Search Thermo Fisher Scientific
Next Generation siRNAs to Make Your Silencing Roar
- >
- 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
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
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.
图2. Each data point represents a minimum of 30 siRNAs to 10 gene targets, tested in triplicate: 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.
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.
图2. Each data point represents a minimum of 30 siRNAs to 10 gene targets, tested in triplicate: 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.
Improved Efficacy and Potency Increase Consistency of Phenotypic Results
Figure 3 shows a compilation of data from three suppliers' siRNAs (n= 126) to seven gene targets.
图3. 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.
图3. 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. 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.
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 4B shows that enhanced guide strand bias strongly correlates with enhanced knockdown.
图4. 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.
• 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.
图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. <
• 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.
图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. 请注意，修饰未改变 PLK 和 WEE1 siRNAs 的预期有丝分裂和凋亡表型。 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. 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.
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 4B shows that enhanced guide strand bias strongly correlates with enhanced knockdown.
图4. 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.
• 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.
图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. <
• 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.
图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. 请注意，修饰未改变 PLK 和 WEE1 siRNAs 的预期有丝分裂和凋亡表型。 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. 各种工作的结果就是获得了最优品质的即用型纯化siRNA。
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