Quantitative assessment of filter-based cDNA microarrays: gene expression profiles of human T-lymphoma cell lines

MOTIVATION: While the use of cDNA microarrays for functional genomic analysis has become commonplace, relatively little attention has been placed on false positives, i.e. the likelihood that a change in measured radioactive or fluorescence intensity may reflect a change in gene expression when, in fact, there is none. Since cDNA arrays are being increasingly used to rapidly distinguish biomarkers for disease detection and subsequent assay development (Wellman et al., Blood, 96, 398-404, 2000), the impact of false positives can be significant. For the use of this technology, it is necessary to develop quantitative criteria for reduction of false positives with radioactively-labeled cDNA arrays. RESULTS: We used a single source of RNA (HuT78 T lymphoma cells) to eliminate sample variation and quantitatively examined intensity ratios using radioactively labeled cDNA microarrays. Variation in intensity ratios was reduced by processing microarrays in side-by-side (parallel mode) rather than by using the same microarray for two hybridizations (sequential mode). Based on statistical independence, calculation of the expected number of false positives as a function of threshold showed that a detection limit of [log(2)R] >0.65 with agreement from three replicates could be used to identify up- or down-modulated genes. Using this quantitative criteria, gene expression differences between two related T lymphoma cell lines, HuT78 and H9, were identified. The relevance of these findings to the known functional differences between these cell types is discussed.     

Transcriptional signatures of normal human mammary epithelial cells in response to benzo[a]pyrene exposure: a comparison of three microarray platforms

Microarrays are used to study gene expression in a variety of biological systems. A number of different platforms have been developed, but few studies exist that have directly compared the performance of one platform with another. The goal of this study was to determine array variation by analyzing the same RNA samples with three different array platforms. Using gene expression responses to benzo[a]pyrene exposure in normal human mammary epithelial cells (NHMECs), we compared the results of gene expression profiling using three microarray platforms: photolithographic oligonucleotide arrays (Affymetrix), spotted oligonucleotide arrays (Amersham), and spotted cDNA arrays (NCI). While most previous reports comparing microarrays have analyzed pre-existing data from different platforms, this comparison study used the same sample assayed on all three platforms, allowing for analysis of variation from each array platform. In general, poor correlation was found with corresponding measurements from each platform. Each platform yielded different gene expression profiles, suggesting that while microarray analysis is a useful discovery tool, further validation is needed to extrapolate results for broad use of the data. Also, microarray variability needs to be taken into consideration, not only in the data analysis but also in specific probe selection for each array type.     

Characterization of variability in large-scale gene expression data: implications for study design

Large-scale gene expression measurement techniques provide a unique opportunity to gain insight into biological processes under normal and pathological conditions. To interpret the changes in expression profiles for thousands of genes, we face the nontrivial problem of understanding the significance of these changes. In practice, the sources of background variability in expression data can be divided into three categories: technical, physiological, and sampling. To assess the relative importance of these sources of background variation, we generated replicate gene expression profiles on high-density Affymetrix GeneChip oligonucleotide arrays, using either identical RNA samples or RNA samples obtained under similar biological states. We derived a novel measure of dispersion in two-way comparisons, using a linear characteristic function. When comparing expression profiles from replicate tests using the same RNA sample (a test for technical variability), we observed a level of dispersion similar to the pattern obtained with RNA samples from replicate cultures of the same cell line (a test for physiological variability). On the other hand, a higher level of dispersion was observed when tissue samples of different animals were compared (an example of sampling variability). This implies that, in experiments in which samples from different subjects are used, the variation induced by the stimulus may be masked by non-stimuli-related differences in the subjects' biological state. These analyses underscore the need for replica experiments to reliably interpret large-scale expression data sets, even with simple microarray experiments.     

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.     

Gene expression does not change significantly in C3H 10T(1/2) cells after exposure to 847.74 CDMA or 835.62 FDMA radiofrequency radiation

In vitro experiments with C3H 10T(1/2) mouse cells were performed to determine whether Frequency Division Multiple Access (FDMA) or Code Division Multiple Access (CDMA) modulated radiofrequency (RF) radiations induce changes in gene expression. After the cells were exposed to either modulation for 24 h at a specific absorption rate (SAR) of 5 W/ kg, RNA was extracted from both exposed and sham-exposed cells for gene expression analysis. As a positive control, cells were exposed to 0.68 Gy of X rays and gene expression was evaluated 4 h after exposure. Gene expression was evaluated using the Affymetrix U74Av2 GeneChip to detect changes in mRNA levels. Each exposure condition was repeated three times. The GeneChip data were analyzed using a two-tailed t test, and the expected number of false positives was estimated from t tests on 20 permutations of the six sham RF-field-exposed samples. For the X-ray-treated samples, there were more than 90 probe sets with expression changes greater than 1.3-fold beyond the number of expected false positives. Approximately one-third of these genes had previously been reported in the literature as being responsive to radiation. In contrast, for both CDMA and FDMA radiation, the number of probe sets with an expression change greater than 1.3-fold was less than or equal to the expected number of false positives. Thus the 24-h exposures to FDMA or CDMA RF radiation at 5 W/kg had no statistically significant effect on gene expression.     

