Dr Alfred Grech, Dr Stephen West, Dr Ramon Tonna


The transcriptome is the collection of RNAs transcribed by a cell or tissue. Transcriptomics is an integral component with other -omics technology. Specifically, it is being used with them in translational research to understand health and disease at different levels, be it  single-cell resolution, a tissue or a patient. The knowledge gained will and is being translated in the prevention and management of human diseases, including Covid-19.


The term transcriptome was coined in 1996 by Charles Auffrey, a distinguished researcher at the French National Centre for Scientific Research. The transcriptome is the collection of RNAs transcribed and that includes tRNA (transfer RNA), rRNA (ribosomal RNA), mRNA (messenger RNA) and ncRNA (noncoding RNA). The transcriptome is dynamic and changes temporally and spatially in various cells of the body. Its profiling with the help of bioinformatics is and will provide knowledge of its functionality in human health and disease, including Covid-19. With other –omic technology (genomics, epigenomics, proteomics, metabolomics, microbiomics), transcriptomic profiling is being applied to understand normalcy and pathology, be that of a single-cell, a tissue or a patient. Indeed, transcriptomic profiling is becoming instrumental for stratification of patients, for the selection of better targeted therapies (personalized medicine), and for biomarkers in diagnosis and prognosis. Moreover transcriptomic data is guiding drug repositioning, saving resources and time, and thus speeding the development of drugs.1


The first method to profile the human transcriptome was ‘EST (Expressed Sequence Tags) sequencing’. Later on, other methods included ‘SAGE’ (Serial Analysis of Gene Expression) and ‘microarray’.  Today, NGS (Next Generation Sequencing)-based RNA-Seq is the method of choice. Notably, the most commonly method used is Illumina sequencing.

RNA-Seq is able to detect the expression of all the genes and also the different species of the RNA world. This yields huge amounts of data that might hold back its use clinically because of costs and feasibility. A practical solution is available in targeted RNA-Seq methods like ‘AmpliSeq-RNA panels’ that are made to study a relatively small number of gene sets (usually 150 to 900 genes).2

Of course, in order to cater for the explosion of data coming from transcriptomics and other –omic platforms, there is constantly the need to find the best bioinformatic pipelines.


There is a lot of research worldwide to understand the molecular mechanisms involved in carcinogenesis. It has now been well-established that mutations in genomic regions that do not code for proteins are also responsible for carcinogenesis. These regions are still transcribed into ncRNAs and their aberrant expression is linked with different types of cancer.

Amongst the thousands of ncRNAs, research is showing that two classes of non-coding RNAs, (i) long non-coding RNAs (lncRNAs) and (ii) small nucleolar RNAs (snoRNAs) co-ordinate metastasis. Creaet al.3 propose that some snoRNAs and lncRNAs synergistically interact with protein-coding genes, generating a metastatic cellular phenotype. Moreover, they point out that lncRNAs and snoRNAs expression profiles can be potential biomarkers and be of use in the diagnosis and prognosis in cancer. And since the silencing of these lncRNAs and snoRNAs in models causes the death of cancerous cells and their metastasis, Creaet al. propose that they can be potential new targets for cancer treatment as well.

A specific example of a lncRNA implicated in carcinogenesis and the integrated application of –omics (proteomics, genomics and transcriptomics) is that done by Wuet al.4 The lncRNA is called HOTAIR and stands for HOXTranscript Antisense Intergenic RNA. The study showed that HOTAIR is dysregulated in hepatocellular carcinoma (HCC). Specifically, HOTAIR inhibition was found to be associated with dysregulation of several transcripts and proteins. Functional bioinformatic studies of the data collected showed that these transcripts and proteins relate to biological circuits in cancer. Furthermore, the study showed that HOTAIR caused cell proliferation partly by its regulation of OGFr expression (opioid growth factor receptor), the latter being known to have a negative regulation on cell proliferation in HCC.

