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Comparison of manual and semi-automatic RNA extraction methods using two-tailed RT-qPCR for absolute quantification as part of the sepsis diagnosis research
University of Skövde, School of Bioscience.
2023 (English)Independent thesis Basic level (degree of Bachelor), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Nowadays, sepsis has become a major healthcare problem. Its variance of symptoms and the lack of time to act makes it greatly difficult to treat. An early diagnosis using biomarkers, particularly miRNA, could potentially increase the patient’s prognosis as well as reduce the use of antibiotics for the treatment. The lack of method optimization for miRNA extraction and quantification calls for investigation prior to the construction of a multi-biomarker panel for sepsis diagnosis. The aim of this project was to examine and compare manual and semi-automatic extraction methodologies through the small RNA quantity and RNA quality, as well as test the detection and quantification abilities of the novel technique, two-tailed RT-qPCR. 30 extractions have been performed, their extracted elutions have been subjected to quality and quantity control and detection and absolute quantification through the two-tailed RT-qPCR. The results show no significant differences between the quantity and quality of the RNA extracted using both methods. Time management, on the contrary, reported significant differences between the two methods. On the other hand, the two-tailed RT-qPCR successfully amplified the miRNA candidate from as little as 100 µL of healthy plasma. The absolute quantification showed the miRNA candidate’s low concentration in plasma. Moreover, the qPCR efficiency was irregular during the project which may alert of contamination or unspecific primers. However, the melt curve showed a single amplicon which suggests great specificity. The detection and quantification of the miRNA candidate have been successful, though further investigation is recommended.

Place, publisher, year, edition, pages
2023. , p. 37
National Category
Medical Bioscience
Identifiers
URN: urn:nbn:se:his:diva-22891OAI: oai:DiVA.org:his-22891DiVA, id: diva2:1777840
Subject / course
Bioscience
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Available from: 2023-06-30 Created: 2023-06-30 Last updated: 2023-06-30Bibliographically approved

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CiteExportLink to record
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