. (A) The ratio of guide to passenger strand knockdown is shown for 46 siRNAs with and without the Silencer Select modifications. (B) The average ratio of guide to passenger strand knockdown is plotted versus luciferase knockdown (guide strand reporter) for 6 Silencer Select siRNAs, 6 siRNAs designed using the new algorithm but without Silencer Select chemical modifications, and 30 competitor siRNAs.
• Off-target Effects Reduced in Array Experiments. When added to three unique negative control siRNA sequences, which should not target any transcript, the Silencer Select modifications reduced the number of differentially expressed genes, compared to mock-transfected cells by an average of 83% (Figure 5). When the same experiment was performed using gene-specific siRNAs, reduction of differentially expressed genes by up to 72% were attributable to the modifications alone.
Figure 5. Silencer Select Modifications Reduce Number of Off-target Differentially Expressed Genes. Three negative control siRNAs with and without the Silencer Select modifications (30 nM) were individually transfected in quadruplicate into HeLa cells. RNA was isolated using MagMAX™-96 Total RNA Isolation Kit and prepared for array analysis using the MessageAmp™ II aRNA Amplification Kit. The labeled aRNA was analyzed on an Affymetrix Human Genome U133 Plus 2.0 Array in triplicate. The y-axis indicates the average number of differentially expressed genes (i.e., those showing ≥2-fold change in expression (p<0.001) versus mock-transfected samples).
• Off-target Effects Reduced in Cell-based Assays. Applied Biosystems scientists have carefully studied the impact of the Silencer Select modifications on 53 siRNAs in cell-based assays (Figure 6). The siRNAs studied included sequences shown in previous studies to have strong off-target phenotypes. When compared to unmodified siRNAs of the same sequence, modified siRNAs retained the expected phenotypes, yet in 10 of 11 cases in which clear off-target effects were observed with unmodified siRNAs, the Silencer Select modifications completely eliminated the off-target effects. This study highlights the power of the Silencer Select modifications to provide cleaner, more consistent cell biology data.
Figure 6. Silencer Select siRNA Modifications Reduce Off-target Effects and Yield More Reliable Phenotypic Data. 53 siRNAs, including older designs previously noted to elicit off-target phenotypes, were transfected into U-2 OS cells at 30 nM in both unmodified and Silencer Select-modified formats. Mitosis and apoptosis were measured 48 hr later. Data are expressed relative to negative control siRNA-transfected cells. Note that the expected mitosis and apoptosis phenotypes for PLK and WEE1 siRNAs are preserved with the modifications. In contrast, the off-target apoptotic phenotypes elicited by 10 unmodified siRNAs were completely eliminated with addition of the Silencer Select modifications. Black=similar mitosis/apoptosis levels as control. Green=downregulation. Red=upregulation.
Each potential siRNA sequence generated by the new siRNA design algorithm must pass a rigorous bioinformatic filtering process. The sequences of each siRNA strand are compared to all other transcripts from the genome of interest. siRNAs with too many matches to transcripts other than their intended target are eliminated. The siRNA sequences are then filtered to remove siRNAs that are predicted to have strong off-target apoptotic phenotypes. This process uses the Silencer Select Toxicity Classifier, a new algorithm designed to eliminate sequences predicted to elicit a toxic response. The bioinformatic filtering process also involves scanning siRNA sequences for known antiviral response motifs and naturally occurring miRNA seed regions. These potentially problematic siRNA sequences are not used. Finally, siRNA sequences are ranked according to the frequency of potential siRNA seed region matches to the 3' untranslated region (UTR) of off-target transcripts. Only those siRNAs that have the highest rankings from the siRNA design process, pass all specificity filters, and have the fewest predicted 3' UTR–siRNA seed region matches to off-target transcripts qualify as Silencer Select Pre-designed siRNAs.
Modifications Reduce Off-target Effects by Up to 90% and Enhance Guide Strand Bias
Although siRNA design and bioinformatic filters can greatly improve siRNA specificity, further improvements can be made with carefully selected chemical modifications. Figure 4A demonstrates that the modifications used in Silencer Select siRNAs enhance the guide strand bias (preferential incorporation of siRNA guide strand over passenger strand into the RNA-induced silencing complex, RISC). Figure 4B shows that enhanced guide strand bias strongly correlates with enhanced knockdown. The Silencer Select modifications resulted in dramatic improvements in siRNA specificity, measured both by gene expression arrays and in cell-based assays.
