Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/136070
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Type: Journal article
Title: Haemolysis Detection in MicroRNA-Seq from Clinical Plasma Samples
Author: Smith, M.D.
Leemaqz, S.Y.
Jankovic-Karasoulos, T.
McAninch, D.
McCullough, D.
Breen, J.
Roberts, C.T.
Pillman, K.A.
Citation: Genes, 2022; 13(7):1288-1-1288-15
Publisher: MDPI AG
Issue Date: 2022
ISSN: 2073-4425
2073-4425
Statement of
Responsibility: 
Melanie D. Smith, Shalem Y. Leemaqz, Tanja Jankovic-Karasoulos, Dale McAninch, Dylan McCullough, James Breen, Claire T. Roberts, and Katherine A. Pillman
Abstract: The abundance of cell-free microRNA (miRNA) has been measured in blood plasma and proposed as a source of novel, minimally invasive biomarkers for several diseases. Despite improvements in quantification methods, there is no consensus regarding how haemolysis affects plasma miRNA content. We propose a method for haemolysis detection in miRNA high-throughput sequencing (HTS) data from libraries prepared using human plasma. To establish a miRNA haemolysis signature we tested differential miRNA abundance between plasma samples with known haemolysis status. Using these miRNAs with statistically significant higher abundance in our haemolysed group, we further refined the set to reveal high-confidence haemolysis association. Given our specific context, i.e., women of reproductive age, we also tested for significant differences between pregnant and nonpregnant groups. We report a novel 20-miRNA signature used to identify the presence of haemolysis in silico in HTS miRNA-sequencing data. Further, we validated the signature set using firstly an all-male cohort (prostate cancer) and secondly a mixed male and female cohort (radiographic knee osteoarthritis). Conclusion: Given the potential for haemolysis contamination, we recommend that assays for haemolysis detection become standard pre-analytical practice and provide here a simple method for haemolysis detection.
Keywords: microRNA; plasma; biomarker; prediction; haemolysis; bioinformatics
Rights: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).
DOI: 10.3390/genes13071288
Grant ID: http://purl.org/au-research/grants/nhmrc/GNT1174971
Published version: http://dx.doi.org/10.3390/genes13071288
Appears in Collections:Medicine publications

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