Experimental Design & Analysis of Protein Array Data: Applying Methods from cDNA Arrays.

Blood ◽  
2004 ◽  
Vol 104 (11) ◽  
pp. 4280-4280
Author(s):  
Jeanette E. Eckel ◽  
Antje Hoering ◽  
Irene Ghobrial
Keyword(s):  
Gene Expression ◽  
Experimental Design ◽  
Differential Expression ◽  
Protein Microarray ◽  
Random Noise ◽  
Reference Sample ◽  
Fold Change ◽  
Protein Array ◽  
Microarray Technology ◽  
Cdna Arrays

Abstract It appears that a number of recent manuscripts using protein microarray technology are using equivalent analysis procedures that the gene-expression microarray community implemented in their infancy. That is, utilizing a classic reference design such that the ratio of the sample of interest to a reference sample is the response of interest and assessing fold change to determine differential expression. For example, recent publications have concluded that proteins with a fold change less than 0.7 or greater than 1.3 demonstrate significant down- or up-regulated differential expression, respectively. However, fold change is an unreliable measure of differential expression and statistical models that distinguish true signal from random noise should be utilized instead of fold changes. Over the last half decade a tremendous amount of research has been devoted to gene-expression microarrays to vastly improve on the areas of experimental design, normalization and statistical analyses to assess differential expression and classification and these methods are directly applicable to protein microarray technology. Thus, the objective is to review the statistical methodology that has been developed for two-color cDNA arrays that is directly applicable to protein arrays. Examples are provided from a mantle-cell lymphoma protein-array experiment.

Study of Basic Concepts on the Development of Protein Microarray - Gene Expression Profiling

2016 ◽  
pp. 263-295
Author(s):  
P. Sivashanmugam ◽  
Arun C. ◽  
Selvakumar P.
Keyword(s):  
Gene Expression ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Protein Microarray ◽  
Microarray Technology ◽  
Microarray Gene Expression ◽  
High Throughput Technology ◽  
Multiple Cell ◽  
Stressed Cell ◽  
Microarray Gene

The physical and biological activity of any organisms is mainly depended on the genetic information which stored in DNA. A process at which a gene gives rise to a phenotype is called as gene expression. Analysis of gene expression can be used to interpret the changes that occur at biological level of a stressed cell or tissue. Hybridization technology helps to study the gene expression of multiple cell at a same time. Among them microarray technology is a high- throughput technology to study the gene expression at transcription level (DNA) or translation level (Protein). Analysis the protein only can predict the accurate changes that happens in a tissue, when they are infected by a disease causing organisms. Protein microarray mainly used to identify the interactions and activities of proteins with other molecules, and to determine their function for a system at normal state and stressed state. The scope of this chapter is to outline a detail description on the fabrication, types, data analysis, and application of protein microarray technology towards gene expression profiling.

Evolution of gene expression patterns in a model of branching morphogenesis

1999 ◽  
Vol 277 (4) ◽  
pp. F650-F663 ◽  
Author(s):  
Anna Pavlova ◽  
Robert O. Stuart ◽  
Martin Pohl ◽  
Sanjay K. Nigam
Keyword(s):  
Gene Expression ◽  
Differential Expression ◽  
Expression Patterns ◽  
Branching Morphogenesis ◽  
Computational Method ◽  
Metanephric Mesenchyme ◽  
Ureteric Bud ◽  
Cdna Arrays ◽  
Apoptotic Genes ◽  
Collecting System

