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Journal of Biomedicine and Biotechnology
Volume 2009 (2009), Article ID 396808, 7 pages
http://dx.doi.org/10.1155/2009/396808
Methodology Report

Automation of cDNA Synthesis and Labelling Improves Reproducibility

1Division of Gene Technology, School of Biotechnology, Royal Institute of Technology, AlbaNova University Center, 10691 Stockholm, Sweden
2Department of Genetics and Pathology, Rudbeck Laboratory, Uppsala University, 75185 Uppsala, Sweden
3Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 171 77 Stockholm, Sweden
4Nordiag AB, P.O. Box 70036, 100 44 Stockholm, Sweden

Received 31 March 2009; Accepted 26 July 2009

Academic Editor: Momiao Xiong

Copyright © 2009 Daniel Klevebring et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Background. Several technologies, such as in-depth sequencing and microarrays, enable large-scale interrogation of genomes and transcriptomes. In this study, we asses reproducibility and throughput by moving all laboratory procedures to a robotic workstation, capable of handling superparamagnetic beads. Here, we describe a fully automated procedure for cDNA synthesis and labelling for microarrays, where the purification steps prior to and after labelling are based on precipitation of DNA on carboxylic acid-coated paramagnetic beads. Results. The fully automated procedure allows for samples arrayed on a microtiter plate to be processed in parallel without manual intervention and ensuring high reproducibility. We compare our results to a manual sample preparation procedure and, in addition, use a comprehensive reference dataset to show that the protocol described performs better than similar manual procedures. Conclusions. We demonstrate, in an automated gene expression microarray experiment, a reduced variance between replicates, resulting in an increase in the statistical power to detect differentially expressed genes, thus allowing smaller differences between samples to be identified. This protocol can with minor modifications be used to create cDNA libraries for other applications such as in-depth analysis using next-generation sequencing technologies.