2 Oct 2023

Understanding Human disease transcriptomics


Transcriptome profiling for human disease

Transcriptome analysis has been a key area of human disease research for decades, and transcriptomics has seen transformative advancements in recent times with the emergence of next-generation sequencing (NGS) technology. The transcriptome refers to all RNA transcribed by the genome, and is comprised of various RNA subtypes, including mRNA, miRNA, rRNA, tRNA, and more. Since each of these RNA classes plays a functional role, understanding their regulation is a key part of seeing the wider ‘functional genome’ picture.

The transcriptome can undergo substantial change in response to certain environmental stimuli or physiological/pathological states. By analyzing the transcriptome in specific tissue or cell types, it is possible to reveal novel insights into the human genome, including gene structure and function, gene expression, regulation, and genome plasticity [1]. As such, transcriptome analysis not only maps the transcribed human genome, but also reveals valuable information on genome function.

One of the primary uses of transcriptomics is to gain a better understanding of key genomic processes that contribute to the pathology of human diseases. Transcriptome profiling has emerged as one of the most effective approaches to understanding disease mechanisms, and can be a powerful tool in the identification of new drug targets and the diagnosis and treatment of human diseases.

In this blog, we discuss the role of transcriptome profiling in human disease research and discover the unique insights it can provide.

Techniques used in human disease transcriptomics

Several different techniques can be used to study the human transcriptome. One of the first to be used successfully was microarray analysis. Once the standard for transcriptome analysis, microarrays use a hybridization-based method that relies on specific predefined probes to detect the presence of relevant RNA transcripts in a sample. While microarrays can be useful at detecting known transcriptome alterations, the technique does not reveal the full transcriptomic landscape. As such, microarrays are limited in their ability to identify novel disease-relevant transcriptome markers.

The emergence of NGS technology gave rise to RNA sequencing (RNA-seq), a NGS technique that enables the identification and quantification of all RNA transcripts present in a sample. RNA-seq technology has revolutionized transcriptome analysis, providing a platform for the quantification of gene expression and characterization in a single run. The technique provides more in-depth and precise transcriptome data that can be used to identify novel genes, splice isoforms, and fusion transcripts, while also revealing insights into non-coding RNA [2].

RNA-seq technology has undergone further advancements with the introduction of single-cell RNA-seq (scRNA-seq). This state-of-the-art approach enables analysis of the transcriptomes of individual cells. The approach can reveal the heterogeneity of RNA transcripts within individual cells and help scientists understand the cell types and functions within highly organized tissues and organs. scRNA-seq can be especially valuable in gaining a deeper understanding of the molecular mechanism of complex disease states, such as cancer.

Applications of human disease transcriptomics

Transcriptome profiling for human disease often relies on the construction of a full-length transcript to obtain expression and characterization of the full transcriptomic landscape. Transcriptome assembly can be reference-guided, where a reference genome exists, or de novo, when a reference genome is not available.

Reference-guided assembly is more straightforward, and involves mapping sequenced reads to the reference genome before transcript assembly. In contrast, de novo transcripts are constructed directly from the overlapping sequenced reads and is a more complex process reliant on advanced bioinformatics. Thanks to the availability of human reference genomes, most applications in human disease research are reference-guided, but de novo can be valuable in determining unknowns.

Either way, scRNA-Seq has found various applications in human disease research:

Diagnostics and disease profiling

Reference-guided RNA-seq has been used to identify disease-causing mutations in a variety of genetic disorders, and has enabled researchers to attribute genetic aberrations and nucleotide polymorphisms to complex diseases and traits. Furthermore, transcriptome profiling has emerged as a valuable tool for molecular diagnostics, with broad applications across diverse areas of human health, including disease diagnosis, prognosis, and therapeutic decision making.

For example, RNA-seq technology is providing insights into cancer research and treatment. scRNA-seq can reveal in-situ transcriptomic information from a single tumor cell, determine its expression and heterogeneity, and uncover the underlying molecular cause of disease [3]. This data can then be used for diagnostic or prognostic purposes, or to guide personalized treatment.

Discover more about personalized medicine

Human pathogen transcriptomes

Transcriptome profiling can be of great value in revealing the molecular changes that occur during infection by bacteria and viruses. By analyzing mRNA expression changes in the pathogen or infected host cell, researchers can identify gene expression variations, discover novel virulence factors, monitor antibiotic resistance, and reveal host-pathogen immunological interactions. RNA-seq was widely implemented to investigate COVID-19 infection during the pandemic, and helped to map immune and inflammatory mechanisms of the disease [4].

Thanks to the increasing sensitivity and accelerated throughput provided by modern RNA-seq, so-called ‘dual RNA-seq’ studies are now possible. These studies can simultaneously resolve full-length transcripts in the pathogen and the host to better understand the infection pathology and immune response.

Responses to environment

Environmental factors such as exposure to chemicals, pollutants, and dietary factors can have a significant impact on gene expression. Transcriptome profiling can be of great value in the identification of genes and markers that react to and mitigate environmental stresses.

RNA-seq has been successfully used in this way to investigate the effects of air pollution on respiratory health. One study indicated that exposure to pollution was more determinant of respiratory health than inherited genetics, highlighting the damaging effects of air pollutant exposure on the lungs [5].

A powerful tool for understanding complex diseases

As we have seen, human disease transcriptomics offers the ability to gain unique insights into the dynamic molecular mechanisms underlying complex human diseases. The ability to perform total RNA sequencing has enabled the discovery of countless biomarkers of disease as well as a growing list of novel therapeutic targets.

Now, with the emergence of high-throughput long sequencing platforms such as SMRT-sequencing from PacBio, the once crucial requirement for a comprehensive reference genome and transcriptome assembly is gradually diminishing.

Are you thinking about starting a transcriptomics research project?

At Eremid, we have integrated numerous transcriptomics platforms into our genomic services lab. With the latest in transcriptomics technology, and our expert team on-hand, we have the in-house ability to support a variety of transcriptomics applications, including:

  • RNA-sequencing
  • 3’ RNA-sequencing
  • Full-length mRNA sequencing (Iso-Seq)
  • Small RNA sequencing
  • Target RNA profiling
  • Single cell RNA-Sequencing

Discuss a project with us today


  1. Casamassimi, A., Federico, A., Rienzo, M., Esposito, S., & Ciccodicola, A. (2017). Transcriptome Profiling in Human Diseases: New Advances and Perspectives. International journal of molecular sciences, 18(8), 1652.
  2. Wang, Z., Gerstein, M., & Snyder, M. (2009). RNA-Seq: a revolutionary tool for transcriptomics. Nature reviews. Genetics, 10(1), 57–63.
  3. Ergin, S., Kherad, N., & Alagoz, M. (2022). RNA sequencing and its applications in cancer and rare diseases. Molecular biology reports, 49(3), 2325–2333.
  4. Bass, A., Liu, Y., & Dakshanamurthy, S. (2021). Single-Cell and Bulk RNASeq Profiling of COVID-19 Patients Reveal Immune and Inflammatory Mechanisms of Infection-Induced Organ Damage. Viruses, 13(12), 2418.
  5. Holloway, J. W., Savarimuthu Francis, S., Fong, K. M., & Yang, I. A. (2012). Genomics and the respiratory effects of air pollution exposure. Respirology (Carlton, Vic.), 17(4), 590–600.
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