Determining a significant change in protein expression with DeCyder during a pair-wise comparison using two-dimensional difference gel electrophoresis

Two-dimensional difference gel electrophoresis (DIGE) is a tool for measuring changes in protein expression between samples involving pre-electrophoretic labeling with cyanine dyes. Here we assess a common method to analyze DIGE data using the DeCyder software system. Experimental error was studied by a series of same sample comparisons. Aliquots of sample were labeled with N-hydroxyl succinimidyl ester-derivatives of Cy2, Cy3, and Cy5 dyes and run together on one gel. This allowed assessment of how experimental error influenced differential expression analysis. Bias in the log volume ratios was observed, which could be explained by differences in dye background. Further complications are caused by significant gel-to-gel variation in the spot volume ratio distributions. Using DeCyder alone results in an inability to define ratio thresholds for 90 or 95% confidence. An alternative normalization method was thus applied which resulted in improved data distribution and allowed greater sensitivity in analysis. When combined with a standardizing function, this allowed gel-independent thresholds for 90% confidence. The new approach, detailed here, represents a method to greatly improve the success of DIGE data analysis.     

Evaluation of two-dimensional differential gel electrophoresis for proteomic expression analysis of a model breast cancer cell system

The technique of fluorescent two-dimensional (2D) difference gel electrophoresis for differential protein expression analysis has been evaluated using a model breast cancer cell system of ErbB-2 overexpression. Labeling of paired cell lysate samples with N-hydroxy succinimidyl ester-derivatives of fluorescent Cy3 and Cy5 dyes for separation on the same 2D gel enabled quantitative, sensitive, and reproducible differential expression analysis of the cell lines. SyproRuby staining was shown to be a highly sensitive and 2D difference gel electrophoresis-compatible method for post-electrophoretic visualization of proteins, which could then be picked and identified by matrix-assisted laser-desorption ionization mass spectroscopy. Indeed, from these experiments, we have identified multiple proteins that are likely to be involved in ErbB-2-mediated transformation. A triple dye labeling methodology was used to identify proteins differentially expressed in the cell system over a time course of growth factor stimulation. A Cy2-labeled pool of samples was used as a standard with all Cy3- and Cy5-labeled sample pairs to facilitate cross-gel quantitative analysis. DeCyder (Amersham Biosciences, Inc.) software was used to distinguish clear statistical differences in protein expression over time and between the cell lines.     

Statistical aspects of quantitative real-time PCR experiment design

Experiments using quantitative real-time PCR to test hypotheses are limited by technical and biological variability; we seek to minimise sources of confounding variability through optimum use of biological and technical replicates. The quality of an experiment design is commonly assessed by calculating its prospective power. Such calculations rely on knowledge of the expected variances of the measurements of each group of samples and the magnitude of the treatment effect; the estimation of which is often uninformed and unreliable. Here we introduce a method that exploits a small pilot study to estimate the biological and technical variances in order to improve the design of a subsequent large experiment. We measure the variance contributions at several ‘levels’ of the experiment design and provide a means of using this information to predict both the total variance and the prospective power of the assay. A validation of the method is provided through a variance analysis of representative genes in several bovine tissue-types. We also discuss the effect of normalisation to a reference gene in terms of the measured variance components of the gene of interest. Finally, we describe a software implementation of these methods, powerNest, that gives the user the opportunity to input data from a pilot study and interactively modify the design of the assay. The software automatically calculates expected variances, statistical power, and optimal design of the larger experiment. powerNest enables the researcher to minimise the total confounding variance and maximise prospective power for a specified maximum cost for the large study.     

