March 2006, Volume 6, Issue 1

Technical Issues Involved in Obtaining Reliable Data from Microarray Experiments

James C. Fuscoe ^1,2,3,7, William S. Branham ^1,2,3, Cathy D. Melvin ^1,2,3, Varsha G. Desai ^1,2,3, Carrie L. Moland ^1,2,3, Tao Han ^1,2,3, Leming Shi ^3,4, Weida Tong ^3,4, Adam T. Scully ⁵, and Robert R. Delongchamp ⁶

¹ Center for Functional Genomics
² Division of Genetic and Reproductive Toxicology
³ Division of Systems Toxicology
⁴ Center for Toxicoinformatics
⁵ Office of Real Property Service
⁶ Division of Biometry and Risk Assessment
National Center for Toxicological Research (NCTR)
U.S. Food and Drug Administration
Jefferson, Arkansas 72079

⁷ Corresponding Author’s email: james.fuscoe@fda.hhs.gov,  Tel: 870-543-7126, Fax: 870-543-7682

Microarrays show great promise in advancing the understanding of many biological phenomena, including toxicity and effectiveness of the many products regulated by the U.S. Food and Drug Administration. In addition, devices based on the microarray technology have the potential to individualize diagnosis and treatment of disease, as well as monitor efficacy of treatment regimens. To realize these expectations, reliable and reproducible measurements are essential. Currently, there are no generally accepted standards for performing and analyzing a microarray study. Without such quality assurance standards among the microarray community, it will be difficult to move this technology into the regulatory framework where it holds such promise. Here, we describe observations and approaches undertaken at the NCTR Center for Functional Genomics to examine and optimize steps in the complex microarray procedure with the aim of decreasing variability in order to generate reliable data. These observations illustrate some of the subtle technical issues that can easily be overlooked in microarray experiments. Among the factors that can influence microarray experiments are microarray printing procedures, oligonucleotide characteristics, RNA quality, and environmental factors.

Introduction 

The mission of the National Center for Toxicological Research (NCTR) is to conduct peer-reviewed scientific research that supports and anticipates the U.S. Food and Drug Administration’s (FDA's) current and future regulatory needs (http://www.fda.gov/nctr/overview/mission.htm). This involves fundamental and applied research specifically designed to define biological mechanisms of action underlying the toxicity of products regulated by the FDA. It also includes the development of methods to improve assessment of human exposure, susceptibility, and risk. The Center for Functional Genomics at the NCTR has implemented DNA microarray technology that enables the evaluation of the effects of chemical toxicants on gene expression as well as the discovery of new biomarkers. To acquire high quality data that will permit definitive conclusions to be made about gene expression, it is important that all steps involved in this technology are optimized and standardized to reduce experimental error. Such need for standardization has been recently reviewed [1, 2]. The experiments reported here describe approaches undertaken to optimize each step and decrease variability in microarray technology in order to generate reliable data. In addition, an evaluation of automated microarray hybridization instruments is provided. Examples are given of spotted oligonucleotide glass slide microarrays, although many of the lessons learned are applicable to in situ synthesized microarrays, such as those from Affymetrix and Agilent Technologies [3-5], and cDNA microarrays [6, 7].

Microarray technology allows the relative gene expression levels to be determined among sets of biological samples, including tissue and cell samples from experimental animal models, humans, lower organism models, etc. In the field of toxicogenomics, which applies new high-throughput genomic technologies to toxicology, a typical application would involve the determination of gene expression changes associated with exposure to a toxic compound with the aim to understand mechanism or develop biomarkers of risk. A microarray experiment utilizes a complex multi-step process that is illustrated in Figure 1 [see Figure 1 following this paragraph]. Initially, a microarray of selected genes (called probes) is fabricated on a microscope slide. The genes in the experiments reported here are unique short oligonucleotides of approximately 50-80 nucleotides in length that are designed to have minimal homology with other genes. Collections of large numbers of genes (10,000 to 40,000) from various organisms, including the rat and mouse toxicology model systems, are available from commercial sources. These genes come in multi-well microplates with each gene in a well. The genes need to be dissolved in a printing buffer and often transferred to new daughter plates before the printing process starts. The transfer is too error-prone for laboratory workers to perform manually and requires a high capacity, liquid-handling robot and sample-tracking software. The gene solutions are then deposited at known positions on the glass microscope slide and, after processing the slide to firmly attach the genes to the slide, to remove salt solutions, and to block reactive sites on the glass surfaces, the microarray is ready for use in an experiment.

