RNA-Seq and Microarray Analysis - Research Paper Example

RNA-Seq and Microarray Analysis Grade (November 22, Article Summaries: RNA-Seq and Microarray AnalysisMooney M, et al. (2013). Comparative RNA-Seq and Microarray Analysis of Gene Expression Changes in B-Cell Lymphomas of Canis familiaris. PLoS ONE 8(4): 1-14. The need to understand human plastic diseases has led to the development of the comparative oncology field of study. Domestic dogs are considered the perfect models for the study. This is because dogs form spontaneous tumors and at the same time they have a tendency towards certain types of cancer (Mooney, et al., 2013). Further the field of animal veterinary and the discipline of human medicine applies the same tools for diagnosis and therapy. Additionally, the response of the canines to chemotherapies has more similarity to that of humans, when compared to any other model that could possibly be considered. Therefore, dogs become the best models for indentifying the genetic underpinnings that are associated with cancer in humans (Mooney, et al., 2013). The fact that dogs and humans depict similarities in cancer treatment and responses also allow for the opportunity to apply advanced cancer diagnostic tools. This is meant to enhance the understanding of the possible treatment of cancerous diseases for the benefit of both dogs and man (Mooney, et al., 2013). The typified canine lymphoma in dogs presents some analogous characteristics with the human Non-Hodgkin’s Lymphoma (NHL) in form of biological and other clinical features. This makes it possible to understand this type of cancer in humans (Mooney, et al., 2013). The canine lymphoma is the most common canine cancerous disease in dogs accounting for 24% occurrence. On the other hand, the NHL is the most common cancer in the USA, while it has almost doubled throughout the world in the last 35 years (Mooney, et al., 2013). Canine cancer is a multi-centric lymphadenopathy cancer that has no other organs involvement, requiring the same type of treatment that apply the multi-agent protocol, which is the standard of care for the NHL cancer in humans (Mooney, et al., 2013). Therefore, the understanding of the canine lymphoma is acceptable for enabling the understanding of the diagnostic process and therapeutic treatment of the NHL patients (Mooney, et al., 2013). The methodology applied in this study first sought to attain the consent of the dog owners that were participating in the study. This was followed by seeking for the approval of the Institutional Animal Care and Use Committee since it the body responsible ensuring the welfare of animals subjected under studies. The sample applied in the study was 30 dogs, from which Fine needle aspirates (FNAs) were collected for analysis (Mooney, et al., 2013). The dogs qualifying for the study required to have a lymph node tumor diameter measuring more than 20 mm. it is from these nodes that the FNAs were collected at intervals starting at 00 hrs, followed by another collection at 06hrs and then a final collection at 24 hrs. The collection of the FNA samples was done at three different sites of the Animal Clinical Investigation (ACI), from where the 30 samples were shipped to the Clinical Reference Laboratory (CRL) where they were subject to seven days observation after the treatment. The lymph nodes tumor volume was assessed, and the results were applied to classify the participants in the samples as either respondents or non-respondents (Mooney, et al., 2013). From the results, five samples of responders and five samples of the non-responders were then selected and subjected to further tests of sequencing RNA transcriptome. In addition, four other FNAs from non-diseases lymph nodes were also collected to be applied for control tests (Mooney, et al., 2013). Therefore, the fourteen samples formed the population sample for the study that were subjected to the sample test analysis to give the final results. The analysis of the samples applied both the RNA-Seq and Microarray to obtain genome-wide expression profiles, where a total of fourteen samples were used in the case of comparison of the RNA-Seq with microarray, where the comparison of the normal biopsies and the B-cell lymphoma required the use of 70% of the study samples (Mooney, et al., 2013). The analysis entailed the formation of cytology slides, which were then subjected to immune-histochemistry (IHC) of the FNAs in order to determine the lymphoblast content. The analysis of the B-cell lymphoma cells required the application of the Multi-Drug Resistant analysis (Mooney, et al., 2013). The visual inspection and longitudinal distributional analysis were applied on all 30 samples in order to determine the microarray components in the samples. On the other hand, the Next Generation