Microarrays : Current Technology, Innovations and Applications [1 ed.] 9781908230591, 9781908230492

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Microarrays Current Technology, Innovations and Applications

Edited by Zhili He Caister Academic Press

Microarrays

Current Technology, Innovations and Applications

Edited by Zhili He Department of Microbiology and Plant Biology Institute for Environmental Genomics University of Oklahoma Norman, OK USA

Caister Academic Press

Copyright © 2014 Caister Academic Press Norfolk, UK www.caister.com British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library ISBN: 978-1-908230-49-2 (hardback) ISBN: 978-1-908230-59-1 (ebook) Description or mention of instrumentation, software, or other products in this book does not imply endorsement by the author or publisher. The author and publisher do not assume responsibility for the validity of any products or procedures mentioned or described in this book or for the consequences of their use. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, without the prior permission of the publisher. No claim to original U.S. Government works.

Contents

Contributorsv Prefaceix 1 2

Microarrays for Microbial Community Analysis at a Glance

Zhili He and Jizhong Zhou

1

Software Tools for the Selection of Oligonucleotide Probes for Microarrays23 Nicolas Parisot, Jérémie Denonfoux, Eric Dugat-Bony, Eric Peyretaillade and Pierre Peyret

3

Development and Evaluation of Functional Gene Arrays with GeoChip as an Example

61

4

Microarray Data Analysis

79

5

Microarray of 16S rRNA Gene Probes for Quantifying Population Differences Across Microbiome Samples

99

Qichao Tu, Ye Deng, Jizhong Zhou and Zhili He Ye Deng and Zhili He

Alexander J. Probst, Pek Yee Lum, Bettina John, Eric A. Dubinsky, Yvette M. Piceno, Lauren M. Tom, Gary L. Andersen, Zhili He and Todd Z. DeSantis

6

GeoChip Applications in Bioremediation Studies

121

7

GeoChip Applications for Analysing Soil and Water Microbial Communities in Oil-contaminated Sites

137

GeoChip Analysis of Soil Microbial Community Responses to Global Change

157

Soil Functional Gene Microarrays and Applications in Plant– Microbe Interactions

169

Joy D. Van Nostrand and Jizhong Zhou

Yuting Liang, Liangyin Yi and Chaopeng Song

8

Kai Xue, Zhili He, Joy D. Van Nostrand and Jizhong Zhou

9

Lilia C. Carvalhais, Vivian Rincon-Florez and Peer M. Schenk

iv  | Contents

10

Bioleaching Microarrays for Profiling Microbial Communities in Acid Mine Drainage and Bioleaching Ecosystems Huaqun Yin and Xueduan Liu

11

181

Applications of Phylogenetic Microarrays to Profiling of Human Microbiomes195 Oleg Paliy and Vijay Shankar

12

Microbial Diagnostic Microarrays for Detection and Typing of Waterborne Pathogens Tanja Kostić

13

217

Broad-spectrum Viral and Bacterial Pathogen Detection by Microarrays229 Kevin McLoughlin, Crystal Jaing, Shea Gardner, Nicholas A. Be, James B. Thissen and Tom Slezak

Index239

Contributors

Gary L. Andersen Ecology Department Earth Sciences Division Lawrence Berkeley National Laboratory Berkeley, CA USA [email protected]

Jérémie Denonfoux Conception, Ingénierie et Développement de l’Aliment et du Médicament (CIDAM) Clermont Université Université d’Auvergne Clermont-Ferrand France [email protected]

Nicholas A. Be Physical & Life Sciences Directorate Lawrence Livermore National Laboratory Livermore, CA USA

Todd Z. DeSantis Second Genome, Inc. South San Francisco, CA USA

[email protected]

[email protected]

Lilia C. Carvalhais School of Agriculture and Food Sciences The University of Queensland Brisbane Queensland Australia

Eric A. Dubinsky Ecology Department Earth Sciences Division Lawrence Berkeley National Laboratory Berkeley, CA USA

[email protected]

[email protected]

Ye Deng Department of Botany and Microbiology Institute for Environmental Genomics University of Oklahoma Norman, OK USA

Eric Dugat-Bony Conception, Ingénierie et Développement de l’Aliment et du Médicament (CIDAM) Clermont Université Université d’Auvergne Clermont-Ferrand France

[email protected]

[email protected]

vi  | Contributors

Shea Gardner Computations Directorate Lawrence Livermore National Laboratory Livermore, CA USA [email protected]