Figure 1. A microarray experiment. The top part of the figure shows steps in the sample preparation process while the bottom part shows steps in the fabrication of the microarray. The samples are hybridized to the microarrays (center). The net result of this complex multi-step procedure is increased understanding of the effects of chemical toxicants.

The preparation of samples is also a multi-step process that starts with the collection of tissue or cells in a manner that preserves the RNA integrity. The RNAs are then used to create fluorescent targets that will bind by hybridization to their specific genes on the microarray. These targets are applied to the microarray slides under conditions that will allow specific and efficient binding. The targets that do not bind are subsequently washed away, leaving only the specifically bound targets. Using a high resolution fluorescence scanner, the fluorescent intensity at each gene position on the microarray is determined, and this value is used as a measure of expression of each gene.  These data are then analyzed and used to develop knowledge about the effect of a particular drug, toxicant, or disease on the biological system. Suggested starting points have been published for those new to developing and using DNA microarray technology [8, 9]. However, optimizing all of these steps is a challenge, and there is ample opportunity for experimental variability to mask true biological effects. This manuscript discusses some of the main technical issues that must be considered and optimized to produce high quality microarray data.

Gene expression is often altered as a result of toxicant exposure and thus is a sensitive, measurable end-point for toxicity that may serve as an early warning of compromised health. The challenges are to identify those genes that respond to toxicant exposure, to discover novel gene interactions, and to improve the knowledge of complex regulatory networks and cross-communication between different pathways during various chemical exposures. Microarray analyses provide a potential solution in that they measure the expression of thousands of genes simultaneously, providing data on the patterns of mRNA expression for most genes expressed in cells [6, 7, 10-12]. An important aspect of this technology is its use as a tool for the identification of molecular mechanisms of toxicity [13-15]. Such an approach enables researchers to identify single genes or whole genetic pathways that are involved in conferring resistance or sensitivity to toxic substances. Based on signature expression profiles of known toxicants, this technology holds promise to allow the characterization of unknown toxic compounds and an understanding of mechanisms of action of their toxicity [16-20].

Typically, toxicologists have used rodent bioassays that require high doses, often take years to complete, and are expensive to identify potentially hazardous substances. This, coupled with traditional methods in molecular biology working on a “one gene at a time” basis, severely limits throughput for mechanism-based studies.  By contrast, the analysis of the expression of thousands of genes in one experiment allows investigators to address important biological questions that have not been easily addressed with traditional expression-based technologies, such as Northern blots, in situ hybridizations, or RNase protection assays. Thus, DNA microarray technology, which can be used to analyze changes in genome-wide patterns of gene expression, is one new methodological advance that can dramatically accelerate the way toxicological problems are investigated [13].

