What is Gene Expression Profiling?

Gene Expression Profiling

Gene expression profiling, also known as transcriptomics, involves the systematic compilation of cDNA sequence fragments derived from both qualitative and quantitative analyses of mRNA populations. This intricate process entails the unbiased construction of cDNA libraries sourced from cells or tissues in a specific physiological state, followed by extensive cDNA sequencing. This comprehensive approach enables the characterization of the type and abundance of gene expression within a given cell or tissue under distinct conditions. The resultant dataset is commonly referred to as a gene expression profile.

In contrast to gene chip technology, transcriptome sequencing boasts advantages such as precise quantification, high reproducibility, a broad detection range, and enhanced sensitivity and specificity. These attributes address the limitations associated with gene chips.

Please read our article: Differential Gene Expression (DGE) Analysis in RNA Sequencing.

Digital Gene Expression Profiling

Digital Gene Expression (DGE) zooms in on the isolation of mRNA from a particular state of a specific tissue or cell within a species. Through the construction of unbiased cDNA libraries and the application of high-throughput sequencing and biochemical analysis techniques, DGE facilitates a comprehensive, cost-effective, and expeditious characterization of gene expression patterns and their abundance in a specified species, tissue, or cellular state.

Digital RNA-Seq, also known as UMI-RNA-Seq, represents a next-generation sequencing (NGS) methodology designed to mitigate sequence-dependent PCR bias by introducing unique molecular identifiers (UMIs) to barcode RNA molecules before amplification. This innovative approach not only enables the realization of transcriptomic sequencing, akin to conventional RNA-Seq, but also effectively diminishes biases and errors inherent in the sequencing process.

Please refer to our article for more information: Unique Molecular Identifiers (UMIs): Enhancing Accuracy and Precision in RNA Sequencing.

Use of unique molecular identifiers (UMIs). (Chaudhary et al., 2018)

Advantages of Digital RNA Sequencing (DGE Analysis)

• Digitized Signal: Sequencing directly yields the sequence without interference from background noise or cross-hybridization. The resolution is exceptional, allowing for the identification of differences as small as a single base between sequences.
• High Throughput: A single sequencing run produces an impressive 3 million sequences, enabling the detection of 100,000 to 300,000 sequence tags in a comprehensive manner.
• High Accuracy and Reproducibility: The correlation between two independent experiments conducted in the same laboratory exceeds 99%, while experiments conducted in different laboratories exhibit a remarkable correlation of up to 98%. This underscores the technology's precision and reproducibility.
• Wide Detection Threshold: The technology spans a broad detection threshold, covering six orders of magnitude. It is adept at detecting low abundance transcripts, even as scarce as 2 copies, and can precisely quantify hundreds of thousands of copies of highly expressed transcripts.
• Wide Detection Range: Independent of known genes, the technology allows for the quantitative analysis of both normal and low abundance transcripts. It is versatile in detecting de novo transcripts and antisense strand transcripts, expanding its applicability and analytical scope.

Gene Expression and Regulatory Networks in Drug Discovery

The integration of gene regulatory networks in drug discovery is multifaceted, encompassing the identification of pivotal drug targets and the systematic elucidation of drug modes of action. This information serves a spectrum of applications, ranging from the discovery and diagnosis of biomarkers to the prediction and prognosis of diseases. Notably, the utility of gene regulatory networks is accentuated in the realm of complex diseases like cancer, where mutations in interconnected pathways play a pivotal role. The interplay of genes in related pathways, often directly interacting with each other, makes gene regulatory networks particularly effective in unraveling potential drug targets.

Please read our article: RNA Sequencing Derived Co-expression Network Analysis Accelerates Functional Genomics Research.

In the domain of drug design, leveraging gene regulatory networks necessitates the integration of diverse data types, including protein interaction networks and metabolic networks. This holistic approach enhances our understanding of the intricate molecular landscape. Furthermore, the incorporation of clinical data, such as survival outcomes and drug response data, enables gene regulatory networks to contribute significantly to the realm of personalized medicine.

Our high-throughput gene expression screening service, employing advanced RNA-seq technology, offers a powerful tool for capturing dynamic gene expression changes. This service allows simultaneous access to the expression profiles of thousands of genes across different tissues, specific time points, and various conditions. The gene expression profiling data generated through this service serves as a valuable resource for phenotyping cellular and drug responses, enabling rapid, accurate, and comprehensive drug target and drug screening.