HOTAIR is also dysregulated (specifically it is overexpressed) in breast cancer.  This over-expression is responsible in metastasis through HOTAIR recruitment of another complex molecule called PolycombRepressive Complex 2 (PRC2), which then silences additional genes, besides the HOXD gene cluster. In 2016, Meredith et al.5carried out a proteomic analysis and found that other proteins are associated with HOTAIR’s action. One such significant interaction is that between HOTAIR and hnRNP (heterogeneous nuclear ribonucleoprotein) A2/B1. This interaction is central to chromatin structure regulation in cells of breast cancer. Indeed, the authors found that knocking down A2/B1 reduced PRC2 activity and also, cell invasion.

Also regarding HOTAIR, Tan et al.6 from their study propose that the lncRNA HOTAIR can be used as a new biomarker in the prognosis and diagnosis for glioblastoma multiforme (GBM), a primary brain tumour that is very aggressive in adults. In fact, they found that HOTAIR expression is associated with high-grade brain tumours. Several other studies are showing HOTAIR as a potential clinical biomarker in oesophageal squamous cell carcinoma, gastric cancer, thyroid cancer, and colorectal cancer amongst others.

There are other lncRNs besides HOTAIR that are being studied in cancer, like the lncRNA EBLN3P that promotes liver cancer progression7 and several other lncrNAs (A2M-AS1, DLEU2, LINC01133, LINC00675, MIR155HG, SLC25A25-AS1, LINC01857, LOC642852 (LINC00205), ITGB2-AS1, TSPOAP1-AS1 and PSMB8-AS1) implicated in pancreatic cancer.8

Other studies are showing that snoRNAs also have apivotal role in tumorigenesis and can be used as biomarkers.9 They have been found to be dysregulated in multiple cancers like those of the lung, digestive tract and urologic tract where they participate in tumour cell proliferation and metastasis.10


Schizophrenia and bipolar disorder are two mental illnesses affecting more than 2% of adults. Their pathophysiology remains unclear but transcriptomic studies are starting to reveal some of the mechanisms involved, implicating dysfunctions in GABA, glutamate and immunological pathways.11

In their study, Labontéet al.12 analysed the known sexual dimorphism in major depressive disorder (MDD). They analysed transcriptomicallysix brain regions in patients with MDD. They also compared the human transcriptome profiles with that of a mouse model. They characterised sex-specific gene networks, like the female-specific hub gene Dusp6 (Dual Specificity Phosphatase 6) which is downregulated by increased ERK (extracellularsignal-regulated kinase) signalling and increased pyramidal neuron excitability. They propose that their findings are further investigated in order to guide treatments for MDD based on its sexual dimorphism.

Also, Gandalet al.13 profiled the transcriptome of patients with schizophrenia, depression, bipolar disorder, autism and alcoholism with a control of healthy people. They found that there are transcriptional perturbations that are shared, implicating that these neuropsychiatric disorders have molecular pathways that are convergent.

In posttraumatic stress disorder (PTSD), Girgentiet al.14 have used RNAseq to study transcriptome signatures of PTSD patients. They found aberrant expression of genes in the brain and in peripheral blood cells. These findings are starting to reveal the molecular and cellular mechanisms that underlie PTSD and so will help in its management.


Using transcriptomic profiling, Tran et al.15 analysed post-mortem brains in patients with ASD to understand the culprit molecular mechanisms involved. Specifically they profiled ‘adenosine-to-inosine (A-to-I) editing’, the latter being the commonest type of RNA editing and is a mechanism that produces RNA and protein diversity, which is not coded from the genome directly. Adenosine-to-inosine (A-to-I) editing is done by RNA-editing enzymes called ADAR (adenosine deaminase acting on RNA) proteins. Their study showed global hypo-editing in ASD brains involving many synaptic genes. Thus, they propose that dysregulation of RNA-editing in ASD might be one of the pathomechanisms involved.

