Profound influence of microarray scanner characteristics on gene expression ratios: analysis and procedure for correction

Background  

High throughput gene expression data from spotted cDNA microarrays are collected by scanning the signal intensities of the corresponding spots by dedicated fluorescence scanners. The major scanner settings for increasing the spot intensities are the laser power and the voltage of the photomultiplier tube (PMT). It is required that the expression ratios are independent of these settings. We have investigated the relationships between PMT voltage, spot intensities, and expression ratios for different scanners, in order to define an optimal scanning procedure.     

Correcting log ratios for signal saturation in cDNA microarrays

MOTIVATION: Pixel saturation occurs when the pixel intensity exceeds a threshold and the recorded pixel intensity is truncated. Microarray experiments are commonly afflicted with saturated pixels. As a result, estimators of gene expression are biased, with the amount of bias increasing as a function of the proportion of pixels saturated. Saturation is directly related to the photomultiplier tube (PMT) voltage settings and RNA abundance and is not necessarily associated with poor array or poor spot quality. When choosing PMT settings, higher PMT settings are desired because of improved signal-to-noise ratios of low-intensity spots. This improved signal is somewhat offset by saturation of high-intensity spots. In practice, spots with saturated pixels are discarded or the biased value is used. Neither of these approaches is appealing, particularly the former approach when a highly expressed gene is discarded because of saturation. RESULTS: We present a method to correct for saturation using pixel-level data. The method is based on a censored regression model. Evaluations on several arrays indicate that the method performs well. Simulation studies suggest that the method is robust under certain model violations.     

Quality assurance for polychromatic flow cytometry

This protocol outlines a three-part quality assurance program to optimize, calibrate and monitor flow cytometers used to measure cells labeled with five or more fluorochromes (a practice known as polychromatic flow cytometry). The initial steps of this program (system optimization) ensure that the instrument's lasers, mirrors and filters are optimally configured for the generation and transmission of multiple fluorescent signals. To determine the sensitivity and dynamic range of each fluorescence detector, the system is then calibrated by measuring fluorescence over a range of photomultiplier tube (PMT) voltages by determining the PMT voltage range and linearity (Steps 2-10) and validating the PMT voltage (Steps 11-17). Finally, to ensure consistent performance, we provide procedures to monitor the precision, accuracy and sensitivity of fluorescence measurements over time. All three aspects of this program should be performed upon installation, or whenever changes occur along the flow cytometer's optical path. However, only a few of these procedures need to be carried out on a routine basis.     

Functional studies on transfected cell microarray analysed by linear regression modelling

Transfected cell microarray is a promising method for accelerating the functional exploration of the genome, giving information about protein function in the living cell. The microarrays consist of clusters of cells (spots) overexpressing or silencing a particular gene product. The subsequent analysis of the phenotypic consequences of such perturbations can then be detected using cell-based assays. The focus in the present study was to establish an experimental design and a robust analysis approach for fluorescence intensity data, and to address the use of replicates for studying regulation of gene expression with varying complexity and effect size. Our analysis pipeline includes measurement of fluorescence intensities, normalization strategies using negative control spots and internal control plasmids, and linear regression (ANOVA) modelling for estimating biological effects and calculating P-values for comparisons of interests. Our results show the potential of transfected cell microarrays in studying complex regulation of gene expression by enabling measurement of biological responses in cells with overexpression and downregulation of specific gene products, combined with the possibility of assaying the effects of external stimuli. Simulation experiments show that transfected cell microarrays can be used to reliably detect even quantitatively minor biological effects by including several technical and experimental replicates.     

Fluorescence ratio measurements of double-labeled probes for multiple in situ hybridization by digital imaging microscopy.

To expand the multiplicity of the in situ hybridization (ISH) procedure, which is presently limited by the number of fluorochromes spectrally separable in the microscope, a digital fluorescence ratio method is proposed. For this purpose, chromosome-specific repetitive probes were double-labeled with two haptens and hybridized to interphase nuclei of human peripheral blood lymphocytes. The haptens were immunocytochemically detected with specific antibodies conjugated with the fluorochromes FITC or TRITC. The FITC and TRITC fluorescence intensities of spots obtained with different double-haptenized probes were measured, and the fluorescence ratio was calculated for each ISH spot. Combinations of different haptens, such as biotin, digoxigenin, fluorescein, sulfonate, acetyl amino fluorene (AAF), and mercury (Hg) were used. The fluorescence intensity ratio (FITC/TRITC) of the ISH spots was fairly constant for all combinations used, with coefficients of variation between 10 and 30%. To study the feasibility of a probe identification procedure on the basis of probe hapten ratios, one probe was double-labeled with different ratios, by varying the relative concentrations of the modified nucleotides (biotin-11-dUTP and digoxigenin-11-dUTP) in the nick-translation reaction. Measurement of the FITC and TRITC intensities of the ISH spots showed that the concentration of modified nucleotides used in the labeling procedures was reflected in the mean fluorescence intensity of the ISH spots. Furthermore, the ratio distributions showed little overlap due to the relatively small coefficients of variation. The results indicate that a multiple ISH procedure based on fluorescence ratio imaging of double-labeled probes is feasible.     