Sequencing (NGS) was the technique applied for the analysis of the RNA-Seq, after which the freeze-and-squeeze technique was applied to extract DNAs that were later purified. The statistical analysis applied the R language and environment to form computations and graphs (Mooney, et al., 2013). The surrogate variable analysis and the seqinR package were also applied for the analysis of the multivariate and univariate analysis, respectively (Mooney, et al., 2013). The results of the study indicated that different genes in the normal lymph nodes with B-cell lymphoma diseases and the ones without diseases were identified through applying both the RNA Microarray and RNA-Seq technologies (Mooney, et al., 2013). The P13K genes of the B-cell lymphoma were observed to be elevated in the dog samples, which is the same case that happens in humans. This way, dogs were observed to be excellent models for identifying the genetic basis that are associated with lymphoma (Mooney, et al., 2013). Peng J, et al. (2014). RNA-Seq and Microarrays Analyses Reveal Global Differential Transcriptomes of Mesorhizobium huakuii 7653R between Bacteroids and Free-Living Cells. PLoS ONE 9(4): 1-18. The symbiotic relationship between plants and bacteria for agricultural fertilization has been the subject of study for the longest time. Rhizobia are bacteria that are specifically capable of establishing a symbiotic relationship with legumes through the formation of the nodules rooting structure (Peng, et al., 2014). The process of forming the symbiotic relationship entails the transformation of the bacteria in terms of its size, shape and also the DNA component constitution. The dramatic change of the bacteria causes them to become bacteroids that are terminally differentiated (Peng, et al., 2014). The symbiotic relationship between the Rhizobia bacteria and the legume plant known as the Inverted Repeat Lacking Clade (IRLC) occurs when the plant provides the Rhizobia bacteria with dicarboxylic acids which in turn provide the plant with reductant, carbon and energy (Peng, et al., 2014). On the other hand, the Rhizobia bacteria convert the energy and carbon obtained from the legume plant into ammonium, through the conversion of the Nitrogen found in the bacteroids. The ammonium is then secreted and absorbed back by the plant (Peng, et al., 2014). The importance of this symbiotic relationship between the IRLC legume plant and the Rhizobia bacteria is an important natural agricultural fertilization process. This process enables the legume plant to increase the nutritional content absorption from the soil. One of the Rhizobium bacteria that has the symbiotic relationship capability is the Mesorhizobium huakuii 7653R bacteria, which is either found as a free-living bacterium in the soil or in its symbiotic relationship with the A. sinicus plant (Peng, et al., 2014). Thus, the M huakuii 7653R induces the ammonium and nitrogen fixation on the A. sinicus plant that is used both as forage and a green manure that grows in Eastern Asia (Peng, et al., 2014). Transcriptone analysis has been applied to analyze and then unravel the complex genes functional genomics (Peng, et al., 2014). The unraveling of the gene system can be undertaken through the traditional quantitative PCR which is suitable for analyzing and unraveling the gene pattern and component for a few genes, or the use of the Microarrays that unravels the multiple and complex host-microbial genetic constitution (Peng, et al., 2014). The complex multi-gene study through the Microarrays technology has also been the basis of the study of the symbiotic relationship between the Rhizobium/pea-vetch, which has in turn created a deep insight into the development of the bacteroids and their functioning (Peng, et al., 2014). Nevertheless, even with the extensive study in this area, the numerous studies have only focused on the analysis of individual metabolic pathways. This then means that obtaining a global perspective of the symbiotic interaction mechanism between the bacteria and their host legume plants is currently limited (Peng, et al., 2014). Further, the limited analysis of this symbiotic relationship through a simple individual metabolic analysis makes it difficult to understand the complex metabolic changes that occur in bacteroids, when compared to the free-living bacterium (Peng, et al., 2014). However, after the decrease in the cost of the next generation technologies that are applied in gene sequencing, RNA sequencing (RNA-Seq) has emerged as the new preferred method for gene transcription studies (Peng, et al., 2014). The Microarrays still remains an important method in the analysis of the gene transcription. Thus, the new RNA-Seq does not come to replace it, but rather to complement the data that is obtained from the Microarrays