Investigating sample pooling strategies for DIGE experiments to address biological variability

If biological questions are to be answered using quantitative proteomics, it is essential to design experiments which have sufficient power to be able to detect changes in expression. Sample subpooling is a strategy that can be used to reduce the variance but still allow studies to encompass biological variation. Underlying sample pooling strategies is the biological averaging assumption that the measurements taken on the pool are equal to the average of the measurements taken on the individuals. This study finds no evidence of a systematic bias triggered by sample pooling for DIGE and that pooling can be useful in reducing biological variation. For the first time in quantitative proteomics, the two sources of variance were decoupled and it was found that technical variance predominates for mouse brain, while biological variance predominates for human brain. A power analysis found that as the number of individuals pooled increased, then the number of replicates needed declined but the number of biological samples increased. Repeat measures of biological samples decreased the numbers of samples required but increased the number of gels needed. An example cost benefit analysis demonstrates how researchers can optimise their experiments while taking into account the available resources.     

Theoretical and experimental comparisons of gene expression indexes for oligonucleotide arrays

MOTIVATION: Oligonucleotide expression arrays exhibit systematic and reproducible variation produced by the multiple distinct probes used to represent a gene. Recently, a gene expression index has been proposed that explicitly models probe effects, and provides improved fits of hybridization intensity for arrays containing perfect match (PM) and mismatch (MM) probe pairs. RESULTS: Here we use a combination of analytical arguments and empirical data to show directly that the estimates provided by model-based expression indexes are superior to those provided by commercial software. The improvement is greatest for genes in which probe effects vary substantially, and modeling the PM and MM intensities separately is superior to using the PM-MM differences. To empirically compare expression indexes, we designed a mixing experiment involving three groups of human fibroblast cells (serum starved, serum stimulated, and a 50:50 mixture of starved/stimulated), with six replicate HuGeneFL arrays in each group. Careful spiking of control genes provides evidence that 88-98% of the genes on the array are detectably transcribed, and that the model-based estimates can accurately detect the presence versus absence of a gene. The use of extensive replication from single RNA sources enables exploration of the technical variability of the array.     

MarVis: a tool for clustering and visualization of metabolic biomarkers

Background

Gene set analysis based on Gene Ontology (GO) can be a promising method for the analysis of differential expression patterns. However, current studies that focus on individual GO terms have limited analytical power, because the complex structure of GO introduces strong dependencies among the terms, and some genes that are annotated to a GO term cannot be found by statistically significant enrichment.

Results

We proposed a method for enriching clustered GO terms based on semantic similarity, namely cluster enrichment analysis based on GO (CeaGO), to extend the individual term analysis method. Using an Affymetrix HGU95aV2 chip dataset with simulated gene sets, we illustrated that CeaGO was sensitive enough to detect moderate expression changes. When compared to parent-based individual term analysis methods, the results showed that CeaGO may provide more accurate differentiation of gene expression results. When used with two acute leukemia (ALL and ALL/AML) microarray expression datasets, CeaGO correctly identified specifically enriched GO groups that were overlooked by other individual test methods.

Conclusion

By applying CeaGO to both simulated and real microarray data, we showed that this approach could enhance the interpretation of microarray experiments. CeaGO is currently available at http://chgc.sh.cn/en/software/CeaGO/.     

Possible sources of dye-related signal correlation bias in two-color DNA microarray assays

DNA microarray analyses commonly use two spectrally distinct fluorescent labels to simultaneously compare different mRNA pools. Signal correlation bias currently limits accepted resolution to twofold changes in gene expression. This bias was investigated by (i) examining fluorescence and absorption spectra and changes in relative fluorescence of DNAs labeled with the Cy3, Cy5, Alexa Fluor 555, and Alexa Fluor 647 dyes and by (ii) using homotypic hybridization assays to compare the Cy dye pair with the Alexa Fluor dye pair. Cy3 or Cy5 dye-labeled DNA exhibited reduced fluorescence and absorption anomalies that were eliminated by nuclease treatment, consistent with fluorescence quenching that arises from dye-dye or dye-DNA-dye interactions. Alexa Fluor 555 and Alexa Fluor 647 dye-labeled DNA exhibited little or no such anomalies. In microarray hybridization, the Alexa Fluor dye pair provided higher signal correlation coefficients (R2) than did the Cy dye pair; at the 95% prediction level, a 1.3-fold change in gene expression was significant using the Alexa Fluor dye pair. Lowered signal correlation of the Cy dye pair was associated with high variance in Cy5 dye signals. These results indicate that fluorescence quenching may be a source of signal bias associated with the Cy dye pair.     