An a posteriori strategy for enhancing gene discovery in anonymous cDNA microarray experiments

MOTIVATION: Because of the high cost of sequencing, the bulk of gene discovery is performed using anonymous cDNA microarrays. Though the clones on such arrays are easier and cheaper to construct and utilize than unigene and oligonucleotide arrays, they are there in proportion to their corresponding gene expression activity in the tissue being examined. The associated redundancy will be there in any pool of possibly interesting differentially expressed clones identified in a microarray experiment for subsequent sequencing and investigation. An a posteriori sampling strategy is proposed to enhance gene discovery by reducing the impact of the redundancy in the identified pool. RESULTS: The proposed strategy exploits the fact that individual genes that are highly expressed in a tissue are more likely to be present as a number of spots in an anonymous library and, as a direct consequence, are also likely to give higher fluorescence intensity responses when present in a probe in a cDNA microarray experiment. Consequently, spots that respond with low intensities will have a lower redundancy and so should be sequenced in preference to those with the highest intensities. The proposed method, which formalizes how the fluorescence intensity of a spot should be assessed, is validated using actual microarray data, where the sequences of all the clones in the identified pool had been previously determined. For such validations, the concept of a repeat plot is introduced. It is also utilized to visualize and examine different measures for the characterization of fluorescence intensity. In addition, as confirmatory evidence, sequencing from the lowest to the highest intensities in a pool, with all the sequences known, is compared graphically with their random sequencing. The results establish that, in general, the opportunity for gene discovery is enhanced by avoiding the pooling of different biological libraries (because their construction will have involved different hybridization episodes) and concentrating on the clones with lower fluorescence intensities.     

Microarray gene expression analysis free of reverse transcription and dye labeling

A new microarray system has been developed for gene expression analysis using cationic gold nanoparticles with diameters of 250 nm as a target detection reagent. The approach utilizes nonlabeled target molecules hybridizing with complementary probes on the array, followed by incubation in a colloidal gold solution. The hybridization signal results from the precipitation of nanogold particles on the hybridized spots due to the electrostatic attraction of the cationic gold particles and the anionic phosphate groups in the target DNA backbone. In contrast to conventional fluorescent detection, this nanoparticle-based detection system eliminates the target labeling procedure. The visualization of hybridization signals can be accomplished with a flatbed scanner instead of a confocal laser scanner, which greatly simplifies the process and reduces the cost. The sensitivity is estimated to be less than 2 pg of DNA molecules captured on the array surface. The signal from hybridized spots quantitatively represents the amount of captured target DNA and therefore permits quantitative gene expression analysis. Cross-array reproducibility is adequate for detecting twofold or less signal changes across two microarray experiments.     

Statistical estimation of gene expression using multiple laser scans of microarrays

We propose a statistical model for estimating gene expression using data from multiple laser scans at different settings of hybridized microarrays. A functional regression model is used, based on a non-linear relationship with both additive and multiplicative error terms. The function is derived as the expected value of a pixel, given that values are censored at 65 535, the maximum detectable intensity for double precision scanning software. Maximum likelihood estimation based on a Cauchy distribution is used to fit the model, which is able to estimate gene expressions taking account of outliers and the systematic bias caused by signal censoring of highly expressed genes. We have applied the method to experimental data. Simulation studies suggest that the model can estimate the true gene expression with negligible bias. AVAILABILITY: FORTRAN 90 code for implementing the method can be obtained from the authors.     

Microarray scanner calibration curves: characteristics and implications

Background

Microarray-based measurement of mRNA abundance assumes a linear relationship between the fluorescence intensity and the dye concentration. In reality, however, the calibration curve can be nonlinear.

Results

By scanning a microarray scanner calibration slide containing known concentrations of fluorescent dyes under 18 PMT gains, we were able to evaluate the differences in calibration characteristics of Cy5 and Cy3. First, the calibration curve for the same dye under the same PMT gain is nonlinear at both the high and low intensity ends. Second, the degree of nonlinearity of the calibration curve depends on the PMT gain. Third, the two PMTs (for Cy5 and Cy3) behave differently even under the same gain. Fourth, the background intensity for the Cy3 channel is higher than that for the Cy5 channel. The impact of such characteristics on the accuracy and reproducibility of measured mRNA abundance and the calculated ratios was demonstrated. Combined with simulation results, we provided explanations to the existence of ratio underestimation, intensity-dependence of ratio bias, and anti-correlation of ratios in dye-swap replicates. We further demonstrated that although Lowess normalization effectively eliminates the intensity-dependence of ratio bias, the systematic deviation from true ratios largely remained. A method of calculating ratios based on concentrations estimated from the calibration curves was proposed for correcting ratio bias.

