The Pack Hunter Approach for Superior Silencing

siPOOLsTM Clean, Reliable & Hassle-Free RNAi Dealing with How siPOOLs improve specificity RNA interference and reliability of

Scientists have been using RNA interference (RNAi) as a rapid and efficient tool to establish gene function. Yet the off-target effects and variable The siPOOL concept performance of short interfering RNAs (siRNAs) remain a troubling drawback, consuming precious time and resources in validation efforts.

off-target Key Problem - Off-target effects of siRNAs Leading cause: miRNA-like transcript downregulation RNA siRNA siPOOLTM 5‘ Fully complementary siRNA-based RNAi AAAAA 3‘ 5‘ RNA cleavage siRNA target RNA Partially 5‘ complementary AAAAA miRNA-based RNAi 5‘ siPOOLs are high complexity, defined siRNA pools containing 30 distinct siRNAs. siPOOLs have a significantly reduced off-target profile and Seed RNA degradation / inhibition 3‘ miRNA more robust on-target gene knockdown. This is attributed to three key features: (2-7 nt) siRNAs typically bind with full complementarity to target RNA transcripts, guiding their degradation by the RNAi machinery. Off-target effects are largely caused by siRNAs mimicking endogenous gene regulators, micro RNAs (miRNAs). As miRNAs require only a 6 base seed Feature 1 match to 3’ untranslated regions (UTR) to trigger transcript downregulation, siRNAs can alter the expression of numerous unintended targets when High complexity pooling processed via this mechanism. Reduced concentration of individual siRNAs Increased diversity of siRNA sequences High siRNA concentrations produce more off-target effects1,2,3. A high Single siRNAs dominate performance of low complexity pools. The high The consequences: complexity pool allows individual siRNAs to be administered at very low complexity of siPOOLs reduces off-target effects more efficiently than Wide-spread off-target gene deregulation Variable on-target gene knockdown concentrations. This substantially dilutes siRNA off-target signatures. low complexity siRNA pools (see Box 1, pg 4).

siRNA conc (nM) 25 10 1 100 nM siRNA 25 Effect 20 Reduced off-target effects Target gene 15 expression

% 10

5 Expression Benefits fold-change 0 0 – 20 30 – 40 50 – 60 70 – 80 90 – 100 Less false results +4 0 -4 20 – 30 40 – 50 60 – 70 80 – 90 Clean, reliable phenotypes % KD with siRNA changes after STAT3 siRNA treatment by microarray analysis. Arrow shows Gene knockdown efficiency by branched DNA assay. Data reconstructed from Simpson et al. STAT3 expression. Data reconstructed from Caffrey et al., 20111 20084 Multiple expression studies like this one show wide-spread off-target Off-target effects reduce the efficiency of on-target knockdown (KD). gene deregulation by siRNAs2,3. High siRNA concentrations produce At 100 nM, nearly half of commercial siRNAs (220 of 541 tested, 41%) had more off-targeteffects. Off-target genes are enriched for 3’ UTR seed-se- knockdown efficiency of < 70% with 4% of siRNAs showing little to no quence matches, indicating significant miRNA-like activity. activity.

2 3 How siPOOLs improve specificity How siPOOLs improve specificity and reliability of gene silencing and reliability of gene silencing

Feature 2 Feature 3 Detailed Bioinformatics-Based Design Quality Production Process siRNAs Transcript Length Mapping image siRNAs Transcript Length Mapping image 30/30 NM_001123066.3 6816 30/30 XM_005257364.4 6767 siPOOL esiRNA Defined, equimolar siRNAs 30/30 NM_001123067.3 5724 30/30 XM_005257365.4 6761 The patented siPOOL production process ensures every siRNA within a

30/30 NM_001203251.1 5631 29/30 XM_005257366.3 6722 marker Scyl1 PolG Traf5 Ago2 Scyl1 PolG Traf5 Ago2 42bp 30/30 NM_001203252.1 5718 30/30 XM_005257367.4 6656 siPOOL is present in equimolar amounts. 30/30 NM_005910.5 5811 30/30 XM_005257368.4 6563 29/30 NM_016834.4 5637 30/30 XM_005257369.4 5789 Highest purity levels 30/30 NM_016835.4 6762 30/30 XM_005257370.4 5766 siPOOLs undergo polyacrylamide gel electrophoresis (PAGE) purificati- 29/30 NM_016841.4 5544 29/30 XM_005257371.4 5615 on to remove impurities and extract full-length siRNAs. This results in the 30/30 XM_005257362.4 6854 highest achievable purity levels of siRNAs. siPOOL: MAPT Symbol: MAPT Gene ID 4137 siRNA CDS boundaries siPOOLs/esiRNAs loaded on a 20% polyacrylamide gel and visualized by EtBr staining5. Complete transcript coverage Optimal siRNA thermodynamics Avoidance of paralogues ­ Multiple siRNAs allow for more robust and Proprietary design algorithms select most Genes sharing highly similar sequences Effects complete transcript isoform coverage. potent siRNAs based on thermodynamic (paralogues) are avoided by siPOOLs. Paralo- siPOOLs target XM as well as NM transcripts properties that favour guide strand loading into gue filtering is applied genome-wide. Defined, highly pure siRNA that evade immune stimulation (Box 2) the RNA-induced silencing complex (RISC).