Xueduan Liu School of Minerals Processing and Bioengineering Key Laboratory of Biometallurgy of Ministry of Education Central South University Changsha China

Zhili He Department of Microbiology and Plant Biology Institute for Environmental Genomics University of Oklahoma Norman, OK USA

[email protected]

[email protected] [email protected]

[email protected]

Crystal Jaing Physical & Life Sciences Directorate Lawrence Livermore National Laboratory Livermore, CA USA [email protected]

Pek Yee Lum Ayasdi, Inc. Palo Alto, CA USA

Kevin McLoughlin Computations Directorate Lawrence Livermore National Laboratory Livermore, CA USA [email protected]

Bettina John Second Genome, Inc. South San Francisco, CA USA

Oleg Paliy Boonshoft School of Medicine Wright State University Dayton, OH USA

[email protected]

[email protected]

Tanja Kostić Health & Environment Department Austrian Institute of Technology GmbH Bioresources Unit Tulln an der Donau Austria

Nicolas Parisot Conception, Ingénierie et Développement de l’Aliment et du Médicament (CIDAM) Clermont Université Université d’Auvergne Clermont-Ferrand France

[email protected] Yuting Liang State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil Science Chinese Academy of Sciences Nanjing China [email protected]

[email protected] Pierre Peyret Conception, Ingénierie et Développement de l’Aliment et du Médicament (CIDAM) Clermont Université Université d’Auvergne Clermont-Ferrand France [email protected]

Contributors |  vii

Eric Peyretaillade Conception, Ingénierie et Développement de l’Aliment et du Médicament (CIDAM) Clermont Université Université d’Auvergne Clermont-Ferrand France

Tom Slezak Computations Directorate Lawrence Livermore National Laboratory Livermore, CA USA

[email protected]

Chaopeng Song Changzhou University Jiangsu China

Yvette M. Piceno Ecology Department Earth Sciences Division Lawrence Berkeley National Laboratory Berkeley, CA USA [email protected] Alexander J. Probst Second Genome, Inc. South San Francisco, CA USA [email protected] Vivian Rincon-Florez School of Agriculture and Food Sciences The University of Queensland Brisbane Queensland Australia [email protected] Peer M. Schenk School of Agriculture and Food Sciences The University of Queensland Brisbane Queensland Australia [email protected] Vijay Shankar Boonshoft School of Medicine Wright State University Dayton, OH USA [email protected]

[email protected]

[email protected] James B. Thissen Physical & Life Sciences Directorate Lawrence Livermore National Laboratory Livermore, CA USA [email protected] Lauren M. Tom Ecology Department Earth Sciences Division Lawrence Berkeley National Laboratory Berkeley, CA USA [email protected] Qichao Tu Department of Botany and Microbiology Institute for Environmental Genomics University of Oklahoma Norman, OK USA [email protected] Joy D. Van Nostrand Department of Microbiology and Plant Biology Institute for Environmental Genomics University of Oklahoma Norman, OK USA [email protected]

viii  | Contributors

Kai Xue Department of Microbiology and Plant Biology Institute for Environmental Genomics University of Oklahoma Norman, OK USA [email protected] Liangyin Yi Changzhou University Jiangsu China [email protected]

Huaqun Yin School of Minerals Processing and Bioengineering Key Laboratory of Biometallurgy of Ministry of Education Central South University Changsha China [email protected] Jizhong Zhou Department of Microbiology and Plant Biology Institute for Environmental Genomics University of Oklahoma Norman, OK USA [email protected]

Preface

Microarray technology has become a revolutionary tool in environmental microbiology and microbial ecology for analysing microbial communities. As a metagenomic tool, various forms of microarrays have been developed and used to understand the diversity, composition, structure, function, dynamics and evolution of microbial communities from different habitats, and link the microbial community structure with environmental factors and ecosystem functioning. However, a comprehensive source that systematically describes such microarrays in terms of oligonucleotide probe design, microarray construction, data analysis, and applications in different environments is lacking. This book meets such a need with a focus on current microarray technologies and their applications in environmental microbiology. This book is divided into 13 chapters with each describing a particular aspect or use of microarray technology. Chapter 1 provides a general introduction of various microarrays used for microbial community analysis, describes crucial issues in the application of microarray technologies, highlights recent applications in environmental microbiology, and discusses the advantages and limitations of microarray technologies compared to high-throughput sequencing technologies. Chapter 2 summarizes current software tools available for selecting oligonucleotide probes for microarrays, the algorithms used, and probe selection criteria. Chapter 3 describes technical details for designing, developing and evaluating functional gene arrays (FGAs) with the GeoChip FGA as an example, and includes gene selection, sequence retrieval and verification, probe design and verification, and computational

