Increased DNA microarray hybridization specificity using sscDNA targets
© Barker et al; licensee BioMed Central Ltd. 2005
Received: 17 February 2005
Accepted: 22 April 2005
Published: 22 April 2005
The most widely used amplification method for microarray analysis of gene expression uses T7 RNA polymerase-driven in vitro transcription (IVT) to produce complementary RNA (cRNA) that can be hybridized to arrays. However, multiple rounds of amplification are required when assaying very small amounts of starting RNA. Moreover, certain cRNA-DNA mismatches are more stable than the analogous cDNA-DNA mismatches and this might increase non-specific hybridization. We sought to determine whether a recently developed linear isothermal amplification method (ribo-SPIA) that produces single stranded cDNA would offer advantages over traditional IVT-based methods for microarray-based analyses of transcript expression.
A single round of ribo-SPIA amplification produced sufficient sscDNA for hybridizations when as little as 5 ng of starting total RNA was used. Comparisons of probe set signal intensities obtained from replicate amplifications showed consistently high correlations (r = 0.99). We compared gene expression in two different human RNA samples using ribo-SPIA. Compared with one round IVT, ribo-SPIA had a larger dynamic range and correlated better with quantitative PCR results even though we used 1000-fold less starting RNA. The improved dynamic range was associated with decreases in hybridization to mismatch control probes.
The use of amplified sscDNA may offer substantial advantages over IVT-based amplification methods, especially when very limited amounts of starting RNA are available. The use of sscDNA targets instead of cRNA targets appears to improve hybridization specificity.
DNA microarrays are a powerful tool for global analysis of gene transcript expression. The initial studies using arrays required large amounts of starting material in order to reliably detect sample signals. Since that time, improvements in sample preparation, amplification and labeling methods [1–5] have reduced the starting material requirement to ~1–5 μg of total RNA . Efforts to use smaller amounts of starting material have focused on PCR [7, 8] and multiple rounds of T7 RNA polymerase in vitro transcription [IVT] [9–12]. PCR based methods have been successfully used to amplify subnanogram quantities of RNA from as little as a single cell [13, 14], but these approaches have not been widely adopted. Most attempts to perform arrays using submicrogram amounts of RNA have relied on 2 or 3 rounds of linear amplification using IVT, but this approach has proven to be time consuming and technically demanding. In our hands, two round IVT is necessary to prepare samples from 5–50 ng total RNA and the amplification typically takes 4–5 days to complete. Others have reported a 10% decrease in sensitivity in detection of differentially expressed genes with the addition of a second IVT round .
Results and discussion
Yield of sscDNA
sscDNA yield from ribo-SPIA experiments
sscDNA Total Yield (μg)
Mouse liver 1
Mouse liver 2
Mouse liver 3
Mouse liver 4
Mouse liver 5
Mouse liver 6
No Input RNA 1
No Input RNA 2
Size of sscDNA products
sscDNA preparations were analyzed by electrophoresis using an Agilent 2100 BioAnalyzer. sscDNAs ranged widely in size and the median size was typically slightly greater than 1 kb (data not shown). sscDNAs were fragmented in preparation for hybridization resulting in fragments of ~50–200 bp. These results are similar to those previously obtained using this method [16, 17].
Reproducibility of microarray hybridization results
Correlations between signal intensities for replicate hybridizations
Range of correlations1
Mouse liver 1–3
Mouse liver 4–6
Differential gene expression measurement
Mismatch probe signal intensities from sscDNA and cRNA hybridizations.
> 2× median*
> 4× median*
> 8× median*
14.1 ± 1.8%
5.0 ± 0.8%
2.0 ± 0.3%
26.6 ± 1.4%
12.9 ± 0.9%
5.4 ± 0.6%
Comparison of expression measurements made with sscDNA, cRNA, and qPCR
We wished to compare how measurements made using amplified sscDNA and microarrays compared with measurements made using other approaches. We began by comparing results obtained using sscDNA and cRNA microarray hybridizations for all 12,625 probe sets on the arrays. Since the sscDNA and cRNA methods would be expected to introduce different systematic biases, we were not surprised that direct correlations between signal intensities obtained with the two different methods showed show relatively poor agreement (r = 0.72–0.75 for K562 and r = 0.68–0.70 for sUHR, as opposed to r = 0.98–0.99 between replicates performed using the same sample preparation method). The finding indicates that it will not be useful to directly compare one array hybridized with sscDNA to another one hybridized with cRNA.
