What is Transcript Quantification?

Transcript quantification is the process of measuring the abundance or expression level of different RNA transcripts within a biological sample. Transcripts are copies of DNA molecules that serve as templates for protein synthesis during gene expression. However, genes can often produce multiple alternative transcripts through a process called alternative splicing, where different combinations of exons (coding regions) are included or excluded from the final transcript.

Transcript quantification provides information about the relative abundance of these alternative transcripts in a given sample. This process is particularly important because different transcripts may have distinct functions or regulatory roles, and their expression levels can vary in different tissues, developmental stages, or disease conditions.

Quantitative transcriptomic analysis. (Klimmeck et al., 2014)

How to Do Quantitative Transcriptome Analysis Using RNA Sequencing?

Sample Preparation and Quality Control

Sample preparation is a critical step in RNA-seq experiments. Considerations include sample type, tissue collection, RNA extraction methods, and RNA quality assessment. Adequate quality control measures such as RNA integrity assessment (e.g., RNA integrity number), RNA concentration determination, and sample purity evaluation (e.g., spectrophotometry) should be implemented to ensure high-quality RNA for downstream analysis.

Check our Sample Submission and Preparation Guidelines for details.

Library Preparation Strategies

Library preparation methods determine the sequencing fragments and are crucial for accurate transcript quantification. Different strategies exist, such as poly(A) enrichment, ribosomal RNA depletion, and strand-specific library preparation. Selection of the appropriate method depends on the research question, sample type, and available resources. Careful consideration of the library preparation strategy is necessary to minimize biases and capture the full transcriptome representation.

Sequencing Platform Selection

Several sequencing platforms, such as Illumina, PacBio, and Oxford Nanopore, are available for RNA-seq. Platform selection depends on factors like sequencing depth requirements, budget, read length, and desired applications (e.g., transcript quantification, isoform discovery). Illumina platforms are commonly used due to their high throughput, accuracy, and cost-effectiveness for most applications.

Experimental Replication and Statistical Considerations

Replication is essential for robust statistical analysis and to capture biological variability. Adequate replication design, considering biological and technical replicates, is crucial for detecting differentially expressed genes and reducing false positives. Statistical considerations involve appropriate experimental design, sample size determination, and choosing appropriate statistical tests or algorithms for downstream analysis.

Diverse Applications of Transcriptome Quantification

Gene Expression Profiling

Transcriptome quantification plays a pivotal role in gene expression profiling by enabling the measurement of gene expression levels across different conditions or developmental stages. This application has been widely used in various fields, including cancer research. For instance, in a study on breast cancer, transcriptome quantification revealed differentially expressed genes associated with tumor aggressiveness and response to therapy. By analyzing the gene expression profiles, researchers identified key biomarkers and potential therapeutic targets for personalized treatment strategies.

Alternative Splicing and Transcript Isoforms

RNA-Seq, with its ability to capture the entire transcriptome, enables the characterization of alternative splicing events and identification of transcript isoforms. This application has been instrumental in understanding the functional diversity of genes. For example, in the study of neuronal development, transcriptome quantification unveiled complex alternative splicing patterns associated with the differentiation of specific neuronal subtypes. By deciphering these intricate regulatory mechanisms, researchers gained insights into the molecular basis of neurodevelopmental disorders.

Learn more information about RNA Splicing and How to Target It.

Non-Coding RNA Analysis

Transcriptome quantification provides a valuable tool for analyzing non-coding RNAs, such as microRNAs, long non-coding RNAs, and circular RNAs. These investigations contribute to understanding the roles of these non-coding RNAs in gene regulation, development, and diseases. For instance, in a study on cardiovascular disease, transcriptome quantification revealed dysregulated long non-coding RNAs involved in the progression of heart failure. These findings highlighted the potential of long non-coding RNAs as diagnostic markers and therapeutic targets.

Pathway Analysis and Functional Annotation

Integration of transcriptome quantification data with functional annotation databases enables pathway analysis, identifying molecular pathways and biological functions associated with specific gene expression patterns. This approach has been applied in numerous studies, including research on infectious diseases. By analyzing transcriptome data from host-pathogen interactions, researchers identified dysregulated pathways involved in immune responses against viral infections. This information helped uncover potential targets for antiviral therapies and vaccine development.

Reference:

1. Klimmeck, Daniel, et al. "Transcriptome-wide profiling and posttranscriptional analysis of hematopoietic stem/progenitor cell differentiation toward myeloid commitment." Stem cell reports 3.5 (2014): 858-875.