Leveraging RNA Sequencing in Pharmaceutical Research

RNA-based measurements have the potential for broad applications across diverse areas of human health, including disease diagnosis, prognosis, and therapeutic selection. Traditionally, methods like polymerase chain reaction (PCR) have been used to measure expression, but this often comes with limitations, such as being time consuming and expensive. In contrast, RNA sequencing (RNA-Seq) has emerged as a high-throughput technology for studying the whole transcriptome—the total cellular content of RNAs—providing insights into the expression patterns of thousands of genes. This wealth of information enables a deeper understanding of functional pathways and gene regulation.

In addition, RNA-Seq can unveil critical details on alternative splicing, allele-specific expression, unannotated exons, and novel transcripts of both differentially expressed genes and noncoding RNA. RNA-Seq also has important applications in drug development as it can identify drug-related genes, fusion proteins in cancer, and microRNAs (miRNA) involved in developing drug resistance.

RNA analysis in drug development

One of the most significant applications of RNA analysis in pharmaceuticals is identifying therapeutic targets, which has historically been a major challenge. With RNA-Seq, researchers can detect drug-induced changes in gene expression, allowing for the assessment of a drug’s global transcriptional effects and significantly accelerating target identification.

Another area where RNA-Seq excels is in detecting fusion genes, which are only expressed in cancer cells. Fusion genes result from the hybridization of two or more regions of typically separated genes, producing fusion proteins with elevated activity levels. Fusions can be recognized in RNA-Seq data using a variety of algorithms. Additionally, when RNA-Seq data is combined with whole-genome sequencing, the fusion genes that code for proteins can be directly identified. By uncovering these fusion genes and proteins, researchers gain further insights into cancer biology, potentially leading to the development of targeted therapies.

This wealth of information enables a deeper understanding of functional pathways and gene regulation.

The emergence of drug resistance has been a significant obstacle in healthcare, limiting the efficacy of many treatments over time. RNA analysis has played a pivotal role in combating this by identifying the miRNAs—a class of noncoding RNA that regulate gene expression—involved in developing drug resistance. miRNA sequencing (miRNA-Seq) is a type of RNA-Seq that identifies millions of small RNA sequence tags so researchers can systematically evaluate miRNA expression. By comparing the expression profiles of drug-resistant and non-resistant cells, it is possible to pinpoint which miRNAs are involved in developing drug resistance. This knowledge provides insights into the underlying mechanisms and offers potential targets for novel therapeutic interventions aimed at overcoming resistance and improving treatment outcomes.

Single-cell RNA-Seq and pharmacogenomics

Traditional bulk RNA-Seq provides an average gene expression profile across a mixture of cells, possibly masking crucial individualized information. In contrast, single-cell RNA-Seq (scRNA-Seq) makes it possible to analyze the transcriptome of individual cells, revealing cellular heterogeneity, transcriptional differences, lineage, and essential information about gene expression at a single-cell resolution. In addition, small single-cell changes can often lead to system-wide changes. Identifying distinct cell populations can further reveal unique drug responses and genetic variations, which has significant implications for pharmacogenomics.

Pharmacogenomics studies how an individual's genetic variation influences their therapeutic response based on drug metabolism, efficacy, and potential for side effects. With recent advances, it can potentially optimize treatment regimens and improve patient outcomes. More specifically, when combined with scRNA-Seq, a deeper understanding of genetic variations at the cellular level can be achieved, allowing clinicians to tailor treatment plans to individual patients.

While the potential of scRNA-Seq in pharmacogenomics is promising, its application as a diagnostic tool has been hindered by the high cost per sample. As technology continues to evolve, efforts are being made to reduce costs and make scRNA-Seq more accessible.

Recent innovations

The field of RNA analysis continues to evolve, giving rise to innovations that will progress pharmaceutical research. Some notable discoveries include digital RNA with perturbation of genes (DRUG-Seq) and long-read sequencing technologies. DRUG-Seq is a high-throughput RNA-Seq method used in drug discovery, allowing researchers to obtain an unbiased profile of the entire transcriptome with reduced hands-on time and cost. DRUG-Seq achieves this through multiplexing, where many libraries are pooled and simultaneously sequenced to minimize expensive and labor-intensive library preparation. The data generated can provide valuable information on a drug’s mechanism of action and its potential off-target effects, further streamlining the drug development process.

Integrating artificial intelligence and machine learning with RNA-Seq holds immense promise for further advancing pharmaceutical research.

Traditional RNA-Seq struggles when identifying full-length transcripts, particularly for long or complex genes. However, long-read sequencing technologies have emerged as a solution. Long-read RNA-Seq enables the sequencing of full-length transcripts for isoform characterization on a transcriptome-wide scale and quantification at both the gene and isoform levels. By providing a more accurate and comprehensive view of the transcriptome, long-read sequencing enables researchers to uncover previously elusive insights into cellular processes and disease mechanisms.

A glimpse into the future

Integrating artificial intelligence (AI) and machine learning (ML) with RNA-Seq holds immense promise for further advancing pharmaceutical research. ML is a branch of AI that can create models from data and is mainly used for classification and prediction analysis to help distinguish characteristics such as subtype and status. Various AI mathematical and statistical methods should be used to extract effective information and discover new biomarkers. Using all the modes of AI can help with processing and interpreting vast amounts of data and identifying patterns and relationships that would typically go unnoticed. Leveraging AI in research will ultimately increase knowledge of the complexities of gene expression, enabling further discovery.

Another area of future development in RNA analysis is spatial transcriptomics. Spatial transcriptomics uses intact tissue sections to detect transcriptional activity with a high level of sensitivity at the cellular level. Before this, cells and tissues were dissociated and homogenized to isolate RNA transcripts, resulting in the loss of spatial context. Comparatively, spatial transcriptomics allows gene expression to be mapped in its native state. A key benefit of this method is the ability to link structure and activity, as the spatial profile of individual genes is inherently tied to their biological function. With this information, researchers can connect gene expression profiles to their cellular location and morphology, quantify differences in gene expression among cells, and gain insights into the cause and effect of diseases.

Spatial transcriptomics can also be applied on the level of a single cell in combination with scRNA-Seq to add positional information. This involves the analysis of gene expression with spatial context at the level of an individual cell, as cells, even those in the same environment, have unique expression patterns.

Overall, RNA-Seq is a powerful and indispensable pharmaceutical research tool that provides comprehensive insights into gene expression and regulation. If researchers continue to leverage its capabilities, there will likely be a wave of targeted and personalized therapies that improve patient outcomes across various diseases.