Navigating the Challenges of MicroRNA Quantification

In this short article, we will provide an overview of the needs and challenges associated with the quantification of miRNAs for expression analysis.

MicroRNAs (miRNAs) play a key role in regulating protein production by binding to messenger RNA (mRNA) in the cells of humans, animals, and plants. While conventionally considered repressive, miRNAs are now recognised for their dual capacity to enhance gene expression [1,2]. Despite ongoing exploration of individual miRNA roles, their significance in maintaining organism stability and health is clear.

Examining disease and homeostatic disruptions reveals significant miRNA level changes due to processes such as:

• Tissue-specific release of miRNAs during cell and tissue damage and organ injury.
• Hijacking of miRNA expression by cancers and viruses, or introduction of pathogen-derived miRNAs [3].
• Regulation of immunity and recovery pathways in response to disease.

This heightened interest in clinical miRNA expression patterns as diagnostic biomarkers is fueled by the discovery of disease-specific miRNAs circulating in the bloodstream [4]. MiRNAs, with potential conservation across species, also emerge as promising translational biomarkers in drug development, offering early indications of drug response compared to protein changes.

In summary, miRNA biomarkers possess diagnostic potential in the clinical setting and could transform medical treatments. For drug development, they offer risk reduction during clinical trials and treatment, minimise harm to animals, and enhance the development of more effective medical treatments [6].

However, these advancements require high-quality tools for measuring miRNA expression levels.

Approaches in Quantifying MicroRNAs

With over 1900 human miRNA genes and 2600 mature miRNA sequences identified [7], RNA sequencing and array-based methods, though essential for extensive panels, can be cost-prohibitive for routine or high-throughput quantification.

An alternative involves the selection of smaller miRNA panels coupled with techniques that maximise information extraction. The method of choice for quantifying smaller miRNA panels is the widely adopted Reverse Transcription-Quantitative Polymerase Chain Reaction (RT-qPCR).

Challenges Associated with RT-qPCR for microRNAs

PCR is a very powerful technique that we owe much to. It's hard to imagine genomics or biotechnology without this laboratory workhorse.

Despite the utility of standard PCR techniques, RT-qPCR for microRNAs and other types of short RNA requires highly trained personnel due to its complex protocols, troubleshooting demands, and data analysis intricacies.

A set of critical challenges complicate the pursuit of robust data:

1.      Variability due to pre-processing steps: miRNAs are unsuitable for standard RT-primer strategies as they are too short for a primer pair to bind. This short length reduces the PCR efficiency because of low melting temperatures. Common solutions involve extension of the template through polyadenylation or step-loop RT-primers, both of which may compromise specificity and require careful primer design [8].

2.      Sample variability: Pre-analytic factors such as sample collection, processing, storage, and isolation methods can influence sample quality. Different tissue and sample types require different extraction techniques, leading to optimisation challenges. The short length of the target RNA, contamination, and low GC content in plasma hinder traditional extraction methods, resulting in sample-to-sample variability [9].

3.      Choice of Controls: As mentioned above, RT-qPCR involves various factors that can contribute to quantification errors. This includes variations in starting material, sample collection, RNA preparation, quality, and reverse transcription efficiency. As a result, a large set of controls is required, including normalisation against both endogenous and exogenous miRNAs. Selecting appropriate controls can be challenging and has resulted in a lack of standardisation across studies [10].

4.      Specificity: microRNAs exist in different forms and stages of maturity, with functions that a scientist may wish to distinguish in a PCR study:

• Isomirs: These are microRNA variations that are identical in sequence but have small additions or deletions at each end which are hard for PCR primers to differentiate [11]. Depending on the scenario, a scientist may wish to quantify specific isomirs or the total related isomir content of a sample.
  
• miRNA families: A similar challenge is seen for selectively picking up microRNA family members that have very different functions but vary by small numbers of base changes in their core sequence [12].
  
• Mature/precursor miRNA: Active, mature miRNAs are responsible for binding to mRNA, but precursor miRNA may also be of interest. These forms both have similar sequences but differ by length and secondary structure so distinguishing them through primer-based methods poses a challenge.
  

In the rapidly evolving field of miRNA research, better tools for quantification are needed to support discoveries in diagnostics and drug development. At Nanovery, we see the potential of microRNAs and understand the challenges researchers face.  

If you're facing challenges in microRNA quantification, we invite you to connect with us. Let's collaborate to advance miRNA-based applications for better health.

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References

[1] Shang, R., Lee, S., Senavirathne, G. et al. microRNAs in action: biogenesis, function and regulation. Nat Rev Genet 24, 816–833 (2023). https://doi.org/10.1038/s41576-023-00611-y

[2] Jame-Chenarboo F, Ng HH, Macdonald D, Mahal LK. High-Throughput Analysis Reveals miRNA Upregulating α-2,6-Sialic Acid through Direct miRNA-mRNA Interactions. ACS Cent Sci. 2022 Nov 23;8(11):1527-1536. doi: 10.1021/acscentsci.2c00748.

[3] Peng, Y., Croce, C. The role of MicroRNAs in human cancer. Sig Transduct Target Ther 1, 15004 (2016). https://doi.org/10.1038/sigtrans.2015.4

[4] Pozniak, T.; Shcharbin, D.; Bryszewska, M. Circulating microRNAs in Medicine. Int. J. Mol. Sci. 2022, 23, 3996. https://doi.org/10.3390/ijms23073996

[5] Ha M, Pang M, Agarwal V, Chen ZJ. Interspecies regulation of microRNAs and their targets. Biochim Biophys Acta. 2008 Nov;1779(11):735-42. doi: 10.1016/j.bbagrm.2008.03.004.

[6] Oda S, Yokoi T. Recent progress in the use of microRNAs as biomarkers for drug-induced toxicities in contrast to traditional biomarkers: A comparative review. Drug Metab Pharmacokinet. 2021 Apr;37:100372. doi: 10.1016/j.dmpk.2020.11.007.

[7] Kozomara A, Birgaoanu M, Griffiths-Jones S. miRBase: from microRNA sequences to function. Nucleic Acids Res. 2019 Jan 8;47(D1):D155-D162. doi: 10.1093/nar/gky1141.

[8] Dellett M, Simpson DA. Considerations for optimization of microRNA PCR assays for molecular diagnosis. Expert Rev Mol Diagn. 2016;16(4):407-14. doi: 10.1586/14737159.2016.1152184.

[9] MacLellan, S.A., MacAulay, C., Lam, S. et al. Pre-profiling factors influencing serum microRNA levels. BMC Clin Pathol 14, 27 (2014). https://doi.org/10.1186/1472-6890-14-27

[10] Ban E, Song EJ. Considerations and Suggestions for the Reliable Analysis of miRNA in Plasma Using qRT-PCR. Genes (Basel). 2022 Feb 10;13(2):328. doi: 10.3390/genes13020328.

[11] Glogovitis, I.; Yahubyan, G.; Würdinger, T.; Koppers-Lalic, D.; Baev, V. isomiRs–Hidden Soldiers in the miRNA Regulatory Army, and How to Find Them? Biomolecules 2021, 11, 41. https://doi.org/10.3390/biom11010041

[12] Chirshev E, Oberg KC, Ioffe YJ, Unternaehrer JJ. Let-7 as biomarker, prognostic indicator, and therapy for precision medicine in cancer. Clin Transl Med. 2019 Aug 28;8(1):24. doi: 10.1186/s40169-019-0240-y.