Barbara Jenko Bizjan1, 2*, Bine Stančič1, Iva Sabolić3, Maja Štalekar1 and Uršula Prosenc Zmrzljak1
1BIA Separations CRO, Labena d.o.o., Ljubljana, Slovenia
2Clinical Institute of Special Laboratory Diagnostics, University Children’s Hospital, UMC, Ljubljana, Slovenia
3eDNA labs, Labena d.o.o., Zagreb, Croatia
barbara.jenko [at] biaseparationscro.com
Abstract
Background: microRNA might serve as a predictive biomarker for treatment response in stem cell treatment in knee osteoarthritis. Different sample types are going to be collected to enlighten the true biological role. MicroRNA analysis necessitates diverse approaches based on the sample type. In this study, we examined microRNA profiles in plasma samples, synovial fluid, and adipose-derived fat tissue. We conducted a comparative analysis of different microRNA analysis methods to assess the data.
Methods: The first approach involved a series of steps, including adapter trimming, quality filtering, size filtering, and mapping of all reads to the human reference genome (GRCh38.p12). Subsequently, genome-mapped reads were aligned to known miRNA sequences from miRBase. Reads that did not match miRNAs were subjected to further classification using additional databases, such as RNAcentral. The second pipeline also encompassed adapter trimming, quality filtering, and size filtering. Additionally, it involved collapsing individual reads into repeat sequences, followed by alignment to the mature index of miRBase. Unaligned reads were classified as isomiRs based on their alignment to the hairpin index of miRBase.
Results: We processed sequences from three plasma samples, three adipose fat tissue samples, and three synovial fluid samples. Although there were slight variations in microRNA read counts, the average ratio between counts was 0.92 (SD=0.29). Notably, the second pipeline yielded higher read counts compared to the first pipeline.
Conclusion: The results obtained from both microRNA bioinformatic pipelines demonstrated similar outcomes, suggesting that the choice of pipeline is unlikely to have a significant impact on the derived biological insights.
Keywords: bioinformatics, microRNA, sequencing
Funding: This work was performed as a part of project: “Development of advanced prediction tool for successful and optimized treatment course in pathological joint changes based on quantification of inflammatory biomarkers – PredicTest” which is co-funded by EUREKA member countries and the European Union Horizon 2020 Framework Programme