and experimental evaluation of the specificity, sensitivity and quantification of the developed microarray. Chapter 4 focuses on microarray data analysis, including data extraction from microarray images, positive spot determination, data comparison and normalization, and data mining using statistical and network approaches. Chapter 5 shows the power of recently-developed bioinformatics tools and the G3 PhyloChip™, a 16S rRNA gene microarray, to understand microbial taxonomical diversity and the structure of microbial communities. Chapters 6, 7 and 8 provide a few example applications of the GeoChip to analyse microbial communities from different ecosystems or environments, such as bioremediation systems (Chapter 6), oil-contaminated soils and waters (Chapter 7), and global-change-affected soils (Chapter 8). In Chapters 9, 10, 11, 12 and 13, several specific microarrays are described in terms of their design, construction and application, including soil functional gene microarrays for analysing plant–microbe interactions (Chapter 9), bioleaching microarrays for profiling acid mine drainage and bioleaching microbial communities (Chapter 10), human microbiome microarrays for analysing the human microbiome (Chapter 11), diagnostic microarrays for detecting and typing waterborne pathogens (Chapter 12), and pathogen microarrays for broad spectrum viral and bacterial detection (Chapter 13). This book provides useful and rich source of information about current microarray technologies and their applications in microbial community analysis. The goal of this work is to systematically introduce current microarray technology and applications for microbial community

x  | Preface

analysis. It is primarily intended for researchers who are interested in microarray technologies and use them in the study of environmental microbiology and microbial ecology. I am in debt to my colleagues who have devoted a lot of effort to the development and application of microarray technology at the Institute for Environmental Genomics, University of Oklahoma. I am very grateful to all contributors for writing these informative chapters. I want to thank Hugh Griffin at Horizon Scientific Press (Caister

Academic Press) for providing kind assistance with the publication of this book. I also want to thank my family members for their support. This work was partially supported by the Oklahoma Applied Research Support (OARS), Oklahoma Center for the Advancement of Science and Technology (OCAST) through AR11-035, and by ENIGMA (Ecosystems and Networks Integrated with Genes and Molecular Assemblies), US Department of Energy through contract No. DE-AC02-05CH11231. Zhili He Institute for Environmental Genomics Department of Microbiology and Plant Biology University of Oklahoma Norman, OK USA

Microarrays for Microbial Community Analysis at a Glance Zhili He and Jizhong Zhou

Abstract Microorganisms are the most diverse group of organisms, and play important and distinctive roles in their ecosystems, such as biogeochemical cycling of carbon, nitrogen, sulfur, phosphorus and metals, and biodegradation or stabilization of environmental contaminants. They also interact with their peers and/or other organisms (e.g. plants, animals) to form a complicated food web, significantly impacting ecosystem functions and services. However, understanding the diversity, composition, structure, functions, activities and dynamics of microbial communities remains challenging. Over the past decade, microarray-based technologies have been developed to address such a challenge. In this chapter, we provide an introduction of various microarrays for microbial community analysis, describe crucial issues in applications of microarray technologies for addressing fundamental scientific questions, and highlight their recent applications in environmental microbiology. In addition, we discuss the advantages and limitations of microarray technologies compared to high-throughput sequencing technologies as well as challenges and future directions. Introduction Microorganisms are considered the most diverse group of organisms, phylogenetically and functionally on the planet. They can live in every imaginable environment, and play important and distinctive roles in their ecosystems, such as biogeochemical cycling of carbon (C), nitrogen (N), sulfur (S), phosphorus (P), and metals (e.g.

