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Profiling of Single-Cell Transcriptomes.

  • Chen W Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland.
  • Gardeux V Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland.
  • Meireles-Filho A Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland.
  • Deplancke B Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland.
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  • 2017-09-09
Published in:
  • Current protocols in mouse biology. - 2017
ASAP
Smart-seq2
Tn5
Web tool
analysis pipeline
gene expression
scRNA-seq
single-cell
tagmentation
transcriptomics
Animals
Base Sequence
Gene Library
Humans
Mice
Sequence Analysis, RNA
Single-Cell Analysis
Transcriptome
English Complex biological systems are composed of multiple cell types whose transcriptional activity can vary due to differences in cell state, environmental stimulation, or intrinsic programs. Conventional bulk analysis methods capture the average transcriptional programs of the cell population, thus missing the unique cellular signature of each single cell. In recent years, the development of single-cell RNA-sequencing (scRNA-seq) technologies has provided a powerful approach to dissect the cellular heterogeneity of complex biological systems. However, such approaches require specialized equipment or are costly. In this article, we describe an improved Smart-seq2-based method to profile the transcriptome of hundreds of single cells simultaneously, without utilizing commercial kits or requiring any specialized single-cell capture/library preparation tools. Moreover, we introduce the Automated Single-cell Analysis Pipeline (ASAP), which allows researchers without strong computational expertise to explore scRNA-seq data using a wide range of commonly used algorithms and sophisticated visualization tools. © 2017 by John Wiley & Sons, Inc.
Language
  • English
Open access status
green
Identifiers
Persistent URL
https://folia.unifr.ch/global/documents/256039
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