In studies of RNA abundance and gene expression, no one technique can answer all of the questions that need to be asked. So it is necessary to use a variety of experimental methods in concert. Two RNA detection and measurement techniques that complement each other well for this purpose are RNA Fluorescence in situ hybridization (RNA-FISH) and Reverse Transcription quantitative Polymerase Chain Reaction (RT-qPCR).

RNA-FISH is commonly used for in situ detection of individual RNA molecules in tissues or cells. In this assay, samples are fixed and the endogenous RNA is hybridized with short probes that are labeled with a fluorescent marker. The probes are designed to be complementary to the section of target RNA under investigation. And the hybridization of probes to an individual endogenous RNA molecule makes it detectable in situ by fluorescence microscopy (1).

RT-qPCR, on the other hand, is an excellent approach for quantifying cellular RNA and has become a technique of choice for examining changes in one or more genes of interest. The first step in this method is toconvert the RNA template into complementary DNA (cDNA) using a reverse transcriptase. This is followed by qPCR using fluorescent probes to measure and quantify the amplification of the target sequence.

The Benefits of Using RNA-FISH and RT-qPCR in Concert

Combining RT-qPCR and RNA-FISH has many potential applications, from neuroscience to cancer diagnosis. Both methods use fluorescence as a signal and have the ability to detect endogenous transcripts, but the value in combining these two techniques comes from the fact that RNA FISH and RT-qPCR can provide complementary results, each overcoming limitations of the other.

While RNA-FISH provides spatial information on RNA distribution in a population of cells or tissue sample, RT-qPCR complements those results by quantifying the average expression level within the entire sample, or specimen. RT-qPCR is notable for its potential for high throughput and quantitative results that are less prone to subjective interpretation. But, a disadvantage of RT-qPCR is that it destroys the morphology of the sample during RNA extraction, which is, of course, not the case for RNA-FISH (2,3).

Taking advantage of these properties, a team of European researchers opted to use RT-qPCR and RNA-FISH in concert to develop a mathematical model to predict in detail RNA production, splicing and 3’-end maturation in budding yeast. In this study, RNA FISH was used to determine the average number of RNA molecules per cell in a population; while RT-qPCR was used to distinguish the products of the different steps (4).

Researchers from the University of Geneva also illustrated the strength of combining these two techniques, as they were able to localize individual RNAs within different cellular compartments. Using a combination of qPCR and RNA FISH, the team quantified overall sense and antisense RNA levels, as well as followed transcription bursts moving from the nucleus to the cytoplasm (5).

Multiplexing Properties

Like qPCR, RNA FISH also has multiplexing capabilities. As RNA-FISH takes advantage of the same dyes, researchers can perform multiplex experiments using both techniques. This is becoming a popular approach in co-expression studies, to identify which cells can express a particular set of genes.

An example comes from the field of neuroscience, where co-expression of certain genes is indicative of neuronal firing and activation. By using RNA FISH, researchers can literally ‘see’ which cells co-express those particular genes and where they’re located. In addition, the level of co-expression can be quantified by qPCR and regional variations in co-expression can be identified in tissues or cells through RNA FISH.

Localized Gene Expression

Another important benefit of combining these two techniques is an ability to observe regional gene expression differences. For instance, brain regions are often defined by the neuropeptides they express. The RNAs for those neuropeptides are localized in a specific brain region, such as the hypothalamus or the amygdala; and other brain regions may not express that particular RNA at all. If relying solely on qPCR, studies examining these gene expression changes in the brain would involve careful dissection of regions of interest, before any RNA analysis. However, in reality, it would be extremely difficult to ensure that the region of interest is uniformly dissected in every sample.

In this case, complementing these studies with RNA FISH can ensure researchers always correctly identify the area of interest within the whole tissue sample. RNA FISH not only allows the detection of which cells express a gene in a tissue population and zoom in on those cells, but also allows the quantification of the number of RNA molecules per cell and how many cells express those molecules in the organism.

Detecting Cell-to-Cell Variation

Even within the same region of interest or same type of cells, cell populations are not all alike, and some may be high expressers of a certain gene while others may express low levels or not at all. Using qPCR by itself would not take into account cell-to-cell variation that can be detected by RNA-FISH. This approach is especially important in studies of cellular differentiation, development, and stem cell research.

Again using both techniques, UK and Italian researchers identified a series of gene markers to discriminate between cell populations at different levels of differentiation. The differences in transcriptional profiles raise interesting questions about how these cell interact with other cells and the environment (6).

Another example comes from Harvard, where scientists analyzed cell-to-cell variability of alternative RNA splicing for two particular genes. In this case, both RNA FISH and qPCR were used to screen candidate genes and measure RNA abundance (7).


From the examples presented, it’s easy to see the many advantages of combining the two techniques:  Using both techniques, researchers can obtain absolute measurements of transcript numbers obtained from RT-qPCR, and combine these with specific information about RNA localization within cell populations, and individual cells using RNA-FISH. Together, these results can not only build a picture of the localization of RNA expression and the rates of RNA synthesis and degradation within a tissue, cell population or individual cell, but also where in the cell these mechanisms occur and what organelles are involved.

References and Further Reading

  1. Imaging individual mRNA molecules using multiple singly labeled probes. Nat Methods 5(10):877–879 (2008)
  2. Single-Molecule Approaches to Stochastic Gene Expression. Annu Rev Biophys. 38:255–270 (2009)
  3. Gene expression profiling in single cells from the pancreatic islets of Langerhans reveals lognormal distribution of mRNA levels. Genome Res. 15(10): 1388–1392 (2005).
  4. RiboSys, a high-resolution, quantitative approach to measure the in vivo kinetics of pre-mRNA splicing and 39-end processing in Saccharomyces cerevisiae. RNA 16:2570–2580 (2010)
  5. Bimodal expression of PHO84 is modulated by early termination of antisense transcription. Nature structural & molecular biology, 20: 851-859 (2013)
  6. Single-cell gene expression profiling reveals functional heterogeneity of undifferentiated human epidermal cells Development 140, 1433-1444 (2013)
  7. Cell-to-cell variability of alternative RNA splicing. Mol Syst Biol. 7:506 (2011).

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