1

arsenic, copper, iron, zinc), antibiotic resistance, stress responses, and biodegradation or stabilization of environmental contaminants. They also interact with themselves and/or other organisms (e.g. plants, animals) to form a complicated food web in ecosystems, significantly impacting ecosystem functions and services. Therefore, one of the most important goals of microbial ecology is to understand the diversity, composition, structure, function, dynamics, and evolution of microbial communities and their relationships with environmental properties and ecosystem functioning. However, microbial ecologists face several challenges towards this goal. First, microorganisms are generally too small to see or characterize with approaches for plant or animal studies. Second, an extremely high diversity has been observed in microbial communities with 2000–50,000 microbial species (Curtis et al., 2002; Hong et al., 2006; Roesch et al., 2007; Schloss and Handelsman, 2006; Torsvik et al., 2002) and even up to millions of species (Gans et al., 2005). In addition, a vast majority of microorganisms (>99%) are currently unculturable (Amann et al., 1995; Whitman et al., 1998), making it even more difficult to study their functional ability. Finally, establishing mechanistic linkages between microbial diversity and ecosystem functioning is even more difficult. To address these challenges, culture-independent high-throughput metagenomic technologies for microbial community analysis are necessary. In the past 20 years, many culture-independent approaches have been developed and used for microbial community analysis, including PCRbased cloning analysis, denaturing gradient gel electrophoresis (DGGE), terminal-restriction

2  | He and Zhou

fragment length polymorphism (T-RFLP), quantitative PCR, and in situ hybridization. However, both resolution and coverage of these methods are limited for providing a comprehensive view of a microbial community. For example, for functional genes, clone libraries comprising over 2000 clones were still insufficient to cover the nifH diversity of the Chesapeake Bay (Steward et al., 2004). Also, most of these approaches rely on an initial PCR amplification step, which introduces well-known biases (Lueders and Friedrich, 2003; Suzuki and Giovannoni, 1996; Warnecke et al., 1997). In addition, these PCR-based analyses are time-consuming and expensive, especially when many genes or samples are examined. Therefore, high throughput metagenomic technologies are necessary to provide a rapid, specific, sensitive, quantitative and comprehensive analysis of microbial communities and their relationships with environmental factors and ecosystem functioning. Microarrays have become a widely used technology since the first microarray was designed to monitor gene expression in Arabidopsis thaliana (Schena et al., 1995). A microarray is also called chip, which is constructed on a solid surface (e.g. a glass slide or silicon thin-film cell) with different types of probes to assay a large number of targets (e.g. genes, proteins/peptides, antibodies, carbohydrates) simultaneously. Based on probe/target types, various microarrays, such as DNA arrays (Brodie et al., 2006; Hazen et al., 2010; He et al., 2007, 2010a; Rhee et al., 2004; Wu et al., 2001), protein arrays (Melton, 2004), and carbohydrate arrays (Houseman and Mrksich, 2002) have been developed. Among those types of microarrays, DNA microarrays are the most sophisticated and the most widely used. First, hundreds to thousands of organism-specific DNA microarrays have been developed to examine gene expression under different conditions for understanding gene function, regulation and network in a specific organism, or simple communities (Dennis et al., 2003; Schena et al., 1995). Second, the potential application of microarrays in environmental microbiology studies was first demonstrated with 16S rRNA gene probes for detection of key genera of nitrifying bacteria (Guschin et al., 1997). Third, various types of microarrays have been developed (Zhou, 2003) for studying microbial communities

and their linkages with environmental factors and ecosystem functioning (Brodie et al., 2006; Hazen et al., 2010; He et al., 2007, 2010a,b, 2014; Rhee et al., 2004; Wu et al., 2001; Zhou et al., 2012). Some examples are briefly introduced below. Phylogenetic oligonucleotide arrays (POA) are designed to examine phylogenetic relatedness or community composition using 16S rRNA or other conserved genes, and the currently most comprehensive POA is the PhyloChip G3 as a general tool to profile microbial communities from different environments (Hazen et al., 2010), and other POAs were developed to study microbial communities from specific environments, such as Microbiota Array (Paliy et al., 2009), HITChip (Rajilić-Stojanović et al., 2009) for human gut microbiomes, COMPOCHIP for compost-degrading microbial communities (Franke-Whittle et al., 2009), and SRP-PhyloChip for detecting sulfate-reducing microorganisms (Loy et al., 2002). Functional gene arrays (FGA) are special microarrays containing probes targeting functional genes involved in various biogeochemical cycling processes, such as C, N, P, S, and metal cycling, antibiotics, and virulence factors, which are very useful as signatures for monitoring the physiological status, and functional potential and activities of microbial communities (Zhou, 2003), and GeoChip is the most comprehensive FGA to functionally profile microbial communities (He et al., 2007, 2010a; Lu et al., 2012a; Rhee et al., 2004; Wu et al., 2001), and other FGAs are to specifically detect certain functional processes or specific ecosystems, such as nitrogen cycling (Steward et al., 2004; Taroncher-Oldenburg et al., 2003), methane oxidation (Bodrossy et al., 2006), virulence (Miller et al., 2008), and bioleaching (Yin et al., 2007). Community genome arrays (CGA) are used to determine the relatedness of microbial species or strains or to identify community members. These arrays use whole genomic DNA from pure cultures as probes, eliminating the need for probe design or PCR amplification (Wu et al., 2004). CGAs have been used to compare microbial communities from different environments (Wu et al., 2004), to examine communities from acid mine drainage and bioleaching communities (Chen et