Conclusion

It is preferable to scan microarray slides at fixed, optimal gain settings under which the linearity between concentration and intensity is maximized. Although normalization methods improve reproducibility of microarray measurements, they appear less effective in improving accuracy.

     

Unsupervised technique for robust target separation and analysis of DNA microarray spots through adaptive pixel clustering

MOTIVATION: Microarray images challenge existing analytical methods in many ways given that gene spots are often comprised of characteristic imperfections. Irregular contours, donut shapes, artifacts, and low or heterogeneous expression impair corresponding values for red and green intensities as well as their ratio R/G. New approaches are needed to ensure accurate data extraction from these images. RESULTS: Herein we introduce a novel method for intensity assessment of gene spots. The technique is based on clustering pixels of a target area into foreground and background. For this purpose we implemented two clustering algorithms derived from k-means and Partitioning Around Medoids (PAM), respectively. Results from the analysis of real gene spots indicate that our approach performs superior to other existing analytical methods. This is particularly true for spots generally considered as problematic due to imperfections or almost absent expression. Both PX(PAM) and PX(KMEANS) prove to be highly robust against various types of artifacts through adaptive partitioning, which more correctly assesses expression intensity values. AVAILABILITY: The implementation of this method is a combination of two complementary tools Extractiff (Java) and Pixclust (free statistical language R), which are available upon request from the authors.     

Quantitative oligonucleotide microarray data analysis with an artificial standard probe strategy

Quantitative data analysis is an important element in several applications of DNA microarray, including mRNA expression profiling and estimation of infectious doses for pathogens. Here, we introduce an artificial standard probe strategy for quantitative pathogen detection using an oligonucleotide chip as a model system. The standard capture probe sequence was artificially designed to prevent non-specific hybridization with bacterial targets. Based on the fluorescence intensities of artificial standard spots, the raw fluorescence intensity data for specific spots could be corrected to generate linear correlations with target concentrations. Therefore, our novel artificial standard probe may be effectively applied for the correction of chip-to-chip variations and quantitative data analysis of a one-color labeled DNA microarray system.     

Tuning FlaSh: redesign of the dynamics,voltage range,and color of the genetically encoded optical sensor of membrane potential

The optical voltage sensor FlaSh, made from a fusion of a GFP "reporter domain" and a voltage-gated Shaker K(+) channel "detector domain," has been mutagenically tuned in both the GFP reporter and channel detector domains. This has produced sensors with improved folding at 37 degrees C, enabling use in mammalian preparations, and yielded variants with distinct spectra, kinetics, and voltage dependence, thus expanding the types of electrical signals that can be detected. The optical readout of FlaSh has also been expanded from single wavelength fluorescence intensity changes to dual wavelength measurements based on both voltage-dependent spectral shifts and changes in FRET. Different versions of FlaSh can now be chosen to optimize the detection of either action potentials or synaptic potentials, to follow high versus low rates of activity, and to best reflect electrical activity in cell types with distinct voltages of operation.     

A new approach to intensity-dependent normalization of two-channel microarrays

A two-channel microarray measures the relative expression levels of thousands of genes from a pair of biological samples. In order to reliably compare gene expression levels between and within arrays, it is necessary to remove systematic errors that distort the biological signal of interest. The standard for accomplishing this is smoothing "MA-plots" to remove intensity-dependent dye bias and array-specific effects. However, MA methods require strong assumptions, which limit their general applicability. We review these assumptions and derive several practical scenarios in which they fail. The "dye-swap" normalization method has been much less frequently used because it requires two arrays per pair of samples. We show that a dye-swap is accurate under general assumptions, even under intensity-dependent dye bias, and that a dye-swap removes dye bias from a single pair of samples in general. Based on a flexible model of the relationship between mRNA amount and single-channel fluorescence intensity, we demonstrate the general applicability of a dye-swap approach. We then propose a common array dye-swap (CADS) method for the normalization of two-channel microarrays. We show that CADS removes both dye bias and array-specific effects, and preserves the true differential expression signal for every gene under the assumptions of the model.     