Certain cancers are a co-morbidity in patients with ASD. Forés-Martos et al.16 carried out a transcriptomic analysis of ASD brains to support this. They found that 4 cancer types (out of the 22 studied), specifically thyroid, kidney, brain and pancreatic cancers, have transcriptomic dysregulation as occurs in ASD. In particular, the study revealed that both diseases have impairments of the immune system, of oxidative phosphorylation and ATP synthesis. Also, the analysis involving the brain and kidney cancers showed dysregulation in the PI3K/AKT/MTOR axis, which is also found in ASD.


Studies of the transcriptome profiles of auto-immune disease are showing the potential in identifying biomarkers for their early diagnosis. Also, they will definitely help in the identification of new therapies, and may even help prevent specific autoimmune diseases.

For example in rheumatoid arthritis (RA), Li et al.17 studied the role of the transcript MEG3 (maternally expressed gene 3), which is a lncRNA. They showed that MEG3 is protective in RA by inhibiting the over-expression of miR-141 and inactivating AKT/mTOR signalling pathway. The authors propose that MEG3 holds great potential in managing RA.

In systemic lupus erythematosus (SLE), Panousiset al.18 identified transcriptome signatures for SLE susceptibility and severity. Specifically they showed three signatures, (i) a ‘susceptibility signature’ in clinical remission, (ii) an ‘activity signature’ associated with regulation of immune cell metabolism and (iii) a ‘severe signature’ especially in patients with nephritis. They propose that their findings could be used in the diagnosis and stratification of patients and hence assist in personalised management given the vast heterogeneity in SLE.To tackle this heterogeneity, Rai et al.19 had also done a transcriptomic analysis to stratify SLE patients based on autoantibody specificities. Again they identified SLE subgroups implicating specific immunological pathomechanisms that could assist in theranostics of SLE patients.


Transcriptomics together with genomics, proteomics and metabolomics is being used to identify the human microbiome and its gene products.20 It is being shown that each individual has a dynamic microbiome that changes temporally and spatially in the body and that it has a pivotal role in health and disease. Again such information is being translated in the clinical setting for personalized theranostics. For example, in HBV-associated hepatocellular carcinoma, gut microbiome-transcriptome signature studies have shown that patients’ gut microbes have the potential to be used as prognostic biomarkers.  In inflammatory bowel diseases, similar gut microbiome-transcriptome studies during inflammatory bowel disease activity showed functional dysbiosis of the gut microbiome. Such signatures have the potential to monitor the inflammation.


The ongoing COVID-19 pandemic is causing overwhelming mortality world-wide. In order to study the molecular mechanisms of the immune response towards this infectious disease, Xiong et al.21transcriptomically analysed bronchoalveolar lavage fluid (BALF) and peripheral blood mononuclear cells from patients with Covid-19. They found out a disproportionate cytokine release of CCL2/MCP-1, CXCL10/IP-10, CCL3/MIP-1A, and CCL4/MIP1B. Moreover, their transcriptomic signatures showed that the lymphopenia seen in COVID-19 patients might be due to activation of apoptosis and P53 signalling pathway in lymphocytes. They also propose that their transcriptomic data can be further investigated to guide anti-inflammatory therapy.


Along similar transcriptomic pipelines, Fagone et al.22 analysed the pathomechanisms brought about by SARS-CoV-2.  Their transcriptomic profiles indicated potential therapeutic drugs like mitogen-activated protein kinase (MEK), serine-threonine kinase (AKT), mammalian target of rapamycin (mTOR) and I kappa B kinase (IKK) inhibitors.

Gardinassiet al.23 also used transcriptomic platforms to analyse the pathophysiology of this new complex infectious disease. They found amongst other things that there is an over-expression of genes connected to oxidative phosphorylation in BALF and peripheral mononuclear leukocytes. This implicates mitochondrial activity during SARS-CoV-2 infection. They also found increased gene expression associated with the biosynthesis of heme, which is known to be a mechanism that offers protection against oxidative stress. It is also known that hypoxia alters gene expression that code for proteins important for heme biosynthesis. Knowing all this and together with their findings, Gardinassiet al. thus propose that the excessive accumulation of heme might be intensifying the production of pro-inflammatory cytokines or cause the observed increased coagulation inside blood vessels in COVID-19.


