Literature DB >> 21775961

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

Wah Chin Boon1, Karolina Petkovic-Duran, Yonggang Zhu, Richard Manasseh, Malcolm K Horne, Tim D Aumann.   

Abstract

Correlating gene expression with cell behavior is ideally done at the single-cell level. However, this is not easily achieved because the small amount of labile mRNA present in a single cell (1-5% of 1-50 pg total RNA, or 0.01-2.5 pg mRNA, per cell) mostly degrades before it can be reverse transcribed into a stable cDNA copy. For example, using standard laboratory reagents and hardware, only a small number of genes can be qualitatively assessed per cell. One way to increase the efficiency of standard laboratory reverse transcriptase (RT) reactions (i.e. standard reagents in microliter volumes) comprising single-cell amounts of mRNA would be to more rapidly mix the reagents so the mRNA can be converted to cDNA before it degrades. However this is not trivial because at microliter scales liquid flow is laminar, i.e. currently available methods of mixing (i.e. shaking, vortexing and trituration) fail to produce sufficient chaotic motion to effectively mix reagents. To solve this problem, micro-scale mixing techniques have to be used. A number of microfluidic-based mixing technologies have been developed which successfully increase RT reaction yields. However, microfluidics technologies require specialized hardware that is relatively expensive and not yet widely available. A cheaper, more convenient solution is desirable. The main objective of this study is to demonstrate how application of a novel "micromixing" technique to standard laboratory RT reactions comprising single-cell quantities of mRNA significantly increases their cDNA yields. We find cDNA yields increase by approximately 10-100-fold, which enables: greater numbers of genes to be analyzed per cell; more quantitative analysis of gene expression; and better detection of low-abundance genes in single cells. The micromixing is based on acoustic microstreaming, a phenomenon where sound waves propagating around a small obstacle create a mean flow near the obstacle. We have developed an acoustic microstreaming-based device ("micromixer") with a key simplification; acoustic microstreaming can be achieved at audio frequencies by ensuring the system has a liquid-air interface with a small radius of curvature. The meniscus of a microliter volume of solution in a tube provides an appropriately small radius of curvature. The use of audio frequencies means that the hardware can be inexpensive and versatile, and nucleic acids and other biochemical reagents are not damaged like they can be with standard laboratory sonicators.

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Year:  2011        PMID: 21775961      PMCID: PMC3346307          DOI: 10.3791/3144

Source DB:  PubMed          Journal:  J Vis Exp        ISSN: 1940-087X            Impact factor:   1.355


  11 in total

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Review 2.  Strategies for microarray analysis of limiting amounts of RNA.

Authors:  F J Livesey
Journal:  Brief Funct Genomic Proteomic       Date:  2003-04

3.  Microfluidic single-cell mRNA isolation and analysis.

Authors:  Joshua S Marcus; W French Anderson; Stephen R Quake
Journal:  Anal Chem       Date:  2006-05-01       Impact factor: 6.986

4.  Parallel picoliter rt-PCR assays using microfluidics.

Authors:  Joshua S Marcus; W French Anderson; Stephen R Quake
Journal:  Anal Chem       Date:  2006-02-01       Impact factor: 6.986

5.  Transcription factor profiling in individual hematopoietic progenitors by digital RT-PCR.

Authors:  Luigi Warren; David Bryder; Irving L Weissman; Stephen R Quake
Journal:  Proc Natl Acad Sci U S A       Date:  2006-11-10       Impact factor: 11.205

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

Authors:  N Bontoux; L Dauphinot; T Vitalis; V Studer; Y Chen; J Rossier; M-C Potier
Journal:  Lab Chip       Date:  2008-01-31       Impact factor: 6.799

7.  Chaotic micromixing in open wells using audio-frequency acoustic microstreaming.

Authors:  Karolina Petkovic-Duran; Richard Manasseh; Yonggang Zhu; Andrew Ooi
Journal:  Biotechniques       Date:  2009-10       Impact factor: 1.993

8.  Acoustic microstreaming increases the efficiency of reverse transcription reactions comprising single-cell quantities of RNA.

Authors:  Wah Chin Boon; Karolina Petkovic-Duran; Kylie White; Elena Tucker; Anthony Albiston; Richard Manasseh; Malcolm Kenneth Horne; Tim Douglas Aumann
Journal:  Biotechniques       Date:  2011-02       Impact factor: 1.993

9.  SK channel function regulates the dopamine phenotype of neurons in the substantia nigra pars compacta.

Authors:  T D Aumann; I Gantois; K Egan; A Vais; D Tomas; J Drago; M K Horne
Journal:  Exp Neurol       Date:  2008-07-15       Impact factor: 5.330

10.  Accelerated analyte uptake on single beads in microliter-scale batch separations using acoustic streaming: plutonium uptake by anion exchange for analysis by mass spectrometry.

Authors:  Walter F Paxton; Matthew J O'Hara; Shane M Peper; Steven L Petersen; Jay W Grate
Journal:  Anal Chem       Date:  2008-04-29       Impact factor: 6.986

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  5 in total

1.  Two methods for full-length RNA sequencing for low quantities of cells and single cells.

Authors:  Xinghua Pan; Russell E Durrett; Haiying Zhu; Yoshiaki Tanaka; Yumei Li; Xiaoyuan Zi; Sadie L Marjani; Ghia Euskirchen; Chao Ma; Robert H Lamotte; In-Hyun Park; Michael P Snyder; Christopher E Mason; Sherman M Weissman
Journal:  Proc Natl Acad Sci U S A       Date:  2012-12-24       Impact factor: 11.205

2.  Exponential scaling of single-cell RNA-seq in the past decade.

Authors:  Valentine Svensson; Roser Vento-Tormo; Sarah A Teichmann
Journal:  Nat Protoc       Date:  2018-03-01       Impact factor: 13.491

3.  Efficient generation of monoclonal antibodies from single rhesus macaque antibody secreting cells.

Authors:  Weixu Meng; Leike Li; Wei Xiong; Xuejun Fan; Hui Deng; Andrew J Bett; Zhifeng Chen; Aimin Tang; Kara S Cox; Joseph G Joyce; Daniel C Freed; Elizabeth Thoryk; Tong-Ming Fu; Danilo R Casimiro; Ningyan Zhang; Kalpit A Vora; Zhiqiang An
Journal:  MAbs       Date:  2015       Impact factor: 5.857

4.  Co-detection and sequencing of genes and transcripts from the same single cells facilitated by a microfluidics platform.

Authors:  Lin Han; Xiaoyuan Zi; Lana X Garmire; Yu Wu; Sherman M Weissman; Xinghua Pan; Rong Fan
Journal:  Sci Rep       Date:  2014-09-26       Impact factor: 4.379

Review 5.  Differential Expression Analysis of Single-Cell RNA-Seq Data: Current Statistical Approaches and Outstanding Challenges.

Authors:  Samarendra Das; Anil Rai; Shesh N Rai
Journal:  Entropy (Basel)       Date:  2022-07-18       Impact factor: 2.738

  5 in total

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