Global gene expression profiling in early-stage polycystic kidney disease in the Han:SPRD Cy rat identifies a role for RXR signaling

Han:SPRD Cy is a spontaneous rat model of polycystic kidney disease (PKD) caused by a missense mutation in Pkdr1. Cystogenesis in this model is not clearly understood. In the current study, we performed global gene expression profiling in early-stage PKD cyst development in Cy/Cy kidneys and normal (+/+) kidneys at 3 and 7 days of postnatal age. Expression profiles were determined by microarray analysis, followed by validation with real-time RT-PCR. Genes were selected with over 1.5-fold expression changes compared with age-matched +/+ kidneys for canonical pathway analysis. We found nine pathways in common between 3- and 7-day Cy/Cy kidneys. Three significantly changed pathways were designated "Vitamin D Receptor (VDR)/Retinoid X Receptor (RXR) Activation," "LPS/IL-1-Mediated Inhibition of RXR Function," and "Liver X Receptor (LXR)/RXR Activation." These results suggest that RXR-mediated signaling is significantly altered in developing kidneys with mutated Pkdr1. In gene ontology analysis, the functions of these RXR-related genes were found to be involved in regulating cell proliferation and organ morphogenesis. With real-time RT-PCR analysis, the upregulation of Ptx2, Alox15b, OSP, and PCNA, major markers of cell proliferation associated with the RXR pathway, were confirmed in 3- and 7-day Cy/Cy kidneys compared with 3-day +/+ kidneys. The increased RXR protein was observed in both the nucleus and cytoplasm of cystic epithelial cells in early-stage Cy/Cy kidneys, and the RXR-positive cells were strongly positive for PCNA staining. Taken together, cell proliferation and organ morphogenesis signals transduced by RXR-mediated pathways may have important roles for cystogenesis in early-stage PKD in this Pkdr1-mutated Cy rat.     

Using human microarrays to identify differentially expressed genes associated with increased steroidogenesis in boars

Human microarrays are readily available, and it would be advantageous if they could be used to study gene expression in other species, such as pigs. The objectives of this research were to validate the use of human microarrays in the analysis of porcine gene expression, to assess the variability of the data generated, and to compare gene expression in boars with different levels of steroidogenesis. Cytochrome b5 (CYB5) expression was used to assess array detection sensitivity. Samples having high or low CYB5 RNA levels were hybridized to microarrays to determine if the known expression difference could be detected. Six hybridizations were conducted using human microarrays containing 3840 total spots representing 1718 characterized human ESTs. To analyze gene expression in boars with different levels of steroidogenesis, testis RNA from four boars with high levels of plasma estrone sulphate was hybridized to testis RNA from four boars with lower levels. Eight microarray hybridizations were conducted including fluor-flips. Self-self hybridizations were also conducted to assess the variability of array experiments. The Cy5 and Cy3 intensity values for each array were normalized using a locally weighted linear regression (LOESS). Statistical significance was assessed using a Student's t-test followed by the Benjamini and Hochberg multiple testing correction procedure. Quantitative real-time PCR (Q-RT-PCR) was used to verify select gene expression differences. The results show that CYB5 was significantly overexpressed in the high CYB5 sample by 1.8 fold (P < 0.05), verifying the known expression difference. The average log2 ratio of the majority of genes (1643) falls within one standard deviation of the mean, indicating the data were reproducible. In the high versus low steroidogenesis experiment, seven genes were significantly overexpressed in the high group (P < 0.05). Quantitative real-time PCR was used to validate five genes with the highest fold change, and the results corroborated those found by the microarray experiments. The results of the self-self hybridizations showed that no genes were significantly differentially expressed following the application of the Benjamini and Hochberg multiple testing correction procedure. The results presented in this report show that human arrays can be used for gene expression analysis in pigs.     