Next we generated another set of expression measurements that could be used as a basis for comparison for the sscDNA and cRNA array results. qPCR is typically used as "gold standard" to confirm putative differentially expressed genes detected with microarrays. Since we saw a subset of genes for which expression differed between sscDNA and cRNA targets, we next assessed if either method tracked more closely to qPCR. We chose qPCR primers and probe sets from a large group of >1000 sets that have been developed for various studies. From these, primers and probes for four subsets of genes were selected for qPCR. The first set included all genes with >4 fold difference in expression between K562 and sUHR samples as determined using the sscDNA method, the cRNA method, or both methods (53 primer/probe sets). The second set included all other genes in which the two methods disagreed by more than 2-fold (29 primer probe sets). The third set consisted of a group of 33 empirically-derived 'housekeeping genes.' These were all genes that were nearly equally expressed (|M| < 0.1) in K562 and sUHR samples according to both the sscDNA and cRNA methods and gave strong signals (A > 5 for both methods). The fourth set included 8 housekeeping genes that had been previously validated as controls for qPCR in other experiments. We determined the gene copy number for each qPCR primer and probe set and then calculated a measure of relative expression, M = log2 (K562 copy number)/(sUHR copy number), that could be directly compared to M values from arrays. 37 putative duplicate probe sets from 17 genes probe sets were hand-curated to confirm that they would correspond to the predicted qPCR product. In two cases probe sets were found to be misidentified in the GeneChip annotation and were removed from the analysis. In the remaining cases of duplication, the qPCR and microarray values were averaged across the duplicates. The final set of 106 curated genes and the associated data can be found at http://asthmagenomics.ucsf.edu.
We examined the suitability of a new isothermal linear amplification method for application to Affymetrix GeneChip microarrays. We performed a series of tests using starting amounts of RNA ranging from 5 to 100 ng for amplification yield and reproducibility. The amplification reactions consistently produced sufficient sscDNA for multiple array hybridizations. Pairwise comparison of technical replicates hybridized to microarrays by regression analysis showed excellent consistency. When we used sscDNA to analyze differential gene expression between two samples, we found a larger dynamic range than that obtained with cRNA hybridizations. The improved performance appears to be related to increased sscDNA hybridization specificity. The data obtained using this new method also more closely matched the results from qRT-PCR than data obtained using standard IVT reactions, even though the amount of starting RNA used was 1000-fold less. This new amplification method is a useful alternative approach for preparing targets that is especially well-suited for experiments involving small amounts of starting material.
Clontech Human Universal Reference Pool total RNA (cUHR), derived by pooling RNA from a variety of human tissues, was purchased from BD Biosciences and used in Experiment 1. Mouse total liver RNA was isolated by standard methods from C57/BL6 mice according to procedures approved by the UCSF Committee on Animal Research and used in Experiment 2. For Experiment 3, we used Stratagene Human Universal Reference Pool and K562 erythroleukemia total RNAs from the same batches used in a previous study . All samples were assessed for size and integrity using the Agilent 2100 BioAnalyzer RNA 6000 Nano LabChip assay. RNA and DNA samples were quantified using a NanoDrop ND-1000 spectrophotometer.
sscDNA samples were prepared using the NuGEN Technologies Ovation RNA amplification and Biotin Labeling system (Version 1.0) according to the manufacturer's directions from the indicated amount of starting RNA (5–100 ng). All reactions were performed in 0.2 ml strip PCR tubes in an MJ GeneWorks PTC-100 thermocycler using recommended programs. Since the seal for PCR tubes and caps tends to deteriorate with repeated use, we replaced the caps for each tube before each resealing step in the protocol. Following amplification, sscDNA product was purified using QIAquick PCR purification kits (Qiagen). Samples were fragmented and end labeled with biotin. After stopping, each reaction was concentrated in a Microcon YM-3 column to a final volume of ~20 μl. The concentrated material was purified using a Centri-Sep 100 spin column (Princeton Separations). Negative control reactions were prepared by replacing input RNA with the appropriate volume of RNase free water.
