Abstract
This protocol details a method to analyze two tissue samples at the transcriptomic level using microarray analysis, ingenuity pathway analysis (IPA) and gene set enrichment analysis (GSEA). Methods such as these provide insight into the mechanisms underlying biological differences across two samples and thus can be applied to interrogate a variety of processes across different tissue samples, conditions, and the like. The full method detailed below can be applied to determine the effects of muscle-specific Notch1 activation in the mdx mouse model and to analyze previously published microarray data of human liposarcoma cell lines.
Keywords: Microarray, Ingenuity pathway analysis, Gene set enrichment analysis, Knowledge-based software, Transcriptomic-based analysis
Background
Transcriptomic analysis of various cell types is crucial to elucidate the functional elements of a cell, provides insight into cell-specific characteristics and can highlight changes associated with different development or disease stages (Wang et al., 2009). While RNA-sequencing has become increasingly popular, the relative cost and time to analysis may be a burden. Therefore, microarray analysis is an alternative tool for comparing relative gene-expression levels between various mRNA samples (Read et al., 2001). Microarray is commonly used to investigate changes associated with disease states whose gene expression patterns can be inferred or have already been defined (Amaratunga et al., 2007). Ingenuity pathway analysis (IPA, QIAGEN) is commonly used in conjunction with large-scale omics data and provides information about pathways, genes and other signatures that may be significantly altered across different samples. Gene set enrichment analysis (GSEA) uses gene sets and characteristics that have been a priori associated with various diseases or pathways in order to provide biological application to the sample of interest.The methods described below were used by Bi and colleagues to understand the effects of Notch signaling in muscle regeneration and liposarcoma, a common soft-tissue cancer type (Bi et al., 2016a). These methods probed the effects of myofiber-specific Notch activation in a Duchenne’s muscular dystrophy (mdx) mouse model and discovered that over-activation of Notch in the mdx mouse model displayed similar gene-expression patterns as healthy human muscle. Similarly, Bi and colleagues performed microarray analysis, IPA and GSEA to find that over-activation of Notch in mouse inguinal white adipose tissue shares signatures of human liposarcoma (Bi et al., 2016b). Both of these studies underscore the importance of comparative analyses when using animal models and since many microarray datasets are available online, gene set enrichment analysis (GSEA) can be used to evaluate already published datasets with respect to the investigator’s interest at relatively low cost. Discoveries such as these are imperative towards developing therapeutic targets and furthering our understanding of biological processes and how their perturbance may influence human disease.
Materials and Reagents
Equipment
Software
Procedure
Data analysis
This part of the protocol includes the analysis of the microarray results and subsequent gene-set enrichment and ingenuity pathway analyses to determine candidate genes and enriched biological pathways/processes (respectively). Following candidate gene selection, real-time PCR analysis is performed to validate candidates (for an overview of workflow see Figure 5). Figure 5. Overview of the workflow for microarray analysis. RNA is isolated from tissue samples (or cells) of interest. Quality of RNA is determined prior to proceeding with generation of cRNA, hybridization and data acquisition. Analysis of the microarray data is performed by the Agilent software. From there, the Gene Set Enrichment Analysis software is employed to yield genes that are significantly enriched in the assayed sample. Microarray data will also be used for Ingenuity Pathway Analysis (IPA) (and can be used for Gene Ontology [GO] analysis) to reveal pathways or biological processes that are enriched in the target sample.
Notes
Acknowledgments
The study is partially supported by NIH grants R01CA212609 and R01AR071649. Protocol was adapted from Bi et al. (2016a and 2016b) listed in the References below.
Competing interests
The authors declare no conflicts of interest or competing interests.
Ethics
All procedures involving the use of animals were performed in accordance with the guidelines presented by Purdue University’s Animal Care and Use Committee (PACUC).
References
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