Perspective on Transcriptome Analysis

Vennam Sowmya Sri^*
*Correspondence: Vennam Sowmya Sri, Department of Pharmacy, Jawaharlal Nehru Technological University, Hyderabad, Telangana, India, Email:

Editorial

A transcriptome is an organism's whole set of messenger RNA, or mRNA, molecules. The array of mRNA transcripts produced in a particular cell or tissue type is referred to as the "transcriptome."

What is Transcriptomics used for?

Transcriptomics technologies are methods for studying an organism's transcriptome, which is the total number of RNA transcripts. An organism's information content is stored in its genome's DNA and expressed through transcription.

What is transcriptome composed of?

The transcriptome consists of coding mRNA which comprise around 1-4 percent of its entirety and non-coding RNAs which comprise the rest of the genome and do not give rise to proteins.

What can a transcriptome tell us?

The transcriptome can tell us when and where each gene in an individual's cells, tissues, and organs is turned on or off. It works like a dimmer switch, allowing for much finer control of gene expression by determining whether a gene is 10% active or 70% active.

Transcriptome analysis

Transcriptome Analysis is the use of high-throughput technologies to examine the transcriptome, or the entire set of RNA transcripts produced by the genome, under specific conditions or in a specific cell. Transcription profiling, which tracks changes in a cell's overall activity rather than just one or a few genes, is utilised in a variety of biomedical research fields, including illness diagnosis, biomarker identification, risk evaluation of new medications and environmental toxins, and so on. To determine the alterations associated with the mutant phenotype, transcription profiling can be used to loss-offunction and gain-of-function mutants. Transcriptomic approaches have been especially beneficial in determining gene functions. Transcriptomics also enables the discovery of pathways that react to or alleviate environmental stress. Disease-associated gene fusions, single nucleotide polymorphisms, and even allele-specific expression can all be detected using RNA-Seq.

Uses of transcriptome analysis

The most typical application of transcriptome analysis is to compare specific pairings of samples. Different external environmental factors, such as hormonal influences or pollutants, could explain the variances. The comparison of healthy and illness states is more widespread. Transcriptomics analyses in cancer, for example, address classification, pathogenic pathways, and even outcome prediction. Beyond anatomical location and histology, transcriptome studies can help classify cancer. Gene-based benchmarks can be used to predict tumour prognosis and therapy response using outcome predictions. These methods are already being used in personalised medicine and cancer patient treatment.

Molecular analysis can be used to characterise organisms and tissues at various stages of development. The transcriptomes of stem cells aid in the comprehension of cellular differentiation and embryonic development processes. Transcriptome analysis is an excellent tool for discovering therapy targets because of its wide approach.

Specific example: Transcriptome analysis applied to human skin

Because skin is so easily accessible, it was one of the first subjects to be studied using ‘omics,' and dermatology was one of the first to embrace the methods. After serum stimulation, cultured fibroblasts showed a classic example of coordinated transcriptional regulation. Serum promotes not only a quick resumption of the cell cycle, but also a physiological involvement of fibroblasts in wound healing, which is typical of a wound-healing response]. The reactions of epidermal keratinocytes to UV radiation, hormones, vitamins, infections, inflammatory and immunomodulating cytokines, toxins, and allergens, as well as the alterations associated with epidermal differentiation, have all been studied.

The gene profiles for keratinocytes, melanocytes, endothelia, adipocytes, immune cells, hair follicles, sebaceous, sweat, and apocrine glands were created using the expression signatures that describe the numerous cell types in human skin. This resource supplied a SkinSig resource, which was subsequently utilised to study 18 skin ailments, enabling in-context interpretation of things like immune cell influx in inflammation or differentiation changes in cornification disorders.

We may predict a significant increase in the use of transcriptome analysis in the future. It can provide greater understanding and more exact diagnoses of diseases when translated to the bedside. This, of course, necessitates further technological advancements, both in lab-bench components that reduce costs while ensuring reproducibility and accuracy, as well as in computer-based components, such as algorithms that allow physicians to make diagnoses rapidly and accurately. This method will become commonplace in a generation.