Optimized RT-qPCR and a Novel Normalization Method for Validating Circulating MiRNA Biomarkers in Ageing-related Diseases

Overview

Journal Sci Rep

Specialty Science

Date 2023 Nov 27

PMID 38012217

Authors

Andrew Want

Karolina Staniak

Wioleta Grabowska-Pyrzewicz

Aleksandra Fesiuk

Anna Barczak

Tomasz Gabryelewicz

Agnieszka Kulczynska-Przybik

Barbara Mroczko

Urszula Wojda

Affiliations

Soon will be listed here.

Abstract

Circulating miRNAs have potential as minimally invasive biomarkers for diagnosing various diseases, including ageing-related disorders such as Alzheimer's disease (AD). However, the lack of standardization in the common analysis method, RT-qPCR, and specifically in the normalization step, has resulted in inconsistent data across studies, hindering miRNA clinical implementation as well as basic research. To address this issue, this study proposes an optimized protocol for key steps in miRNA profiling, which incorporates absorbance-based haemolysis detection for assessing sample quality, double spike-in controls for miRNA isolation and reverse transcription, and the use of 7 stable normalizers verified in an aging population, including healthy subjects and individuals at different stages of Alzheimer's disease (140 subjects). The stability of these 7 normalizers was demonstrated using our novel method called BestmiRNorm for identifying optimal normalizers. BestmiRNorm, developed utilizing the Python programming language, enables the assessment of up to 11 potential normalizers. The standardized application of this optimized RT-qPCR protocol and the recommended normalizers are crucial for the development of miRNAs as biomarkers for AD and other ageing-related diseases in clinical diagnostics and basic research.

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References

Heckl C, Lang A, Ruhm A, Sroka R, Duffield T, Vogeser M . Spectrophotometric evaluation of hemolysis in plasma by quantification of free oxyhemoglobin, methemoglobin, and methemalbumin in presence of bilirubin. J Biophotonics. 2021; 14(5):e202000461. DOI: 10.1002/jbio.202000461. View

OBryant S, Gupta V, Henriksen K, Edwards M, Jeromin A, Lista S . Guidelines for the standardization of preanalytic variables for blood-based biomarker studies in Alzheimer's disease research. Alzheimers Dement. 2014; 11(5):549-60. PMC: 4414664. DOI: 10.1016/j.jalz.2014.08.099. View

Vickers K, Palmisano B, Shoucri B, Shamburek R, Remaley A . MicroRNAs are transported in plasma and delivered to recipient cells by high-density lipoproteins. Nat Cell Biol. 2011; 13(4):423-33. PMC: 3074610. DOI: 10.1038/ncb2210. View

OBryant S, Mielke M, Rissman R, Lista S, Vanderstichele H, Zetterberg H . Blood-based biomarkers in Alzheimer disease: Current state of the science and a novel collaborative paradigm for advancing from discovery to clinic. Alzheimers Dement. 2016; 13(1):45-58. PMC: 5218961. DOI: 10.1016/j.jalz.2016.09.014. View

Hampel H, OBryant S, Molinuevo J, Zetterberg H, Masters C, Lista S . Blood-based biomarkers for Alzheimer disease: mapping the road to the clinic. Nat Rev Neurol. 2018; 14(11):639-652. PMC: 6211654. DOI: 10.1038/s41582-018-0079-7. View

Schmittgen T, Livak K . Analyzing real-time PCR data by the comparative C(T) method. Nat Protoc. 2008; 3(6):1101-8. DOI: 10.1038/nprot.2008.73. View

Kahn S, Watkins B, BERMES Jr E . An evaluation of a spectrophotometric scanning technique for measurement of plasma hemoglobin. Ann Clin Lab Sci. 1981; 11(2):126-31. View

Bartel D . Metazoan MicroRNAs. Cell. 2018; 173(1):20-51. PMC: 6091663. DOI: 10.1016/j.cell.2018.03.006. View

Palmqvist S, Tideman P, Cullen N, Zetterberg H, Blennow K, Dage J . Prediction of future Alzheimer's disease dementia using plasma phospho-tau combined with other accessible measures. Nat Med. 2021; 27(6):1034-1042. DOI: 10.1038/s41591-021-01348-z. View

10.

Valadi H, Ekstrom K, Bossios A, Sjostrand M, Lee J, Lotvall J . Exosome-mediated transfer of mRNAs and microRNAs is a novel mechanism of genetic exchange between cells. Nat Cell Biol. 2007; 9(6):654-9. DOI: 10.1038/ncb1596. View

11.

Murray M, Watson H, Ward D, Bailey S, Ferraresso M, Nicholson J . "Future-Proofing" Blood Processing for Measurement of Circulating miRNAs in Samples from Biobanks and Prospective Clinical Trials. Cancer Epidemiol Biomarkers Prev. 2017; 27(2):208-218. PMC: 5812437. DOI: 10.1158/1055-9965.EPI-17-0657. View

12.

Zhou C, Keshavarz Hedayati M, Zhu X, Nielsen F, Levy U, Kristensen A . Optofluidic Sensor for Inline Hemolysis Detection on Whole Blood. ACS Sens. 2018; 3(4):784-791. DOI: 10.1021/acssensors.8b00030. View

13.

Gislefoss R, Berge U, Lauritzen M, Langseth H, Wojewodzic M . A Simple and Cost-Effective Method for Measuring Hemolysis in Biobank Serum Specimens. Biopreserv Biobank. 2021; 19(6):525-530. DOI: 10.1089/bio.2021.0037. View

14.

Nagaraj S, Zoltowska K, Laskowska-Kaszub K, Wojda U . microRNA diagnostic panel for Alzheimer's disease and epigenetic trade-off between neurodegeneration and cancer. Ageing Res Rev. 2018; 49:125-143. DOI: 10.1016/j.arr.2018.10.008. View

15.

Jack Jr C, Bennett D, Blennow K, Carrillo M, Dunn B, Budd Haeberlein S . NIA-AA Research Framework: Toward a biological definition of Alzheimer's disease. Alzheimers Dement. 2018; 14(4):535-562. PMC: 5958625. DOI: 10.1016/j.jalz.2018.02.018. View

16.

Abdelsalam M, Ahmed M, Osaid Z, Hamoudi R, Harati R . Insights into Exosome Transport through the Blood-Brain Barrier and the Potential Therapeutical Applications in Brain Diseases. Pharmaceuticals (Basel). 2023; 16(4). PMC: 10144189. DOI: 10.3390/ph16040571. View

17.

Andersen C, Jensen J, Orntoft T . Normalization of real-time quantitative reverse transcription-PCR data: a model-based variance estimation approach to identify genes suited for normalization, applied to bladder and colon cancer data sets. Cancer Res. 2004; 64(15):5245-50. DOI: 10.1158/0008-5472.CAN-04-0496. View

18.

Bustin S, Benes V, Garson J, Hellemans J, Huggett J, Kubista M . The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin Chem. 2009; 55(4):611-22. DOI: 10.1373/clinchem.2008.112797. View

19.

Thomou T, Mori M, Dreyfuss J, Konishi M, Sakaguchi M, Wolfrum C . Adipose-derived circulating miRNAs regulate gene expression in other tissues. Nature. 2017; 542(7642):450-455. PMC: 5330251. DOI: 10.1038/nature21365. View

20.

Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A . Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol. 2002; 3(7):RESEARCH0034. PMC: 126239. DOI: 10.1186/gb-2002-3-7-research0034. View