Microarrays for Microbial Community Analysis |  3

al., 2009), and to determine prokaryotic species relatedness (Wu et al., 2008). Metagenomic arrays (MGA) use environmental clone library inserts as probes (Sebat et al., 2003), and were used to examine microbial communities from marine environments (Rich et al., 2008). Many studies have demonstrated that microarrays are a revolutionized tool able to provide specific, sensitive, and possibly quantitative analyses of microbial communities in a rapid, high-throughput, and parallel manner. Microarrays for microbial community analysis at a glance In this book, we are interested in microarrays for environmental microbiology. This chapter first briefly introduces two most generally used microarrays, PhyloChip and GeoChip, and their applications. Probe design, array development and evaluation, and data analysis are described in Chapter 2, 3 and 4, respectively. Second, PhyloChip is described in detail (Chapter 5). Also, applications of GeoChip analysis of microbial communities from different habitats, such as soil, water, sediment, and extreme environments are used as examples (Chapters 6, 7 and 8). In addition, some currently available specific microarrays are introduced (Chapters 9–13). PhyloChip PhyloChip is fabricated using photolithographybased Affymetrix technology with 25-mer oligonucleotide probes to discriminate conserved 16S rRNA gene sequences in a complex microbial community. Updated from a previous version PhyloChip G2 containing 8741 OTUs and 842 subfamilies with 297,851 probes (Brodie et al., 2006), the most recent version of PhyloChip G3 has probes targeting ~60,000 OTUs, representing two domains, 147 phyla, 1123 classes, 1219 orders, 1464 families, and 10,993 subfamilies within the archaea and bacteria (Hazen et al., 2010). PhyloChip has been used extensively to analyse microbial communities from a variety of habitats, such as air (Brodie et al., 2007), soil (Brodie et al., 2006; He et al., 2012b), water (Dubinsky et al., 2012; Hazen et al., 2010; Lee et al., 2012), sediments (Beazley et al., 2012), and

animal/human microbiomes (Lemon et al., 2010; Nelson et al., 2011). Generally, 16S rRNA genes are isolated and amplified from microbial community DNA and then biotin labelled for PhyloChip hybridization (Hazen et al., 2010). Typically, 0.5–2.0 µg PCR amplicons or ~2.0 µg total RNA are needed for PhyloChip hybridization (DeAngelis et al., 2011; Lee et al., 2012). The PhyloChip exhibits a detection limit of 107 copies or 0.01% of nucleotides hybridized to the array (Brodie et al., 2007; DeAngelis et al., 2011). PhyloChip hybridization is generally performed at 48°C (DeAngelis et al., 2011; Lee et al., 2012). The labelled nucleic acids are digitally imaged for detection and quantification, and the resulting data are analysed with various statistical methods. Although the traditional bias-prone PCR amplification used before hybridization is generally believed not to be strictly quantitative, good correlations were reported between microarray signal intensities and quantitative PCR copy numbers over 5 orders of magnitude (Brodie et al., 2007; Lemon et al., 2010). Recently, two PCR-independent methods were developed. One is to directly hybridize 16S rRNAs (dirRNA) to PhyloChip (Lee et al., 2012), and the other is to convert rRNA to double-stranded cDNA (dscDNA), which is then hybridized to PhyloChip (DeAngelis et al., 2011). The direct RNA hybridization method was used to analyse a microbial community performing reductive dechlorination at a trichloroethene (TCE)-contaminated site (Lee et al., 2012). Novel insights were obtained in terms of the microbial ecology and population dynamics at the TCE-contaminated field site for in situ reductive dechlorination processes (Lee et al., 2012). More details about PhyloChip development, data analysis, and applications are described in Chapter 5. GeoChip GeoChip is constructed with 50-mer oligonucleotide probes (He et al., 2005b) and has evolved over several generations (He et al., 2007, 2010a, 2012a; Lu et al., 2012a; Rhee et al., 2004; Wu et al., 2001). The most recent GeoChip (version 5.0) contains about 167,000 probes covering ~395,000 coding sequences from ~1500 functional gene families related to microbial (archaea,