The FDA anticipates the use of DNA microarray-based medical devices, as well as the submission of toxicogenomics data for support of investigational new drug applications (INDs), new drug applications (NDAs), and biologics license applications (BLAs) [21]. This will create new challenges in the evaluation of such state-of-the-art technology. In response, the FDA has taken a proactive position by sponsoring workshops [22], publishing regulatory science perspectives [23], and creating draft guidance documents such as “Pharmacogenetic Tests and Genetic Tests for Heritable Markers; Draft Guidance for Industry and FDA Staff” (www.fda.gov/cdrh/oivd/guidance/1549.pdf). In November 2003, the FDA issued the “Draft Guidance for Industry: Pharmacogenomic Data Submissions” ((http://www.fda.gov/cder/guidance/5900dft.pdf) to encourage the use of toxicogenomics data during drug development. This document outlines how and what data should be presented to FDA and how it will be used. In addition, the FDA is encouraging the voluntary submission of toxicogenomics data sets so the Agency can “be prepared to appropriately evaluate the anticipated future submissions” and so FDA scientists can develop an understanding of relevant scientific issues. The MicroArray Quality Control (MAQC) project, a large multi-institution collaboration involving FDA Centers, major providers of microarray platforms and RNA samples, EPA, NIST, academic laboratories, and other stakeholders, has also been recently implemented to provide quality control tools to the microarray community. The outcome of the MAQC project will be large publicly available reference datasets along with readily accessible reference RNA samples. These will be used to help avoid procedural failures and to develop guidelines for microarray data analysis. Complete information can be found at: (http://www.fda.gov/nctr/science/centers/toxicoinformatics/maqc/index.htm) Thus, the FDA is anticipating that pharmacogenomics data, including DNA microarray data, will become important information in the assessment of INDs, NDAs, and BLAs. Because of the complex nature of collecting such data and the potential high degree of variability introduced to the data-sets by sub-optimal procedures and unrecognized sources of variability, observations and experiences with these issues are reported here. In addition, guidance is offered on how such problems can be overcome.

Fabrication of Microarrays

Oligonucleotide microarrays. Spotted oligonucleotide arrays exhibit a number of advantages over cDNA arrays [24, 25]. For example, oligonucleotides can be synthesized such that homologous sequences between genes can be excluded, thereby enhancing specificity. In addition, a given gene can be represented by a set of different oligonucleotides targeting different regions or exons, thereby allowing for the detection of splice variants, or the discrimination of closely related genes, strains, or species. Also, cross-hybridization, which can severely mask true gene expression changes, is more of a problem with cDNA arrays than with oligonucleotide arrays. Oligonucleotide microarrays, therefore, offer potentially greater specificity and are an alternative to expensive cDNA library maintenance and amplification.

Microarray printing. The first step in conducting microarray experiments is to print (or spot) the oligonucleotide solutions onto a series of glass microscope slides that have been coated with a substrate capable of binding the oligonucleotides. Many variables (e.g., printing pins, printing buffers, pin washing procedures, oligonucleotide sources, slide substrates, temperature, and humidity) must be optimized to insure high quality arrays. Many of these variables have been examined in some detail for specific combinations of printing pins, printing buffers, temperature/ humidity, etc. [26-29]. In the studies described here, all microarrays were printed using ArrayIt SMP3 printing pins (TeleChem International, Inc., Sunnyvale, Calif.) on the GeneMachines OmniGrid 100 printer (Genomic Solutions, Ann Arbor, Mich.). The ArrayIt SMP3 pins print oligonucleotides in a volume of 0.6 nl with diameters of approximately 100 µm. Rat, mouse, and human oligonucleotide libraries were purchased from BD Biosciences Clontech (San Jose, Calif.) and mouse oligonucleotides from MWG Biotech (High Point, N.C.).  Either ArrayIt Micro Spotting Plus or MWG Spotting Buffer A printing buffers were used to print the oligonucleotides from 384-well polypropylene microplates. Satisfactory probe spots were obtained using GeneMachines OmniGrid 100 instrument settings of a dip time (the time the printing pins are immersed in the oligonucleotide solution) of 500 milliseconds and the minimum print time (the time the pins are in contact with the slide substrate). Following the dipping of the pins in the oligonucleotide solutions, the excess liquid is removed by “blotting” the pins using 12 consecutive taps at a spacing of 475 µm on a glass blot pad coated with poly-L-lysine. These instrument settings are adjustable and may need fine-tuning for various combinations of DNA, spotting buffer, slide surface chemistry, etc. After testing combinations of these parameters, the described settings became a standard for the present studies. Settings will also depend on the particular microarray printer used. The goal is to standardize as many aspects of the microarray process as possible in order to reduce variability.

