Literature DB >> 18305863

Integrating whole transcriptome assays on a lab-on-a-chip for single cell gene profiling.

N Bontoux1, L Dauphinot, T Vitalis, V Studer, Y Chen, J Rossier, M-C Potier.   

Abstract

To correlate gene expression profiles to fundamental biological processes such as cell growth, differentiation and migration, it is essential to work at the single cell level. Gene expression analysis always starts with the relatively low efficient reverse transcription (RT) of RNA into complementary DNA (cDNA), an essential step as unprocessed RNAs will not be analysed further. In this paper, we present a novel method for RT that uses microfluidics to manipulate nanolitre volumes. We compare our method to conventional protocols performed in microlitre volumes. More specifically, reverse transcription was performed either in a polydimethylsiloxane (PDMS) rotary mixer or in a tube, using a single cell amount of mouse brain RNA (10 pg), and was followed by a template-switching PCR (TS-PCR) amplification step. We demonstrate that, using the microfluidic protocol, 74% of the genes expressed in mouse brain were detected, while only 4% were found with the conventional approach. We next profiled single neuronal progenitors. Using our microfluidic approach, i.e. performing cell capture, lysis and reverse transcription on-chip followed by TS-PCR amplification in tube, a mean of 5000 genes were detected in each neuron, which corresponds to the expected number of genes expressed in a single cell. This demonstrates the outstanding sensitivity of the microfluidic method.

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Year:  2008        PMID: 18305863     DOI: 10.1039/b716543a

Source DB:  PubMed          Journal:  Lab Chip        ISSN: 1473-0189            Impact factor:   6.799


  34 in total

1.  Identifying individual DNA species in a complex mixture by precisely measuring the spacing between nicking restriction enzymes with atomic force microscope.

Authors:  Jason Reed; Carlin Hsueh; Miu-Ling Lam; Rachel Kjolby; Andrew Sundstrom; Bud Mishra; J K Gimzewski
Journal:  J R Soc Interface       Date:  2012-03-28       Impact factor: 4.118

Review 2.  Measurement of single-cell dynamics.

Authors:  David G Spiller; Christopher D Wood; David A Rand; Michael R H White
Journal:  Nature       Date:  2010-06-10       Impact factor: 49.962

Review 3.  Single-cell and regional gene expression analysis in Alzheimer's disease.

Authors:  Ruby Kwong; Michelle K Lupton; Michal Janitz
Journal:  Cell Mol Neurobiol       Date:  2012-01-22       Impact factor: 5.046

Review 4.  Microfluidic single-cell analysis of intracellular compounds.

Authors:  Tzu-Chiao Chao; Alexandra Ros
Journal:  J R Soc Interface       Date:  2008-10-06       Impact factor: 4.118

Review 5.  Single cell optical transfection.

Authors:  David J Stevenson; Frank J Gunn-Moore; Paul Campbell; Kishan Dholakia
Journal:  J R Soc Interface       Date:  2010-01-11       Impact factor: 4.118

6.  Analysis of gene expression at the single-cell level using microdroplet-based microfluidic technology.

Authors:  Pascaline Mary; Luce Dauphinot; Nadège Bois; Marie-Claude Potier; Vincent Studer; Patrick Tabeling
Journal:  Biomicrofluidics       Date:  2011-06-03       Impact factor: 2.800

7.  Increasing cDNA yields from single-cell quantities of mRNA in standard laboratory reverse transcriptase reactions using acoustic microstreaming.

Authors:  Wah Chin Boon; Karolina Petkovic-Duran; Yonggang Zhu; Richard Manasseh; Malcolm K Horne; Tim D Aumann
Journal:  J Vis Exp       Date:  2011-07-11       Impact factor: 1.355

8.  Single Cell Multiplex Reverse Transcription Polymerase Chain Reaction After Patch-clamp.

Authors:  Gabrielle Devienne; Benjamin Le Gac; Juliette Piquet; Bruno Cauli
Journal:  J Vis Exp       Date:  2018-06-20       Impact factor: 1.355

9.  An automated microfluidic device for assessment of mammalian cell genetic stability.

Authors:  Yan Chen; Baoyue Zhang; Hongtao Feng; Weiliang Shu; Gina Y Chen; Jiang F Zhong
Journal:  Lab Chip       Date:  2012-10-21       Impact factor: 6.799

10.  T7-based linear amplification of low concentration mRNA samples using beads and microfluidics for global gene expression measurements.

Authors:  Jason G Kralj; Audrey Player; Hope Sedrick; Matthew S Munson; David Petersen; Samuel P Forry; Paul Meltzer; Ernest Kawasaki; Laurie E Locascio
Journal:  Lab Chip       Date:  2008-12-15       Impact factor: 6.799

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