Home Search Contact Print Sign In
Products Services Resource Center Ordering About Exiqon
microRNA Research
mRNA & lncRNA Research
DNA Research
Custom LNA™ Oligos
LNA™ Phosphoramidites
RNA Isolation
Microarray Analysis
Real-time PCR
Northern Blotting
In Situ Hybridization
Functional Analysis
RNA Isolation
Microarray Analysis
Real-time PCR
In Situ Hybridization
SNP Detection
Microarray Analysis
In Situ Hybridization
RNA Isolation Services
microRNA PCR Services
Sequencing Services
Custom Pharma Service
Application Stories
Top 5 Tips: Successful microRNA inhibition

MicroRNA inhibition is a powerful technique for determining the function of a microRNA. Here, we have assembled five important tips for successful silencing experiments.

1. Choose the right inhibitor

Successful outcome of microRNA functional analysis starts with a careful choice of the inhibitor best suited for your experiment.

  • Check whether your microRNA of interest belongs to a family of potentially functionally redundant microRNAs. If these family members are expressed in your system then you should consider using our family inhibitors.
  • If you are working with a very highly expressed microRNA or hard-to-transfect cells then you should consider using our Power inhibitors. Due to their high stability Power inhibitor are also ideally suited for experiments where long duration inhibition is required.
  • Our regular inhibitors are probably the best option if you are working with easy-to-transfect cells or with cells deriving from muscle or CNS that don't tolerate Power inhibitors well.
  • If you don't find your microRNA of interest among our predesigned inhibitors we can custom design it for you.
  • If your work is ultimately aimed at studying microRNA function in vivo then you should consider using a custom designed in vivo inhibitor for your initial in vitro characterization work – so that results are directly comparable.

2. Optimize your transfection

Optimizing transfection efficiencies is crucial for maximizing microRNA inhibition while minimizing secondary effects. Transfection efficiency varies according to cell type and the transfection reagent used. The optimal combination of cell type, transfection reagent and transfection conditions must be determined empirically. For more information download our microRNA inhibitor manual.

3. Develop an assay to monitor antisense activity

Before attempting to analyze the consequences of suppressing microRNA activity you need to ascertain that efficient microRNA inhibition is achieved by transfection with the LNA™ oligo. Consequently, you need to develop an assay for microRNA activity. Use functional assays! Look for de-repression of validated or predicted microRNA targets and remember that you may have to look at protein levels since microRNAs often have only a modest direct effect on target mRNA stability. Alternatively, develop a plasmid-based assay by inserting microRNA target sites in the 3’UTR of a reporter gene such as luciferase. Don’t rely on microRNA qPCR! It is prone to yield false positive results (1 – see also our tech note) and if the LNA™ oligo ends up in the RNA sample it might interfere with the RT-qPCR assay.

4. Determine the right amount of inhibitor and right time to look for phenotypic effects – perform proper controls

All oligonucleotides are cytotoxic and have secondary effects at high concentrations. Perform dose response studies to determine the lowest concentration of LNA™ oligo at which efficient microRNA inhibition is achieved. Use this concentration to study the phenotypic consequences of microRNA inhibition. Remember that microRNA function may be required only in limited window of time. It can therefore be a useful exercise to play around with the time of transfection and the time of phenotypic observation. Always include a negative control to evaluate the contribution of nonspecific effects of transfection with an oligonucleotide. Use one of our generic control oligos or even better ask us to custom design a mismatch control in which we have changed the position of a few of the nucleotides of your particular inhibitor.

5. Know your biology and validate your microRNA targets with target site blockers

Good knowledge of the pathways and key proteins involved in the biology is key to interpreting the phenotypic consequences of microRNA inhibition. microRNA target prediction tools can generate lists of hundreds of potential targets for most microRNAs. The challenge is to narrow these down to a manageable short list of targets relevant to your biological system. There are numerous bioinformatics tools (2-6) that will help you interpret your microRNA target list by gene ontology and human phenotype ontology analysis and cross checking with biological pathway and protein-protein interaction databases. But the benefit of this analysis relies on the correct choice of gene ontology terms and pathways and subsequent literature searches on the candidate genes. Once you have created your short list you can validate the microRNA candidate targets by verifying that gene expression is de-repressed upon microRNA inhibition. The next question is then whether de-repression of any of these validated targets is associated with the observed phenotype. One way of approaching this problem is to prevent microRNA interaction with a particular mRNA of interest by masking the microRNA target site with an LNA™ antisense oligo instead of inhibiting the microRNA. This is an elegant way of assessing which of the many microRNA targets that are major contributors to the phenotype observed upon microRNA inhibition. Contact us for custom design of target site blockers.

1. Torres AG, Fabani MM, Vigorito E, Gait MJ. MicroRNA fate upon targeting with anti-microRNA oligonucleotides as revealed by an improved Northern-blot-based method for microRNA detection. RNA. 2011 May;17(5):933-43.
2. Jiao X, Sherman BT, Huang DW, Stephens R, Baseler MW, Lane HC, Lempicki RA. DAVID-WS: A Stateful Web Service to Facilitate Gene/Protein List Analysis. Bioinformatics. 2012 Apr 27. [Epub ahead of print]
3. Sealfon RS, Hibbs MA, Huttenhower C, Myers CL, Troyanskaya OG. GOLEM: an interactive graph-based gene-ontology navigation and analysis tool. BMC Bioinformatics. 2006 Oct 10;7:443
4. Berriz GF, Beaver JE, Cenik C, Tasan M, Roth FP. Next generation software for functional trend analysis. Bioinformatics. 2009 Nov 15;25(22):3043-4.
5. Antonov AV, Schmidt EE, Dietmann S, Krestyaninova M, Hermjakob H. R spider: a network-based analysis of gene lists by combining signaling and metabolic pathways from Reactome and KEGG databases. Nucleic Acids Res. 2010 Jul;38(Web Server issue):W78-83. Epub 2010 Jun 2.
6. Reimand J, Arak T, Vilo J. g:Profiler--a web server for functional interpretation of gene lists (2011 update). Nucleic Acids Res. 2011 Jul;39(Web Server issue):W307-15. Epub 2011 Jun 6


microRNA inhibitor products
Learn about our various products for microRNA inhibition
Read more

List of publications
See our list of selected papers
View list

Application stories
Check out these case stories using microRNA inhibition
Read more

Identify microRNA targets
RNA pull down techniques uncover non-canonical interactions
Read TechNote

  Privacy   Sitemap   Legal