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Optimization of extraction of circulating RNAs from plasma--enabling small RNA sequencing.

ABSTRACT: There are several protocols and kits for the extraction of circulating RNAs from plasma with a following quantification of specific genes via RT-qPCR. Due to the marginal amount of cell-free RNA in plasma samples, the total RNA yield is insufficient to perform Next-Generation Sequencing (NGS), the state-of-the-art technology in massive parallel sequencing that enables a comprehensive characterization of the whole transcriptome. Screening the transcriptome for biomarker signatures accelerates progress in biomarker profiling for molecular diagnostics, early disease detection or food safety. Therefore, the aim was to optimize a method that enables the extraction of sufficient amounts of total RNA from bovine plasma to generate good-quality small RNA Sequencing (small RNA-Seq) data. An increased volume of plasma (9 ml) was processed using the Qiagen miRNeasy Serum/Plasma Kit in combination with the QIAvac24 Plus system, a vacuum manifold that enables handling of high volumes during RNA isolation. 35 ng of total RNA were passed on to cDNA library preparation followed by small RNA high-throughput sequencing analysis on the Illumina HiSeq2000 platform. Raw sequencing reads were processed by a data analysis pipeline using different free software solutions. Seq-data was trimmed, quality checked, gradually selected for miRNAs/piRNAs and aligned to small RNA reference annotation indexes. Mapping to human reference indexes resulted in 4.8±2.8% of mature miRNAs and 1.4±0.8% of piRNAs and of 5.0±2.9% of mature miRNAs for bos taurus.

SUBMITTER: Spornraft M

PROVIDER: S-EPMC4167851 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Optimization of extraction of circulating RNAs from plasma--enabling small RNA sequencing.

Spornraft Melanie M Kirchner Benedikt B Haase Bettina B Benes Vladimir V Pfaffl Michael W MW Riedmaier Irmgard I

PloS one 20140917 9

There are several protocols and kits for the extraction of circulating RNAs from plasma with a following quantification of specific genes via RT-qPCR. Due to the marginal amount of cell-free RNA in plasma samples, the total RNA yield is insufficient to perform Next-Generation Sequencing (NGS), the state-of-the-art technology in massive parallel sequencing that enables a comprehensive characterization of the whole transcriptome. Screening the transcriptome for biomarker signatures accelerates pro ...[more]

PMID: 25229651

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Project description:Next-generation transcriptome sequencing (RNA-Seq) has become the standard practice for studying gene splicing, mutations and changes in gene expression to obtain valuable, accurate biological conclusions. However, obtaining good sequencing coverage and depth to study these is impeded by the difficulties of obtaining high quality total RNA with minimal genomic DNA contamination. With this in mind, we evaluated the performance of Phenol-free total RNA purification kit (Amresco) in comparison with TRI Reagent (MRC) and RNeasy Mini (Qiagen) for the extraction of total RNA of Pseudomonas aeruginosa which was grown in glucose-supplemented (control) and polyethylene-supplemented (growth-limiting condition) minimal medium. All three extraction methods were coupled with an in-house DNase I treatment before the yield, integrity and size distribution of the purified RNA were assessed. RNA samples extracted with the best extraction kit were then sequenced using the Illumina HiSeq 2000 platform.TRI Reagent gave the lowest yield enriched with small RNAs (sRNAs), while RNeasy gave moderate yield of good quality RNA with trace amounts of sRNAs. The Phenol-free kit, on the other hand, gave the highest yield and the best quality RNA (RIN value of 9.85 ± 0.3) with good amounts of sRNAs. Subsequent bioinformatic analysis of the sequencing data revealed that 5435 coding genes, 452 sRNAs and 7 potential novel intergenic sRNAs were detected, indicating excellent sequencing coverage across RNA size ranges. In addition, detection of low abundance transcripts and consistency of their expression profiles across replicates from the same conditions demonstrated the reproducibility of the RNA extraction technique.Amresco's Phenol-free Total RNA purification kit coupled with DNase I treatment yielded the highest quality RNAs containing good ratios of high and low molecular weight transcripts with minimal genomic DNA. These RNA extracts gave excellent non-biased sequencing coverage useful for comprehensive total transcriptome sequencing and analysis. Furthermore, our findings would be useful for those interested in studying both coding and non-coding RNAs from precious bacterial samples cultivated in growth-limiting condition, in a single sequencing run.

Project description:ObjectiveAging is a complicated process that triggers age-related disease susceptibility through intercellular communication in the microenvironment. While the classic secretome of senescence-associated secretory phenotype (SASP) including soluble factors, growth factors, and extracellular matrix remodeling enzymes are known to impact tissue homeostasis during the aging process, the effects of novel SASP components, extracellular small noncoding RNAs (sncRNAs), on human aging are not well established.MethodsHere, by utilizing 446 small RNA-seq samples from plasma and serum of healthy donors found in the Extracellular RNA (exRNA) Atlas data repository, we correlated linear and nonlinear features between circulating sncRNAs expression and age by the maximal information coefficient (MIC) relationship determination. Age predictors were generated by ensemble machine learning methods (Adaptive Boosting, Gradient Boosting, and Random Forest) and core age-related sncRNAs were determined through weighted coefficients in machine learning models. Functional investigation was performed via target prediction of age-related miRNAs.ResultsWe observed the number of highly expressed transfer RNAs (tRNAs) and microRNAs (miRNAs) showed positive and negative associations with age respectively. Two-variable (sncRNA expression and individual age) relationships were detected by MIC and sncRNAs-based age predictors were established, resulting in a forecast performance where all R 2 values were greater than 0.96 and root-mean-square errors (RMSE) were less than 3.7 years in three ensemble machine learning methods. Furthermore, important age-related sncRNAs were identified based on modeling and the biological pathways of age-related miRNAs were characterized by their predicted targets, including multiple pathways in intercellular communication, cancer and immune regulation.ConclusionIn summary, this study provides valuable insights into circulating sncRNAs expression dynamics during human aging and may lead to advanced understanding of age-related sncRNAs functions with further elucidation.

Dataset Information

Optimization of extraction of circulating RNAs from plasma--enabling small RNA sequencing.

Publications

Optimization of extraction of circulating RNAs from plasma--enabling small RNA sequencing.

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