Sepsis, defined as a life-threatening organ dysfunction, is a condition triggered by an adverse immune reaction often leading to a considerable cost in human lives. A fast and early detection is the cornerstone for treating sepsis, however, current therapeutic standard relies on blood culturing, a slow and non-specific indicator. Modern research has heightened an interest in a new set of biomarkers collectively named, microRNA, to fight against sepsis induced mortality. MicroRNAs are highly stable in biofluids and attractive candidates as biomarkers due to being detectable by non-invasive means, however, methods for their detection remains unclear. The study at hand aimed to optimize microRNA extraction from 100 μL initial blood plasma and subsequentially quantify a target microRNA-223 with the newly developed two-tailed RT-qPCR priming technology (TATAA Biocenter AB). Blood plasma was taken from self-assessed healthy donors and microRNA extraction was conducted using the miRNeasy Serum/Plasma advanced kit (QIAGEN) and QIAcube® (QIAGEN). Each extraction was analysed in a Qubit 3.0 (Thermo Fisher Scientific) and DS-11+spectrophotometer (DeNovix). Absolute quantification was used to quantify microRNA, two-tailed RT-qPCR to detect and obtain a Cq-value in a 7300 Real-Time PCR System (Applied Biosystems). Using this system, a standard curve was optimized to achieve a 103% efficiency and correlation coefficient R2=0.99 to secure technical excellence. The two-tailed RT-qPCR platform returned quantifiable microRNA-223 data which allowed for a theoretical profiling of microRNA-223 by absolute quantification. The study demonstrated a promising setting of using two-tailed RT-qPCR to detect and characterize microRNAs extracted from human plasma for future biomarker research.
Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:his-20227 |
Date | January 2021 |
Creators | Nilsson, Andreas |
Publisher | Högskolan i Skövde, Institutionen för biovetenskap |
Source Sets | DiVA Archive at Upsalla University |
Language | English |
Detected Language | English |
Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
Format | application/pdf |
Rights | info:eu-repo/semantics/openAccess |
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