Whole Transcriptome Sequencing (WTS) / Total RNA-Seq microbiologystudy

Whole transcriptome sequencing (WTS), also known as total RNA sequencing (RNA-Seq) is a method of RNA sequencing that identifies all types of RNA molecules present in an organism or a biological sample at a specific time.

It sequences the total RNA transcripts in a sample including both coding and non-coding transcripts and provides detailed information about the gene expression and regulation across the entire transcriptome. 

The transcriptome constitutes the complete set of RNA transcripts present in a specific cell. Studying the transcriptome is important for understanding the functions of genomes and the mechanisms involved in development and diseases. WTS allows the detailed study of gene expression patterns, splicing events, non-coding RNA, and the discovery of novel RNA species.

Traditionally, gene expression and RNA transcripts were studied using low-throughput methods such as Northern blots and quantitative PCR. Further developments in gene expression analysis led to the emergence of tag-based methods like Serial Analysis of Gene Expression (SAGE) and hybridization-based methods like microarrays. However, these methods provide limited information as they rely on targeted known genes and pre-designed probes. The development of next-generation sequencing (NGS) led to the beginning of total RNA sequencing which allowed a detailed study of transcriptome and led to a better understanding of gene expression. 

Principle of Whole Transcriptome Sequencing

The principle of whole transcriptome sequencing involves isolating and sequencing all RNA molecules from a sample including both coding and non-coding RNAs. It involves using high-throughput sequencing technologies to study the entire RNA content of a given sample. 

The process of WTS begins with the extraction of RNA which is converted into complementary DNA (cDNA) and prepared into sequencing libraries. High-throughput sequencing is performed to generate raw data which is processed by aligning the reads to a reference genome or by performing de novo assembly. The assembled data is analyzed to quantify gene expression and identify transcript variants.  

Process of Whole Transcriptome Sequencing

All RNA sequencing methods have a similar workflow. WTS differs from other transcriptome sequencing methods as it allows the sequencing of all RNA molecules. 

The general workflow includes the following steps:

1. Sample Preparation/Total RNA Isolation

The first step involves extracting the total RNA from biological samples or organisms of interest. Since ribosomal RNA (rRNA) constitutes a large portion of total RNA, it is often depleted to reduce its presence in the sequencing data. rRNA depletion helps increase the coverage of less abundant transcripts and avoids wasting sequencing resources. 

2. cDNA Synthesis

The isolated RNA should be converted into a more stable DNA form suitable for sequencing. This is done by reverse transcription which converts the RNA into cDNA.

3. Library Construction

The cDNA is fragmented into smaller pieces to facilitate sequencing. Fragmentation can be done by enzymatic digestion or mechanical methods. Then, adapters are attached to the ends of the cDNA fragments through ligation. This allows the fragments to be recognized and processed by the sequencing platform. These fragments are amplified using Polymerase Chain Reaction (PCR).

4. Sequencing

The prepared cDNA library is sequenced using either single-end or paired-end methods. Single-end sequencing reads one end of each fragment, while paired-end sequencing reads both ends, providing more detailed information about each fragment. This step generates millions of short reads corresponding to the RNA transcripts.

5. Data Analysis

After sequencing, the raw sequence data is analyzed using different bioinformatics tools. Initially, the sequencing data undergoes quality control to ensure the integrity of the raw data before further analysis. Then, the sequencing reads are aligned to a reference transcriptome or assembled de novo to reconstruct the transcriptome. The aligned or assembled reads are used to generate a detailed transcriptome profile to identify all transcripts expressed in the sample. Advanced analyses may include differential expression analysis, alternative splicing detection, and gene fusion identification.

Methods of Whole Transcriptome Sequencing

1. Whole Transcriptome Shotgun Sequencing

• Whole transcriptome shotgun sequencing is a type of WTS that randomly sequences cDNA fragments. It uses the principle of shotgun sequencing applied in genomics but targets RNA converted to cDNA. 
• It has roots in earlier sequencing technologies like Expressed Sequence Tag (EST) sequencing. EST was one of the earlier methods for transcriptomics. It involves sequencing short segments of cDNA derived from mRNA. 