Effects Benefits Co-operative knockdown Maximal potency High specificity Avoid toxicity and off-target effects from immune stimulation Benefits Robust, efficient and specific on-target knockdown Box 2 Off-targets from immune stimulation esiRNA siPOOL Box 1 siRNA with known Long double-stranded RNA fragments tend to off-target activity stimulate the immune response as seen in this 6 6 Why 30? Because 4 are not enough! transcriptome-wide expression profile of Scyl1 Low complexity pools of 4 siRNAs are commonly siPOOL esiRNA-treated MCF7 cells (left). Upregulated 4 4 complexity genes in esiRNA-treated cells were largely inter- used and marketed as “smart pools”. We show here 2 2 that an siRNA with known off-target activity against # siRNAs / pool 1 4 15 60 feron response genes which were not induced 0 0 MAD2 gene required high complexity pools of > 15 single „smart in siPOOL-treated cells (right). The immune log2 fold change log2 fold change Control siRNA Pool„ siPOOL siRNAs to sufficiently reduce off-target effects. response can also be stimulated by impurities -2 Scyl 1 -2 Scyl 1 1.4 in siRNA preparations, producing toxicity and Similar results were obtained when off-target activity 1.2 off-target effects that interfere with functional 2 4 6 8 10 12 2 4 6 8 10 12 was assayed by a MAD2 3’UTR-linked luciferase 1 read-outs. average expression average expression reporter, MAD2 protein expression and MAD2 functi- 0.8 off-target effect onal assay (mitotic escape). % off-target 0.4 luciferase activity 0.2 0 conc.(nM) 1 3 10 1 3 10 1 3 10 1 3 10 1 3 10 4 5 Data with siPOOLs – Data with siPOOLs – Proof of concept Proof of concept

Reduced off-target effects with siPOOLs Efficient on-target knockdown with siPOOLs

1 nM siPOOL siRNA siPOOL 35

3 3 HeLa cells 30 Various standard cell lines 2 2 25 1 1 3 nM 20 1 nM 0 0

% genes 15 log2 fold change log2 fold change 48 h -1 -1 10 24 h -2 -2 target gene target gene 5 Human Gene 1.0 ST Real-time quantitati- 4 6 8 10 12 14 4 6 8 10 12 14 microarray 0 ve PCR (rtqPCR) average expression average expression (Affymetrix) 0 – 20 20 – 30 30 – 40 40 – 50 50 – 60 60 – 70 70 – 80 80 – 90 90 – 100 % KD siPOOL

Specific reagents should only affect their target. siPOOLs exhibit potent knockdown efficiencies at low nanomolar concentrations. At 1 nM, a large proportion of siPOOLs (178 of 233 tested, 76%) Transcriptome-wide profiling revealed a single siRNA can induce numerous off-target genes (red dots) while a siPOOL against the same target gene had > 70% gene knockdown efficiency1 (for indirect comparison, refer pg 2). (green dot), and containing the non-specific siRNA, had greatly reduced off-target effects. Robust on-target knockdown with siPOOLs Phenotypes you can trust with siPOOLs