Pin washing. Because each printing pin is used multiple times during a print run (410 times for printing 20,000 mouse oligonucleotides onto a glass microscope slide), every pin must be thoroughly cleaned after it prints each oligonucleotide onto the slides and before it dips into the next oligonucleotide solution. Any “carry-over” of oligonucleotide from one spot to the next would result in erroneous data. To assess potential oligonucleotide carry-over, a test oligonucleotide of 80 nucleotides (complementary to the rat tubulin gene; rTUBA1) was labeled with a fluorescent dye (Alexa 532) and was printed using the GeneMachines default wash procedure outlined in Figure 2A [see par A of Figure 2 following this paragraph]. These data [see Figure 3 following this paragraph] indicate a carry-over of rTUBA1 of approximately 5% and would present an error in gene expression data if this were a real experiment. To eliminate oligonucleotide carry-over, both the number of pin washes and the wash times were increased by over 4-fold [see part B of Figure 2 following this paragraph].

Figure 2. Pin washing procedures. Default pin washing procedures (A) were modified (B) to insure complete removal of each oligonucleotide from the printing pin prior to loading the next oligonucleotide. While increasing the pin wash times increases the length of the print run, these procedures eliminate any carry-over of oligonucleotides from one feature to the next.

Figure 3. Carry-over of rTUBA1 during microarray printing. (A) Alexa 532-labeled oligonucleotide rTUBA1 (20 µM) was printed as the first tap on the slide (rTUBA1). After the pin was cleaned using the default wash procedure shown in Fig. 2A, the pin was dipped into water (no oligonucleotide) and then tapped onto the slide (Water). The pin was again cleaned and dipped into printing buffer before tapping on the slide (Printing Buffer). (B) The mean Cy3 channel signals from 4 arrays printed on 3 slides are shown. Using the wash procedure outlined in Fig. 2A, sufficient rTUBA1 remained on the pin to cause a carry-over Cy3 channel signal that was 5% of rTUBA signal.

A method to determine if carry-over is a problem within a print run is available in the microarrays described here. When printing large collections of oligonucleotides from 384-well plates, the last plate is not completely filled with oligonucleotides; the remaining wells contain printing buffer only. Since the printer is not stopped until the contents of all the wells have been printed, there are blank features printed by each printing pin from the buffer-only wells. If carry-over exists, fluorescent signal would be detected in these blank features after hybridization. Figure 4 shows an analysis of the median feature intensities, after hybridization, of the last oligonucleotide printed by each of the 16 printing pins plus the next five blank features. The data indicate that the modified wash procedure (Figure 2B) eliminates any oligonucleotide carry-over. Thus, probe carry-over contamination can be monitored for each printing pin on each microarray slide.

Figure 4. Oligonucleotide carry-over analysis. The means of the Cy3 and Cy5 channel feature intensities of the last printed oligonucleotide (L) of each of 16 subarrays is shown. In addition, the feature intensities of spots printed from the following 5 wells (which contained printing buffer alone) are shown (1-5). The modified pin washing procedure (Fig. 2B) eliminated any signal in the blank feature locations. This is data from a 4000 rat gene microarray hybridized with a rat tissue sample.