Using Human Microarrays to Identify Differentially Expressed Genes Associated with Increased Steroidogenesis in Boars

Human microarrays are readily available, and it would be advantageous if they could be used to study gene expression in other species, such as pigs. The objectives of this research were to validate the use of human microarrays in the analysis of porcine gene expression, to assess the variability of the data generated, and to compare gene expression in boars with different levels of steroidogenesis. Cytochrome b5 (CYB5) expression was used to assess array detection sensitivity. Samples having high or low CYB5 RNA levels were hybridized to microarrays to determine if the known expression difference could be detected. Six hybridizations were conducted using human microarrays containing 3840 total spots representing 1718 characterized human ESTs. To analyze gene expression in boars with different levels of steroidogenesis, testis RNA from four boars with high levels of plasma estrone sulphate was hybridized to testis RNA from four boars with lower levels. Eight microarray hybridizations were conducted including fluor-flips. Self-self hybridizations were also conducted to assess the variability of array experiments. The Cy5 and Cy3 intensity values for each array were normalized using a locally weighted linear regression (LOESS). Statistical significance was assessed using a Student's t-test followed by the Benjamini and Hochberg multiple testing correction procedure. Quantitative real-time PCR (Q-RT-PCR) was used to verify select gene expression differences. The results show that CYB5 was significantly overexpressed in the high CYB5 sample by 1.8 fold (P < 0.05), verifying the known expression difference. The average log2 ratio of the majority of genes (1643) falls within one standard deviation of the mean, indicating the data were reproducible. In the high versus low steroidogenesis experiment, seven genes were significantly overexpressed in the high group (P < 0.05). Quantitative real-time PCR was used to validate five genes with the highest fold change, and the results corroborated those found by the microarray experiments. The results of the self-self hybridizations showed that no genes were significantly differentially expressed following the application of the Benjamini and Hochberg multiple testing correction procedure. The results presented in this report show that human arrays can be used for gene expression analysis in pigs.     

Bivariate microarray analysis: statistical interpretation of two-channel functional genomics data

Conventional statistical methods for interpreting microarray data require large numbers of replicates in order to provide sufficient levels of sensitivity. We recently described a method for identifying differentially-expressed genes in one-channel microarray data 1. Based on the idea that the variance structure of microarray data can itself be a reliable measure of noise, this method allows statistically sound interpretation of as few as two replicates per treatment condition. Unlike the one-channel array, the two-channel platform simultaneously compares gene expression in two RNA samples. This leads to covariation of the measured signals. Hence, by accounting for covariation in the variance model, we can significantly increase the power of the statistical test. We believe that this approach has the potential to overcome limitations of existing methods. We present here a novel approach for the analysis of microarray data that involves modeling the variance structure of paired expression data in the context of a Bayesian framework. We also describe a novel statistical test that can be used to identify differentially-expressed genes. This method, bivariate microarray analysis (BMA), demonstrates dramatically improved sensitivity over existing approaches. We show that with only two array replicates, it is possible to detect gene expression changes that are at best detected with six array replicates by other methods. Further, we show that combining results from BMA with Gene Ontology annotation yields biologically significant results in a ligand-treated macrophage cell system.     

A Sensitive,Reproducible and Objective Immunofluorescence Analysis Method of Dystrophin in Individual Fibers in Samples from Patients with Duchenne Muscular Dystrophy

Duchenne muscular dystrophy (DMD) is characterized by the absence or reduced levels of dystrophin expression on the inner surface of the sarcolemmal membrane of muscle fibers. Clinical development of therapeutic approaches aiming to increase dystrophin levels requires sensitive and reproducible measurement of differences in dystrophin expression in muscle biopsies of treated patients with DMD. This, however, poses a technical challenge due to intra- and inter-donor variance in the occurrence of revertant fibers and low trace dystrophin expression throughout the biopsies. We have developed an immunofluorescence and semi-automated image analysis method that measures the sarcolemmal dystrophin intensity per individual fiber for the entire fiber population in a muscle biopsy. Cross-sections of muscle co-stained for dystrophin and spectrin have been imaged by confocal microscopy, and image analysis was performed using Definiens software. Dystrophin intensity has been measured in the sarcolemmal mask of spectrin for each individual muscle fiber and multiple membrane intensity parameters (mean, maximum, quantiles per fiber) were calculated. A histogram can depict the distribution of dystrophin intensities for the fiber population in the biopsy. This method was tested by measuring dystrophin in DMD, Becker muscular dystrophy, and healthy muscle samples. Analysis of duplicate or quadruplicate sections of DMD biopsies on the same or multiple days, by different operators, or using different antibodies, was shown to be objective and reproducible (inter-assay precision, CV 2–17% and intra-assay precision, CV 2–10%). Moreover, the method was sufficiently sensitive to detect consistently small differences in dystrophin between two biopsies from a patient with DMD before and after treatment with an investigational compound.     