Branching morphogenesis of the ureteric bud in response to unknown signals from the metanephric mesenchyme gives rise to the urinary collecting system and, via inductive signals from the ureteric bud, to recruitment of nephrons from undifferentiated mesenchyme. An established cell culture model for this process employs cells of ureteric bud origin (UB) cultured in extracellular matrix and stimulated with conditioned media (BSN-CM) from a metanephric mesenchymal cell line (H. Sakurai, E. J. Barros, T. Tsukamoto, J. Barasch, and S. K. Nigam. Proc. Natl. Acad. Sci. USA 94: 6279–6284, 1997.). In the presence of BSN-CM, the UB cells form branching tubular structures reminiscent of the branching ureteric bud. The pattern of gene regulation in this model of branching morphogenesis of the kidney collecting system was investigated using high-density cDNA arrays. Software and analytical methods were developed for the quantification and clustering of genes. With the use of a computational method termed “vector analysis,” genes were clustered according to the direction and magnitude of differential expression in n-dimensional log-space. Changes in gene expression in response to the BSN-CM consisted primarily of differential expression of transcription factors with previously described roles in morphogenesis, downregulation of pro-apoptotic genes accompanied by upregulation of anti-apoptotic genes, and upregulation of a small group of secreted products including growth factors, cytokines, and extracellular proteinases. Changes in expression are discussed in the context of a general model for epithelial branching morphogenesis. In addition, the cDNA arrays were used to survey expression of epithelial markers and secreted factors in UB and BSN cells, confirming the largely epithelial character of the former and largely mesenchymal character of the later. Specific morphologies (cellular processes, branching multicellular cords, etc.) were shown to correlate with the expression of different, but overlapping, genomic subsets, suggesting differences in morphogenetic mechanisms at these various steps in the evolution of branching tubules.

scholarly journals RNA Profiling for Biomarker Discovery: Practical Considerations for Limiting Sample Sizes

2005 ◽  
Vol 21 (1) ◽  
pp. 43-48 ◽  
Author(s):  
Danny J. Kelly ◽  
Sujoy Ghosh
Keyword(s):  
Gene Expression ◽  
Polymerase Chain Reaction ◽  
Microarray Data ◽  
Biomarker Discovery ◽  
Reference Sample ◽  
Fold Change ◽  
Chain Reaction ◽  
Rna Profiling ◽  
Polymerase Chain ◽  
Signal Correlation

We have compared microarray data generated on Affymetrix™chips from standard (8 micrograms) or low (100 nanograms) amounts of total RNA. We evaluated the gene signals and gene fold-change estimates obtained from the two methods and validated a subset of the results by real time, polymerase chain reaction assays. The correlation of low RNA derived gene signals to gene signals obtained from standard RNA was poor for less to moderately abundant genes. Genes with high abundance showed better correlation in signals between the two methods. The signal correlation between the low RNA and standard RNA methods was improved by including a reference sample in the microarray analysis. In contrast, the fold-change estimates for genes were better correlated between the two methods regardless of the magnitude of gene signals. A reference sample based method is suggested for studies that would end up comparing gene signal data from a combination of low and standard RNA templates; no such referencing appears to be necessary when comparing fold-changes of gene expression between standard and low template reactions.

scholarly journals Differences in articular hip cartilage gene expression between femoroacetabular impingement (FAI) and end-stage hip osteoarthritis

2020 ◽  
Vol 8 (7_suppl6) ◽  
pp. 2325967120S0039
Author(s):  
John Reuter ◽  
Gillian Soles ◽  
Cheryl Ackert-Bicknell ◽  
Brian Giordano ◽  
Benjamin Kuhns
Keyword(s):  
Gene Expression ◽  
Femoral Head ◽  
Differential Expression ◽  
Rna Sequencing ◽  
Femoroacetabular Impingement ◽  
Molecular Mechanisms ◽  
Hip Osteoarthritis ◽  
Fold Change ◽  
Cam Deformity ◽  
End Stage

Objectives: The morphological deformities in Femoroacetabular Impingement (FAI) have been associated with hip osteoarthritis (OA), however the molecular mechanisms for OA initiation and progression are poorly understood. The purpose of this study was to use whole genome RNA sequencing to characterize differences in gene expression articular cartilage samples isolated from patients undergoing surgery for FAI and idiopathic OA. We hypothesized that there would be significant differences in genes expression in pathways related to inflammation as well as cartilage and bone turnover. Methods: 20 patients undergoing either hip arthroscopy for FAI (5 male, 5 female) or total hip arthroplasty (5 male, 5 female) for end-stage osteoarthritis were included in the study. FAI patients required a Cam deformity with an Alpha Angle greater than 55 while patients with dysplasia (LCEA<25) or prior hip surgery were excluded. Exclusion criteria for the THA cohort included dysplasia, and post-traumatic OA or inflammatory OA. Cartilage samples were obtained over the Cam deformity prior to femoroplasty in the FAI group or over anterosuperior femoral head-neck junction in the OA group following extraction of the femoral head. Following RNA isolation, Next Generation RNA sequencing was performed to evaluate gene expression. Differential expression data was incorporated into the Ingenuity Pathway Analysis (IPA) platform to identify differences in canonical signaling pathways associated with osteoarthritis. Results: There were 3531 genes that were significantly differentially expressed between the FAI and OA cohorts. Of these, there were 27 genes that were upregulated by a greater than 2 log-fold change in the OA cohort and 524 genes that were upregulated by a greater than 2 log-fold change in the FAI cohort. There was significant differential expression in genes related to cartilage metabolism (Table 1) and canonical osteoarthritis pathways involving BMP, TGFβ, and Wnt signaling. (Table 2). Additionally, FAI samples had significant upregulation of EGF-ERBB mediated signaling which compared to osteoarthritic tissue. Conclusion: The results of the present study support our hypothesis that there are significant differences in gene expression between FAI and OA samples in multiple pathways that are implicated in osteoarthritis. Osteoarthritis samples had increased expression of cartilage breakdown and inflammation while femoroacetabular impingement samples had greater expression of chondroprotective genes. Further study of cartilage samples from FAI patients may provide insight into the molecular mechanisms of osteoarthritis progression. [Table: see text][Table: see text]

