ArticleCellular and molecular life sciences : CMLS2016
Deep characterization of blood cell miRNomes by NGS.
Article in Cellular and molecular life sciences : CMLS, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- MicroRNA in diagnosis and therapy monitoring of early-stage triple-negative breast cancer.Scientific reports · 2018Trial
- Identifying Putative Causal Links between MicroRNAs and Severe COVID-19 Using Mendelian Randomization.Cells · 2021Article
- MicroRNAs as Candidate Biomarkers for Alzheimer's Disease.Non-coding RNA · 2021Review
- MicroRNA Expression Differences in Blood-Derived CD19+ B Cells of Methotrexate Treated Rheumatoid Arthritis Patients.Frontiers in immunology · 2021Article
- Common diseases alter the physiological age-related blood microRNA profile.Nature communications · 2020Article
- HumiR: Web Services, Tools and Databases for Exploring Human microRNA Data.Biomolecules · 2020Article
- xMD-miRNA-seq to generate near in vivo miRNA expression estimates in colon epithelial cells.Scientific reports · 2018Article
- Big Strides in Cellular MicroRNA Expression.Trends in genetics : TIG · 2018Review
- Sources to variability in circulating human miRNA signatures.RNA biology · 2017Article
- A comprehensive, cell specific microRNA catalogue of human peripheral blood.Nucleic acids research · 2017Article
- Web-based NGS data analysis using miRMaster: a large-scale meta-analysis of human miRNAs.Nucleic acids research · 2017Article
- QuickMIRSeq: a pipeline for quick and accurate quantification of both known miRNAs and isomiRs by jointly processing multiple samples from microRNA sequencing.BMC bioinformatics · 2017Article
- Combining miRNA and mRNA Expression Profiles in Wilms Tumor Subtypes.International journal of molecular sciences · 2016Article
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Authors and funding
20 authors.
Funding
No grant is acknowledged in the PubMed record.
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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