All samples were placed in standard Affymetrix hybridization buffer. The sample denaturation time for the sscDNA samples was reduced from 5 to 2 minutes and hybridization time increased from 16 to 20 hours as recommended by NuGEN Technologies. cUHR gene expression was assayed using Affymetrix Human Genome U133A GeneChip arrays (Experiment 1). Mouse liver RNA was assayed using Murine Genome Mu6500A arrays (Experiment 2). K562 and sUHR RNAs were assayed using Human Genome U95Av2 arrays (Experiment 3). One template independent sample was also analyzed using a Human Genome U95Av2 array (Experiment 4). Arrays were stained with phycoerythrin-streptavidin according to the manufacturer's instructions. Metrics for all sample hybridizations including scaling factors, mean background intensities, and percent present calls have been provided (see Additional File 1). Each set of data was normalized independently using RMAExpress software http://stat-www.berkeley.edu/~bolstad/RMAExpress/RMAExpress.html. K562 and sUHR microarray data were also analyzed using Microarray Suite 4.0 in order to calculate PM-MM values for each transcript probe set. Probe level analyses were performed using the BioConductor  affy analysis package . All microarray data have been deposited in the Gene Expression Omnibus (GEO) database under the accession numbers GSM41384 – GSM41393, GSM41433 – GSM41438 and GSM4843 – GSM4847.
Real-time (RT) PCR was used to measure the expression of selected genes in sUHR and K562 cells. Gene-specific primers for multiplex real time RT-PCR were designed for each gene of interest using "Primer Express" software (Perkin-Elmer) and purchased from Biosearch Technologies. Sequence data for all oligonucleotides primers has been provided (see Additional File 2). First strand cDNA synthesis was performed using total RNA, Powerscript reverse transcriptase (BD Biosciences), and random hexamer primers. Real time amplification was performed using an ABI Prizm7900 and Invitrogen Universal Master Mix. Relative gene copy numbers (GCN) were calculated as described previously . GeNorm  was used to select the two most stable housekeeping genes across all specimens for normalization.
This study was supported in part in the General Clinical Research Center at San Francisco General Hospital and supported by Grant 5-MO1-RR00083 from the Division of Research Resources, National Institutes of Health, the UCSF NHLBI Shared Microarray Facility (NIH grant HL072301) and the UCSF Sandler Center for Basic Research in Asthma. We thank Yanxia Hao, Jennifer Gregg, Agnes Paquet and Michael Salazar for excellent technical assistance, Ron Davis for helpful discussions and Karena Essex for assistance in preparation of the manuscript.
- Luo L, Salunga RC, Guo H, Bittner A, Joy KC, Galindo JE, Xiao H, Rogers KE, Wan JS, Jackson MR, Erlander MG: Gene expression profiles of laser-captured adjacent neuronal subtypes. Nat Med. 1999, 5: 117–122-10.1038/4806.PubMedView ArticleGoogle Scholar
- Ohyama H, Zhang X, Kohno Y, Alevizos I, Posner M, Wong DT, Todd R: Laser capture microdissection-generated target sample for high-density oligonucleotide array hybridization. Biotechniques. 2000, 29: 530–536-PubMedGoogle Scholar
- Luzzi V, Holtschlag V, Watson MA: Expression profiling of ductal carcinoma in situ by laser capture microdissection and high-density oligonucleotide arrays. Am J Pathol. 2001, 158: 2005–2010-PubMedPubMed CentralView ArticleGoogle Scholar
