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Identification of extracellular miRNA in archived serum samples by next-generation sequencing from RNA extracted using multiple methods.

ABSTRACT: miRNAs act as important regulators of gene expression by promoting mRNA degradation or by attenuating protein translation. Since miRNAs are stably expressed in bodily fluids, there is growing interest in profiling these miRNAs, as it is minimally invasive and cost-effective as a diagnostic matrix. A technical hurdle in studying miRNA dynamics is the ability to reliably extract miRNA as small sample volumes and low RNA abundance create challenges for extraction and downstream applications. The purpose of this study was to develop a pipeline for the recovery of miRNA using small volumes of archived serum samples. The RNA was extracted employing several widely utilized RNA isolation kits/methods with and without addition of a carrier. The small RNA library preparation was carried out using Illumina TruSeq small RNA kit and sequencing was carried out using Illumina platform. A fraction of five microliters of total RNA was used for library preparation as quantification is below the detection limit. We were able to profile miRNA levels in serum from all the methods tested. We found out that addition of nucleic acid based carrier molecules had higher numbers of processed reads but it did not enhance the mapping of any miRBase annotated sequences. However, some of the extraction procedures offer certain advantages: RNA extracted by TRIzol seemed to align to the miRBase best; extractions using TRIzol with carrier yielded higher miRNA-to-small RNA ratios. Nuclease free glycogen can be carrier of choice for miRNA sequencing. Our findings illustrate that miRNA extraction and quantification is influenced by the choice of methodologies. Addition of nucleic acid- based carrier molecules during extraction procedure is not a good choice when assaying miRNA using sequencing. The careful selection of an extraction method permits the archived serum samples to become valuable resources for high-throughput applications.

SUBMITTER: Gautam A

PROVIDER: S-EPMC5025515 | biostudies-literature | 2016 Oct

REPOSITORIES: biostudies-literature

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Identification of extracellular miRNA in archived serum samples by next-generation sequencing from RNA extracted using multiple methods.

Gautam Aarti A Kumar Raina R Dimitrov George G Hoke Allison A Hammamieh Rasha R Jett Marti M

Molecular biology reports 20160810 10

miRNAs act as important regulators of gene expression by promoting mRNA degradation or by attenuating protein translation. Since miRNAs are stably expressed in bodily fluids, there is growing interest in profiling these miRNAs, as it is minimally invasive and cost-effective as a diagnostic matrix. A technical hurdle in studying miRNA dynamics is the ability to reliably extract miRNA as small sample volumes and low RNA abundance create challenges for extraction and downstream applications. The pu ...[more]

PMID: 27510798

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Project description:Extracellular vesicles (EVs) are intercellular communicators with key functions in physiological and pathological processes and have recently garnered interest because of their diagnostic and therapeutic potential. The past decade has brought about the development and commercialization of a wide array of methods to isolate EVs from serum. Which subpopulations of EVs are captured strongly depends on the isolation method, which in turn determines how suitable resulting samples are for various downstream applications. To help clinicians and scientists choose the most appropriate approach for their experiments, isolation methods need to be comparatively characterized. Few attempts have been made to comprehensively analyse vesicular microRNAs (miRNAs) in patient biofluids for biomarker studies. To address this discrepancy, we set out to benchmark the performance of several isolation principles for serum EVs in healthy individuals and critically ill patients. Here, we compared five different methods of EV isolation in combination with two RNA extraction methods regarding their suitability for biomarker discovery-focused miRNA sequencing as well as biological characteristics of captured vesicles. Our findings reveal striking method-specific differences in both the properties of isolated vesicles and the ability of associated miRNAs to serve in biomarker research. While isolation by precipitation and membrane affinity was highly suitable for miRNA-based biomarker discovery, methods based on size-exclusion chromatography failed to separate patients from healthy volunteers. Isolated vesicles differed in size, quantity, purity and composition, indicating that each method captured distinctive populations of EVs as well as additional contaminants. Even though the focus of this work was on transcriptomic profiling of EV-miRNAs, our insights also apply to additional areas of research. We provide guidance for navigating the multitude of EV isolation methods available today and help researchers and clinicians make an informed choice about which strategy to use for experiments involving critically ill patients.