siRNA siPOOL siRNA siPOOL 120 120 2.0 2.0

• • • •• • 100 • 100 A549 cells • • A549 cells • • • • • • • 1.5 • 1.5 • • • • • • • 80 80 • • • • • • • • • • • • • • • • • • • • •• • • • • • • • • • 1 nM • • • • • • • • • 3 nM • • 60 • • 60 1.0 • • • •• • 1.0 • • • • • • • • • • • • • • • • • • • • • • • •••• • • • • • • • • • • • • • • •• • • • • • •• • •• • 40 • 40 • • • • • •• • viability siRNA 1 • • • • • • • • viability siPOOL 1 • • • • • • 0.5 • • • • 0.5 • • • • • • • 72 h • • • • • 24 h •• • ••• • •• • •• • • • • % remaining mRNA siRNA 1 % remaining • • • 20 •••• • • • mRNA siPOOL% remaining 1 20 • •• • • • • • • • • • • • • • •• • •• • • •• • • •• • 2 2 0 R=0.4 0 R=0.9 0.0 R =0.19 0.0 R =0.71 Real-time quantitati- AlamarBlue cell 0 20 40 60 80 100 120 0 20 40 60 80 100 120 ve PCR (rtqPCR) 0.0 0.5 1.0 1.5 2.0 0.0 0.5 1.0 1.5 2.0 viability assay % remaining mRNA siRNA 2 % remaining mRNA siPOOL 2 viability siRNA 2 viability siPOOL 2 (Thermo Fisher) If reliable and specific, two reagents that target the same gene should produce similar phenotypes6. siRNAs vary strongly in their knockdown efficiency. We screened 36 genes using two siPOOLs per gene and three siRNAs per gene from a commercially available library and measured their effect on Two siPOOLs against the same gene gave similar knockdown efficiencies (good correlation, R=0.9) while knockdown with single siRNAs was far cell viability. Only siPOOLs produced consistent phenotypes. more variable (poor correlation, R=0.4). This shows greater robustness and reproducibility of siPOOL-mediated knockdown.

6 7 Further benefits of using siPOOLs Customer Data with siPOOLs

■ One gene – one siPOOL. Unlike other siRNA reagents that require ■ Custom siPOOL design with sequence information available. Case study 1: Highly efficient and paralogue-specific knockdown testing of multiple reagents, a single siPOOL is sufficient to reliably All siPOOL design is undertaken by us and custom design requests knockdown the gene of interest. siPOOLs have been cited in various can be incorporated e.g. custom species, isoforms or transcript publications - refer to website: Resources > Publications. Our land- regions. Sequence information of the siRNAs within the siPOOL is SMARCA2 siPOOL SMARCA4 siPOOL mark paper, Hannus et al., 2014, can be referenced when using available upon request. A siPOOL-transcript map to visualize siRNA siPOOLs for gene function validation. For further validation, binding sites can also be provided. 10 nM 10 nM 0.02 nM 0.02 nM

siPOOL-resistant rescue constructs are also available (see pg 11). Untreated Neg. siPOOL Untreated Neg. siPOOL . 1.60 1.40 Easy . A siPOOL is applied the same way as other rtq-PCR 1.40 1.20 ■  ■ Support guaranteed. Gene knockdown efficiency can vary with siRNA reagents, meaning wide-ranging compatibility with transfection 1.20 1.00

the characteristics of the gene as well as transfection efficiency. A SMARCA2 1.00 1.07 1.25 1.00 1.02

0.80 1.08 1.04

reagents at similar conditions previously established with cell lines 0.92 0.80 0.93 1.00 0.92

high level of support is provided after purchase of siPOOLs to make 0.89

0.60 0.79 0.60 0.77

of choice. A standard transfection protocol is available on our 0.73 sure that siPOOLs are performing to your satisfaction. If siPOOLs fail 0.40 0.35 0.40

website under Resources > Protocols. Please contact us if you have RNA levels Relative RNA levels Relative to meet your expectations, we endeavor to fully evaluate the reasons 0.17 0.20 0.20 0.04 0.10 0.08 0.04 0.05 0.03 0.03 questions regarding siRNA transfection. why and generate a re-design when possible. Our support ranges 0 0.03 0.00 from in-house siPOOL knockdown validation, bioinformatics analysis and transfection optimization. 10 nM 10 nM 0.02 nM 0.02 nM 1.80 Untreated Neg. siPOOL 1.20 Untreated Neg. siPOOL 1.60 rtq-PCR 1.00 1.40

1.20 0.80 1.00 SMARCA4 0.96 1.00 0.60 1.20 1.08 1.08 0.80 1.02 1.03 0.94 1.22 1.00 1.03 0.92 0.60 0.93 0.40 0.54 1.02 0.40 0.23 0.13 0.42 0.12 Relative RNA levels Relative RNA levels Relative

0.20 0.11 0.34 0.20 0.09 0.08 0.08 Various applications of siPOOLs: 0 0

At 0.3 nM, siPOOLs produced 95% knockdown of SMARCA2 and 87% knockdown of SMARCA4 in HT1080 fibrosarcoma cell line as quantified by ■ Target validation (further validation available with siPOOL-resistant rescue constructs) Case study 1 and 4 real-time quantitative PCR. No cross-reactivity was observed between the paralogue-specific siPOOLs.