Microarray slide substrates and background fluorescence.  In construction of oligonucleotide microarrays, the probes must bind to a substrate (thin reactive coating) applied to a microscope slide. In addition to high levels of oligonucleotide binding, the substrate should be stable, have low inherent fluorescence, be free of localized optical anomalies, be cost-competitive, and offer ease of downstream processing of printed microarrays. An assessment of several microarray substrates produced in-house and from different commercial sources was conducted. These included poly-L-lysine produced in-house and purchased from Erie Scientific Co. (Portsmouth, N.H.); epoxy from GeneMachines, MWG, and Full Moon BioSystems, Inc. (Sunnyvale, Calif.); and aminosilane from Clontech. All of the slides were clear and free of surface defects; however, there were slight differences in inherent background fluorescence [see Figure 5 following this paragraph]. While the signal from the Cy5 channel was quite low and uniform for all the slide types [see part A of Figure 5 following this paragraph], the signal from the Cy3 channel exhibited greater variability between slide types with the signal from the Full Moon BioSystems epoxy slides and the Clontech aminosilane slides significantly higher than those from the other slides. The background fluorescence from the Clontech aminosilane slides was approximately twice that of the poly-L-lysine coated slides [see part B of Figure 5 following this paragraph]. Such background fluorescence will negatively impact the signal-to-noise ratio and result in reduced ability to detect significant gene expression changes. Similar findings have been reported by others [29].

Figure 5. Background fluorescence from microarray substrates. Glass slides with the following substrate coatings were examined for background fluorescence in both the Cy5 (A) and Cy3 (B) channels. Microarray slides were: (1) in-house prepared poly-L-lysine coated slide; (2) poly-L-lysine coated slide from Erie Scientific; epoxy coated slide from MWG (3), GeneMachines (4), or Full Moon BioSystems (5); and aminosilane coated slide from Clontech (6). Slides were scanned using the Axon 4000B microarray scanner with laser power of 100% and photomultiplier gains set to 600. The data are means +/- SD of the median intensities of 20,160 spots (100 mm diameter) covering the printable area on each slide. Note different y-axis scales in (A) and (B).

Figure 6. Feature intensity as a function of oligonucleotide concentration - poly-L-lysine coated slides. Alexa 532-labeled oligonucleotide rTUBA1 was printed onto poly-L-lysine coated slides from Erie Scientific at concentrations of 40, 20, and 10 µM. Following post-processing (A) or mock hybridization (B), the microarrays were scanned. When normalized to the 40 µM concentration, the ratios of Cy3 channel signal intensities were 1, 0.71, and 0.56 after post-processing and 1, 0.53, and 0.34 after mock hybridization. The spotting buffer and water (*) were at background levels.

Inactivating reactive sites on the poly-L-lysine slides after printing of the oligonucleotides (post-processing) can be accomplished using bovine serum albumin (BSA) as a blocking agent in an open laboratory environment. By contrast, the succinic anhydride post-processing procedure recommended for aminosilane coated slides requires 1-methyl-2-pyrrolidone, which must be used in a fume hood. Based on the scan data, ease of post-processing, and subjective evaluation of other criteria listed above, the poly-L-lysine slides exhibited low background fluorescence and were cost-effective for microarray experiments. Other combinations of slide substrates and blocking reagents may provide adequately low levels of background fluorescence, but it is important to find such combinations and standardize them in order to reduce variability.

Feature intensity as a function of oligonucleotide concentration. For microarray experiments to yield accurate and meaningful data, the oligonucleotide printed onto the substrate must be in excess of the amount of each individual labeled cDNA applied during hybridization. Most microarrays are constructed using oligonucleotide solutions in the range of 20 µM to 50 µM. To assess the binding capacity of poly-L-lysine slides, various concentrations of a fluorescently labeled oligonucleotide (rTUBA1 as described above) were printed on the slides and the signal quantified. Micro Spotting Solution Plus (ArrayIt) spotting buffer and water were also printed on these arrays as negative controls. Microarrays were scanned after post-processing and after a mock hybridization, i.e., using hybridization buffer only with no labeled cDNA. Figure 6 [see Figure 6 two paragraphs above] shows that relatively good proportionality exists between the amount of oligonucleotide printed and the feature intensity signal detected after post processing or after a mock hybridization. Little signal is lost during the hybridization.

