Literature DB >> 24782542

Microfluidic single-cell whole-transcriptome sequencing.

Aaron M Streets1, Xiannian Zhang1, Chen Cao2, Yuhong Pang2, Xinglong Wu2, Liang Xiong2, Lu Yang2, Yusi Fu2, Liang Zhao3, Fuchou Tang4, Yanyi Huang3.   

Abstract

Single-cell whole-transcriptome analysis is a powerful tool for quantifying gene expression heterogeneity in populations of cells. Many techniques have, thus, been recently developed to perform transcriptome sequencing (RNA-Seq) on individual cells. To probe subtle biological variation between samples with limiting amounts of RNA, more precise and sensitive methods are still required. We adapted a previously developed strategy for single-cell RNA-Seq that has shown promise for superior sensitivity and implemented the chemistry in a microfluidic platform for single-cell whole-transcriptome analysis. In this approach, single cells are captured and lysed in a microfluidic device, where mRNAs with poly(A) tails are reverse-transcribed into cDNA. Double-stranded cDNA is then collected and sequenced using a next generation sequencing platform. We prepared 94 libraries consisting of single mouse embryonic cells and technical replicates of extracted RNA and thoroughly characterized the performance of this technology. Microfluidic implementation increased mRNA detection sensitivity as well as improved measurement precision compared with tube-based protocols. With 0.2 M reads per cell, we were able to reconstruct a majority of the bulk transcriptome with 10 single cells. We also quantified variation between and within different types of mouse embryonic cells and found that enhanced measurement precision, detection sensitivity, and experimental throughput aided the distinction between biological variability and technical noise. With this work, we validated the advantages of an early approach to single-cell RNA-Seq and showed that the benefits of combining microfluidic technology with high-throughput sequencing will be valuable for large-scale efforts in single-cell transcriptome analysis.

Entities:  

Keywords:  embryonic stem cell; genomics; lab on chip

Mesh:

Substances:

Year:  2014        PMID: 24782542      PMCID: PMC4024894          DOI: 10.1073/pnas.1402030111

Source DB:  PubMed          Journal:  Proc Natl Acad Sci U S A        ISSN: 0027-8424            Impact factor:   11.205


  42 in total

1.  Resolution of cell fate decisions revealed by single-cell gene expression analysis from zygote to blastocyst.

Authors:  Guoji Guo; Mikael Huss; Guo Qing Tong; Chaoyang Wang; Li Li Sun; Neil D Clarke; Paul Robson
Journal:  Dev Cell       Date:  2010-04-20       Impact factor: 12.270

2.  Insightful tales from single embryonic cells.

Authors:  Hendrik Marks; Gert Jan C Veenstra; Hendrik G Stunnenberg
Journal:  Cell Stem Cell       Date:  2010-05-07       Impact factor: 24.633

Review 3.  Stochasticity and cell fate.

Authors:  Richard Losick; Claude Desplan
Journal:  Science       Date:  2008-04-04       Impact factor: 47.728

4.  CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification.

Authors:  Tamar Hashimshony; Florian Wagner; Noa Sher; Itai Yanai
Journal:  Cell Rep       Date:  2012-08-30       Impact factor: 9.423

5.  Chip in a lab: Microfluidics for next generation life science research.

Authors:  Aaron M Streets; Yanyi Huang
Journal:  Biomicrofluidics       Date:  2013-01-31       Impact factor: 2.800

6.  Tumour evolution inferred by single-cell sequencing.

Authors:  Nicholas Navin; Jude Kendall; Jennifer Troge; Peter Andrews; Linda Rodgers; Jeanne McIndoo; Kerry Cook; Asya Stepansky; Dan Levy; Diane Esposito; Lakshmi Muthuswamy; Alex Krasnitz; W Richard McCombie; James Hicks; Michael Wigler
Journal:  Nature       Date:  2011-03-13       Impact factor: 49.962

7.  Tracing the derivation of embryonic stem cells from the inner cell mass by single-cell RNA-Seq analysis.

Authors:  Fuchou Tang; Catalin Barbacioru; Siqin Bao; Caroline Lee; Ellen Nordman; Xiaohui Wang; Kaiqin Lao; M Azim Surani
Journal:  Cell Stem Cell       Date:  2010-05-07       Impact factor: 24.633

Review 8.  Development and applications of single-cell transcriptome analysis.

