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Although bioinformatics plays a big part in identifying putative miRNAs, they also need to be experimentally verified in the lab. A range of techniques have been developed to overcome the challenges of miRNA profiling.
Here we review the most popular methods currently in use. Use our table to see at a glance which method is most suited for your experiments and click on a technique for more detail.
|Technique||When to use||Benefits||Drawbacks|
|Multiplex miRNA profiling|
One of the most popular techniques for validating and accurately quantifying miRNAs is quantitative real time PCR (qPCR). As well as being sensitive and quantitative, qPCR is also relatively inexpensive and flexible making it the preferred choice for validating novel miRNAs and for use in relatively small experiments.
This technique begins with the conversion of miRNA to cDNA. With the length of a miRNA being comparable to that of a typical DNA primer, cDNA synthesis from miRNAs presents its own challenges. The solution to this is to make the molecule longer, either by incorporating a poly(A) tail or stem-loop structure.
Once miRNA has been converted to cDNA it can be assayed using the same approach as a conventional qPCR experiment. Amplification is initiated with an miRNA-specific primer and a stem-loop/poly(A) primer. Either SYBR® Green or a TaqMan® probe is used to detect the amplified product.
However, qPCR has its limitations: large experiments can become quite labor intensive to perform. Moreover, unlike conventional qPCR, only one flanking primer can be specific to the miRNA, so care must be taken to ensure only one product is being amplified, especially when using SYBR® Green.
The short template length can prove a particularly problematic issue when trying to distinguish miRNAs that may only differ by a handful of bases; melting temperatures can be very low and very similar. The use of novel probes such as locked nucleic acids have been developed that mitigate specificity issues, but sample throughput remains a limitation for large studies (Vester et al., 2004).
Arrays are typically chosen for larger studies covering multiple miRNA targets. While they are the least quantitative of the three miRNA assay methods, conventional DNA oligonucleotide arrays are a relatively inexpensive way to measure hundreds of targets at once.
Thousands of probes can be easily spotted on slides, or built up by photolithography, potentially enabling the parallel tracking of all known miRNAs. Arrays are probed by hybridizing fluorescently labeled DNA or RNA samples. The brightness of individual spots can be used to infer relative changes in expression between samples.
As with qPCR, distinguishing similar sequences may be problematic, but careful selection of control probes, stringent washing and analysis can mitigate the issue. The maturity of the array platform is a significant benefit here, as there are well developed protocols and purpose built analysis tools available.
This method of miRNA quantification uses the high-throughput capability of next-generation sequencing (NGS) platforms. While it cannot quantify miRNA levels with the molar resolution of qPCR, deep sequencing of miRNA does have the advantage of being able to sample all miRNAs present in a sample, whether the researcher knows the sequence or not, making it an ideal discovery tool. Furthermore, as sequences are read directly, RNA-seq can distinguish closely related miRNAs and isoforms.
The most popular NGS approach uses TruSeq kits from Illumina to add sequencing adapters to an RNA library, which is then size fractionated to isolate miRNAs, prior to being run on one of Illumina's sequencing platforms. Illumina uses reversible dye-terminator sequencing to simultaneously sequence millions of library fragments in parallel.
On the face of it, this would seem to be the one method to rule them all: it can distinguish miRNAs at a single base resolution, it doesn’t require upfront knowledge of an miRNA’s existence, it can determine relative miRNA expression levels and, better yet, tagged libraries can be used for multiplexing.
However, of the three methods discussed, NGS does require the most input material, and even then miRNA-seq can’t match the sensitivity of qPCR.
Library construction, especially amplification, is potentially a sizeable source of bias and requires a good degree of technical skill to undertake (Baker et al., 2010). Data analysis can also be relatively challenging, especially for a labs that may not have a resident bioinformatician.
Multiplex miRNA profiling assays using Firefly particle technology are a more recent addition to the range of tools available to assay miRNAs. A key benefit of this technique, is its ability to allow the validation of multiple miRNAs across a range of samples, without the labor intensive workflow or large sample requirement of other techniques.
This technique is dependent on hydrogel particles that contain custom selected probes against target miRNAs. miRNAs bind to these probes and are then ligated to adaptor sequences for detection or pre-detection amplification.
Particles are optimized for use with common bench-top flow cytometers, allowing detection without specialized lab equipment. In addition, data analysis is relatively straightforward and does not require advanced bioinformatics skills.
This technology can be used directly with crude biofluids, and preparation of an RNA library is not necessary. The high sensitivity of the assay means that miRNA profiling can be achieved from input of as little as 10 μl of plasma or serum, or 100 pg purified RNA.
In truth, each of the techniques described can be considered largely complimentary; experimental aims and material considerations will likely dictate which tool a lab opts for first.
Studies focused on one or two miRNAs with relatively few test groups are likely to opt for qPCR as the primary assay, whereas experiments trying to discover new miRNA variants will look to an NGS solution. For larger studies examining multiple miRNAs at once, microarrays or multiplex miRNA assays using Firefly particle technology are more suitable.
Whatever the choice, given the difficulties involved in handling, verifying and interpreting miRNA data, the consensus view is that best practice is to verify the results of one technique by using a second where possible (Baker et al., 2010).
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