What is Transcriptomics?
Transcriptomics is the study of the transcriptome—the complete set of RNA transcripts that are produced by the genome, under specific circumstances or in a specific cell—using high-throughput methods, such as microarray analysis. Comparison of transcriptomes allows the identification of genes that are differentially expressed in distinct cell populations, or in response to different treatments. It is 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 (Blackburn, 2017).
How Transcriptomics is measured
According to Blackburn (2017), the analysis of RNAs relies on expertise in next generation sequencing technologies, all of which currently require RNA to be reversed transcribed or copied into complementary DNA (cDNA) for sequencing. Up until recently that 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, labeled 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.
, RNA-seq is more flexible and can be used to discover novel variants, as well as to study known variants even though it is complex. In terms 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 it is 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 & Data Crunch
RNA molecules are weaker than DNA molecules and more prone to breaking down as it is a challenging material to work with. 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. Moreover there is a lack of standardization of the methods used in RNA-sequencing 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, the RNA levels in cells are constantly changing, unlike the static and stable DNA genome. Making sense of these fluctuations, determining what is ‘normal’ and what is not, is even harder since it will not be consistent between individuals.
The Human Transcriptome
Transcriptome analysis has become essential in terms of basic research, clinical, and translational studies. Due to the greater information content of the output, and also due to decreasing sequencing costs, sequencing technologies have gradually replaced microarray techniques. Presently, industrial platforms have been exhibiting a high degree of standardization that contributed to the allowance of reproducible analyses for academic core facilities and commercial service providers. Together with other “omics” technologies, transcriptomics are an essential component in world-wide efforts to understand normalcy and disease at many levels, from the single cell to the tissue and the patient.
When it comes to clinical and experimental pathology, in order to stratify cancer patients and select the best available therapies, the approaches of transcriptomic are now a standard strategy for class discovery and class prediction. The data collection and analysis of transcriptomic in systems biology and systems medicine are instrumental for the development of future targeted therapies. The issue of personalized medicine, the requirement to study the effects of old and novel drugs on the genetic program, and the strong need for predictive markers in the clinic are expected to increase the application of transcriptomics furthermore. Areas such as cell-to-cell variation in transcriptomes, alternative mRNA splicing, and noncoding RNAs will increasingly be explored in development, normal physiology, and disease. Knowledge frontiers will be pushed towards deciphering the mechanisms of transcriptome deregulation by deciphering the master regulators of the transcriptome and the networks in which they function.
The other studies that are subject to the human transcriptome have also provided new insights into the progression of maturation of distinct brain regions and neocortical areas (Kang et al., 2011; Pletikos et al., 2014). For example, the trajectories (Fig. 13.8) of regional and areal gene expression during brain maturation were shown to have similar shapes across the neocortex, with steep increases during mid- and late fetal development. The major inflection point on the curve detailing these trajectories is observed during late infancy. With varying degrees of deviations in different development periods, these trajectories are generally, synchronized. The average deviation of the areal trajectory varied more among neocortical areas during fetal than postnatal periods. Medial prefrontal cortex and inferior temporal cortex appear to mature faster than other areas prenatally, whereas the dorsolateral prefrontal cortex transcriptome reached mature levels more slowly. Global levels of transcriptome maturation become more synchronized among neocortical areas during postnatal development.
Whereas global transcriptome trajectories are generally synchronized across the brain, the trajectories of certain neurodevelopmental processes were observed to vary dramatically from each other and among brain regions. For example, major neurodevelopmental processes, including dendrite and synapse development, myelination exhibit differences in their onset times, and neural cell proliferation and migration, rates of increase and decrease, and shapes between each other and among brain regions, which provides a molecular basis for observations of regional differences in these developmental processes (Kang et al., 2011). Similarly, the expression trajectories of genes encoding neuronal markers and neurotransmitter receptors show differences across brain regions (Kang et al., 2011). Some of the processes develop in a species-dependent manner. For example, the delayed expression of genes associated with synaptic functions has been observed in the postnatal human prefrontal cortex, compared with chimpanzee and rhesus macaque (Somel et al., 2009).
(Figure 13.8. Neocortical regional/areal transcriptional trajectories become more synchronized during postnatal development. Prenatal development exhibits more deviation than postnatal development. (A) A maturational trajectory plot showing the Pearson correlation of gene expression in each sample to the corresponding averaged gene expression in young adulthood (solid line) or mid-fetal development (dashed line). (B) Bar plots showing the average deviation of the areal trajectory from the average overall maturational trajectory (maturational difference index). Error bars represent standard deviation. MFC, medial prefrontal cortex; OFC, orbital prefrontal cortex; DFC, dorsolateral prefrontal cortex, M1C, primary motor (M1) cortex; S1C, somatosensory (S1) cortex; IPC, posterior inferior parietal cortex; A1C, primary auditory (A1C) cortex; STC, superior temporal cortex; ITC, inferior temporal cortex; V1C, primary visual (V1) cortex.)
Considerations for the use of transcriptomics in identifying the ‘genes that matter’ for environmental adaptation
In a 2015 study by Tyler Evans in his journal of experimental biology, transcriptomics has emerged as a powerful approach for exploring physiological responses to the environment. However, like any other experimental approach, transcriptomics has its limitations. Transcriptomics has been criticized as an inappropriate method to identify genes with large impacts on adaptive responses to the environment because: (1) genes with large impacts on fitness are rare; (2) a large change in gene expression does not necessarily equate to a large effect on fitness; and (3) protein activity is most relevant to fitness, and mRNA abundance is an unreliable indicator of protein activity. In this review, these criticisms are re-evaluated in the context of recent systems-level experiments that provide new insight into the relationship between gene expression and fitness during environmental stress. In general, these criticisms remain valid today, and indicate that exclusively using transcriptomics to screen for genes that underlie environmental adaptation will overlook constitutively expressed regulatory genes that play major roles in setting tolerance limits. In the standard practices in transcriptomic data analysis, pipelines may also be limiting insight by prioritizing highly differentially expressed and conserved genes over those genes that undergo moderate fold-changes and cannot be annotated. While these data certainly do not undermine the continued and widespread use of transcriptomics within environmental physiology, they do highlight the types of research questions for which transcriptomics is best suited and the need for more gene functional analyses. Such information is pertinent at a time when transcriptomics has become increasingly tractable and many researchers may be contemplating integrating transcriptomics into their research programs.
What this all means
Transcriptomics in particular the 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 the examples of these panel tests currently available in clinics 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 normalize 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 (Blackburn, 2017).
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 (Blackburn, 2017).
