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A cost effective 5? selective single cell transcriptome profiling approach with improved UMI design.

ABSTRACT: Single cell RNA sequencing approaches are instrumental in studies of cell-to-cell variability. 5? selective transcriptome profiling approaches allow simultaneous definition of the transcription start size and have advantages over 3? selective approaches which just provide internal sequences close to the 3? end. The only currently existing 5? selective approach requires costly and labor intensive fragmentation and cell barcoding after cDNA amplification. We developed an optimized 5? selective workflow where all the cell indexing is done prior to fragmentation. With our protocol, cell indexing can be performed in the Fluidigm C1 microfluidic device, resulting in a significant reduction of cost and labor. We also designed optimized unique molecular identifiers that show less sequence bias and vulnerability towards sequencing errors resulting in an improved accuracy of molecule counting. We provide comprehensive experimental workflows for Illumina and Ion Proton sequencers that allow single cell sequencing in a cost range comparable to qPCR assays.

SUBMITTER: Arguel MJ

PROVIDER: S-EPMC5397152 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

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A cost effective 5΄ selective single cell transcriptome profiling approach with improved UMI design.

Arguel Marie-Jeanne MJ LeBrigand Kevin K Paquet Agnès A Ruiz García Sandra S Zaragosi Laure-Emmanuelle LE Barbry Pascal P Waldmann Rainer R

Nucleic acids research 20170401 7

Single cell RNA sequencing approaches are instrumental in studies of cell-to-cell variability. 5΄ selective transcriptome profiling approaches allow simultaneous definition of the transcription start size and have advantages over 3΄ selective approaches which just provide internal sequences close to the 3΄ end. The only currently existing 5΄ selective approach requires costly and labor intensive fragmentation and cell barcoding after cDNA amplification. We developed an optimized 5΄ selective wor ...[more]

PMID: 27940562

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Project description:Covalently closed circular RNAs are neoteric to the eukaryotic family of long non-coding RNAs emerging as a result of 5'-3' backsplicing from exonic, intronic, or intergenic regions spanning the parental gene. Owing to their unique structure and stability, circular RNAs have a multitude of functional properties such as micro-RNA and protein sponges, direct and indirect modulators of gene expression, protein translation, and many unproven activities apart from being potential biomarkers. However, due to their low abundance, most of the global circular RNA identification is carried out by high-throughput NGS-based approaches requiring millions of sequencing reads. This lag in methodological advancements demands for newer, more refined, and efficient identification techniques. Here, we aim to show an improved version of our previously reported template-dependent multiple displacement amplification (tdMDA)-NGS method by superimposing the ribosomal depletion step and use of H minus reverse transcriptase and RNase H. Implication of tdMDA using highly replicative Phi29 DNA polymerase after minimizing the linear and ribosomal RNA content further intensifies its detection limit toward even the abysmally expressing circular RNA at a low NGS depth, thereby decreasing the cost of identifying a single circular RNA. A >11-fold and >6-fold increase in total circular RNA was identified from the improved-tdMDA-NGS method over the traditional method of circRNA sequencing using DCC and CIRI2 pipelines, respectively, from Oryza sativa subsp. Indica. Furthermore, the reliability of the improved-tdMDA-NGS method was also asserted in HeLa cell lines, showing a significant fold difference in comparison with the existing traditional method of circRNA sequencing. Among the identified circular RNAs, a significant percentage from both rice (∼58%) and HeLa cell lines (∼84%) is found to be matched with the previously reported circular RNAs, suggesting that the improved-tdMDA-NGS method can be adapted to detect and characterize the circular RNAs from different biological systems.

Dataset Information

A cost effective 5? selective single cell transcriptome profiling approach with improved UMI design.

Publications

A cost effective 5΄ selective single cell transcriptome profiling approach with improved UMI design.

Similar Datasets

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