The statistics of identifying differentially expressed genes in Expresso and TM4: a comparison

Background  

Analysis of DNA microarray data takes as input spot intensity measurements from scanner software and returns differential expression of genes between two conditions, together with a statistical significance assessment. This process typically consists of two steps: data normalization and identification of differentially expressed genes through statistical analysis. The Expresso microarray experiment management system implements these steps with a two-stage, log-linear ANOVA mixed model technique, tailored to individual experimental designs. The complement of tools in TM4, on the other hand, is based on a number of preset design choices that limit its flexibility. In the TM4 microarray analysis suite, normalization, filter, and analysis methods form an analysis pipeline. TM4 computes integrated intensity values (IIV) from the average intensities and spot pixel counts returned by the scanner software as input to its normalization steps. By contrast, Expresso can use either IIV data or median intensity values (MIV). Here, we compare Expresso and TM4 analysis of two experiments and assess the results against qRT-PCR data.     

Profiling Expression Patterns and Isolating Differentially Expressed Genes by cDNA Microarray System with Colorimetry Detection

A high-density cDNA microarray with colorimetry detection system to simultaneously monitor the expression of many genes on nylon membrane is described and characterized. To quantify the expression of genes and to isolate differentially expressed genes, the southern hybridization process on filter membranes was employed. The levels of gene expression were represented by color intensities generated by colorimetric reactions in place of hazardous radioisotopes or costly laser-induced fluorescence detection. The gene expression patterns on nylon membranes were digitized by devices such as an economical flatbed scanner or a digital camera. The quantitative information of gene expression was retrieved by image analysis software. Quantitative comparison of the northern dot-blotting method with the microarray system is described. Applications employing single-color detection as well as dual-color detection to isolate differentially expressed genes among thousands of genes are demonstrated.     

The detection of a transgenic soybean biochip using gold label silver stain technology

A method for the rapid detection of transgenic soybean crops based on a combination of gene chip and "gold label silver stain" (GLSS) technologies has been established. To ensure the specificity of this method, the CaMV35S promoter and Nos terminator were selected as probes because they are both exogenous genes that are specific to transgenic soybean plants. The addition of biotin-modified dUTPs to a polymerase chain reaction (PCR) system can produce amplified nucleic acid segments containing biotin. These labeled PCR products then hybridize with specific probes on the chip and are subsequently bound by streptavidin-modified gold nanoparticles (GNPs). Due to the catalytic nature of the GNPs, silver staining can be used to visualize the hybridized probes, which appear as signals in varying shades of gray. The intensity value of the gray signals can be obtained using a general scanner. Silver staining for 10 min was determined to produce the optimal signal-to-noise ratio. In addition, this method was shown to be highly specific and had a detection sensitivity of 288.57 pg/μL.     

Bayesian Hierarchical Model for Estimating Gene Expression Intensity Using Multiple Scanned Microarrays

We propose a method for improving the quality of signal from DNA microarrays by using several scans at varying scanner sen-sitivities. A Bayesian latent intensity model is introduced for the analysis of such data. The method improves the accuracy at which expressions can be measured in all ranges and extends the dynamic range of measured gene expression at the high end. Our method is generic and can be applied to data from any organism, for imaging with any scanner that allows varying the laser power, and for extraction with any image analysis software. Results from a self-self hybridization data set illustrate an improved precision in the estimation of the expression of genes compared to what can be achieved by applying standard methods and using only a single scan.     

Noise sampling method: an ANOVA approach allowing robust selection of differentially regulated genes measured by DNA microarrays

MOTIVATION: A crucial step in microarray data analysis is the selection of subsets of interesting genes from the initial set of genes. In many cases, especially when comparing a specific condition to a reference, the genes of interest are those which are differentially expressed. Two common methods for gene selection are: (a) selection by fold difference (at least n fold variation) and (b) selection by altered ratio (at least n standard deviations away from the mean ratio). RESULTS: The novel method proposed here is based on ANOVA and uses replicate spots to estimate an empirical distribution of the noise. The measured intensity range is divided in a number of intervals. A noise distribution is constructed for each such interval. Bootstrapping is used to map the desired confidence levels from the noise distribution corresponding to a given interval to the measured log ratios in that interval. If the method is applied on individual arrays having replicate spots, the method can calculate an overall width of the noise distribution which can be used as an indicator of the array quality. We compared this method with the fold change and unusual ratio method. We also discuss the relationship with an ANOVA model proposed by Churchill et al. In silico experiments were performed while controlling the degree of regulation as well as the amount of noise. Such experiments show the performance of the classical methods can be very unsatisfactory. We also compared the results of the 2-fold method with the results of the noise sampling method using pre and post immortalization cell lines derived from the MDAH041 fibroblasts hybridized on Affymetrix GeneChip arrays. The 2-fold method reported 198 genes as upregulated and 493 genes as downregulated. The noise sampling method reported 98 gene upregulated and 240 genes downregulated at the 99.99% confidence level. The methods agreed on 221 genes downregulated and 66 genes upregulated. Fourteen genes from the subset of genes reported by both methods were all confirmed by Q-RT-PCR. Alternative assays on various subsets of genes on which the two methods disagreed suggested that the noise sampling method is likely to provide fewer false positives.