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

Marie-Jeanne Arguel1, Kevin LeBrigand1, Agnès Paquet1

  • 1Université Côte d'Azur, CNRS, Institut de Pharmacologie Moléculaire et Cellulaire, F06560 Sophia Antipolis, France.

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|December 13, 2016
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Summary

We optimized 5′ selective single-cell RNA sequencing (scRNA-seq) for cost-effective and accurate transcriptome profiling. This new protocol reduces labor and expense, making advanced cell variability studies more accessible.

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Area of Science:

  • Molecular Biology
  • Genomics
  • Bioinformatics

Background:

  • Single-cell RNA sequencing (scRNA-seq) is crucial for understanding cell-to-cell variability.
  • 5′ selective transcriptome profiling offers advantages over 3′ selective methods by defining transcription start sites.
  • Existing 5′ selective methods are costly and labor-intensive due to post-amplification barcoding.

Purpose of the Study:

  • To develop an optimized, cost-effective, and labor-efficient 5′ selective scRNA-seq workflow.
  • To improve the accuracy of molecule counting in scRNA-seq data.
  • To enable single-cell sequencing at a cost comparable to quantitative PCR (qPCR) assays.

Main Methods:

  • Implemented cell indexing prior to fragmentation, compatible with Fluidigm C1 microfluidic devices.
  • Designed optimized unique molecular identifiers (UMIs) to minimize sequence bias and errors.
  • Provided comprehensive workflows for Illumina and Ion Proton sequencing platforms.

Main Results:

  • Significant reduction in cost and labor for 5′ selective scRNA-seq.
  • Enhanced accuracy in molecule counting due to improved UMIs.
  • Achieved single-cell sequencing costs comparable to qPCR.

Conclusions:

  • The developed protocol offers a more accessible and accurate 5′ selective scRNA-seq method.
  • This advancement facilitates deeper investigation into cell-to-cell variability.
  • The workflow democratizes high-resolution transcriptome profiling for broader research applications.