- Dunmire V, Wu C, Symmans WF, Zhang W: Increased yield of total RNA from fine-needle aspirates for use in expression microarray analysis. Biotechniques. 2002, 33: 890–896-Google Scholar
- Luzzi V, Mahadevappa M, Raja R, Warrington JA, Watson MA: Accurate and reproducible gene expression profiles from laser capture microdissection, transcript amplification, and high density oligonucleotide microarray analysis. J Mol Diagn. 2003, 5: 9–14-PubMedPubMed CentralView ArticleGoogle Scholar
- Eberwine J, Kacharmina JE, Andrews C, Miyashiro K, McIntosh T, Becker K, Barrett T, Hinkle D, Dent G, Marciano P: mRNA expression analysis of tissue sections and single cells. J Neurosci. 2001, 21: 8310–8314-PubMedGoogle Scholar
- Livesey FJ, Furukawa T, Steffen MA, Church GM, Cepko CL: Microarray analysis of the transcriptional network controlled by the photoreceptor homeobox gene Crx. Curr Biol. 2000, 10: 301–310-10.1016/S0960-9822(00)00379-1.PubMedView ArticleGoogle Scholar
- Iscove NN, Barbara M, Gu M, Gibson M, Modi C, Winegarden N: Representation is faithfully preserved in global cDNA amplified exponentially from sub-picogram quantities of mRNA. Nat Biotechnol. 2002, 20: 940–943-10.1038/nbt729.PubMedGoogle Scholar
- Eberwine J, Yeh H, Miyashiro K, Cao Y, Nair S, Finnell R, Zettel M, Coleman P: Analysis of gene expression in single live neurons. Proc Natl Acad Sci U S A. 1992, 89: 3010–3014-PubMedPubMed CentralView ArticleGoogle Scholar
- Baugh LR, Hill AA, Brown EL, Hunter CP: Quantitative analysis of mRNA amplification by in vitro transcription. Nucleic Acids Res. 2001, 29: e29-10.1093/nar/29.5.e29.PubMedPubMed CentralView ArticleGoogle Scholar
- Kamme F, Salunga R, Yu J, Tran DT, Zhu J, Luo L, Bittner A, Guo HQ, Miller N, Wan J, Erlander M: Single-cell microarray analysis in hippocampus CA1: Demonstration and validation of cellular heterogeneity. J Neurosci. 2003, 23: 3607–3615-PubMedGoogle Scholar
- Scherer A, Krause A, Walker JR, Korn A, Niese D, Raulf F: Early prognosis of the development of renal chronic allograft rejection by gene expression profiling of human protocol biopsies. Transplantation. 2003, 75: 1323–1330-PubMedView ArticleGoogle Scholar
- Chiang MK, Melton DA: Single-cell transcript analysis of pancreas development. Dev Cell. 2003, 4: 383–393-10.1016/S1534-5807(03)00035-2.PubMedView ArticleGoogle Scholar
- Tietjen I, Rihel JM, Cao Y, Koentges G, Zakhary L, Dulac C: Single-cell transcriptional analysis of neuronal progenitors. Neuron. 2003, 38: 161–175-10.1016/S0896-6273(03)00229-0.PubMedView ArticleGoogle Scholar
- Gold D, Coombes K, Medhane D, Ramaswamy A, Ju Z, Strong L, Koo JS, Kapoor M: A comparative analysis of data generated using two different target preparation methods for hybridization to high-density oligonucleotide microarrays. BMC Genomics. 2004, 5: 2 (http://www.biomedcentral.com/1471-2164/5/2). 10.1186/1471-2164-5-2.View ArticleGoogle Scholar
- Dafforn A, Chen P, Deng G, Herrler M, Iglehart D, Koritala S, Lato S, Pillarisetty S, Purohit R, Wang M, Wang S, Kurn N: Linear mRNA amplification from as little as 5 ng total RNA for global gene expression analysis. Biotechniques. 2004, 37: 854–857-PubMedGoogle Scholar
- Singh R, Maganti RJ, Jabba SV, Wang M, Deng G, Heath JD, Kurn N, Wangemann P: Microarray based comparison of three amplification methods for nanogram amounts of total RNA. Am J Physiol Cell Physiol. (in press)-Google Scholar