In a separate experiment, rTUBA1, at concentrations of 40, 20, and 10 µM, with and without Alexa 532 covalently bound, was printed on aminosilane coated slides [see Figure 7 following this paragraph]. Following post processing, these slides were stained with POPO-3 (Molecular Probes, Eugene, Ore.), a highly sensitive dimeric cyanine fluorescent nucleic acid stain, and scanned. These data also show a direct correlation between the amount of oligonucleotide printed onto the slide and the Cy3 intensity signal after post-processing. Thus, under these conditions, the binding of oligonucleotides to the substrate is proportional to the oligonucleotide concentration. Recently, it has been shown that there are significant DNA retention differences among slide types, and that the retention characteristics can decay with time [30]. Our method of evaluating the binding of oligonucleotides will be a useful tool for monitoring any changes in DNA binding and retention properties of slide substrates.

Figure 7. Feature intensity as a function of oligonucleotide concentration - aminosilane coated slides. Oligonucleotide rTUBA1, with and without covalently bound Alexa 532, was printed onto aminosilane-coated slides from Clontech at concentrations of 40, 20, and 10 µM. Following post-processing, the microarrays were stained with the DNA dye POPO3 and scanned. When normalized to the 40 µM concentration, the ratios of Cy3 channel signal intensities are 1, 0.53, and 0.29 for the Alexa 532-labeled rTUBA1 and 1, 0.49, and 0.24 for the rTUBA1 stained with POPO3. The Cy3 signal from the spotting buffer and water (*) were at background levels.

Quality control prior to hybridization. Because of the time, expense, and frequently limited amount of sample RNA available, quality control scans of all post-processed microarray slides are routinely conducted prior to using them for hybridizations. Analysis of the low-level autofluorescence of the oligonucleotides (discussed in detail below) can be used as a tool to monitor feature characteristics, as well as the general quality of the microarrays prior to hybridization. Slides that have dust particles, within or closely adjacent to oligonucleotide features, or that show evidence of possible substrate separation are discarded. This insures that RNA samples and hybridization reagents are not wasted due to a defective or substandard microarray. Figure 8A [see part A of Figure 8 following this paragraph] shows the mean numbers of pixels per microarray feature during a print run of 120 microarrays. As can be seen, there is an approximately 40% decrease in the number of pixels per feature area during a print run. Despite this reduction in the number of pixels per feature, the median feature pixel intensities remain constant [see part B of Figure 8 following this paragraph], indicating that while the absolute amount of oligonucleotide deposited on the slide decreases, the density of the oligonucleotide on the slide remains constant. Figure 8C [see part C of Figure 8 following this paragraph] shows a close correspondence between the number of pixels per feature before and after hybridization. These analyses indicate that, while feature size decreases during a print run, it does not affect the outcome of the data analysis based on median feature intensity.

Figure 8. Feature size characteristics across a print run. Clontech rat oligonucleotides (20 µM) were printed on Erie poly-L-lysine coated slides in MWG Spotting Buffer A. Scans after post-processing were done using the Axon Instruments GenePix 4000B scanner with the laser power set at 100% and the PMT gains set at 600 for both the Cy3 and Cy5 channels. The “Find Irregular Features” option of GenePix Pro 5 software was used to produce the most accurate definition of feature boundaries. (A) The mean number of pixels per feature is presented as a function of the number of microarrays printed with one load of the printing pin. (B) The means of the median feature intensity are presented as a function of the number of microarrays printed. (C) Mean numbers of feature pixels after post processing (■) are shown along with the corresponding mean numbers of feature pixels after hybridization (○) as a function of number of microarrays printed.

