Deep characterization of blood cell miRNomes by NGS
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 differe...
Gespeichert in:
| Hauptverfasser: | , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
August 2016
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| In: |
Cellular and molecular life sciences
Year: 2016, Jahrgang: 73, Heft: 16, Pages: 3169-3181 |
| ISSN: | 1420-9071 |
| DOI: | 10.1007/s00018-016-2154-9 |
| Online-Zugang: | Verlag, Volltext: http://dx.doi.org/10.1007/s00018-016-2154-9 Verlag, Volltext: https://doi.org/10.1007/s00018-016-2154-9 
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| Verfasserangaben: | Eva C. Schwarz, Christina Backes, Arne Knörck, Nicole Ludwig, Petra Leidinger, Cora Hoxha, Gertrud Schwär, Thomas Grossmann, Sabine C. Müller, Martin Hart, Jan Haas, Valentina Galata, Isabelle Müller, Tobias Fehlmann, Hermann Eichler, Andre Franke, Benjamin Meder, Eckart Meese, Markus Hoth, Andreas Keller |
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| 245 | 1 | 0 | |a Deep characterization of blood cell miRNomes by NGS |c Eva C. Schwarz, Christina Backes, Arne Knörck, Nicole Ludwig, Petra Leidinger, Cora Hoxha, Gertrud Schwär, Thomas Grossmann, Sabine C. Müller, Martin Hart, Jan Haas, Valentina Galata, Isabelle Müller, Tobias Fehlmann, Hermann Eichler, Andre Franke, Benjamin Meder, Eckart Meese, Markus Hoth, Andreas Keller |
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| 520 | |a 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. | ||
| 650 | 4 | |a Blood cells | |
| 650 | 4 | |a FACS | |
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