Gene expression analyses study the occurrence or activity of the formation of a gene product from its coding gene. It is a sensitive indicator of biological activity wherein a changing gene expression pattern is reflected in a change of biological process. Gene expression profiling goes beyond the static information of the genome sequence into a dynamic functional view of an organism’s biology and is a widely used approach in research, clinical and pharmaceutical settings to better understand individual genes, gene pathways, or greater gene activity profiles. Gene expression analysis can be achieved through a variety of means, however real-time PCR has risen as the most popularly used approach and the range of products in the Meridian portfolio are well placed to support these studies.
Gene Expression – Approaches and Techniques
Gene expression analysis typically involves the isolation or capture of transcribed RNA within a sample, followed by amplification and subsequent detection and quantitation.
Absolute quantitation techniques such as serial analysis of gene expression (SAGE) use tags, calculating the number of tags for a given sequence by the total number of tags to gain the overall abundance of a gene product. Microarrays are also very popular for large scale gene expression analysis comparisons, allowing for tens of thousands of targets to be compared simultaneously. The most popular approach is to compare expression patterns from a sample representing the condition of interest to the same set of genes from a reference sample, thus relative expression levels between the two populations can be calculated. This high throughput approach allows for large scale screening of gene pathways or disease-related gene families and is proving a useful approach in disease-prognosis studies, or to predict effects of chemicals or drugs on biological processes in pharmaceutical research. Gene expression using next generation sequencing (NGS) technology, RNA-Seq, is gaining ground and enables the measurement and comparison of millions of random position reads that are subsequently mapped and aligned to each gene, providing a measure of gene expression at an unprecedented level of detail.
For validation of high throughput gene expression analyses and for the analysis of moderate numbers of genes, real-time reverse transcriptase PCR (RT-qPCR) is the method of choice, allowing for accurate relative (or in some cases absolute) quantification of cDNA in a sample, representing gene expression levels of the target of interest. Meridian offers a full range of sample preparation, cDNA synthesis and qPCR reagents, as well as custom assay design services, to assist RT-qPCR gene expression analysis studies.
Gene Expression Using qPCR – Technical Considerations
Although RT-qPCR is considered the gold standard for accurate measurement of gene expression, the true accuracy and subsequent usability of RT-qPCR data is greatly dependent on experimental design, overall workflow and analysis techniques.
There are a number of critical issues across the entire workflow from sample selection and processing, use of appropriate controls and calculation methods, as well as overall experimental design and reporting strategies. Some key elements are outlined below. However, Meridian recommends the MIQE (Minimum Information for Publication of Quantitative Real-Time PCR Experiments) Guidelines1 for proper RT-qPCR experimental design.
In addition to using an appropriate number of replicate samples for statistical significance, an effective gene expression analysis must ensure that comparison samples are meaningful for the biological question under investigation.
Integration of additional controls beyond the standard positive, negative or internal amplification controls is essential, including inter-run calibration methods, sample extraction and integrity controls, pre-amplification controls and appropriate validation of reference genes used for data normalization.
Normalization of Data
Given the sensitivity of RT-qPCR methods, ultimate calculations of gene expression variation are a result of both true biological variation between the samples being measured, as well as technical variation introduced throughout the experimental procedure.
Thus reference genes are chosen to represent stable expression levels between the two samples and are used to normalize results and hopefully remove the technical variation from the final calculations. Choosing stably expressed reference genes is crucial to the success of a gene expression experiment and the right gene for a given experiment will be dependent on a range of variables including the organism being studied, the tissue types being sampled and the overall experimental conditions. A group of reference genes, as opposed to a single candidate, is best practice and allows for more reliable quantitation.
Data Analysis and Reporting
Results obtained from RT-qPCR experiments are logarithmic values that need to be converted to a linear quantity relative to the calibrator or reference sample.
There are a number of models, software programs and calculation approaches to choose from, including those that take into account reaction efficiencies different to an assumed 100% PCR efficiency. Results may vary depending on the calculation method chosen, along with the number or type of reference genes used for normalization. Once relative expression levels have been calculated, appropriate statistical analysis is required to ensure any conclusions drawn from the data are valid and biologically relevant.
Gene Expression - Applications
The most common use of gene expression analysis is to compare expression levels of one or more genes from different samples.
Interesting and common comparisons include:
- Normal vs Disease
- Mutant vs Wild-type
- Before and after treatment
- Time course during development or across treatment regime
- Spatial variation within organs, tissues or other sample types
As research advances and methods improve to gain more accurate pictures of expression patterns linked to specific states, gene expression analyses is moving from the research arena into clinical space. Further, through combining the growing single nucleotide polymorphism (SNP) data with gene expression variation analysis, a more comprehensive picture of phenotype can be obtained. As access to high throughput technology improves and data analysis programs continue to manage the growing complexity of generated data, future applications for gene expression analysis can only improve further.
1. The MIQE Guidelines: (Minimum Information for Publication of Quantitative Real-Time PCR Experiments) Guidelines. S. Bustin et al, Clinical Chemistry 55:4, 611-622 (2009)