Nearly all of the cells in an organism carry the same genetic material and so the same collection of genes, but different cells have different roles and so have different profiles of genes active at any one time. Gene expression analysis provides a snapshot of these genetic patterns, and quantitative PCR (real-time PCR) plays a role in the process.



Applications for gene expression analysis in research

Gene expression analysis helps researchers to unpack how genes are regulated, and how gene expression changes in different conditions, at different times of the day, and at different stages of lifecycles, for example during cell division, in disease states in humans and animals, or during disease and pathogen-defence responses in plants. This includes when genes are expressed and overexpressed, and when their expression is suppressed or blocked.

Applications for gene expression analysis in research include:

  • Basic biological research, including developmental biology
  • Understanding cell division and cell pathways
  • Quantitation of RNA and gene products
  • siRNA/RNAi experiments
  • Quality control and assay validation
  • Microarray validation
  • Low-fold copy number discrimination
  • Biomarker discovery and validation
  • Phenotypic analysis of cells and tissues
  • Gene expression changes in disease e.g. cancer

PCR in gene expression profiling

Gene expression is generally measured in terms of expression of messenger RNA (mRNA), which is transcribed from DNA and then used as a template to code for proteins. However, proteins are not the only gene products, and assays are being developed to profile microRNAs (miRNAs), which have regulatory roles, and other non-coding forms of RNA, including ribosomal RNA (rRNA) and transfer RNA (tRNA).

In gene expression profiling using whole-genome or focused microarrays, the mRNA is isolated and converted to cDNA (complementary DNA) using reverse transcription. The cDNA fragments are then amplified using qPCR and the quantity and proportions of the original RNA calculated based on the fluorescent signals.

Researchers can use gene expression profiling to understand how gene expression changes in disease, for example comparing the gene expression profiles of normal cells and cancer cells from different tumours in the same individual, or of the same type of tumours in different individuals. By comparing gene expression profiles in samples taken from people before and after they develop disease, or in different stages of the disease, could help find diagnostic and prognostic biomarkers.

Gene expression profiling can be used to investigate how gene expression changes in people who respond differently to the same drug, potentially identifying or validating predictive biomarkers that could be used in personalised medicine.

Suzanne Elvidge is a freelance science, biopharma, business and health writer with more than 20 years of experience. She is editor of Genome Engineering, a blog that monitors the latest developments in genome engineering and that aims to educate (and sometimes to entertain!) and has written for a range of online and print publications including FierceBiomarkers, FierceDrugDelivery, European Life Science, the Journal of Life Sciences (now the Burrill Report), In Vivo, Life Science Leader, Nature Biotechnology, PR Week and Start-Up. She specialises in writing on pharmaceuticals, biotechnology, healthcare, science, lifestyle and green living, but can write on any topic given enough tea and chocolate biscuits. She lives just beyond the neck end of nowhere in the Peak District with her second-hand bookseller husband and two second-hand cats.