Authors:  Fuchou Tang; Kaiqin Lao; M Azim Surani
Journal:  Nat Methods       Date:  2011-04       Impact factor: 28.547

9.  Defining cell populations with single-cell gene expression profiling: correlations and identification of astrocyte subpopulations.

Authors:  Anders Ståhlberg; Daniel Andersson; Johan Aurelius; Maryam Faiz; Marcela Pekna; Mikael Kubista; Milos Pekny
Journal:  Nucleic Acids Res       Date:  2010-11-25       Impact factor: 16.971

10.  An improved single-cell cDNA amplification method for efficient high-density oligonucleotide microarray analysis.

Authors:  Kazuki Kurimoto; Yukihiro Yabuta; Yasuhide Ohinata; Yukiko Ono; Kenichiro D Uno; Rikuhiro G Yamada; Hiroki R Ueda; Mitinori Saitou
Journal:  Nucleic Acids Res       Date:  2006-03-17       Impact factor: 16.971

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  89 in total

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Authors:  Kalina Paunovska; David Loughrey; Cory D Sago; Robert Langer; James E Dahlman
Journal:  Adv Mater       Date:  2019-08-20       Impact factor: 30.849

2.  Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells.

Authors:  Allon M Klein; Linas Mazutis; Ilke Akartuna; Naren Tallapragada; Adrian Veres; Victor Li; Leonid Peshkin; David A Weitz; Marc W Kirschner
Journal:  Cell       Date:  2015-05-21       Impact factor: 41.582

Review 3.  Revolutionizing immunology with single-cell RNA sequencing.

Authors:  Haide Chen; Fang Ye; Guoji Guo
Journal:  Cell Mol Immunol       Date:  2019-02-22       Impact factor: 11.530

4.  Is microfluidics the "assembly line" for CRISPR-Cas9 gene-editing?

Authors:  Fatemeh Ahmadi; Angela B V Quach; Steve C C Shih
Journal:  Biomicrofluidics       Date:  2020-11-24       Impact factor: 2.800

5.  Single-Cell Sequencing and Organoids: A Powerful Combination for Modelling Organ Development and Diseases.

Authors:  Yuebang Yin; Peng-Yu Liu; Yinghua Shi; Ping Li
Journal:  Rev Physiol Biochem Pharmacol       Date:  2021       Impact factor: 5.545

6.  An open-pattern droplet-in-oil planar array for single cell analysis based on sequential inkjet printing technology.

Authors:  Chenyu Wang; Wenwen Liu; Manqing Tan; Hongbo Sun; Yude Yu
Journal:  Biomicrofluidics       Date:  2017-07-20       Impact factor: 2.800

Review 7.  Estimation methods for heterogeneous cell population models in systems biology.

Authors:  Steffen Waldherr
Journal:  J R Soc Interface       Date:  2018-10-31       Impact factor: 4.118

8.  Translational Opportunities for Microfluidic Technologies to Enable Precision Epigenomics.

Authors:  Yi Xu; Steven R Doonan; Tamas Ordog; Ryan C Bailey
Journal:  Anal Chem       Date:  2020-06-04       Impact factor: 6.986

Review 9.  Quantitative imaging of lipid droplets in single cells.

Authors:  Anushka Gupta; Gabriel F Dorlhiac; Aaron M Streets
Journal:  Analyst       Date:  2019-01-28       Impact factor: 4.616

10.  Single cell-resolution western blotting.

Authors:  Chi-Chih Kang; Kevin A Yamauchi; Julea Vlassakis; Elly Sinkala; Todd A Duncombe; Amy E Herr
Journal:  Nat Protoc       Date:  2016-07-28       Impact factor: 13.491

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