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Custom long non-coding RNA capture enhances detection sensitivity in different human sample types.

ABSTRACT: Long non-coding RNAs (lncRNAs) are a heterogeneous group of transcripts that lack protein coding potential and display regulatory functions in various cellular processes. As a result of their cell- and cancer-specific expression patterns, lncRNAs have emerged as potential diagnostic and therapeutic targets. The accurate characterization of lncRNAs in bulk transcriptome data remains challenging due to their low abundance compared to protein coding genes. To tackle this issue, we describe a unique short-read custom lncRNA capture sequencing approach that relies on a comprehensive set of 565,878 capture probes for 49,372 human lncRNA genes. This custom lncRNA capture approach was evaluated on various sample types ranging from artificial high-quality RNA mixtures to more challenging formalin-fixed paraffin-embedded tissue and biofluid material. The custom enrichment approach allows the detection of a more diverse repertoire of lncRNAs, with better reproducibility and higher coverage compared to classic total RNA-sequencing.

SUBMITTER: Morlion A

PROVIDER: S-EPMC8682977 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

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Custom long non-coding RNA capture enhances detection sensitivity in different human sample types.

Morlion Annelien A Everaert Celine C Nuytens Justine J Hulstaert Eva E Vandesompele Jo J Mestdagh Pieter P

RNA biology 20210901 sup1

Long non-coding RNAs (lncRNAs) are a heterogeneous group of transcripts that lack protein coding potential and display regulatory functions in various cellular processes. As a result of their cell- and cancer-specific expression patterns, lncRNAs have emerged as potential diagnostic and therapeutic targets. The accurate characterization of lncRNAs in bulk transcriptome data remains challenging due to their low abundance compared to protein coding genes. To tackle this issue, we describe a unique ...[more]

PMID: 34470578

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Project description:BackgroundMitochondrial genome abnormalities can lead to mitochondrial dysfunction, which in turn affects cellular biology and is closely associated with the development of various diseases. The demand for mitochondrial DNA (mtDNA) sequencing has been increasing, and Illumina and MGI are two commonly used sequencing platforms for capture-based mtDNA sequencing. However, there is currently no systematic comparison of mtDNA sequencing performance between these two platforms. To address this gap, we compared the performance of capture-based mtDNA sequencing between Illumina's NovaSeq 6000 and MGI's DNBSEQ-T7 using tissue, peripheral blood mononuclear cell (PBMC), formalin-fixed paraffin-embedded (FFPE) tissue, plasma, and urine samples.ResultsOur analysis indicated a high degree of consistency between the two platforms in terms of sequencing quality, GC content, and coverage. In terms of data output, DNBSEQ-T7 showed higher rates of clean data and duplication compared to NovaSeq 6000. Conversely, the amount of mtDNA data obtained by per gigabyte sequencing data was significantly lower in DNBSEQ-T7 compared to NovaSeq 6000. In terms of detection mtDNA copy number, both platforms exhibited good consistency in all sample types. When it comes to detection of mtDNA mutations in tissue, FFPE, and PBMC samples, the two platforms also showed good consistency. However, when detecting mtDNA mutations in plasma and urine samples, significant differenceof themutation number detected was observed between the two platforms. For mtDNA sequencing of plasma and urine samples, a wider range of DNA fragment size distribution was found in NovaSeq 6000 when compared to DNBSEQ-T7. Additionally, two platforms exhibited different characteristics of mtDNA fragment end preference.ConclusionsIn summary, the two platforms generally showed good consistency in capture-based mtDNA sequencing. However, it is necessary to consider the data preferences generated by two sequencing platforms when plasma and urine samples were analyzed.

Project description:BackgroundAtypical porcine pestivirus (APPeV), also known as Pestivirus scrofae, is a member of the Pestivirus genus within the Flaviviridae family. Experimental infections have directly linked APPeV to congenital tremor (CT) type A-II in congenitally infected piglets born to challenged sows. Here, we report the assessment of the prevalence of APPeV in Hungarian pig herds and the influence of different sample types on detection rates.ResultsAltogether, 2650 blood serum, 198 oral fluid and 163 processing fluid samples were obtained via a systemic approach from 26 Hungarian farms and one Slovakian farm. The samples originated from different age groups and were analyzed via reverse transcription-quantitative polymerase chain reaction (RT-qPCR). The estimated prevalence of APPeV was determined to be 66.67% in the sampled farms, indicating the widespread distribution of the virus within Hungary. Within the positive farms, APPeV genetic material was detected in the serum (21%), processing fluid (57%), and oral fluid (72%) samples. Notably, in some farms, the presence of APPeV was confirmed in only specific sample types, and five farms had APPeV in all three sample types. Age group analysis revealed that 10-week-old animals had the highest positivity rate in their blood serum (27%), whereas 20-week-old animals presented the highest rate in their oral fluid samples (59%). Processing fluid and oral fluid samples proved to be valuable for noninvasive diagnostic matrices, allowing for efficient population-level virus detection. We determined the partial NS2-3 coding region of 15 Hungarian strains and a Slovakian strain, and our phylogenetic analysis revealed that very similar strains can be found on different farms.ConclusionIn conclusion, our study provides insights into APPeV prevalence in Hungarian pig herds, emphasizing the importance of different sample types for accurate diagnostics. These findings contribute to our understanding of the virus's distribution across different age groups.

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Custom long non-coding RNA capture enhances detection sensitivity in different human sample types.

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Custom long non-coding RNA capture enhances detection sensitivity in different human sample types.

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