Comparative analysis of molecular aberration in different genders for identification of key pathways in hepatocellular carcinoma.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e23192-e23192
Author(s):  
Li Yu ◽  
Feng Tieshan ◽  
Lifeng Li ◽  
Shifu Chen ◽  
Liu Xiaoliang
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Differential Expression ◽  
Mutation Frequency ◽  
Molecular Mechanisms ◽  
Rho Gtpase ◽  
Enrichment Analysis ◽  
Fold Change ◽  
P Value ◽  
Males And Females

e23192 Background: The incidence rate of hepatocellular carcinoma (HCC) varies significantly between genders, being higher in men than in women. While the molecular mechanisms remain unexplored, we systematically analyzed the gene expression and SNV signature to identify key molecular aberrations and pathways. Methods: Gene expression and simple nucleotide variation data of 407 HCC patients with HCC including 140 females and 267 males were collected. We identified genes with differential mutation frequency in two cohorts using Fisher’s exact test (p-value < 0.05), and Deseq2 to identify differential expression genes (FDR < 0.05 and fold change > 2). Enrichment analysis was applied using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and the Reactome database (p adjust < 0.05). Results: In total, 103 genes with differential mutation frequency in two cohorts were identified. Of these genes, 57 genes were differentially expressed, and the number of up-regulated genes in males and females were 21 and 36, respectively. The genes that show significant up-regulation in males are KDM5D and ANKFN1 which have the log2(fold change) of 7.49 and 4.45. The genes that show significant up-regulation in females are SYT13 and SCD5 which have the log2(fold change) of 2.33 and 2.29. The result of enrichment analysis showed that the up-regulated genes in males and females were involved in different biological pathways. In males, the up-regulated genes mainly participated in the PPAR signaling pathway. In females, the up-regulated genes mainly participated in the Rho GTPase cycle, regulation of insulin secretion and integration of energy metabolism. Conclusions: In this study, 57 genes with differential mutation frequency and differential expression between males and females with HCC were identified based on TCGA dataset. Enrichment analysis result indicated that these genes are mainly involved in signaling pathways relevant to carcinogenesis and metabolism.

Custom microarray for glycobiologists: considerations for glycosyltransferase gene expression profiling

2002 ◽  
Vol 69 ◽  
pp. 135-142 ◽  
Author(s):  
Elena M. Comelli ◽  
Margarida Amado ◽  
Steven R. Head ◽  
James C. Paulson
Keyword(s):  
Gene Expression ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Affymetrix Genechip ◽  
Microarray Technology ◽  
Gene Arrays ◽  
Glycosyltransferase Gene

The development of microarray technology offers the unprecedented possibility of studying the expression of thousands of genes in one experiment. Its exploitation in the glycobiology field will eventually allow the parallel investigation of the expression of many glycosyltransferases, which will ultimately lead to an understanding of the regulation of glycoconjugate synthesis. While numerous gene arrays are available on the market, e.g. the Affymetrix GeneChip® arrays, glycosyltransferases are not adequately represented, which makes comprehensive surveys of their gene expression difficult. This chapter describes the main issues related to the establishment of a custom glycogenes array.

scholarly journals High heterogeneity undermines generalization of differential expression results in RNA-Seq analysis

