Exploring the underlying causes of genetic diseases requires methods more advanced than whole genome sequencing (WGS) and whole exome sequencing (WES). Next-gen RNA-seq analysis techniques give researchers the opportunity to identify the mutations that lie in the non-coding and regulatory region of the genome.

The data generated from NGS RNA-seq is massive. The first challenge all researchers have to overcome is the analysis of the enormous volume of data each RNA sequencing generates. Ready-to-use software tools and publication-ready templates allow the researchers to analyze RNA-seq content in record time. Technologies like cloud-based data storage and replicable analytics reports support high throughput sequencing techniques and the subsequent interpretation of data.

RNA sequencing can potentially identify several disease-specific biomarkers, gene expression profiling, and host response to pathogens. Understanding the correlation of the biomarkers with human pathology and exploring the expression profile in genetic disorders can open new windows for personalized treatment for individual patients.

How has NGS catalyzed the evolution of transcriptome studies?

Next-generation RNA sequencing is displacing microarrays quite rapidly in eminent research facilities across the world. It can potentially detect the allele-specific expression of genes, gene fusion, detection of novel transcripts, and identification of alternative splicing sites within the sample population. That allows the researchers to take a look at the entire pool of RNA present in the sample, some of which might be coming from the pathogen. This constitutes meta-transcriptomics. The simultaneous measurement of altered gene expression supports the detection of infection by allowing the researchers to investigate the dynamics between the pathogen(s) and the host.

Precise identification of disease-causing agents in a given human sample 

Several bacterial and viral diseases have specific gene expression signatures. Pathogen-specific diseases like typhoid fever, dengue fever, malaria, HIV, tuberculosis, tularemia, and human respiratory syncytial virus infection have distinct gene expression signatures that are usually present in the whole blood sample of the affected individual. Multi-cohort, meta-transcriptome analysis has paved the pathway for economic and quick clinical diagnostics for individual patients. NGS RNA analysis can be appointed for the tracking of disease progression, and monitor the effects of the treatment after the diagnosis.

Till date, the pathogen-based assays are limited. It has led to the emergence of alternative diagnostic methods that can determine the precise nature of the pathogen infecting the given host. RNA-sequencing techniques explore the relationship between the host’s genome and protein expression. Therefore, the presence of “external” protein encoding machinery from the pathogen is quickly and accurately detected by leveraging this method. In most instances, the blood RNA expression profile gives the researchers a thorough insight into the variety of viral and bacterial pathogens that might be playing a role in the infection.

Since each infectious microbe (virus or bacterium) elicits a distinct blood RNA expression profile, it enables the differentiation between distinct pathogens (S. pneumoniae, E. coli, respiratory syncytial virus, and rhinovirus). While the exact number of biomarkers necessary for the discrimination between two pathogens can vary, the number of biomarkers usually stay between 10 and 20 transcripts. A recent study shows a stark exception, where only two biomarkers were enough to distinguish between viral and bacterial infections among febrile children.

Predicting the onset of hereditary and genetic diseases

The possibility of determining the gene expression signatures can allow early disease diagnosis, even before the first symptoms of an infection set in. In the case of contagious diseases, it can prevent transmission, and it can improve the prognosis. The presence of standardized analytics tools and technology like the kind Basepair Tech offers allows the accurate translation of gene expression signatures in terms of biomarkers for diagnosis, prognosis, and theranostics. 

Several research institutes across the globe are using biomarkers in cancer to determine the type of tumor, stage of cancer, and the prognosis based on the information. It is now possible to predict the chances of the recurrence of breast cancer in a patient through high throughput detection of biomarkers of the disease in the provided tumor (cDNA) sample. In spite of being a cost-intensive technique, it is quite accurate thanks to the efficiency of the analysis of the sequencing involved.

