Introduction
One of the biggest surprises of the human genome project was that the number of protein coding genes found in our genome was unexpectedly small: a mere 20,000 versus the anticipated 100,000 or more. This presented a real conundrum for the research community – how does our incredible level of phenotypic complexity and diversity arise from such a modest set of genes? Discovering the world of non-coding RNAs revealed the answer, thanks to new transcriptomic technologies such as microarrays and next generation sequencing (NGS) enabling new avenues in RNA research. Today, new classes of RNAs are being discovered on a regular basis that do not code for proteins, but instead have a hand in genetic regulatory control and a wide range of cellular activities. Amazingly diverse and changeable, these transcripts have the potential to produce any number of splice variants, and in the latest catalog compiled by the ENCODE project, [1] we now know that there are approximately 60,000 genes and ~200,000 RNA species. This RNA revolution has created a fundamental shift in how researchers now view “the central dogma”, challenging the traditional idea that DNA is the master and RNA merely the messenger. The revelation of RNA’s vast number and diverse role in gene regulation has brought to light the many possibilities for using RNA as an indicator of biological states: the biomarker.
Indeed, the widespread use of transcriptomic profiling in cancer research over recent years has proven that, like protein, RNA is a rich source of clinically valuable biomarkers for diagnosis, prognosis and predicting therapeutic response. [2, 3] Although such transcriptomic profiling may identify many potential biomarkers, translating these discoveries into the clinic for routine RNA biomarker measurement presents challenges in terms of established analytical technologies. While it is commonplace to detect and visualize DNA and proteins in their native context within single cells, until now the best routine measurement tools for RNA analysis have been those that detect and quantify RNA in solution. However, these methods only provide an “average” measurement in a cell population, masking the incredible level of cell-to-cell variation in RNA expression. With its central role in cell physiology, protein has been the more popular biomarker traditionally, providing functional insights into disease states while also lending itself to wellestablished detection techniques. The lack of effective RNA in situ detection methods has often resulted in the use of DNA and protein as surrogates for those RNA biomarkers initially discovered, however this can be problematic. Changes in RNA expression may not result from DNA alterations and may not correlate with protein levels – or there may be no protein counterparts at all in the case of non-coding RNAs. The bottom line is, when utilizing RNA biomarkers initially discovered during microarray or RNA-seq programs, the best approach is direct RNA measurement in situ, since the use of a DNA or protein surrogate, or solution-based RNA analysis, inevitably leads to information loss, compromising the full diagnostic utility of the RNA biomarker. The discovery of the “new world” of RNA has sparked an unprecedented drive towards better tools to characterize the complexity of RNA – in terms of quantity, function and spatial distribution. Presenting a vital piece of the puzzle in elucidating the role played by RNA in disease states, pinpointing the localization of specific RNAs within cells and tissue architecture is an important factor in realizing its true potential as a biomarker. Here we examine the utility of RNA as a biomarker, and how this is profoundly linked to the developing technologies now available for its detection, localization and validation.
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