Scientists are really interested in understanding how the cells in our bodies function, and there are many different ways that they can do that. To understand how these different techniques work, let’s think back to one of the most fundamental concepts in biology: The Central Dogma. Remember that the DNA in our cells gets transcribed into RNA molecules which then get translated into protein. To understand how a cell functions, a researcher could look at the DNA, RNA, or proteins in the cell. Since proteins are the molecules that perform most of the tasks within cells, it is often useful to analyze them directly. However, sometimes that isn’t the best or easiest approach, so scientists also look at the RNA to understand which genes are being expressed. Looking at gene expression can allow us to make inferences about gene function, and if you already know the function of a gene, looking at expression can help you extrapolate what the cell is doing at the protein level. Let’s take a look at an example of how scientists can analyze the RNA inside of a cell.
Let’s say we’re researchers interested in understanding how the cells of our immune system change with age. Our research team is specifically interested in studying macrophages, which are a critically important type of white blood cell that find and destroy foreign pathogens from around the body. We’ve identified a gene – let’s call it gene A – that is involved in the process of breaking down a pathogen once the macrophage has engulfed it. This is a novel gene, so we don’t yet have the means to analyze the protein abundance. But, we can get some preliminary information about the role the gene is playing by looking at transcript levels. We want to know if the expression of this gene increases, decreases, or remains constant throughout the aging process. We obtain two tissue samples: one from a young donor and one from an elderly donor. We then isolate the macrophages from the tissue and now we want to analyze the RNA transcript levels of gene A within each group of macrophages to better understand how age impacts the expression of Gene A. The technique we are going to utilize to do this is called quantitative polymerase chain reaction or qPCR. Let’s take a look at how this technique works.
qPCR is a special variation on the traditional version of the polymerase chain reaction – or PCR – method, so let’s briefly review the basics of how PCR works. PCR is a lab technique used to amplify and detect segments of DNA. This process involves three main steps: denaturation, annealing, and extension. In the denaturation step, the hydrogen bonds that hold the two strands of DNA together are broken, which separates them into two separate single strand DNA molecules. The primers then anneal to the single strands of DNA and use each strand as a template to generate two new double-stranded DNA molecules. These steps are repeated in a temperature-dependent manner for many cycles in order to amplify the section of DNA you want to examine. In qPCR, we will rely on the same principles of PCR with two key differences. First, qPCR relies on an RNA template instead of DNA, and, second, instead of analyzing the product after many rounds of amplification, qPCR allows us to monitor the amplification that takes place during each cycle.
Now let’s go back to our analysis of gene A and talk through the qPCR process. The first step in analyzing our gene of interest is to isolate the RNA from both the young and the elderly macrophages, much the same as we would with DNA in regular PCR. To keep things simple, let’s just look at the macrophages from the young person since we will treat the macrophages from the elderly person the same way. So, we start by taking the RNA out of the macrophages, but we immediately run into a problem: RNA isn’t very stable and degrades quite quickly. When scientists realized this many years back, they came up with a clever solution to this problem. Once we have our RNA template, we can go through an additional step, called reverse transcription, to stabilize the RNA before we begin our analysis. Reverse transcription relies on an enzyme called reverse transcriptase, which some viruses use to replicate their RNA-based genomes. When RNA molecules are exposed to reverse transcriptase in the presence of a buffer and free nucleotides, their code serves as a template to produce complementary DNA, or cDNA. This hybrid molecule is much more stable than RNA and can be used in the subsequent qPCR assay.
After creating cDNA for both samples, it’s time to actually start the qPCR reaction. For this experiment, we will have five samples. The first is our negative control which does not contain any cDNA. Excluding the template from the reaction helps us ensure that any changes in transcript levels we observe are due to the correct amplification of our cDNA and not some random signal. The second and third samples will allow us to monitor the levels of something called a housekeeping gene in both our young and old samples. Genes that fall into this category are constantly expressed within most cells at all times. For example, actin is commonly included in qPCR studies as a housekeeping gene because it is an important protein that helps form the cytoskeletal structure of cells and is highly expressed in most cells. I will explain in more detail later exactly how, but housekeeping genes serve as an important reference point for analyzing the expression of your gene of interest. The housekeeping gene also helps you understand whether something was off in your reaction. If you see dramatic changes in amplification of your housekeeping gene between each sample, that likely means that the reaction was flawed and your data won’t be reliable. The fourth and fifth samples will show the expression of gene A in both our young and old donors.
To set up these reactions, we take cDNA from each sample (except the negative control) and mix it with a buffer, free nucleotides, and something called a probe. Like I mentioned before, the beauty of qPCR is that it tells us how much transcript is being produced during each amplification cycle and the probe is what allows us to make this measurement. There are several different types of probes and I won’t get into the details of how each of them work. The main thing to know is that any type of probe fluoresces when double-stranded DNA is created. As rounds of amplification are completed, the fluorescent signal increases. After we’ve mixed all components of the reaction together, we place all of our samples into a special thermocycler that is equipped with a camera to detect changes in fluorescence. These changes are recorded for each sample and plotted on a graph like this.
Once the reaction is complete, it’s finally time to analyze the data and see if we can answer our initial question about whether the expression of gene A changes with age. I won’t go too far into the specifics of how the data is analyzed, but, in a nutshell, the expression of each sample is compared to both the negative control and the housekeeping gene and then the expression levels for each sample are reported with units of relative fold expression. Think about it this way: if I wanted to compare the height of two different mountains, I couldn’t do that without a common reference point, so people use sea level as a reference point. If we set sea level as zero, then we can accurately compare the difference in height between the mountains because we’re starting with the same reference point. If we measured the height of both mountains starting at ground level rather than sea level, we would get a totally different comparison. This is why it is important to start at a common reference. In qPCR, a housekeeping gene basically sets our sea level and serves as a common reference point for any transcript level analysis. That means all of our data is discussed in terms of the fold-difference relative to the housekeeping level. Let’s look at some data from our macrophage example.
We can see that the expression of gene A in the young macrophages (Y) is significantly higher than the expression in the elderly (E) macrophages. Since we used a common reference point (our housekeeping gene), we can be confident that the difference in gene A expression is biologically relevant. This result supports the hypothesis that gene A expression declines with age. Since we know that this gene is involved in the destruction of pathogens inside the macrophage, a decline in expression could have detrimental effects on the function of macrophages throughout the aging process. This experiment could open a new avenue for exploration to further investigate what this gene does and how we could potentially intervene to restore any function that is lost over time.
