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Deep characterization of blood cell miRNomes by NGS

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Abstract

A systematic understanding of different factors influencing cell type specific microRNA profiles is essential for state-of-the art biomarker research. We carried out a comprehensive analysis of the biological variability and changes in cell type pattern over time for different cell types and different isolation approaches in technical replicates. All combinations of the parameters mentioned above have been measured, resulting in 108 miRNA profiles that were evaluated by next-generation-sequencing. The largest miRNA variability was due to inter-individual differences (34 %), followed by the cell types (23.4 %) and the isolation technique (17.2 %). The change over time in cell miRNA composition was moderate (<3 %) being close to the technical variations (<1 %). Largest variability (including technical and biological variance) was observed for CD8 cells while CD3 and CD4 cells showed significantly lower variations. ANOVA highlighted that 51.5 % of all miRNAs were significantly influenced by the purification technique. While CD4 cells were least affected, especially miRNA profiles of CD8 cells were fluctuating depending on the cell purification approach. To provide researchers access to the profiles and to allow further analyses of the tested conditions we implemented a dynamic web resource.

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Acknowledgments

The work of AK, BM is supported by the European Union FP7 (BestAgeing). BM is grateful for support from the German Center for Cardiovascular Research (DZHK). We thank Elmar Krause for support within the FACS facility. We acknowledge funding by the Deutsche Forschungsgemeinschaft (DFG), the collaborative research centers SFB 1027 (Project A2 to MH) and SFB 894 (Project A1 to MH) and the research training group GK 1326 (to ECS and MH). We thank Stephanie Deutscher for excellent technical assistance.

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    Authors and Affiliations

    1. Biophysics, Center for Integrative Physiology and Molecular Medicine, School of Medicine, Saarland University, Homburg, Germany

      Eva C. Schwarz, Arne Knörck, Cora Hoxha, Gertrud Schwär & Markus Hoth

    2. Saarland University, Building E2.1, 66123, Saarbrücken, Germany

      Christina Backes, Thomas Grossmann, Sabine C. Müller, Valentina Galata, Tobias Fehlmann & Andreas Keller

    3. Department of Human Genetics, Saarland University, Homburg, Germany

      Nicole Ludwig, Petra Leidinger, Martin Hart & Eckart Meese

    4. Department of Internal Medicine III, University Hospital Heidelberg, Heidelberg, Germany

      Jan Haas & Benjamin Meder

    5. German Center for Cardiovascular Research (DZHK), Heidelberg, Germany

      Jan Haas & Benjamin Meder

    6. Klaus Tschira Institute for Integrative Computational Cardiology, Heidelberg, Germany

      Jan Haas & Benjamin Meder

    7. Clinical Hemostaseology and Transfusion Medicine, Saarland University, Homburg, Germany

      Isabelle Müller & Hermann Eichler

    8. Kiel University, Kiel, Germany

      Andre Franke

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    1. Eva C. Schwarz
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    Corresponding author

    Correspondence to Christina Backes.

    Additional information

    E. C. Schwarz and C. Backes contributed equally as first authors.

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    Supplemental Figure 1: log10 of mapped reads per sample, samples below the threshold of 0.5 million reads are highlighted

    Supplemental Figure 2: RT-qPCR results for the same miRNAs as presented in Figure 7. The bar height corresponds to the pseudo counts as defined in the Methods

    Supplemental Figure 3: Additional RT-qPCR validation experiments for three miRNAs. Left hand side presents the NGS results (panels A, C, E) while right hand side presents RT-qPCR based pseudo counts (panels B, D, F)

    Supplemental Figure 4: Upper part shows the interface of the web resource with the example input CD3 positive selected vs. CD4 positive selected cells. Middle part presents a volcano plot of the analysis CD3 positive selected vs. CD4 positively selected, detailing the negative logarithm of the p-value vs. the logarithm of fold change. For the same comparison a scatter plot shows the logarithm of median expression for CD3 positive selected cells on the x-axis and for the CD4 positive selected cells on the y-axis. The hierarchical clustering of the 20 most significant miRNAs graphically demonstrates that the respective miRNAs are differentially regulated between both groups. The lower part presents the tabular output

    Supplementary material 1 (PDF 469 kb)

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    Schwarz, E.C., Backes, C., Knörck, A. et al. Deep characterization of blood cell miRNomes by NGS. Cell. Mol. Life Sci. 73, 3169–3181 (2016). https://doi.org/10.1007/s00018-016-2154-9

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    • DOI: https://doi.org/10.1007/s00018-016-2154-9

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