A Robust Protocol to Quantify Circulating Cancer Biomarker MicroRNAs

  • Emma Bell
  • Hannah L. Watson
  • Shivani Bailey
  • Matthew J. MurrayEmail author
  • Nicholas ColemanEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 1580)


Reverse-transcriptase quantitative PCR (RT-qPCR) is a widely used method for quantifying microRNAs (miRNAs) in cells and tissues. However, the quantification of miRNAs in the circulation presents specific challenges. Here, we describe an optimized protocol using a small amount of input material to assess serum sample quality and quantify levels of a panel of up to 20 miRNAs. This is achieved by multiplexing Taqman miRNA stem-loop primers in the reverse transcription step. An additional multiplexed pre-amplification step is used to increase the sensitivity of the final quantification step, which is carried out using standard Taqman qPCR methodology.

Key words

MicroRNA Serum Plasma Cerebrospinal fluid Pre-amplification RT-qPCR 


  1. 1.
    Witwer KW (2015) Circulating microRNA biomarker studies: pitfalls and potential solutions. Clin Chem 61:56–63CrossRefPubMedGoogle Scholar
  2. 2.
    Murray MJ et al (2016) A pipeline to quantify serum and cerebrospinal fluid microRNAs for diagnosis and detection of relapse in paediatric malignant germ-cell tumours. Br J Cancer 114:151–162CrossRefPubMedGoogle Scholar
  3. 3.
    Blondal T et al (2013) Assessing sample and miRNA profile quality in serum and plasma or other biofluids. Methods 59:S1–S6CrossRefPubMedGoogle Scholar
  4. 4.
    Pritchard CC et al (2012) Blood cell origin of circulating microRNAs: a cautionary note for cancer biomarker studies. Cancer Prev Res 5:492–497CrossRefGoogle Scholar
  5. 5.
    Kirschner MB et al (2011) Haemolysis during sample preparation alters microRNA content of plasma. PLoS One 6:e24145CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    McDonald JS et al (2011) Analysis of circulating microRNA: preanalytical and analytical challenges. Clin Chem 57:833–840CrossRefPubMedGoogle Scholar
  7. 7.
    Kroh EM et al (2010) Analysis of circulating microRNA biomarkers in plasma and serum using quantitative reverse transcription-PCR (qRT-PCR). Methods 50:298–301CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Murray MJ et al (2014) Serum levels of mature microRNAs in DICER1-mutated pleuropulmonary blastoma. Oncogene 3:e87CrossRefGoogle Scholar
  9. 9.
    Murray MJ et al (2015) Solid tumors of childhood display specific serum microRNA profiles. Cancer Epidemiol Biomarkers Prev 24:350–360CrossRefPubMedGoogle Scholar
  10. 10.
    Mitchell PS et al (2008) Circulating microRNAs as stable blood-based markers for cancer detection. Proc Natl Acad Sci U S A 105:10513–10518CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Chen C et al (2005) Real-time quantification of microRNAs by stem-loop RT-PCR. Nucleic Acids Res 33:e179CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Tang F et al (2006) 220-plex microRNA expression profile of a single cell. Nat Protoc 1:1154–1159CrossRefPubMedGoogle Scholar
  13. 13.
    Lao K et al (2006) Multiplexing RT-PCR for the detection of multiple miRNA species in small samples. Biochem Biophys Res Commun 343:85–89CrossRefPubMedGoogle Scholar
  14. 14.
    Murray MJ et al (2011) Identification of MicroRNAs from the miR-371~373 and miR-302 clusters as potential serum biomarkers of malignant germ cell tumors. Am J Clin Pathol 135:119–125CrossRefPubMedGoogle Scholar
  15. 15.
    Dieckmann KP et al (2016) MicroRNA miR-371a-3p – A novel serum biomarker of testicular germ cell tumors: evidence for specificity from measurements in testicular vein blood and in neoplastic hydrocele fluid. Urol Int 97:76–83CrossRefPubMedGoogle Scholar
  16. 16.
    Wulfken LM et al (2011) MicroRNAs in renal cell carcinoma: diagnostic implications of serum miR-1233 levels. PLoS One 6:e25787CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Meng X et al (2015) Diagnostic and prognostic potential of serum miR-7, miR-16, miR-25, miR-93, miR-182, miR-376a and miR-429 in ovarian cancer patients. Br J Cancer 113:1358–1366CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Marabita F et al (2016) Normalization of circulating microRNA expression data obtained by quantitative real-time RT-PCR. Brief Bioinform 17:204–212CrossRefPubMedGoogle Scholar
  19. 19.
    Schwarzenbach H et al (2015) Data normalization strategies for MicroRNA quantification. Clin Chem 61:1333–1342CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media LLC 2017

Authors and Affiliations

  1. 1.Department of PathologyUniversity of Cambridge, CambridgeCambridgeUK
  2. 2.Department of Paediatrics, Haematology and OncologyAddenbrooke’s HospitalCambridgeUK
  3. 3.Department of PaediatricsUniversity of Cambridge, Addenbrooke’s HospitalCambridgeUK
  4. 4.Department of HistopathologyAddenbrooke’s HospitalCambridgeUK
  5. 5.AstraZenecaCambridge Science ParkCambridgeUK

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