Schwarz Eva C, Backes Christina, Knörck Arne, Ludwig Nicole, Leidinger Petra, Hoxha Cora, Schwär Gertrud, Grossmann Thomas, Müller Sabine C, Hart Martin, Haas Jan, Galata Valentina, Müller Isabelle, Fehlmann Tobias, Eichler Hermann, Franke Andre, Meder Benjamin, Meese Eckart, Hoth Markus, Keller Andreas
Biophysics, Center for Integrative Physiology and Molecular Medicine, School of Medicine, Saarland University, Homburg, Germany.
Saarland University, Building E2.1, 66123, Saarbrücken, Germany.
Cell Mol Life Sci. 2016 Aug;73(16):3169-81. doi: 10.1007/s00018-016-2154-9. Epub 2016 Feb 13.
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.
系统了解影响细胞类型特异性微小RNA（miRNA）谱的不同因素对于开展前沿生物标志物研究至关重要。我们针对不同细胞类型和不同分离方法，在技术重复中对生物变异性以及细胞类型模式随时间的变化进行了全面分析。上述参数的所有组合均已测量，共产生了108个通过下一代测序评估的miRNA谱。最大的miRNA变异性归因于个体间差异（34%），其次是细胞类型（23.4%）和分离技术（17.2%）。细胞miRNA组成随时间的变化较为适度（<3%），接近技术变异（<1%）。CD8细胞的变异性最大（包括技术和生物变异），而CD3和CD4细胞的变异则显著较低。方差分析表明，所有miRNA中有51.5%受纯化技术的显著影响。虽然CD4细胞受影响最小，但特别是CD8细胞的miRNA谱会因细胞纯化方法而波动。为了让研究人员能够获取这些谱并进一步分析测试条件，我们创建了一个动态网络资源。