RNA Preparation

High quality RNA samples are key to obtaining reliable microarray data. An otherwise successful microarray experiment will be completely invalidated by beginning with samples of degraded RNA. It has been demonstrated that up to three quarters of differential gene expression can be due solely to differences in RNA integrity between samples [31]. Another study confirms the decrease in data quality obtained from degraded sample RNA, although it is suggested that “moderate” degradation may yield usable results [32]. Several widely used RNA isolation methods were investigated for the purification of intact RNA from the livers of mice, including TriReagent [33] (Molecular Research Center, Inc., Cincinnati, Ohio), FastRNA Pro Green Kit for animal tissue (Qbiogene, Inc., Carlsbad, Calif.), and Qiagen RNeasy Kit (Qiagen Inc., Valencia, Calif.). Pieces of fresh liver tissue rapidly removed from humanely euthanized mice were used in all procedures, and RNA was extracted from three tissue samples by each method. The tissue samples processed with Tri-Reagent and the RNeasy Kits were disrupted using the manufacturers recommended conditions with a motor-driven Teflon pestle in a tight-fitting centrifuge tube. The tissue samples processed with the FastRNA Kit were disrupted with the FastPrep cell disruptor (Qbiogene, Inc., Carlsbad, Calif.), which uses a high-speed reciprocating device to propel small beads into the tissue. After RNA isolation, the RNA quality was assessed by capillary electrophoresis using the Bioanalyzer 2100 (Agilent Technologies, Palo Alto, Calif.). As can be seen in Figure 9 [see Figure 9 following this paragraph], the nine samples were all of similar high quality with little degradation. The Bioanalyzer uses software to assess the quality of the RNA based on the electrophoretic tracings and calculates an RNA Integrity Number (RIN) that ranges from 10 for “perfect” intact RNA to 1 for completely degraded RNA. The average RIN was 9.0, with all except one sample being > 8.4. The RNA degradation software (Degradometer) [31] was also evaluated, and a good correlation was found between the RIN values and the Degradometer values (R=0.8). Thus, each method can produce high quality RNA. The Qiagen kit provided advantages of ease of use and consistency

Figure 9. Comparison of RNA isolation procedures. RNA was purified from 3 mouse livers (T1-T3) by 3 different methods (M1: FastPrep; M2: Qiagen RNAeasy kit; M3: TriReagent) as described in the text. The integrity of the RNA was evaluated using the Agilent Bioanalyzer. Electrophoretic tracings are shown along with the RNA integrity number (RIN).

RNA samples dissolved in sterile, RNase-free water are often conveniently stored at -80ş C until use. Extended storage of RNA under these conditions may result in degradation of the RNA. To investigate this, RNA samples were evaluated using the Agilent Bioanalyzer before and after more than two years under these conditions. Table 1 [see Table 1 following this paragraph] shows that the integrity of 4 of 11 samples was significantly reduced and, that overall there was a significant decline in the average RIN value of 0.26 (p = 0.01). While significant, this relatively modest reduction in RNA quality, over a period of two years, suggests that storage of RNA under these conditions for relatively short periods of time may not negatively impact microarray results.

Table 1.  Stability of RNA stored in water at -80ş C.

^a RNA was isolated using the TriReagent method from individual rat livers, and the RNA was dissolved in sterile water and stored in small aliquots at -80ş C.

^b RNA Integrity Number (RIN) was assigned by the Agilent Bioanalyzer software and is a measure of RNA quality as described in the text.

^c Date of evaluation of RNA.

^d Difference between the average of the 8/04 RIN values and the original 6/6/02 RIN values.

^e P-values tested a one-sided hypothesis that the original RNA has a higher RIN score than the RNA stored in water for more than 2 years.