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.     

用DDRT-PCR方法克隆小鼠精子发生早期相关基因的EST

为了避免差异显示技术中的放射性污染 ,并用之于筛选、克隆精子发生早期的相关基因 ,分离纯化了小鼠的原始精原细胞及B型精原细胞 ,提取其总RNA ,逆转录获得cDNA ,以荧光差异显示方法筛选差异表达基因。利用斑点杂交技术对差异片段进行快速鉴定以排除假阳性。选取 16条差异显著的片段做克隆测序 ,通过Gen Bank/Blast比较 ,有 7个片段属于新的EST ,且均表现为在B型精原细胞中表达强度高于原始精原细胞。提交Gen Bank获得注册号。从中选取较有意义的 3条基因片段通过半定量RT PCR方法进一步验证其表达特征。和传统的差异显示方法比较 ,文中所采用的mRNA差异显示技术可快速排除假阳性结果 ,避免同位素标记带来的放射性污染。结果表明所获得的 7个新EST均表现为B型精原细胞中高表达 ,这些表达升高的基因可能与其后精子发生过程中的一系列特殊现象 (如减数分裂、变态成形 )有关 ,为生精细胞的分化做物质上的准备。     

Analysis of the pattern of gene expression during human adipogenesis by DNA microarray

High density DNA microarrays containing over 5000 cDNA clones were used to carry out a comprehensive investigation of gene expression during adipogenesis. Complex probes synthesized from total RNA were hybridized to the arrays to determine the level of mRNA expression of each arrayed gene. Thirty three genes (29 known and 4 ESTs with no identified homologies) have been found to alter their level of expression more than 2.5-fold after differentiation. The quantitative measurement by DNA array was in good agreement with conventional Northern blot analysis of selected genes. Our results demonstrate that utilization of a DNA array is a speedy, efficient and quantitative approach to profile the expression of a large number of genes.     

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.     

Expression profiles of apoptosis genes in mammary epithelial cells

To investigate apoptosis in HC11 mammary epithelial cells, we compared the gene expression profiles of actively growing and serum-starved apoptotic cells using a mouse apoptosis gene array and 33P-labeled cDNA prepared from the RNA of the two cultures. Analysis of the arrays showed that expression of several genes such as clusterin, secreted frizzled related protein mRNA (sFRP-1), CREB-binding protein (CBP), and others was higher in the apoptotic cells whereas expression of certain genes including survivin, cell division cycle 2 homolog A (CDC2), and cyclin A was lower. These expression patterns were confirmed by RT-PCR and/or Northern analyses. We compared the expression of some of these genes in the mouse mammary gland under various physiological conditions. The expression levels of genes (clusterin, CBP, and M6P-R) up-regulated in apoptotic conditions were higher at involution than during lactation. On the other hand, genes (Pin, CDC2) downregulated in apoptotic conditions were relatively highly expressed in virgin and pregnant mice. We conclude that certain genes such as clusterin, sFRP-1, GAS1 and CBP are induced in apoptotic mammary epithelial cells, and others are repressed. Moreover, the apoptosis array is an efficient technique for comparing gene expression profiles in different states of the same cell type.     

Removing technical variability in RNA-seq data using conditional quantile normalization

The ability to measure gene expression on a genome-wide scale is one of the most promising accomplishments in molecular biology. Microarrays, the technology that first permitted this, were riddled with problems due to unwanted sources of variability. Many of these problems are now mitigated, after a decade's worth of statistical methodology development. The recently developed RNA sequencing (RNA-seq) technology has generated much excitement in part due to claims of reduced variability in comparison to microarrays. However, we show that RNA-seq data demonstrate unwanted and obscuring variability similar to what was first observed in microarrays. In particular, we find guanine-cytosine content (GC-content) has a strong sample-specific effect on gene expression measurements that, if left uncorrected, leads to false positives in downstream results. We also report on commonly observed data distortions that demonstrate the need for data normalization. Here, we describe a statistical methodology that improves precision by 42% without loss of accuracy. Our resulting conditional quantile normalization algorithm combines robust generalized regression to remove systematic bias introduced by deterministic features such as GC-content and quantile normalization to correct for global distortions.     

