Effect of long-term plasma storage on the profile of circulating microRNAs

Aim. To assess the impact of long-term storage of biobanked plasma samples on the profile of circulating small non-coding ribonucleic acids (microRNAs).

Material and methods. The study included paired plasma aliquots from 10 patients from the biobank collection of the National Medical Research Center for Therapy and Preventive Medicine. The control group consisted of microRNA samples isolated 1,5 years after plasma collection and then stored in aqueous solution for 3,5 years. In the long-term plasma storage group, microRNA was isolated from a second plasma aliquot after 5 years. All samples were stored at -70 оC. Se­quen­cing was performed for both groups simultaneously on the NextSeq 550 platform (Illumina, USA) using the High Output 1×75 bp protocol.

Results. Principal component analysis based on human microRNA gene expression data (ENCODE v47) revealed heterogeneity between the study groups. In the long-term plasma storage group, compared to the control group, a significant decrease in library concentration and size was observed, as well as a more than twofold increase in expression levels for 31 microRNAs.

Conclusion. Circulating microRNAs demonstrated higher stability du­ring storage in plasma than in aqueous solution. The obtained results in­di­cate the need to consider the storage time of isolated microRNA, along with other preanalytical factors, to improve the reproducibility of micro­RNA studies.

1. Matias-­Garcia PR, Wilson R, Mussack V, et al. Impact of long-term storage and freeze-­thawing on eight circulating microRNAs in plasma samples. PLoS One. 2020;15:e0227648. doi:10.1371/journal.pone.0227648.

2. Sourvinou IS, Markou A, Lianidou ES. Quantification of circulating miRNAs in plasma: effect of preanalytical and analytical pa­ra­meters on their isolation and stability. J Mol Diagn. 2013;15:827-34. doi:10.1016/j.jmoldx.2013.07.005.

3. O’Brien J, Hayder H, Zayed Y, et al. Overview of MicroRNA bio­genesis, mechanisms of actions, and circulation. Front Endocrinol (Lausanne). 2018;9:402. doi:10.3389/fendo.2018.00402.

4. Mikhailina VI, Meshkov AN, Kiseleva AN, et al. MicroRNA as biomarkers of coronary artery disease in real-world practice. Cardiovascular Therapy and Prevention. 2024;23(12):4225. (In Russ.) doi:10.15829/1728-8800-2024-4225.

5. Kiseleva AV, Sotnikova EA, Kutsenko VA, et al. Circulating micro­RNAs and collateral circulation in coronary chronic total occlusion. Cardiovascular Therapy and Prevention. 2024; 23(10):4190. (In Russ.) doi:10.15829/1728-8800-2024-4190.

6. Wang J, Chen J, Sen S. MicroRNA as biomarkers and diagnostics. J Cell Physiol. 2016;231:25-30. doi:10.1002/jcp.25056.

7. Chan S-F, Cheng H, Goh KK-R, et al. Preanalytic Methodological Considerations and Sample Quality Control of Circulating miRNAs. J Mol Diagn. 2023;25:438-53. doi:10.1016/jjmoldx.2023.03.005.

8. Sotnikova EA, Kiseleva AV, Meshkov AN. Effect of plasma and se­rum storage conditions on circulating microRNA levels. Car­dio­vascular Therapy and Prevention. 2024;23(11):4180. (In Russ.) doi:10.15829/1728-8800-2024-4180.

9. Sotnikova EA, Kiseleva AV, Meshkov AN. Preanalytical fac­tors affecting the plasma and serum levels of circulating micro­RNAs. Cardiovascular Therapy and Prevention. 2024;23(11): 4179. (In Russ.) doi:10.15829/1728-8800-2024-4179.

10. Suzuki K, Yamaguchi T, Kohda M, et al. Establishment of pre­analytical conditions for microRNA profile analysis of clinical plasma samples. PLoS One. 2022;17:e0278927. doi:10.1371/journal.pone.0278927.

11. Brunet-­Vega A, Pericay C, Quílez ME, et al. Variability in microRNA recovery from plasma: Comparison of five commercial kits. Anal Biochem. 2015;488:28-35. doi:10.1016/j.ab.2015.07.018.

12. Coenen-­Stass AML, Magen I, Brooks T, et al. Evaluation of methodologies for microRNA biomarker detection by next generation sequencing. RNA Biol. 2018;15:1133-45. doi:10.1080/15476286.2018.1514236.

13. Page K, Guttery DS, Zahra N, et al. Influence of plasma pro­ces­sing on recovery and analysis of circulating nucleic acids. PLoS One. 2013;8:e77963. doi:10.1371/journal.pone.0077963.

14. Balzano F, Deiana M, Dei Giudici S, et al. miRNA Stability in Frozen Plasma Samples. Molecules. 2015;20:19030-40. doi:10.3390/molecules201019030.

15. Kiseleva AV, Vasilyev DK, Soplenkova AG, et al. Association of plasma microRNA levels with different collateral circulation degree in chronic total occlusion patients with coronary artery disease: a pilot study. Cardiovascular Therapy and Prevention. 2024;23(7):4086. (In Russ.) doi:10.15829/1728-8800-2024-4086.

16. Kopylova OV, Ershova AI, Pokrovskaya MS, et al. Population-­nosological research biobank of the National Medical Research Center for Therapy and Preventive Medicine: analysis of bio­sam­p­les, principles of collecting and storing information. Car­dio­vas­cular Therapy and Prevention. 2021;20(8):3119. (In Russ.) doi:10.15829/1728-8800-2021-3119.

17. Smith T, Heger A, Sudbery I. UMI-tools: modeling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy. Genome Res. 2017;27:491-9. doi:10.1101/gr.209601.116.

18. Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 2011;17:10. doi:10.14806/ej.17.1.200.

19. Dobin A, Davis CA, Schlesinger F, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29:15-21. doi:10.1093/bioinformatics/bts635.

20. Danecek P, Bonfield JK, Liddle J, et al. Twelve years of SAMtools and BCFtools. GigaScience. 2021;10:giab008. doi:10.1093/gigascience/giab008.

21. Liao Y, Smyth GK, Shi W. featureCounts: an efficient general pur­pose program for assigning sequence reads to genomic features. Bio­informatics. 2014;30:923-30. doi:10.1093/bioinformatics/btt656.

22. ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature. 2012;489:57-74. doi:10.1038/nature11247.

23. Wickham H. Ggplot2: Elegant graphics for data analysis. New York, NY: Springer; 2009.

24. Kassambara A, Mundt F. Package "factoextra". Extract and visu­alize the results of multivariate data analyses. 2017;76:10-18637.

25. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550. doi:10.1186/s13059-014-0550-8.

26. Mitchell AJ, Gray WD, Hayek SS, et al. Platelets confound the measurement of extracellular miRNA in archived plasma. Sci Rep. 2016;6:32651. doi:10.1038/srep32651.

27. Binderup HG, Houlind K, Madsen JS, et al. Pre-storage centri­fu­gation conditions have significant impact on measured microRNA levels in biobanked EDTA plasma samples. Biochem Biophys Rep. 2016;7:195-200. doi:10.1016/j.bbrep.2016.06.005.

28.