- Wilson CL, Pepper SD, Hey Y, Miller CJ: Amplification protocols introduce systematic but reproducible errors into gene expression studies. Biotechniques. 2004, 36: 498–506-PubMedGoogle Scholar
- Klur S, Toy K, Williams MP, Certa U: Evaluation of procedures for amplification of small-size samples for hybridization on microarrays. Genomics. 2004, 83: 508–517-10.1016/j.ygeno.2003.09.005.PubMedView ArticleGoogle Scholar
- Kenzelmann M, Klären R, Hergenhahn M, Bonrouhi M, Gröne HJ, Schmid W, Schütz G: High-accuracy amplification of nanogram total RNA amounts for gene profiling. Genomics. 2003, 83: 550–558-Google Scholar
- Feldman AL, Costouros NG, Wang E, Qian M, Marincola FM, Alexander HR, Libutti SK: Advantages of mRNA amplification for microarray analysis. Biotechniques. 2002, 33: 906–914-Google Scholar
- Puskás LG, Zvara , Hackler LJ, Van Hummelen P: RNA amplification results in reproducible microarray data with slight ratio bias. Biotechniques. 2002, 32: 1330–1340-Google Scholar
- Wang J, Hu L, Hamilton SR, Coombes KR, Zhang W: RNA amplification strategies for cDNA microarray experiments. Biotechniques. 2003, 34: 394-400.PubMedGoogle Scholar
- Saghizadeh M, Brown DJ, Tajbakhsh J, Chen Z, Kenney MC, Farber DB, Nelson SF: Evaluation of techniques using amplified nucleic acid probes for gene expression profiling. Biomol Eng. 2003, 20: 97–106-PubMedView ArticleGoogle Scholar
- Barczak A, Rodriguez MW, Hanspers K, Koth LL, Tai YC, Bolstad BM, Speed TP, Erle DJ: Spotted long oligonucleotide arrays for human gene expression analysis. Genome Res. 2003, 13: 1775–1785-10.1101/gr.1048803.PubMedPubMed CentralView ArticleGoogle Scholar
- Casey J, Davidson N: Rates of formation and thermal stabilities of RNA:DNA and DNA:DNA duplexes at high concentrations of formamide. Nucleic Acids Res. 1977, 4: 1539–1552-PubMedPubMed CentralView ArticleGoogle Scholar
- Sugimoto N, Nakano M, Nakano SI: Thermodynamics—structure relationship of single mismatches in RNA/DNA duplexes. Biochemistry. 2000, 39: 11270-11281. 10.1021/bi000819p.PubMedView ArticleGoogle Scholar
- Rosenow C, Saxena RM, Durst M, Gingeras TR: Prokaryotic RNA preparation methods useful for high density array analysis: Comparison of two approaches. Nucleic Acids Res. 2001, 29: e112-10.1093/nar/29.22.e112.PubMedPubMed CentralView ArticleGoogle Scholar
- Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, Hornik K, Hothorn T, Huber W, Iacus S, Irizarry R, Leisch F, Li C, Maechler M, Rossini AJ, Sawitzki G, Smith C, Smyth G, Tierney L, Yang JYH, Zhang J: Bioconductor: Open software development for computational biology and bioinformatics. Genome Biol. 2004, 5: R80-10.1186/gb-2004-5-10-r80.PubMedPubMed CentralView ArticleGoogle Scholar
- Gautier L, Cope L, Bolstad BM, Irizarry RA: Affy—analysis of Affymetrix GeneChip data at the probe level. Bioinformatics. 2004, 20: 306–315-10.1093/bioinformatics/btg405.View ArticleGoogle Scholar
- Dolganov GM, Woodruff PG, Novikov AA, Zhang Y, Ferrando RE, Szubin R, Fahy JV: A novel method of gene transcript profiling in airway biopsy homogenates reveals increased expression of a Na+-K+-Cl– cotransporter (NKCC1) in asthmatic subjects. Genome Res. 2001, 11: 1473–1483-10.1101/gr.191301.PubMedView ArticleGoogle Scholar
- Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A, Speleman F: Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol. 2002, 3: research0034.1–0034.11. Available at: http://genomebiology.com/2002/3/7/research/0034.-10.1186/gb-2002-3-7-research0034.View ArticleGoogle Scholar
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