analysis process (Peng, et al., 2014). The application of a multiple method of gene transcription analysis that comprises of the Microarrays and the RNA-Seq is advantageous since it creates a comprehensive picture. Therefore, both the RNA-Seq and the Microarrays technologies were applied towards analyzing the complex gene transcription changes that occurs in the bacterial development in M. huakuii 7653R (Peng, et al., 2014). This makes it possible to display what physiological changes are present during the differentiation of the bacteria. The method applied under this study was to first separate the RNA from the free-living cells, by growing the bacterium M. huakuii 7653R at 280C (Peng, et al., 2014). After this, centrifugation was used to harvest the cultured cells, and then the collected pellets were stored in frozen liquid nitrogen until the RNA was separated (Peng, et al., 2014). On the other hand, the A. sinicus seeds were germinated, planted and grown for 32 days, after which the nodules of the plant were harvested and stored in liquid nitrogen until the RNA was isolated (Peng, et al., 2014). The RNAs were then subjected to the RNA-Seq technology transcriptome analysis in the laboratory (Peng, et al., 2014). The results indicated that CtrA/c-di-GMP is involved in the regulation of the cell cycle during the nitrogen fixation and bacteroid development (Peng, et al., 2014). Additionally, the findings also indicated that the bacA/typA is the one responsible for coordinating and mediating the bacteroid differentiation and adaptation to the host cells environment, to trigger the symbiotic interaction (Peng, et al., 2014). The further analysis of the symbiotic interaction also indicated that it is in the A. sinicus plants nodules that the biological processes are altered and the gene transcription undertaken in order to alter the gene expression level that enhances the differentiation of the bacteroid and the consequent nitrogen fixation (Peng, et al., 2014). Therefore, the RNA-Seq technology has made it possible to understand and create new insights into the understanding of the process of bacteroid differentiation and nitrogen fixation in indeterminate types of roots nodules. This has been difficult to achieve globally using only the traditional Microarray technology (Peng, et al., 2014). Zhao, S., Fung-Leung, W-P., Bittner, A., Ngo K. & Liu, X. (2014). Comparison of RNA-Seq and Microarray in Transcriptome Profiling of Activated T Cells. PLoS ONE 9(1):1-13. RNA-Seq and the Microarray technologies are gene sequencing technologies that have been the subject of many studies in the recent past. However, the most studied aspect of the RNA-Seq and the Microarray are their similarities, while the differences between these gene sequencing technologies have not been studied sufficiently. Microarrays were discovered in the 1990s, and it has remained a most widely applied technology of choice for analyzing DNA expression (Zhao, Fung-Leung, Bittner, Ngo & Liu, 2014). The microarrays technology has made it possible to interrogate thousands of transcripts. This has in turn advanced the biological process of gene identification both in diseased and healthy tissues, as well as the evolution and developmental processes and responses (Zhao, Fung-Leung, Bittner, Ngo & Liu, 2014). Microarray has remained popular in gene and DNA profiling due to the fact that it can easily be afforded by many laboratories (Zhao, Fung-Leung, Bittner, Ngo & Liu, 2014). However, despite the popularity of Microarray in gene and DNA profiling, the method is associated with certain limitations. This calls for the use of a more advanced technological method of gene and DNA analysis. The major limitation associated with Microarrays is the limits of accuracy regarding the expression related to background hybridization measurement of low abundance transcripts. Additionally, the Microarrays technology is limited to the interrogation of genes that are only designed (Zhao, Fung-Leung, Bittner, Ngo & Liu, 2014). To address these limitations, the RNA-Seq gene profiling technology has emerged. This sequencing method applies high-throughput technologies and has ever since shown strong capability to overcome the Microarrays in genome transcription and profiling (Zhao, Fung-Leung, Bittner, Ngo & Liu, 2014). The greatest advantage that is offered by the RNA-Seq gene profiling technology is the novel structure detection ability (Zhao, Fung-Leung, Bittner, Ngo & Liu, 2014). Additionally, it offers the advantage of being capable of avoiding the background hybridization biases that are associated with the Microarrays, since it does not depend on prior selection and designation of probe. However, despite these advantages, the RNA-Seq poses both novel algorithmic and logistical challenges when it