Literature DB >> 20064613

Single-cell gene expression profiling using reverse transcription quantitative real-time PCR.

Anders Ståhlberg1, Martin Bengtsson.   

Abstract

Even in an apparently homogeneous population of cells there are considerable differences between individual cells. A response to a stimulus of a cell population or tissue may be consistent and gradual while the single-cell response might be binary and apparently irregular. The origin of this variability may be preprogrammed or stochastic and a study of this phenomenon will require quantitative measurements of individual cells. Here, we describe a method to collect dispersed single cells either by glass capillaries or flow cytometry, followed by quantitative mRNA profiling using reverse transcription and real-time PCR. We present a single cell lysis protocol and optimized priming conditions for reverse transcription. The large cell-to-cell variability in single-cell gene expression measurements excludes it from standard data analysis. Correlation studies can be used to find common regulatory elements that are indistinguishable at the population level. Single-cell gene expression profiling has the potential to become common practice in many laboratories and a powerful research tool for deeper understanding of molecular mechanisms. Copyright 2010 Elsevier Inc. All rights reserved.

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Year:  2010        PMID: 20064613     DOI: 10.1016/j.ymeth.2010.01.002

Source DB:  PubMed          Journal:  Methods        ISSN: 1046-2023            Impact factor:   3.608


  46 in total

1.  Microfluidic single-cell real-time PCR for comparative analysis of gene expression patterns.

Authors:  Veronica Sanchez-Freire; Antje D Ebert; Tomer Kalisky; Stephen R Quake; Joseph C Wu
Journal:  Nat Protoc       Date:  2012-04-05       Impact factor: 13.491

2.  Metabolic cycling in single yeast cells from unsynchronized steady-state populations limited on glucose or phosphate.

Authors:  Sanford J Silverman; Allegra A Petti; Nikolai Slavov; Lance Parsons; Ryan Briehof; Stephan Y Thiberge; Daniel Zenklusen; Saumil J Gandhi; Daniel R Larson; Robert H Singer; David Botstein
Journal:  Proc Natl Acad Sci U S A       Date:  2010-03-24       Impact factor: 11.205

3.  Embracing Systems Toxicology at Single-Cell Resolution.

Authors:  Qiang Zhang; W Michael Caudle; Jingbo Pi; Sudin Bhattacharya; Melvin E Andersen; Norbert E Kaminski; Rory B Conolly
Journal:  Curr Opin Toxicol       Date:  2019-04-19

Review 4.  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

5.  RNA-sequencing from single nuclei.

Authors:  Rashel V Grindberg; Joyclyn L Yee-Greenbaum; Michael J McConnell; Mark Novotny; Andy L O'Shaughnessy; Georgina M Lambert; Marcos J Araúzo-Bravo; Jun Lee; Max Fishman; Gillian E Robbins; Xiaoying Lin; Pratap Venepally; Jonathan H Badger; David W Galbraith; Fred H Gage; Roger S Lasken
Journal:  Proc Natl Acad Sci U S A       Date:  2013-11-18       Impact factor: 11.205

6.  Differentially expressed micoRNAs in human oocytes.

Authors:  Yan-Wen Xu; Bin Wang; Chen-Hui Ding; Tao Li; Fang Gu; Canquan Zhou
Journal:  J Assist Reprod Genet       Date:  2011-06-07       Impact factor: 3.412

7.  Quantitative Analysis of Glutamate Receptors in Glial Cells from the Cortex of GFAP/EGFP Mice Following Ischemic Injury: Focus on NMDA Receptors.

Authors:  David Dzamba; Pavel Honsa; Martin Valny; Jan Kriska; Lukas Valihrach; Vendula Novosadova; Mikael Kubista; Miroslava Anderova
Journal:  Cell Mol Neurobiol       Date:  2015-05-21       Impact factor: 5.046

Review 8.  Single-cell technologies sharpen up mammalian stem cell research.

Authors:  Philipp S Hoppe; Daniel L Coutu; Timm Schroeder
Journal:  Nat Cell Biol       Date:  2014-10       Impact factor: 28.824

Review 9.  Liquid Biopsies in Oncology and the Current Regulatory Landscape.

Authors:  Lindsay N Strotman; Lori M Millner; Roland Valdes; Mark W Linder
Journal:  Mol Diagn Ther       Date:  2016-10       Impact factor: 4.074

10.  Single-cell qPCR on dispersed primary pituitary cells -an optimized protocol.

Authors:  Kjetil Hodne; Trude M Haug; Finn-Arne Weltzien
Journal:  BMC Mol Biol       Date:  2010-11-12       Impact factor: 2.946

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