In Search of Epigenetic Marks in Testes and Sperm Cells of Differentially Fed Boars

In search of transmittable epigenetic marks we investigated gene expression in testes and sperm cells of differentially fed F0 boars from a three generation pig feeding experiment that showed phenotypic differences in the F2 generation. RNA samples from 8 testes of boars that received either a diet enriched in methylating micronutrients or a control diet were analyzed by microarray analysis. We found moderate differential expression between testes of differentially fed boars with a high FDR of 0.82 indicating that most of the differentially expressed genes were false positives. Nevertheless, we performed a pathway analysis and found disparate pathway maps of development_A2B receptor: action via G-protein alpha s, cell adhesion_Tight junctions and cell adhesion_Endothelial cell contacts by junctional mechanisms which show inconclusive relation to epigenetic inheritance. Four RNA samples from sperm cells of these differentially fed boars were analyzed by RNA-Seq methodology. We found no differential gene expression in sperm cells of the two groups (adjusted P-value>0.05). Nevertheless, we also explored gene expression in sperm by a pathway analysis showing that genes were enriched for the pathway maps of bacterial infections in cystic fibrosis (CF) airways, glycolysis and gluconeogenesis p.3 and cell cycle_Initiation of mitosis. Again, these pathway maps are miscellaneous without an obvious relationship to epigenetic inheritance. It is concluded that the methylating micronutrients moderately if at all affects RNA expression in testes of differentially fed boars. Furthermore, gene expression in sperm cells is not significantly affected by extensive supplementation of methylating micronutrients and thus RNA molecules could not be established as the epigenetic mark in this feeding experiment.     

Microarray analysis of cytoplasmic versus whole cell RNA reveals a considerable number of missed and false positive mRNAs

With no known exceptions, every published microarray study to determine differential mRNA levels in eukaryotes used RNA extracted from whole cells. It is assumed that the use of whole cell RNA in microarray gene expression analysis provides a legitimate profile of steady-state mRNA. Standard labeling methods and the prevailing dogma that mRNA resides almost exclusively in the cytoplasm has led to the long-standing belief that the nuclear RNA contribution is negligible. We report that unadulterated cytoplasmic RNA uncovers differentially expressed mRNAs that otherwise would not have been detected when using whole cell RNA and that the inclusion of nuclear RNA has a large impact on whole cell gene expression microarray results by distorting the mRNA profile to the extent that a substantial number of false positives are generated. We conclude that to produce a valid profile of the steady-state mRNA population, the nuclear component must be excluded, and to arrive at a more realistic view of a cell''s gene expression profile, the nuclear and cytoplasmic RNA fractions should be analyzed separately.     

Evidence for diversity in transcriptional profiles of single hematopoietic stem cells

Hematopoietic stem cells replenish all the cells of the blood throughout the lifetime of an animal. Although thousands of stem cells reside in the bone marrow, only a few contribute to blood production at any given time. Nothing is known about the differences between individual stem cells that dictate their particular state of activation readiness. To examine such differences between individual stem cells, we determined the global gene expression profile of 12 single stem cells using microarrays. We showed that at least half of the genetic expression variability between 12 single cells profiled was due to biological variation in 44% of the genes analyzed. We also identified specific genes with high biological variance that are candidates for influencing the state of readiness of individual hematopoietic stem cells, and confirmed the variability of a subset of these genes using single-cell real-time PCR. Because apparent variation of some genes is likely due to technical factors, we estimated the degree of biological versus technical variation for each gene using identical RNA samples containing an RNA amount equivalent to that of single cells. This enabled us to identify a large cohort of genes with low technical variability whose expression can be reliably measured on the arrays at the single-cell level. These data have established that gene expression of individual stem cells varies widely, despite extremely high phenotypic homogeneity. Some of this variation is in key regulators of stem cell activity, which could account for the differential responses of particular stem cells to exogenous stimuli. The capacity to accurately interrogate individual cells for global gene expression will facilitate a systems approach to biological processes at a single-cell level.     

Effects of filtering by Present call on analysis of microarray experiments

Background

Affymetrix GeneChips^® are widely used for expression profiling of tens of thousands of genes. The large number of comparisons can lead to false positives. Various methods have been used to reduce false positives, but they have rarely been compared or quantitatively evaluated. Here we describe and evaluate a simple method that uses the detection (Present/Absent) call generated by the Affymetrix microarray suite version 5 software (MAS5) to remove data that is not reliably detected before further analysis, and compare this with filtering by expression level. We explore the effects of various thresholds for removing data in experiments of different size (from 3 to 10 arrays per treatment), as well as their relative power to detect significant differences in expression.

Results

Our approach sets a threshold for the fraction of arrays called Present in at least one treatment group. This method removes a large percentage of probe sets called Absent before carrying out the comparisons, while retaining most of the probe sets called Present. It preferentially retains the more significant probe sets (p ≤ 0.001) and those probe sets that are turned on or off, and improves the false discovery rate. Permutations to estimate false positives indicate that probe sets removed by the filter contribute a disproportionate number of false positives. Filtering by fraction Present is effective when applied to data generated either by the MAS5 algorithm or by other probe-level algorithms, for example RMA (robust multichip average). Experiment size greatly affects the ability to reproducibly detect significant differences, and also impacts the effect of filtering; smaller experiments (3–5 samples per treatment group) benefit from more restrictive filtering (≥50% Present).

Conclusion

Use of a threshold fraction of Present detection calls (derived by MAS5) provided a simple method that effectively eliminated from analysis probe sets that are unlikely to be reliable while preserving the most significant probe sets and those turned on or off; it thereby increased the ratio of true positives to false positives.