Unlocking the Value of RNA

Our understanding of RNA is constantly evolving, with exciting discoveries like alternative splicing and RNA catalytic activity only a few decades old (1). The discovery of noncoding RNAs has opened up a new world of nucleic acid function, helping us to understand how life’s incredible phenotypic diversity arises from a relatively small, fixed set of genes. Amazingly intricate and dynamic, alternative splicing has the potential to produce any number of splice variants for a given gene. In fact, according to the most recently published human genome (2), we now have approximately 60,000 human genes transcribing into nearly 200,000 RNA species (see Figure 1 for a more detailed breakdown). The expression levels of those RNAs are highly dynamic, integrating both genetic and epigenetic mechanisms of gene regulation to reflect the state of a biological system – which makes them an excellent choice as biomarkers.

Biomarkers, indicators of biological and disease states, have long been used for diagnostic testing – and in this era of personalized medicine, their importance is growing exponentially. All three of the main macromolecules in the cell – DNA, RNA and protein – can serve as valuable markers of a specific biological trait or measurable change directly associated with a physiological condition or disease status (3). But measuring the biomarkers themselves can prove challenging, as not all of the tools for doing so are created equal in terms of information obtained, technical advances or clinical compatibility.

Which biomarker is best?

Most biomarkers today are discovered as RNA, thanks to transcriptomic discovery programs that rely on the high-throughput capabilities of microarrays and RNA sequencing. Because RNA profiling benefits from rapid and relatively inexpensive next-generation sequencing (NGS) and whole-genome microarrays, RNA is an ideal biomarker choice – and yet, it remains difficult to translate discoveries to the clinic for routine measurement. Why? The blame lies with established analytical technologies. Although it’s commonplace to detect and visualize native DNA and proteins in a single cell, the best routine measurement tools for RNA analysis detect and quantify it in solution, which results in a loss of morphological context. Because we lack appropriate methodology for measuring RNA in situ, many laboratories use DNA and protein as surrogates, which can be problematic for a variety of reasons (see Table 1).

The main issue with analyzing a protein in place of an RNA biomarker is that, because of gene regulation at the transcriptional and post-transcriptional levels, protein and RNA levels rarely exhibit a linear correlation. So even though the expression pattern of an RNA molecule may indicate a particular biological state, its protein counterpart may signal something very different. And for noncoding RNA molecules, which have no protein counterparts, measuring RNA is the only option. An additional complication is that most protein detection techniques – such as immunohistochemistry (IHC), the main method of protein biomarker detection in situ – rely on the use of antibodies, whose quality can be limiting or prevent the detection of scarce or secreted proteins. In some cases, no antibody to the protein of interest exists at all, meaning that researchers must either develop and validate their own over time, or find an alternative detection method.

Like RNA and proteins, DNA biomarkers are also important in research and clinical applications. This is especially true because many structural alterations to DNA are linked to cancer and other diseases, so it’s important to look at the DNA itself using techniques like DNA fluorescence in situ hybridization (FISH), polymerase chain reaction (PCR) and evolving NGS technologies. But structural rearrangements don’t always result in changes to transcriptional activity or levels of expression – so an increased DNA copy number does not necessarily translate to an increased amount of RNA or protein (4).

As DNA and protein surrogates may not recapitulate the information provided by an RNA biomarker, and solution-based RNA analysis takes the expression out of its cellular context, what’s the best way for laboratory medicine professionals to perform routine biomarker profiling following transcriptomic exploration? The answer lies in direct RNA measurement in situ. And at the moment, there’s an unprecedented demand for effective tools to do just that. To gain biological insights, we need the ability to not only detect the RNA, but also to localize it in the cell. It’s this combination of detection and localization that researchers are targeting now in order to take advantage of RNA’s potential as an indicator of health and disease.

The key to precise localization

First applied to RNA in 1981 (5,6), in situ hybridization (ISH) can effectively determine the precise localization of target RNA in single cells. The technique provides the spatial and morphological context we need to understand the physiological and pathological relevance of a given target RNA. For instance, we can detect RNA expression in specific cell types to distinguish between stromal and tumor expression (see Figure 2), or detect it in rare cell populations such as cancer stem cells or circulating tumor cells. So if the answer to the RNA measurement conundrum lies in a technique that was introduced decades ago, why is it not widely used? In the past, ISH was not only time-consuming and difficult to use, but it lacked sensitivity and specificity – deficits that presented a barrier to its becoming commonplace in research and diagnostic pathology labs. But now that there have been some significant advancements to ISH techniques, the outlook is changing.

Nowadays, we have multiplex nucleic acid in situ hybridization technologies that overcome those limitations, enabling single-cell gene expression profiling in situ with detection sensitivity and specificity down to a single molecule (see Figure 3). Incorporating a variety of innovative approaches to probe design and signal amplification, these recent advances (7–11) take in situ RNA detection to an entirely new level. Single-molecule detection allows single-cell transcript quantification to be as simple as counting dots in a cell, which can be automated with advanced image analysis software. Robust probe design strategies also provide compatibility with partially degraded RNA, allowing for robust RNA detection in routine clinical specimens like FFPE tissue. Adopting these techniques on commonly used automated slide staining instruments minimizes manual labor and variability, facilitating seamless integration of RNA biomarker assays into existing pathology lab workflows. The time has come to unlock the full potential of RNA biomarkers.

Because it’s central to the flow of genetic information in the cell, RNA is ideally positioned to reflect cellular physiology, making it an ideal biomarker. The limitations of DNA and protein surrogates, and of analyzing RNA in solution, make it clear that there is no substitute for measuring RNA itself in situ – and thereby providing a direct path from the lab to the clinic. While NGS approaches continue to fuel RNA biomarker discoveries, quantitative RNA biomarker analysis that includes tissue morphology at single-cell resolution, will facilitate rapid validation of novel RNA biomarkers and help us translate them into clinical use.