Improved analysis of brachial artery ultrasound using a novel edge-detection software system.

Brachial artery ultrasound is commonly employed for noninvasive assessment of endothelial function. However, analysis is observer dependent and susceptible to errors. We describe studies on a computerized edge-detection and wall-tracking software program to allow more accurate and reproducible measurement. In study 1, three purpose-built Perspex phantom arteries, 3.00, 4.00, and 6.00 mm in diameter, were measured with the software. There was a mean bias of 11 microm (P < 0.001 at each level) between known and measured values; the mean resolving power of the software was estimated as 8.3 microm. In study 2, the mean intraobserver coefficient of variation of repeated measures of flow-mediated dilation (FMD) using the software (6.7%) was significantly lower than that for traditional manual measurements using the intima-lumen interfaces (24.8%, P < 0.05) and intima-media interfaces (32.5%, P < 0.05). In study 3, 24 healthy volunteers underwent repeat testing twice within 1 wk; the coefficients of variation for between-visit reproducibility of FMD and response to glyceryl trinitrate using the software were 14.7 and 17.6%, respectively. Assuming 80% power and an alpha of 0.05, eight subjects with matched controls would be required, in a parallel designed study, to detect an absolute 2.5% change in FMD. In summary, we have developed a semiautomated computerized vascular ultrasound analysis system that will improve the power of clinical intervention studies to detect small changes in arterial diameter.     

Expression analysis of RNA sequencing data from human neural and glial cell lines depends on technical replication and normalization methods

Background

The potential for astrocyte participation in central nervous system recovery is highlighted by in vitro experiments demonstrating their capacity to transdifferentiate into neurons. Understanding astrocyte plasticity could be advanced by comparing astrocytes with stem cells. RNA sequencing (RNA-seq) is ideal for comparing differences across cell types. However, this novel multi-stage process has the potential to introduce unwanted technical variation at several points in the experimental workflow. Quantitative understanding of the contribution of experimental parameters to technical variation would facilitate the design of robust RNA-Seq experiments.

Results

RNA-Seq was used to achieve biological and technical objectives. The biological aspect compared gene expression between normal human fetal-derived astrocytes and human neural stem cells cultured in identical conditions. When differential expression threshold criteria of |log₂ fold change| > 2 were applied to the data, no significant differences were observed. The technical component quantified variation arising from particular steps in the research pathway, and compared the ability of different normalization methods to reduce unwanted variance. To facilitate this objective, a liberal false discovery rate of 10% and a |log₂ fold change| > 0.5 were implemented for the differential expression threshold. Data were normalized with RPKM, TMM, and UQS methods using JMP Genomics. The contributions of key replicable experimental parameters (cell lot; library preparation; flow cell) to variance in the data were evaluated using principal variance component analysis. Our analysis showed that, although the variance for every parameter is strongly influenced by the normalization method, the largest contributor to technical variance was library preparation. The ability to detect differentially expressed genes was also affected by normalization; differences were only detected in non-normalized and TMM-normalized data.

Conclusions

The similarity in gene expression between astrocytes and neural stem cells supports the potential for astrocytic transdifferentiation into neurons, and emphasizes the need to evaluate the therapeutic potential of astrocytes for central nervous system damage. The choice of normalization method influences the contributions to experimental variance as well as the outcomes of differential expression analysis. However irrespective of normalization method, our findings illustrate that library preparation contributed the largest component of technical variance.