Project description:Next-generation sequencing has emerged as an essential technology for the quantitative analysis of gene expression. In medical research, RNA sequencing (RNA-seq) data are commonly used to identify which type of disease a patient has. Because of the discrete nature of RNA-seq data, the existing statistical methods that have been developed for microarray data cannot be directly applied to RNA-seq data. Existing statistical methods usually model RNA-seq data by a discrete distribution, such as the Poisson, the negative binomial, or the mixture distribution with a point mass at zero and a Poisson distribution to further allow for data with an excess of zeros. Consequently, analytic tools corresponding to the above three discrete distributions have been developed: Poisson linear discriminant analysis (PLDA), negative binomial linear discriminant analysis (NBLDA), and zero-inflated Poisson logistic discriminant analysis (ZIPLDA). However, it is unclear what the real distributions would be for these classifications when applied to a new and real dataset. Considering that count datasets are frequently characterized by excess zeros and overdispersion, this paper extends the existing distribution to a mixture distribution with a point mass at zero and a negative binomial distribution and proposes a zero-inflated negative binomial logistic discriminant analysis (ZINBLDA) for classification. More importantly, we compare the above four classification methods from the perspective of model parameters, as an understanding of parameters is necessary for selecting the optimal method for RNA-seq data. Furthermore, we determine that the above four methods could transform into each other in some cases. Using simulation studies, we compare and evaluate the performance of these classification methods in a wide range of settings, and we also present a decision tree model created to help us select the optimal classifier for a new RNA-seq dataset. The results of the two real datasets coincide with the theory and simulation analysis results. The methods used in this work are implemented in the open-scource R scripts, with a source code freely available at https://github.com/FocusPaka/ZINBLDA.

Project description:BackgroundUnambiguous HLA typing is important in hematopoietic stem cell transplantation (HSCT), HLA disease association studies, and solid organ transplantation. However, current molecular typing methods only interrogate the antigen recognition site (ARS) of HLA genes, resulting in many cis-trans ambiguities that require additional typing methods to resolve. Here we report high-resolution HLA typing of 10,063 National Marrow Donor Program (NMDP) registry donors using long-range PCR by next generation sequencing (NGS) approach on buccal swab DNA.MethodsMultiplex long-range PCR primers amplified the full-length of HLA class I genes (A, B, C) from promotor to 3' UTR. Class II genes (DRB1, DQB1) were amplified from exon 2 through part of exon 4. PCR amplicons were pooled and sheared using Covaris fragmentation. Library preparation was performed using the Illumina TruSeq Nano kit on the Beckman FX automated platform. Each sample was tagged with a unique barcode, followed by 2×250 bp paired-end sequencing on the Illumina MiSeq. HLA typing was assigned using Omixon Twin software that combines two independent computational algorithms to ensure high confidence in allele calling. Consensus sequence and typing results were reported in Histoimmunogenetics Markup Language (HML) format. All homozygous alleles were confirmed by Luminex SSO typing and exon novelties were confirmed by Sanger sequencing.ResultsUsing this automated workflow, over 10,063 NMDP registry donors were successfully typed under high-resolution by NGS. Despite known challenges of nucleic acid degradation and low DNA concentration commonly associated with buccal-based specimens, 97.8% of samples were successfully amplified using long-range PCR. Among these, 98.2% were successfully reported by NGS, with an accuracy rate of 99.84% in an independent blind Quality Control audit performed by the NDMP. In this study, NGS-HLA typing identified 23 null alleles (0.023%), 92 rare alleles (0.091%) and 42 exon novelties (0.042%).ConclusionLong-range, unambiguous HLA genotyping is achievable on clinical buccal swab-extracted DNA. Importantly, full-length gene sequencing and the ability to curate full sequence data will permit future interrogation of the impact of introns, expanded exons, and other gene regulatory sequences on clinical outcomes in transplantation.

Dataset Information

Identification of extracellular miRNA in archived serum samples by next-generation sequencing from RNA extracted using multiple methods.

Publications

Identification of extracellular miRNA in archived serum samples by next-generation sequencing from RNA extracted using multiple methods.

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