Figure 4. Silencer Select siRNA Modifications Enhance Guide Strand Bias, Which Correlates with Knockdown. Luciferase reporter gene constructs with siRNA targets cloned in either the sense (guide strand target) or antisense (passenger strand target) orientation were co-transfected with the corresponding siRNA and a β-galactosidase-encoding control vector. Luciferase and β-galactosidase assays were performed 72 hr later, and knockdown for each strand was calculated relative to negative control siRNA-transfected cells. (A) The ratio of guide to passenger strand knockdown is shown for 46 siRNAs with and without the Silencer Select modifications. (B) The average ratio of guide to passenger strand knockdown is plotted versus luciferase knockdown (guide strand reporter) for 6 Silencer Select siRNAs, 6 siRNAs designed using the new algorithm but without Silencer Select chemical modifications, and 30 competitor siRNAs.
• Off-target Effects Reduced in Array Experiments. When added to three unique negative control siRNA sequences, which should not target any transcript, the Silencer Select modifications reduced the number of differentially expressed genes, compared to mock-transfected cells by an average of 83% (Figure 5). When the same experiment was performed using gene-specific siRNAs, reduction of differentially expressed genes by up to 72% were attributable to the modifications alone.
Figure 5. Silencer Select Modifications Reduce Number of Off-target Differentially Expressed Genes. Three negative control siRNAs with and without the Silencer Select modifications (30 nM) were individually transfected in quadruplicate into HeLa cells. RNA was isolated using MagMAX™-96 Total RNA Isolation Kit and prepared for array analysis using the MessageAmp™ II aRNA Amplification Kit. The labeled aRNA was analyzed on an Affymetrix Human Genome U133 Plus 2.0 Array in triplicate. The y-axis indicates the average number of differentially expressed genes (i.e., those showing ≥2-fold change in expression (p<0.001) versus mock-transfected samples).
• Off-target Effects Reduced in Cell-based Assays. Applied Biosystems scientists have carefully studied the impact of the Silencer Select modifications on 53 siRNAs in cell-based assays (Figure 6). The siRNAs studied included sequences shown in previous studies to have strong off-target phenotypes. When compared to unmodified siRNAs of the same sequence, modified siRNAs retained the expected phenotypes, yet in 10 of 11 cases in which clear off-target effects were observed with unmodified siRNAs, the Silencer Select modifications completely eliminated the off-target effects. This study highlights the power of the Silencer Select modifications to provide cleaner, more consistent cell biology data.
Figure 6. Silencer Select siRNA Modifications Reduce Off-target Effects and Yield More Reliable Phenotypic Data. 53 siRNAs, including older designs previously noted to elicit off-target phenotypes, were transfected into U-2 OS cells at 30 nM in both unmodified and Silencer Select-modified formats. Mitosis and apoptosis were measured 48 hr later. Data are expressed relative to negative control siRNA-transfected cells. Note that the expected mitosis and apoptosis phenotypes for PLK and WEE1 siRNAs are preserved with the modifications. In contrast, the off-target apoptotic phenotypes elicited by 10 unmodified siRNAs were completely eliminated with addition of the Silencer Select modifications. Black=similar mitosis/apoptosis levels as control. Green=downregulation. Red=upregulation.
The collection of individual Silencer Select siRNA products targeting human, mouse, and rat genes is described at right. Each Silencer Select siRNA is synthesized and purified in state-of-the-art facilities and meets the highest quality standards. As part of the rigorous quality control procedures, each RNA oligonucleotide is analyzed by MALDI-TOF mass spectrometry, and analytical HPLC is used to monitor purity. Each annealed siRNA is also assessed by gel electrophoresis to confirm that the strands annealed properly. The result is premium-quality siRNA that is purified and ready to use.
Scientific Contributors
Nitin Puri, Xiaohui (David) Wang, Rajeev Varma, Kathy Latham, Tim Sendera, and Susan Magdaleno • Applied Biosystems, Austin, TX
Scientific Contributors
Nitin Puri, Xiaohui (David) Wang, Rajeev Varma, Kathy Latham, Tim Sendera, and Susan Magdaleno • Applied Biosystems, Austin, TX