Drug repositioning (also called drug repurposing)24 investigates existing drugs for new therapy. It facilitates discovering new mode of actions of old drugs as well as the discovery of new medicines.25In this field, transcriptomic data of diseases is being harnessed to understand the pathological pathways and drug mode of action. All this is guiding drug repositioning, saving resources and time and thus speeding up the development of drugs. For example, Arakelyan et al.1 used transcriptome-guided analysis to reposition infliximab and brodalumab, two already approved biologics. Specifically they found that the former has potential therapeutic action for ulcerative colitis and Crohn’s disease whilst the latter has potential for psoriasis.

Serafin et al.24also point out that drug repositioning studies have pointed to potential drugs that can be used against SARS-CoV-2 and  the  treatment of COVID-19 disease.


Surely, the knowledge gained from the integration of transcriptomics with other –omic platforms (including single cell –omics) will be harnessed to understand the organisation and control of different tissues and of single cells. The data gathered and analysed will also reveal the perturbations/aberrations which are specific to pathological states and this will ultimately provide better patient stratification and targeted theranostics.

Such integrated studies are already being done. For example, in 2019, Schüssler-Fiorenza Rose et al.26 used –omics (specifically, immunomics, transcriptomics, proteomics, metabolomics and microbiomics) together with wearable monitoring devices to study molecular and physiological profiles at the individual level. They targeted type 2 DM individuals and revealed several associated cardiovascular and oncologic pathophysiological pathways.

Integrated –omics, including transcriptomic analysis has also been applied to understand systemic autoimmunity. Fujio et al.27 carried out a study on SLE, the classic prototype of systemic autoimmune disease, and identified several immunological pathways linked to autoimmunity. Moreover they could stratify SLE patients based on their immunological and clinical characteristics. Amongst other things they found that ‘CD8 exhaustion’ is a favourable prognostic feature while ‘IFN score’ is prognostic towards developing systemic autoimmune diseases.

An interesting study is that performed by Hou et al.28 Here they used what they called ‘single-cell triple omics sequencing’ (scTrio-seq), whereby they concurrently did transcriptomic, DNA methylomic, and genomic CNVs (copy-number variations) profiling on a single mammalian cell. They also used scTrio-seq on 25 single human hepatocellular cancer cells and found two subpopulations. They propose that the method applied can be a tool to understand the intricate heterogeneous states of cancer cells.


Single-cell RNA sequencing (scRNA-seq) is a promising platform when it comes to studying the transcriptome of individual cells. Surely, understanding the transcript landscape that make a specific cell will aid further our understanding of the functions of organs better. In fact it is believed that these single-cell molecular profiling will help researchers to reveal novel biological discoveries29 that are being missed with traditional analytical methods that use bulk populations of cells. Hence, single-cell transcriptome profiling will be another tool to understand health and disease.

Studies are already being done to understand cancer, autoimmune diseases, diabetes mellitus, the immune response, tissue regeneration, stem cells, embryogenesis, and much more.

Single cell transcriptomic platforms are still being discovered.30 Indeed, one interesting method called ‘spatially resolved transcriptomics’ is where tissues are cryo-sectioned into thin slices and subsequently each slice is profiled transcriptomically. Thus spatially resolved transcriptomics has the potential to reveal subcellular localization or spatial information of the tissue.


Transcriptome profiling, now even at a single cell resolution, is another valuable supply of knowledge that will lead to a deeper understanding of cell biology and also of the pathophysiology of disease. Together with other –omics, it is already being proposed and in some instances applied in focused personalized theranostics.





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