Comparing and measuring gene expression is certainly an integral part of research—gene expression patterns continue to show us how different cell networks are regulated, and point to new biological pathways and possible treatments for disease.
But one crucial part of gene expression lies in making sure that differences in gene expression are due to gene expression, and NOT variations in the amount of starting material, enzyme efficiencies, and overall transcription levels in different cells and tissues. What can control for all that? Reference genes.
Reference genes, or “housekeeping” genes, are those that express at a constant, known level, regardless of what do you in your experiments. By comparing your test samples reference genes, you can compare RT-PCR results between different types of cells or tissues, and not have to worry about differing sample sizes.
So how do you pick out a reference gene?
Look for stable expression. A lot of literature has been published in this area, so a good lit search will save a lot of lab time (not to mention pain). Jo Vandesompele and colleagues at Ghent University hospital ranked 10 commonly used reference genes, and found four: HPRT1, ACTB, GAPD, and B2M—that were the most stable of the common genes.
Know your tissues. While HPRT1, SDHA, and UBC are the most stable genes in neuroblastoma, Xiaozhu Zhang and others at University of British Columbia found that in neutrophils, GNB2LI, HPRT1, RPL32, ACTB, and B2M were the best choices. Some reference genes are useful in multiple cell types, but not all. Other tissues and cell types may have other “best bets.”
Use more than one. Since there’s bound to be some variation between cells (even cells collected at the same time) as well as some variation among genes, using more than one reference gene keeps your normalization efforts, well, normal. This can also help normalize things in tissues that the literature doesn’t illuminate as well.
One nice thing about reference genes is that you can normalize results with very little starting material (often an issue with RT-PCR analysis). By comparing Ct values of your target and reference genes, you can set normal values of gene expression without calculating mass or other values.
Some tissues are tricky to work with. This truth was lost on me in the early years of grad school because I worked with liver samples. If you’re extracting RNA from liver samples, you’re likely not losing sleep over your massive RNA yields. But for the folks doing RNA extractions with less willing donors, such […]
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