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Weitong Cui ◽  
Huaru Xue ◽  
Lei Wei ◽  
Jinghua Jin ◽  
Xuewen Tian ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Expression ◽  
Small Sample ◽  
Differentially Expressed ◽  
Cancer Type ◽  
Rna Seq ◽  
Sample Sizes ◽  
Large Sample ◽  
Expression Levels ◽  
Gene Expression Levels

Abstract Background RNA sequencing (RNA-Seq) has been widely applied in oncology for monitoring transcriptome changes. However, the emerging problem that high variation of gene expression levels caused by tumor heterogeneity may affect the reproducibility of differential expression (DE) results has rarely been studied. Here, we investigated the reproducibility of DE results for any given number of biological replicates between 3 and 24 and explored why a great many differentially expressed genes (DEGs) were not reproducible. Results Our findings demonstrate that poor reproducibility of DE results exists not only for small sample sizes, but also for relatively large sample sizes. Quite a few of the DEGs detected are specific to the samples in use, rather than genuinely differentially expressed under different conditions. Poor reproducibility of DE results is mainly caused by high variation of gene expression levels for the same gene in different samples. Even though biological variation may account for much of the high variation of gene expression levels, the effect of outlier count data also needs to be treated seriously, as outlier data severely interfere with DE analysis. Conclusions High heterogeneity exists not only in tumor tissue samples of each cancer type studied, but also in normal samples. High heterogeneity leads to poor reproducibility of DEGs, undermining generalization of differential expression results. Therefore, it is necessary to use large sample sizes (at least 10 if possible) in RNA-Seq experimental designs to reduce the impact of biological variability and DE results should be interpreted cautiously unless soundly validated.

scholarly journals Species diversity and spatial distribution of CL/VL vectors: assessing bioclimatic effect on expression plasticity of genes possessing vaccine properties isolated from wild-collected sand flies in endemic areas of Iran

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ali Bordbar ◽  
Parviz Parvizi
Keyword(s):  
Gene Expression ◽  
Spatial Distribution ◽  
Interpolation Method ◽  
Diversity Index ◽  
Fold Change ◽  
Control Measures ◽  
Pcr Analysis ◽  
Physiological Status ◽  
Sand Flies ◽  
Risk Maps

Abstract Background Leishmaniasis is one of the ten most important neglected tropical diseases worldwide. Understanding the distribution of vectors of visceral and cutaneous leishmaniasis (VL/CL) is one of the significant strategic frameworks to control leishmaniasis. In this study, the extent of the bioclimatic variability was investigated to recognize a rigorous cartographic of the spatial distribution of VL/CL vectors as risk-maps using ArcGIS modeling system. Moreover, the effect of bioclimatic diversity on the fold change expression of genes possessing vaccine traits (SP15 and LeIF) was evaluated in each bioclimatic region using real-time PCR analysis. Methods The Inverse Distance Weighting interpolation method was used to obtain accurate geography map in closely-related distances. Bioclimatic indices were computed and vectors spatial distribution was analyzed in ArcGIS10.3.1 system. Species biodiversity was calculated based on Shannon diversity index using Rv.3.5.3. Expression fold change of SP15 and LeIF genes was evaluated using cDNA synthesis and RT-qPCR analysis. Results Frequency of Phlebotomus papatasi was predominant in plains areas of Mountainous bioclimate covering the CL hot spots. Mediterranean region was recognized as an important bioclimate harboring prevalent patterns of VL vectors. Semi-arid bioclimate was identified as a major contributing factor to up-regulate salivary-SP15 gene expression (P = 0.0050, P < 0.05). Also, Mediterranean bioclimate had considerable effect on up-regulation of Leishmania-LeIF gene in gravid and semi-gravid P. papatasi population (P = 0.0109, P < 0.05). Conclusions The diversity and spatial distribution of CL/VL vectors associated with bioclimatic regionalization obtained in our research provide epidemiological risk maps and establish more effectively control measures against leishmaniasis. Oscillations in gene expression indicate that each gene has its own features, which are profoundly affected by bioclimatic characteristics and physiological status of sand flies. Given the efficacy of species-specific antigens for vaccine production, it is essential to consider bioclimatic factors that have a fundamental role in affecting the regulatory regions of environmentally responsive loci for genes used in vaccine design.

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