Preparation of Labeled Targets

Aminoallyl concentration and dye incorporation. The indirect labeling of sample RNA involves the production of cDNA containing amino groups through the inclusion of aminoallyl-dUTP (aa-dUTP) in the reverse transcription reaction. In a subsequent reaction, the amine-reactive fluorescent molecules, cyanine-3 (Cy3) or cyanine-5 (Cy5) [34], are attached to the incorporated amino groups in the cDNA creating the labeled target molecules. Recent studies have described various characteristics of this labeling scheme and have suggested optimal labeling densities and reaction conditions [35-37]. Even though these guidelines exist, it is important to optimize the labeling reactions in an individual laboratory under site-specific conditions. Starting with total RNA from the samples, the effects of the ratio of aa-dUTP to dTTP, the amount of RNA in the reverse transcription reaction, and the amount of reactive fluorescent dyes were examined. Table 2 [see Table 2 following this paragraph] shows that highly fluorescent targets can be created from 10 µg of total RNA, with the highest specific activities (low nucleotide to dye ratios) obtained by using more aa-dUTP and large amounts of reactive dyes. When hybridized to microarrays, it was found that a ratio of aa-dUTP to dTTP of 2:3, and 6,250 pmoles of reactive dyes resulted in acceptable signal-to-noise ratios and was cost effective.

Table 2.  Dye incorporation into cDNA.

^a Sample A (rat liver RNA-1) labeled with Cy3 and sample B (rat liver RNA-2) labeled with Cy5.

Quality of purification columns. The synthesized aminoallyl cDNA was purified with the widely used QIAquick PCR purification columns (Qiagen, Valencia, Calif.) before labeling with the reactive dyes. These columns were then used to remove uncoupled fluorescent dye from the fluorescently labeled cDNA. Therefore, the quality of the columns is a critical factor as faulty columns might result in insufficient recovery or purity of the sample loaded on the column. An experiment was carried out where cDNA synthesized from six RNA samples was pooled together, split into six samples, and passed through three columns each from two different QIAquick PCR purification kits with different lot numbers. Use of one lot of columns resulted in losses of greater than 30% of the cDNA (data not shown).

In another experiment, samples with known amounts of cDNA were passed through columns to check the efficiency of recovery from the columns. Table 3 [see Table 3 following this paragraph] shows that one lot of columns resulted in poor and highly variable recovery of the cDNA (samples 1-5; recovery ranged from 15% to 73%), while the use of columns from a different lot resulted in good recovery of the cDNA (samples 6-9; 84% to 90%). Such poor recovery of cDNA would ultimately affect the quality of the microarray. Thus, these widely used purification columns can be the source of significant loss of cDNA that will result in reduced signal-to-noise ratios. These data also indicate the importance of monitoring cDNA yield at the end of the synthesis and labeling procedures.

Table 3.  Effect of columns on the recoveryof cDNA.

^a cDNA samples 1-5 were passed through a single lot of QIAquick PCR Purification columns while samples 6-9 were passed through a different lot of columns.

These results clearly indicated that the quality of the purification columns is very important for consistent and high-level recovery of cDNA, as well as fluorescently labeled cDNA. Therefore, before initiating any microarray experiment, it may be necessary to perform a pilot experiment with cDNA samples to determine the quality of the purification columns. Indeed, this illustrates the need to institute quality control criteria on all aspects of the microarray procedure.

Effect of dimethylsulfoxide on dye coupling. As described above, aminoallyl-cDNA was labeled with either Cy3 or Cy5 fluorescent molecules. Cyanine dyes provided in lyophilized form were suspended in dimethylsulfoxide (DMSO) prior to labeling reactions. Dimethylsulfoxide is hydroscopic in nature and, as cyanine dyes are rapidly hydrolyzed in water, it is important to use DMSO that has not been exposed to humid air. The effect of DMSO on dye coupling was examined by using DMSO from newly opened vials and DMSO that had been opened and used in the laboratory environments, and then stored in a desiccator for several months. Table 4 [see Table 4 following this paragraph] shows a profound reduction in dye incorporation, as evidenced by a poor nucleotide/dye ratio, as a result of using “old” DMSO compared to freshly opened “new” DMSO. From these results, it is evident that the quality of DMSO is a major factor in generating highly fluorescent targets.

Table 4.  Effect of DMSO on dye incorporation.