Meet another member of the 'omics family: transcriptomics – the study of all the RNA molecules within a cell, otherwise known as the transcriptome. Many studies of the transcriptome focus on messenger (m)RNA molecules only, which reflect the genes that are being actively expressed (as protein products) in a cell or tissue at a given time or in a given situation. However, over 95%of the RNAs in a cell are not translated into a protein, so transcriptomics also includes the study of these non-coding RNAs, which have a dizzying variety of forms and functions.
Understanding and categorising the whole transcriptome, including discovering the role of many of the RNA molecules in the cell, is a daunting scientific and analytical challenge.
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How is it measured?
Analysis of RNAs relies on expertise in next generation sequencing technologies, all of which currently require RNA to be reverse transcribed (copied) into complementary DNA (cDNA) for sequencing. Until recently, microarrays were the most common technique in use in research laboratories. Microscopic spots containing DNA sequences of interest are attached to a solid surface like a microscope slide. cDNA for analysis, labelled with fluorescent markers, is washed over the slide; cDNAs attach to any complementary strands on the slide. Higher levels of binding give a higher fluorescent signal and indicate a higher level of gene activity. This technique is commonly used, for example in panel assays that test for the presence of a known subset of gene transcripts. Another similar technique in use is quantitative reverse transcription (qRT)-PCR, which is used to detect the presence and quantity of RNA by converting it into cDNA via a process called reverse transcription and amplifying the cDNA using PCR (polymerase chain reaction). Both these techniques are simpler and quicker than the main research method now in use, RNA-seq.
Although complex, RNA-seq is more flexible and can be used to discover novel variants, as well as to study known variants. As with qRT-PCR, RNA is extracted from the tissue and then might be filtered to remove RNAs that are not of interest to the experiment. Once reversed transcribed to cDNA, the samples are sequenced as short reads (small DNA sequences) and bioinformatics tools are used to assemble the transcriptome. An important new advance is the ability to directly sequence RNA using long-read nanopore technology.
Technical challenges and a data crunch
RNA is a challenging material to work with. RNA molecules are weaker than DNA molecules and more prone to breaking down. RNA is therefore more sensitive to external conditions such as heat and RNases, enzymes that break down RNA; these are found everywhere, including on the surface of the skin, and are difficult to remove completely. In addition there is a lack of standardisation of the methods used in RNA-seq and other experiments. Different researchers have their preferred methods for RNA extraction and reverse transcription, as well as the extensive bioinformatics analysis needed to interpret their experimental data; this requires significant resources. Furthermore, RNA levels in the cell are constantly changing, unlike the DNA genome which is static and stable. Making sense of these fluctuations – determining what is 'normal' and what is not – is even harder since it will not be consistent between individuals.
What does this all mean?
Transcriptomics – in particular whole transcriptome sequencing– is a technically challenging and highly useful research tool, but it is not yet ready for use in mainstream medicine. Existing clinical applications of transcriptome analysis are restricted to panel tests that make use of microarrays or qRT-PCR, examining the activity of a subset of genes known to provide prognostic information about a disease, informing clinical decisions about how much, or how little, treatment a patient needs.
One of these panel tests currently available in the clinic is the OncotypeDx®, used to assess breast cancer recurrence risk after surgery in patients with oestrogen receptor positive, HER2-negative (ER+/Her2-) tumours. The test measures the gene expression levels of 16 breast cancer associated genes, plus five others used as a form of control to normalise gene expression levels for each patient. The expression levels are used to calculate a risk score; those with a low risk score have a good prognosis and will not need chemotherapy in addition to hormone therapy.
Another test currently in clinical trials is AlloMap®, which rules out acute cellular rejection of heart transplants. This type of immune response is mediated by white blood cells and the risk of such rejection is greatest during the first three months after a transplant. Whilst this type of rejection can be treated using immunosuppression, it can currently only be detected by an invasive heart biopsy. If rejection can be ruled out with a non-invasive test, then some patients can be spared a biopsy.
What are the future challenges for transcriptomics in clinical care?
We will not see any form of transcriptome-wide sequencing in routine clinical care any time soon. Much of the research in this area is still in the discovery phase, investigating what transcriptome findings mean and how they relate to human health and disease. Scientists are still categorising the wealth of non-coding RNAs found in cells and finding out what they do, and as the findings of the ENCODE project demonstrated, this is not without controversy. The immediate uses of transcriptome-type technologies will be in the increasing use of the panel tests already described and others likely to be approved for clinical use in the future, the majority of which will probably be used in oncology. A ‘multi-omic’ future, where genomic, transcriptomic and proteomic information are combined to fully understand a patient's condition may be possible one day – but not yet.