Literature DB >> 21044647

Optimization of whole-transcriptome amplification from low cell density deep-sea microbial samples for metatranscriptomic analysis.

Jieying Wu1, Weimin Gao, Weiwen Zhang, Deirdre R Meldrum.   

Abstract

Limitation in sample quality and quantity is one of the big obstacles for applying metatranscriptomic technologies to explore gene expression and functionality of microbial communities in natural environments. In this study, several amplification methods were evaluated for whole-transcriptome amplification of deep-sea microbial samples, which are of low cell density and high impurity. The best amplification method was identified and incorporated into a complete protocol to isolate and amplify deep-sea microbial samples. In the protocol, total RNA was first isolated by a modified method combining Trizol (Invitrogen, CA) and RNeasy (QIAGEN, CA) method, amplified with a WT-Ovation™ Pico RNA Amplification System (NuGEN, CA), and then converted to double-strand DNA from single-strand cDNA with a WT-Ovation™ Exon Module (NuGEN, CA). The products from the whole-transcriptome amplification of deep-sea microbial samples were assessed first through random clone library sequencing. The BLAST search results showed that marine-based sequences are dominant in the libraries, consistent with the ecological source of the samples. The products were then used for next-generation Roche GS FLX Titanium sequencing to obtain metatranscriptome data. Preliminary analysis of the metatranscriptomic data showed good sequencing quality. Although the protocol was designed and demonstrated to be effective for deep-sea microbial samples, it should be applicable to similar samples from other extreme environments in exploring community structure and functionality of microbial communities.
Copyright © 2010 Elsevier B.V. All rights reserved.

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Year:  2010        PMID: 21044647     DOI: 10.1016/j.mimet.2010.10.018

Source DB:  PubMed          Journal:  J Microbiol Methods        ISSN: 0167-7012            Impact factor:   2.363


  12 in total

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