Determining the role of different types of RNA in pathogenesis

Today, the availability of multiple strategies of RNA isolation allows the research teams to select specific types of RNA found inside a human cell. The RNA sequencing experiments are no longer limited to the messenger RNAs that bear the ribonucleic acid codes for subsequent translation. The methods allow the researchers to explore the world of lncRNA, miRNA, tRNA, rRNA, siRNA, and piRNA. Out of all these types of RNA, miRNA plays a critical role in the regulation of gene expression in cells via repression of translation or the degradation of mRNA. It has regulatory roles in several biological processes, including stress response, cell behavior, and development. On the other hand, siRNA and piRNA work to maintain genomic stability by controlling silencing of the transposons.

Recent studies on lncRNA expression profile highlight its role in the development and progression of Alzheimer’s disease. A high incidence of identified lncRNA correlates to single nucleotide polymorphisms (SNPs) that are common among RNA samples from people affected by the disease. Research shows that the β-site amyloid precursor protein cleaves the enzyme-1 antisense transcript. That initiates a deregulation cascade that eventually creates the pathological symptoms of Alzheimer’s. Another RNA analysisstudy shows the link between lncRNA with a high probability of celiac disease and similar intestinal autoimmune disorders. The correct analysis of the data from the sequencing of lncRNA populations within the sample can demystify the relationship between disease-causing SNPs and their effects on the function of lncRNA.

Biomarkers and drug pathway discovery

Transcriptomics gives a thorough understanding of pathogenesis and disease progression in an individual. In the case of certain chronic diseases like cancer and Alzheimer’s, it provides the scope of development of personalized therapy. Additionally, it makes drug development much more efficient with its ability to decode targetable DNA, RNA, proteins and protein synthesis machinery.

Till date, several animal models were used in drug testing and drug pathway discovery. However, the minute disparity between model systems, in vitro systems and human body made optimization a true challenge. The advancement of NGS and RNA analysis techniques in drug research allows the exploration of susceptible signaling mechanisms through the assay of biomarker expression of pathogens or diseased cells.

Easy methods for RNA analysis of sequencing data promises new directions in the field of clinical medicine and targeted therapeutics. The process can identify different levels of protein expression in normal cells and diseased cells. It allows comparative studies of biomarker expressions and highlights the various targets that can be leveraged by the pharmaceutical industries for efficient drug designing.

Profiling human oocytes for IVF

IVF is an expensive and labor-intensive process. The quality of the oocyte determines the success of the in-vitro fertilization, implantation and maturation significantly. To distinguish between healthy oocytes that will give rise to competent embryos and non-viable oocytes, many researchers turn to NGS transcriptomics.

Studying the biomarkers for the protein synthesis pathways in a sample of oocytes provides thorough understanding of their developmental stage and health. One study shows that over 2000 genes were upregulated in mature oocytes that were developed in vitro. Several of these genes regulate transcription, translation, protein transport and metabolism. Many of them also regulate the cell cycle. On the other hand, the expression of the same genes were at normal level in the oocytes developed in vivo.

Such studies help in the distinction between oocytes that have the potential to develop normally after IVF and those that might have dysregulation of transcription and translation leading to developmental incompetence. NGS sequencing and RNA analysis plays a critical role in determining the health of oocytes developed in vitro for IVF via gene expression profiling or biomarker expression profiling.

How is next-gen RNA-seq influencing the future of diagnosis and treatment of human diseases?

The last couple of years have seen the fast-paced evolution of transcriptomics. Apart from the displacement of micro-array techniques by NGS technology, there has been a sharp rise in the availability of standardized analytics tools for transcriptome analysis. The reliable and replicable analytics reports ensure the fast and accurate determination of pathogenesis among human sample populations. From cancer to malaria, the diagnosis has not only become quicker, but it can also precede disease symptoms. Next-generation RNA-seq allows the identification of genetic markers of certain diseases. It enables disease prediction, diagnosis, prognosis, and theranostics with astounding speed and precision. The low cost of RNA analysisgives hope for the adoption of similar techniques for mass diagnosis and the development of personalized medicine/treatment in the near future.