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Evaluation of quantitative miRNA expression platforms in the microRNA quality control (miRQC) study

Nature Methods volume 11, pages 809–815 (2014)Cite this article

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A Corrigendum to this article was published on 28 August 2014

This article has been updated

Abstract

MicroRNAs are important negative regulators of protein-coding gene expression and have been studied intensively over the past years. Several measurement platforms have been developed to determine relative miRNA abundance in biological samples using different technologies such as small RNA sequencing, reverse transcription–quantitative PCR (RT-qPCR) and (microarray) hybridization. In this study, we systematically compared 12 commercially available platforms for analysis of microRNA expression. We measured an identical set of 20 standardized positive and negative control samples, including human universal reference RNA, human brain RNA and titrations thereof, human serum samples and synthetic spikes from microRNA family members with varying homology. We developed robust quality metrics to objectively assess platform performance in terms of reproducibility, sensitivity, accuracy, specificity and concordance of differential expression. The results indicate that each method has its strengths and weaknesses, which help to guide informed selection of a quantitative microRNA gene expression platform for particular study goals.

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Figure 1: MiRQC study overview.
Figure 2: Titration response and reproducibility.
Figure 3: Platform accuracy.
Figure 4: Detection rate, sensitivity and specificity.
Figure 5: Differential miRNA expression.
Figure 6: Radial plot of performance metric z-scores.

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ArrayExpress

Gene Expression Omnibus

Change history

  • 30 July 2014

    In the version of this article initially published, the author Linda Wong was omitted from the author list. The error has been corrected in the HTML and PDF versions of the article.

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Acknowledgements

We thank J. Nuytens for preparing all RNA samples and E. Lefebvre and T. Demoor for statistical and mathematical support. P. Mestdagh is supported by the Fund for Scientific Research Flanders (FWO); J.V. is supported by the Ghent University Research Fund (BOF).

Author information

Authors and Affiliations

  1. Center for Medical Genetics, Ghent University, Ghent, Belgium

    Pieter Mestdagh & Jo Vandesompele

  2. Novartis Institutes of Biomedical Research, Basel, Switzerland

    Nicole Hartmann, Lukas Baeriswyl, Frank Staedtler & Thomas Peters

  3. Exiqon, Vedbaek, Denmark

    Ditte Andreasen & Peter Mouritzen

  4. Life Technologies, South San Francisco, California, USA

    Nathalie Bernard, Caifu Chen, Kathy Y Lee, Sunali Patel, Elliot J Shelton, Umberto Ulmanella & Linda Wong

  5. Quanta Biosciences, Inc.,, Gaithersburg, Maryland, USA

    David Cheo, Yun Feng & Dave Schuster

  6. Agilent Technologies, Inc.,, Santa Clara, California, USA

    Petula D'Andrade & Stephanie Fulmer-Smentek

  7. Affymetrix, Santa Clara, California, USA

    Mike DeMayo & Julia Gouffon

  8. NanoString Technologies, Seattle, Washington, USA

    Lucas Dennis & Chris Grimley

  9. WaferGen, San Francisco, California, USA

    Stefaan Derveaux & Scott Silveria

  10. Miltenyi Biotec, Bergisch Gladbach, Germany

    Bernhard Gerstmayer, Sabine Peiffer & Silvia Rüberg

  11. Qiagen, Fredrick, Maryland, USA

    Eric Lader & Jonathan M Shaffer

  12. Illumina, Hayward, California, USA

    Shujun Luo & Gary Schroth

  13. Strand Life Sciences, Bangalore, India

    Aishwarya Narayanan & Vamsi Veeramachaneni

  14. Center for Applied Genomics, The Children's Hospital of Philadelphia, Philadelphia, USA

    Toumy Guettouche

Authors
  1. Pieter Mestdagh
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  2. Nicole Hartmann
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  18. Kathy Y Lee
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  19. Shujun Luo
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  20. Peter Mouritzen
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  22. Sunali Patel
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  26. Dave Schuster
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  28. Elliot J Shelton
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  29. Scott Silveria
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  30. Umberto Ulmanella
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  32. Frank Staedtler
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  33. Thomas Peters
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  34. Toumy Guettouche
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  35. Linda Wong
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  36. Jo Vandesompele
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Contributions

P. Mestdagh, J.V. and T.G. designed the study; P. Mestdagh and J.V. performed data analysis and wrote the manuscript; and N.H., L.B., D.A., N.B., C.C., D.C., P.D., M.D., L.D., S.D., Y.F., S.F.-S., B.G., J.G., C.G., E.L., K.Y.L., S.L., P. Mouritzen, A.N., S. Patel, S. Peiffer, S.R., G.S., D.S., J.M.S., E.J.S., S.S., U.U., V.V., F.S. and T.P. produced miRNA expression data. All authors approved the final version of the manuscript.

Corresponding author

Correspondence to Jo Vandesompele.

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Competing interests

D.A. and P. Mouritzen are employed by Exiqon; N.B., C.C., K.L., S.P atel., E.S. and U.U. are employed by Life Technologies; D.C., Y.F. and D.S. are employed by Quanta Biosciences; P.D. and S.F. are employed by Agilent Technologies; M.D. and J.G. are employed by Affymetrix; L.D. and C.G. are employed by NanoString Technologies; S.D. and S.S. are employed by WaferGen; E.L. and J.S. are employed by Qiagen; S.L. and G.S. are employed by Illumina. MiRNA profiling experiments were performed at the expense of Exiqon, Life Technologies, Agilent Technologies, Quanta Biosciences, Qiagen, WaferGen, Illumina and NanoString Technologies.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–10, Supplementary Tables 1–3 and Supplementary Notes 1–13 (PDF 15764 kb)

Supplementary Data

miRQC qPCR Data (ZIP 920 kb)

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Mestdagh, P., Hartmann, N., Baeriswyl, L. et al. Evaluation of quantitative miRNA expression platforms in the microRNA quality control (miRQC) study. Nat Methods 11, 809–815 (2014). https://doi.org/10.1038/nmeth.3014

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