2. Tag-based Sequencing 

• Tag-based sequencing methods like Serial Analysis of Gene Expression (SAGE) focus on sequencing short tags from RNA molecules.
• SAGE was one of the first sequencing-based methods for whole transcriptome analysis.

3. Bulk RNA Sequencing

• Bulk RNA sequencing involves sequencing either mRNA only or the whole transcriptome library. It provides information on the average gene expression level from a bulk sample.

4. Single-cell RNA Sequencing

• Single-cell RNA sequencing studies the transcriptomes of individual cells. It involves isolating and sequencing individual cells from a sample and provides a detailed analysis of the gene expression unlike in bulk RNA sequencing. 

5. Spatial RNA Sequencing

• Spatial transcriptomics is a new method of transcriptome sequencing that combines transcriptomic data with spatial information from samples. It provides detailed spatial information about gene expression.

Advantages of Whole Transcriptome Sequencing

• Whole Transcriptome Sequencing provides a complete view of all transcripts including novel transcripts. This allows the identification of new genes and previously unannotated transcripts. It captures all types of RNA transcripts including both coding and non-coding RNAs across the entire transcriptome. 
• Whole Transcriptome Sequencing provides a detailed view of gene expression.
• Whole Transcriptome Sequencing allows the identification of biomarkers across a wide range of RNA molecules. This is useful for discovering biomarkers associated with diseases and other conditions.
• Whole Transcriptome Sequencing can detect low-abundance transcripts which is useful to study rare gene expression events.
• Whole Transcriptome Sequencing can provide information about the expression of fusion genes which is useful for identifying structural variations and their functions. 
• One significant advantage of RNA sequencing over DNA sequencing is its ability to directly detect variant mRNA transcripts resulting from alternative splicing. This provides insights into the diversity of transcript variants and their roles in biological processes.

Limitations of Whole Transcriptome Sequencing

• Whole Transcriptome Sequencing is expensive compared to other RNA analysis methods. Although the costs have significantly decreased recently, large-scale studies requiring high sequencing depths and full transcriptome coverage can still be expensive. 
• Whole Transcriptome Sequencing generates large volumes of data which requires significant computational resources. Specialized bioinformatics tools and expertise are needed to handle the massive amount of data. 
• It is time-consuming and complex to process the data generated by WTS. 
• Steps like reverse transcription and PCR amplification during library preparation can introduce biases that lead to inaccurate measurement of RNA transcripts. 
• Sequences with extreme GC content can be misrepresented due to biases introduced during sequencing or library preparation. 
• Whole Transcriptome Sequencing is sensitive to RNA quality. Poorly preserved or degraded RNA can lead to inaccurate data.

Applications of Whole Transcriptome Sequencing

• Whole Transcriptome Sequencing measures the expression levels of RNA transcripts and can be used to compare expression profiles in different conditions.
• Whole Transcriptome Sequencing helps in accurately mapping gene structures and constructing gene interaction networks which is useful in identifying the location of exons and introns. This helps in understanding the structure of the genes and their functions.
• Whole Transcriptome Sequencing is used in cancer research to profile the transcriptome of tumors and identify cancer-specific gene expression patterns. 
• Whole Transcriptome Sequencing is useful in identifying RNA biomarkers that are related to specific diseases or conditions which can be used for diagnosis and drug discovery.
• It can be used to understand the mechanisms of various diseases. It helps to understand how diseases disrupt normal cellular functions.
• It can be used in agricultural research to study the transcriptomes of plants which is useful in the development of crops with improved traits.
• It is also used to study the transcriptomes of microbial communities which helps to understand the functional roles of different microbes in various environments. 
• Whole Transcriptome Sequencing also allows the identification of non-coding RNAs and post-transcriptional modifications that regulate gene expression.

References

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