■ Combinatorial gene knockdown Case Study 2 „Our Cancer Drug Discovery group uses siRNA technology on a routine basis for target validation experiments. To guarantee robustness of target validation processes we use several independent siRNAs or siRNA pools for the same target of interest. We often observe however, a discrepancy ■ Selective paralogue/isoform knockdown Case Study 1 and 3 between these reagents in produced cancer phenotypes due to off-target effects, which frequently delivered false-positive results. By using siPOOLs, we were able to overcome this discrepancy. siPOOLs are highly target-specific and show an efficient knockdown of the target already in the range of low nanomolar concentrations. Another advantage of siPOOLs is that they are able to distinguish very specifically between highly ■ Efficient targeting of long non-coding RNAs Case Study 3 homologous transcript sequences. We are very happy with those siPOOLs, which helped to avoid waste of time and costs within the drug discovery process by producing reliable data.“ ■ Target identification with RNAi screening (siPOOL human kinase library and custom libraries available)

■ Disease-associated investigations (enquire about siPOOL toolboxes)

Dr. Mona Malz, PhD Senior Scientist Cancer Drug Discovery German Cancer Research Center (DKFZ) Heidelberg, Germany 8 9 Customer Data with siPOOLs Customer Data with siPOOLs

Case study 2: Combinatorial gene knockdown to study synergistic effects Ms. Jasmine Barra PhD Student „For our research purpose the use of siPOOLs proved to be a key Lab for Molecular Cancer Biology choice. We could overcome the major issues of both cell toxicity and 6 The high efficiency of siPOOLs at low concentrations encourages its off target effects we observed using GAPMERs. use for combinatorial gene knockdown. Prof. Dr. Chris Marine Group A synergistic effect on survival of primary retinal gangliocytes was ob- VIB Center for the Biology of Disease In our hands the siPOOLs performed always with high reprodu- served on combinatorial knockdown of dual leucine zipper kinase (DLK) KU Leuven, Belgium cibility, allowing us to increase the efficiency of knock down of our and leucine zipper kinase (LZK) by Welsbie et al. (published in Neuron, target of interest, despite the poor results given by standard siRNA 4 2017)7. approaches. Moreover siTOOLs could custom design for us isoform-specific „Our lab uses arrayed, high-throughput functional genomic siPOOLs that allowed us to address in a more specific way our screening in primary neurons to identify potential neuroprotective biological questions.“ RGC survivalRGC (RLU) 2 drug targets. Having tested over 75,000 siRNA sequences, it is quite apparent that off-target effects dominate siRNA-mediated pheno- types. In contrast, in our hands, siPOOLs have much greater pre- dictive power in that phenotypes we see with these (and we have Case study 4: Further validation with siPOOL-resistant rescue constructs 0 tested approximately 15) can be reproduced using cells containing 0 0.3 1 3 10 30 conventional knockouts for the same genes. We now routinely use siPOOL (nM) Design of siPOOL-resistant rescue constructs siPOOLs and are moving away from single siRNAs.“ 3.0 DLK+LZK DLK Control LZK Coding Sequence, CDS

Dr. Derek S Welsbie, MD, PhD 2.5 5’ 3’ Assistant Professor of Opthalmology University of California, San Diego, USA 2.0 NNN NNN NNN NNN GTC NNN N 1.5 Case study 3: Knockdown of long non-coding RNAs GTG GTA 3.7 kb GTC 1.0 NEAT 1_1 pA 21.7 kb GTT Complex concentration (nM) NEAT 1_2 0.5 N 1 ASO N 1_2 ASO Val siPOOLs N1 "N1" siPOOLs N1 long form "N1_2" siPOOL-resistant rescue sequences are designed by wobbling 0 Control siPOOL siPOOL ASO KD siPOOLs KD single bases at siRNA recognition sites (orange) till sufficient mis- + rescue siPOOLs were used to knockdown long match is reached to confer siPOOL resistance. The codon-optimi- CTRL N 1 N 1_2 CTRL N 1 N 1_2 non-coding RNA, NEAT1, in MCF7 cells. An iso- 1.0 1.0 zed sequence when expressed via a DNA vector thereby ‘rescues’ Complex formation measured by fluorescence cross-correlation form-specific siPOOL (N1_2) was also generated the loss-of-function phenotype induced by the co-administered spectroscopy was decreased on siPOOL-mediated knockdown of 0.8 0.8 that targets only the long form of NEAT1 (NEAT siPOOL. Rescue constructs are available as sequence data files one labelled binding partner. Complex formation was restored upon 1_2). Both siPOOLs performed comparably with or in sequence-verified standard/custom vectors. expression of siPOOL-resistant rescue construct. 0.6 0.6 antisense oligos (ASO) and induced measurable 0.4 0.4 phenotypic changes. Data kindly provided by Relative RNA levels Relative Relative RNA levels Relative Data as published in Adriaens et. al, Nature 0.2 0.2 Medicine, 20168 0.0 0.0 NEAT 1 NEAT 1_2 NEAT 1 NEAT 1_2

