Comparison of methods for miRNA isolation and quantification from ovine plasma

Wright, Kathryn; Silva, Kumudika de; Purdie, Auriol C.; Plain, Karren M.

doi:10.1038/s41598-020-57659-7

Cited by 63 publications

(63 citation statements)

References 34 publications

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“…This indicates that the Qubit measurements were more consistent for RNA extracted from a larger plasma volume, given it is within the limit of saturation. Good performance of Qubit from 200-µl plasma has also been observed in another recent study 40 .…”

Section: Discussionsupporting

confidence: 72%

Droplet digital polymerase chain reaction-based quantification of circulating microRNAs using small RNA concentration normalization

Gupta

Ndode-Ekane

Puhakka

et al. 2020

Sci Rep

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Quantification of plasma microRNAs (miRNAs) as non-invasive disease biomarkers is subject to multiple technical variabilities. This study aimed to develop an optimized protocol for miRNA quantification from rodent plasma. We hypothesized that a fixed small RNA concentration input for reverse transcription (RT) reaction will provide better miRNA quantification than a fixed RNA volume input. For this, tail-vein plasma was collected from 30 naïve, adult male Sprague-Dawley rats. Plasma hemolysis was measured with NanoDrop-1000 and Denovix DS-11 spectrophotometers. Plasma was then pooled, and RNA was extracted from 50-μl, 100-μl or 200-μl pool aliquots. Small RNA concentration was measured with Qubit miRNA assay. A fixed RNA volume (un-normalized) or a fixed small RNA concentration was used for RT (concentration-normalized). The method was setup with miR-23a-3p and validated with miR-103a-3p and miR-451a. Hemolysis measurements from Denovix and NanoDrop strongly correlated. Qubit revealed increased small RNA concentrations with increasing starting plasma volumes. With concentration-normalization, miRNA levels from 100-µl and 200-µl plasma volume groups mostly normalized to the level of the 50-µl in ddPCR. Our results indicate that miRNA quantification with ddPCR should be performed with small RNA concentration-normalization to minimize variations in eluted RNA concentrations occuring during RNA extraction. MicroRNAs (miRNAs) are small non-coding RNAs (~22 nucleotides long) that regulate the expression of protein-coding genes through translational repression 1. Dysregulation of miRNA levels is observed in many disease conditions, both in the affected tissue and the circulation 2-5. Circulating serum/plasma miRNAs are extremely stable under appropriate sampling and storage conditions 6-10 , and are a potential source of non-invasive diagnostic, prognostic, and predictive biomarkers of disease states 11,12. Despite the stability of circulating plasma miRNAs, there are several challenges associated with their reliable and reproducible quantification as biomarkers. First, hemolysis in plasma impairs the detection of circulating cell-free miRNAs 6,9,13. Second, the total amount of small RNA in the plasma is usually low, making it challenging to obtain accurate concentration measurements using common methods like the NanoDrop or Agilent Bioanalyzer 14. Third, there are no endogenous normalizer miRNAs that can be used as an internal standard 15. Some attempts have been made to overcome these challenges. For example, multiple methods are proposed for detecting plasma hemolysis 16-18. The need to carefully report the protocols used for blood collection, centrifugation, storage, and handling of plasma has been emphasized, because these factors profoundly affect circulating miRNA levels, and the harmonization of these techniques is important for standardizing plasma biomarker research across laboratories 18-22. The addition of carrier RNAs and synthetic spike-ins during RNA extraction and reverse transcription-quantitative polym...

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Section: Discussionsupporting

confidence: 72%

Droplet digital polymerase chain reaction-based quantification of circulating microRNAs using small RNA concentration normalization

Gupta

Ndode-Ekane

Puhakka

et al. 2020

Sci Rep

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“…It has been identified that a number of factors including RNA isolation and detection systems can contribute to this inconsistency. Recently published work from one laboratory, which used different commercial kits to isolate RNA, demonstrated that the recovery of RNA was variable between the commercial kits (El-Khoury et al, 2016;Wright et al, 2020). In addition, the selection of endogenous controls to normalize the target miR expression levels directly affects the results, and such importance has been neglected by some studies.…”

Section: Overall Challenges Of Using Secreted Mirs To Predict Implantmentioning

confidence: 99%

Secreted MicroRNA to Predict Embryo Implantation Outcome: From Research to Clinical Diagnostic Application

Zhou

Dimitriadis

2020

Front. Cell Dev. Biol.

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Embryo implantation failure is considered a leading cause of infertility and a significant bottleneck for in vitro fertilization (IVF) treatment. Confirmed factors that lead to implantation failure involve unhealthy embryos, unreceptive endometrium, and asynchronous development and communication between the two. The quality of embryos is further dependent on sperm parameters, oocyte quality, and early embryo development after fertilization. The extensive involvement of such different factors contributes to the variability of implantation potential across different menstrual cycles. An ideal approach to predict the implantation outcome should not compromise embryo implantation. The use of clinical material, including follicular fluid, cumulus cells, sperm, seminal exosomes, spent blastocyst culture medium, blood, and uterine fluid, that can be collected relatively non-invasively without compromising embryo implantation in a transfer cycle opens new perspectives for the diagnosis of embryo implantation potential. Compositional comparison of these samples between fertile women and women or couples with implantation failure has identified both quantitative and qualitative differences in the expression of microRNAs (miRs) that hold diagnostic potential for implantation failure. Here, we review current findings of secreted miRs that have been identified to potentially be useful in predicting implantation outcome using material that can be collected relatively non-invasively. Developing non-invasive biomarkers of implantation potential would have a major impact on implantation failure and infertility.

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“…Sample quality and processing are important factors affecting the quality and quantity of cfRNAs. Pre-analytical and analytical aspects should be considered since physiological processes such as blood hemolysis of blood during plasma collection can influence the measurement of cfRNAs [ 60 , 61 ]. The methods used for the isolation of exosomes also can also affect the quality of exosomal miRNA profiles [ 51 ].…”

Section: Methodologies For Liquid Biopsymentioning

confidence: 99%

Liquid Biopsy in Pancreatic Cancer: Are We Ready to Apply It in the Clinical Practice?

Heredia-Soto

Rodrı́guez-Salas

Feliú

2021

Cancers

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Pancreatic ductal adenocarcinoma (PDAC) exhibits the poorest prognosis of all solid tumors, with a 5-year survival of less than 10%. To improve the prognosis, it is necessary to advance in the development of tools that help us in the early diagnosis, treatment selection, disease monitoring, evaluation of the response and prognosis. Liquid biopsy (LB), in its different modalities, represents a particularly interesting tool for these purposes, since it is a minimally invasive and risk-free procedure that can detect both the presence of genetic material from the tumor and circulating tumor cells (CTCs) in the blood and therefore distantly reflect the global status of the disease. In this work we review the current status of the main LB modalities (ctDNA, exosomes, CTCs and cfRNAs) for detecting and monitoring PDAC.

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Comparison of methods for miRNA isolation and quantification from ovine plasma

Cited by 63 publications

References 34 publications

Droplet digital polymerase chain reaction-based quantification of circulating microRNAs using small RNA concentration normalization

Droplet digital polymerase chain reaction-based quantification of circulating microRNAs using small RNA concentration normalization

Secreted MicroRNA to Predict Embryo Implantation Outcome: From Research to Clinical Diagnostic Application

Liquid Biopsy in Pancreatic Cancer: Are We Ready to Apply It in the Clinical Practice?

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