comes to analysis (Zhao, Fung-Leung, Bittner, Ngo & Liu, 2014). Thus, while different studies have been assessing the similarity between these two DNA sequencing and gene profiling methods, this study was undertaken to evaluate the existing difference between the two transcription methods. The methodology that was applied in this study was carrying out the analysis of the RNA samples of human T cell using both Microarrays and the RNA-Seq sequencing, to establish which method was better placed to undertake the gene transcription analysis effectively (Zhao, Fung-Leung, Bittner, Ngo & Liu, 2014). While undertaking this analysis, the study was focusing on the differences in these gene transcription methods, as opposed to their similarities. The informed consent of the participants in the study was obtained. The analysis of the results was undertaken using ANOVA software, in order to determine the most sensitive method in detecting the differential gene expression (Zhao, Fung-Leung, Bittner, Ngo & Liu, 2014). The results indicated that the genes with a high expression showed similarities in sensitivity responses from both RNA-Seq sequencing and the Microarray technologies ((Zhao, Fung-Leung, Bittner, Ngo & Liu, 2014). Further, the resuls indicated that the genes with low expression, most especially where the gene expression level was >0.47, the variance in gene expression were higher in RNA-Seq than in the Microarrays analysis (Zhao, Fung-Leung, Bittner, Ngo & Liu, 2014). Nevertheless, there are certain differentiated gene expressions that were found between the two platforms. Microarrays was found to be less effective in the detection of low-level gene expression levels such that it cannot differentiate no from low gene expression (Zhao, Fung-Leung, Bittner, Ngo & Liu, 2014). The RNA-Seq sequencing on the other hand was found to be more effective in detecting the low abundance gene expression, while also being superior in allowing genetic variants and also differentiating critical biological isofoms (Zhao, Fung-Leung, Bittner, Ngo & Liu, 2014). The other major difference between the microarray and the RNA-Seq sequencing platforms is that the RNA-Seq sequencing was found to be broader in its dynamic range, allowing for more differential expression of genes with high folder change (Zhao, Fung-Leung, Bittner, Ngo & Liu, 2014). The RNA-Seq sequencing was also superior in overcoming the technical issues and the biases associated with the microarray limited-detection probe hybridization. Nevertheless, the results indicated that despite the superiority of RNA-Seq sequencing, microarray is still the most dominant method for transcription profiling amongst researchers. This is possibly because the RNA-Seq is a new technological method that is also expensive, and whose data analysis is much complex (Zhao, Fung-Leung, Bittner, Ngo & Liu, 2014). Lessons from the three papers: The lessons we are able to learn from the three articles is that RNA-Seq sequencing stands as a popular method for transcription profiling both in plants, humans and animals diseased and healthy tissues (Zhao, Fung-Leung, Bittner, Ngo & Liu, 2014). This is due to its high detection capability even in circumstances of low abundance gene expression (Mooney, et al., 2013). Further, the RNA-Seq sequencing is a superior method when it comes to the analysis of the novel and undersigned probe gene expressions, such as in the case of the symbiotic relationships between Rhizobia bacteria and the legume plant A. sinicus plant (Peng, et al., 2014). In this respect, the RNA-Seq sequencing has come in handy, to help in addressing the technical and bias issues related to cross-hybridization and the limited detection of probes (Zhao, Fung-Leung, Bittner, Ngo & Liu, 2014). Nevertheless, the cross-hybridization RNA-Seq sequencing technology has not found much applicability among researchers. This is due to the fact that the method is relatively new compared to the dominantly used microarray gene transcription analysis (Zhao, Fung-Leung, Bittner, Ngo & Liu, 2014). The cost factor and the complexity of analysis of the RNA-Seq sequencing data is yet the other factors contributing to its low popularity and use amongst researchers. References Mooney M, et al. (2013). Comparative RNA-Seq and Microarray Analysis of Gene Expression Changes in B-Cell Lymphomas of Canis familiaris. PLoS ONE 8(4): 1-14. Peng J, et al. (2014). RNA-Seq and Microarrays Analyses Reveal Global Differential Transcriptomes of Mesorhizobium huakuii 7653R between Bacteroids and Free-Living Cells. PLoS ONE 9(4): 1-18. Zhao, S., Fung-Leung, W-P., Bittner, A., Ngo K. & Liu, X. (2014). Comparison of RNA-Seq and Microarray in Transcriptome Profiling of Activated T Cells. PLoS ONE 9(1):1-13. 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