     

Find Pairs: The Module for Protein Quantification of the PeakQuant Software Suite

Abstract Accurate quantification of proteins is one of the major tasks in current proteomics research. To address this issue, a wide range of stable isotope labeling techniques have been developed, allowing one to quantitatively study thousands of proteins by means of mass spectrometry. In this article, the FindPairs module of the PeakQuant software suite is detailed. It facilitates the automatic determination of protein abundance ratios based on the automated analysis of stable isotope-coded mass spectrometric data. Furthermore, it implements statistical methods to determine outliers due to biological as well as technical variance of proteome data obtained in replicate experiments. This provides an important means to evaluate the significance in obtained protein expression data. For demonstrating the high applicability of FindPairs, we focused on the quantitative analysis of proteome data acquired in (14)N/(15)N labeling experiments. We further provide a comprehensive overview of the features of the FindPairs software, and compare these with existing quantification packages. The software presented here supports a wide range of proteomics applications, allowing one to quantitatively assess data derived from different stable isotope labeling approaches, such as (14)N/(15)N labeling, SILAC, and iTRAQ. The software is publicly available at http://www.medizinisches-proteom-center.de/software and free for academic use.     

Spotting and validation of a genome wide oligonucleotide chip with duplicate measurement of each gene

The quality of DNA microarray based gene expression data relies on the reproducibility of several steps in a microarray experiment. We have developed a spotted genome wide microarray chip with oligonucleotides printed in duplicate in order to minimise undesirable biases, thereby optimising detection of true differential expression. The validation study design consisted of an assessment of the microarray chip performance using the MessageAmp and FairPlay labelling kits. Intraclass correlation coefficient (ICC) was used to demonstrate that MessageAmp was significantly more reproducible than FairPlay. Further examinations with MessageAmp revealed the applicability of the system. The linear range of the chips was three orders of magnitude, the precision was high, as 95% of measurements deviated less than 1.24-fold from the expected value, and the coefficient of variation for relative expression was 13.6%. Relative quantitation was more reproducible than absolute quantitation and substantial reduction of variance was attained with duplicate spotting. An analysis of variance (ANOVA) demonstrated no significant day-to-day variation.     

Selective Labelling of Cell-surface Proteins using CyDye DIGE Fluor Minimal Dyes

Surface proteins are central to the cell''s ability to react to its environment and to interact with neighboring cells. They are known to be inducers of almost all intracellular signaling. Moreover, they play an important role in environmental adaptation and drug treatment, and are often involved in disease pathogenesis and pathology (1). Protein-protein interactions are intrinsic to signaling pathways, and to gain more insight in these complex biological processes, sensitive and reliable methods are needed for studying cell surface proteins. Two-dimensional (2-D) electrophoresis is used extensively for detection of biomarkers and other targets in complex protein samples to study differential changes. Cell surface proteins, partly due to their low abundance (1 2% of cellular proteins), are difficult to detect in a 2-D gel without fractionation or some other type of enrichment. They are also often poorly represented in 2-D gels due to their hydrophobic nature and high molecular weight (2). In this study, we present a new protocol for intact cells using CyDye DIGE Fluor minimal dyes for specific labeling and detection of this important group of proteins. The results showed specific labeling of a large number of cell surface proteins with minimal labeling of intracellular proteins. This protocol is rapid, simple to use, and all three CyDye DIGE Fluor minimal dyes (Cy 2, Cy 3 and Cy 5) can be used to label cell-surface proteins. These features allow for multiplexing using the 2-D Fluorescence Difference Gel Electrophoresis (2-D DIGE) with Ettan DIGE technology and analysis of protein expression changes using DeCyder 2-D Differential Analysis Software. The level of cell-surface proteins was followed during serum starvation of CHO cells for various lengths of time (see Table 1). Small changes in abundance were detected with high accuracy, and results are supported by defined statistical methods.Open in a separate windowClick here to view.^{(76M, flv)}