4  | He and Zhou

bacteria, fungi, protists) C, N, S and P cycling, energy metabolism, antibiotic resistance, metal homeostasis and resistance, secondary metabolism, electron transfer, organic remediation, stress responses, bacteriophages, virulence, and soil beneficial microorganisms. GeoChips also use other phylogenetic markers such as gyrB to track phylogeny. GeoChip is the most comprehensive functional gene array developed for biogeochemical, ecological and environmental analyses, which can provide the information simultaneously on functions of microbial communities for hundreds of thousands of signature genes important for ecosystem functions. To fabricate GeoChip, all sequences of interest are automatically retrieved from public databases by key word search, followed by HMMER confirmation to identify correct sequences via seed sequences with validated functions. Both gene- and group-specific probes are designed based on sequence identity, continuous stretch and free energy using the CommOligo program (He et al., 2005b; Li et al., 2005; Liebich et al., 2006). Usually with a common oligonucleotide reference standard (CORS) method for data comparison and normalization (Liang et al., 2010), the designed array is manufactured commercially through in situ synthesis technologies (e.g. Roche NimbleGen), or home-made by an array spotter (He et al., 2012a). Next, nucleic acids (DNA or RNA) are extracted and purified from environmental samples. The purified DNA (0.5–1.0 µg) is then labelled with fluorescent dyes before hybridization with GeoChip. If DNA/RNA is not enough, the community DNA (Wu et al., 2006), or RNA (Gao et al., 2007) is amplified prior to labelling. After washing away free nucleic acids, the fluorescently labelled and captured DNA/cDNA molecules are digitally imaged by a laser scanner, and this image is used to assess the abundance of target DNA/RNA species. Various statistical tools are available to rapidly analyse these hybridization data for addressing fundamental questions in microbial ecology (He et al., 2012c). One of most important criteria for microbial detection is specificity. This is particularly important for analysing environmental samples since there are numerous homologous sequences for each gene present in a sample. Typically, GeoChip

hybridizes with environmental DNAs in presence of 40–50% formamide at 42–50°C (equivalent to 66–80°C), depending on array formats (He et al., 2007, 2010a; Lu et al., 2012a; Rhee et al., 2004; Wu et al., 2001). Various controlled studies demonstrated that such hybridization stringencies could differentiate sequences with 87% were distinguishable at 65°C without formamide added (Wu et al., 2001). Sensitivity Sensitivity is a major concern, especially with the samples from complex environments where many gene variants are expected to be in low abundances. The current level of sensitivity for oligonucleotide arrays using environmental samples is approximately 50–100 ng or 107 cells (Bodrossy et al., 2003; Chen et al., 2009; Rhee et al., 2004; Tiquia et al., 2004; Wu et al., 2001), or approximately 5% of the microbial community (Bodrossy et al., 2003), providing a coverage of most dominant community members. This is an important issue especially for most environmental samples from soils, groundwaters, and marine water columns since the abundance of most populations may be generally lower than the detection limit. As such, the use of microarrays for many environmental studies is very limited. Several strategies have been utilized to increase sensitivity. The most important is the use of target

DNA or RNA amplification to increase sensitivity (Gao et al., 2007; Palka-Santini et al., 2009; Wu et al., 2006). Whole community genome amplification (WCGA) can amplify all of the community DNAs, including those in a low abundance (Wu et al., 2006). WCGA has been shown to provide a representative, sensitive, and quantitative amplification of microbial community DNA using starting amounts of 1.0–250 ng. Use of amplified DNA for hybridization increased the sensitivity of GeoChip hybridization to 10 fg (equivalent to 1 to 2 cells) (Wu et al., 2006). Also, several array modifications can increase sensitivity, although generally there is a trade-off with a decreased specificity, another important concern with microarrays. Increasing the length of oligonucleotide probes increases sensitivity (Denef et al., 2003; He et al., 2005a), although this does decrease specificity (Relogio et al., 2002). Another option is to increase the amount of probe per spot (Cho and Tiedje, 2002; Relogio et al., 2002; Zhou and Thompson, 2002). This strategy is based on the fact that membrane-based arrays are more sensitive than glass-based arrays, probably because of the higher probe concentration (>1 μg/spot for membranes;