10 11 How to order

Via Webshop: 1. Register an account Direct requests: 2. Enter gene name/NCBI ID and select species A quotation can also be obtained by contacting us directly with your 3. Select gene with option to verify on NCBI website requests. Please contact us and we will respond within 24 h: 4. Choose product [email protected] 5. Select payment method - Invoice, Paypal, Visa/Mastercard or direct +49 (0) 89 12501 4800 bank transfer 6. Receive confirmation via Email

Via Our Distributors: Please visit About > Distributors on our website for contact information

About Us siTOOLs Biotech is an agile, science-driven start-up providing innovative gene function analysis tools and services. Based in the dynamic Munich-Mar- tinsried biotech cluster in Germany, siTOOLs was founded in 2013 by experts in the RNA field: Dr. Michael Hannus (previously RNAi Screening Lead from Cenix Bioscience) and Prof. Dr. Gunter Meister (Head of RNA Biochemistry at the University of Regensburg). With initial funding from the German EXIST start-up grant, siTOOLs has grown from a local start-up to a world-wide supplier. siTOOL‘s reagents are routinely being used in target validation for drug discovery in pharma/biotech and in multiple research projects by leading academic labs. We strive towards easing scientists‘ workflows with robust tools and rigorous, scientific support.

Dr. Michael Hannus Prof. Dr. Gunter Meister Learn more:

Website www.sitoolsbiotech.com Lochhamer Str. 29A, Planegg/Martinsried, Germany Blog blog.sitoolsbiotech.com Company Registration: HRB 207818 (Munich)

References 1 Caffrey, D. R., Zhao, J., Song, Z., Schaffer, M. E., Haney, S. A., Subramanian, R. R., … Hughes, J. D. (2011). Sirna off-target effects can be reduced at concentrations that match their individual potency. PLoS ONE, 6(7), e21503. 2 Jackson, A. L., Bartz, S. R., Schelter, J., Kobayashi, S. V, Burchard, J., Mao, M., … Linsley, P. S. (2003). Expression profiling reveals off-target gene regulation by RNAi. Nature Biotechnology, 21(6), 635–637. 3 Birmingham, A., Anderson, E. M., Reynolds, A., Ilsley-Tyree, D., Leake, D., Fedorov, Y., … Khvorova, A. (2006). 3’ UTR seed matches, but not overall identity, are associated with RNAi off-targets. Nat Meth, 3(3), 199–204. 4 Simpson, K. J., Selfors, L. M., Bui, J., Reynolds, A., Leake, D., Khvorova, A., & Brugge, J. S. (2008). Identification of genes that regulate epithelial cell migration using an siRNA screening approach. Nat Cell Biol, 10(9), 1027–1038. 5 Hannus, M., Beitzinger, M., Engelmann, J. C., Weickert, M.-T., Spang, R., Hannus, S., & Meister, G. (2014). siPOOLs: highly complex but accurately defined siRNA pools eliminate off-target effects. Nucleic Acids Research, 42(12), 8049-61. 6 Marine, S., Bahl, A., Ferrer, M., & Buehler, E. (2012). Common seed analysis to identify off-target effects in siRNA screens. Journal of Biomolecular Screening, 17(3), 370–8. 7 Welsbie, D. S., Mitchell, K. L., Jaskula-Ranga, V., Sluch, V. M., Yang, Z., Kim, J., … Zack, D. J. (2017). Enhanced Functional Genomic Screening Identifies Novel Mediators of Dual Leucine Zipper Kinase-Dependent Injury Signaling in Neurons. Neuron, 94(6), 1142–1154.e6. 8 Adriaens, C., Standaert, L., Barra, J., Latil, M., Verfaillie, A., Kalev, P., … Marine, J.-C. (2016). p53 induces formation of NEAT1 lncRNA-containing paraspeckles that modulate replication stress response and chemosensitivity. Nature Medicine, 22(8), 861–868.