During transcriptome sequencing, the initial step involves the removal of rRNA. The rationale behind this procedure stems from the fact that the RNA extracted consists of a wide range of RNA types, with over 80% comprising rRNA. When sequencing is performed, a significant amount of irrelevant rRNA sequence data is generated. However, in most cases, rRNA is not the primary focus of interest. Due to its abundance and limited contribution to transcriptome analysis, rRNA data proves redundant for the target RNAs, such as mRNAs and lncRNAs. This redundancy leads to a wasteful consumption of valuable sequencing resources, as rRNAs account for over 80% of the total RNA but provide minimal transcript information. Consequently, the foremost challenge in RNA sequencing is effectively eliminating rRNA to mitigate these issues.
Ribosomal profiling strategy and points of rRNA depletion. (Chung et al., 2015)
What is Ribosomal RNA (rRNA)?
The transcriptome refers to the complete set of RNA molecules produced in a cell, tissue, or organism at a specific time and under specific conditions. It includes various types of RNA molecules, including both coding RNAs (such as messenger RNA or mRNA) and non-coding RNAs (such as transfer RNA or tRNA, ribosomal RNA or rRNA, microRNA or miRNA, long non-coding RNA or lncRNA, and circular RNA or circRNA).
Ribosomal RNA (rRNA) is a crucial component of the ribosomes, which are cellular structures responsible for protein synthesis. In eukaryotes, the 28S rRNA is one of the major components of the large ribosomal subunit. It is a relatively large RNA molecule and is highly abundant within the cell, accounting for approximately 80% of the total RNA content. Despite its abundance, 28S rRNA is considered metabolically inactive as its primary role is to provide a structural framework within the ribosome for protein synthesis rather than being directly involved in the coding or decoding of genetic information.
The Role of Ribosomal RNA (rRNA)
Following the completion of the Human Genome Project (HGP) and the Encyclopedia of DNA Components (ENCODE) project, it was revealed that a majority of the DNA in the human genome can be transcribed into RNA. However, only 1.5% of the nucleotide sequences are utilized for encoding proteins. The remaining portion, known as non-coding RNA (ncRNA), does not contribute to protein synthesis and is often considered as genomic transcriptional noise. This transcriptome noise, primarily originating from the most abundant member, rRNA, contains invalid information.
Ribosomal RNA plays a crucial role in protein synthesis as a fundamental component of ribosomes. It constitutes approximately 80-90% of the total RNA content within a cell, providing the structural framework for the translation machinery. While essential for cellular survival and growth, the abundance of rRNA poses challenges in accurately detecting and analyzing other RNA species, such as messenger RNA (mRNA) and non-coding RNAs, which may be of greater interest in RNA-Seq experiments.
The primary function of sequencing is to extract valuable biological information. However, rRNA, being the most abundant type of RNA, provides limited insights into the transcriptome. Excessive presence of rRNA can overshadow the expression abundance of other genes, thereby hindering accurate analysis. As a result, rRNA is typically removed from RNA samples prior to sequencing. The efficiency of rRNA removal plays a critical role in maximizing the reads of transcripts, enhancing the overall effectiveness of the sequencing process.
Challenges Posed by rRNA in RNA-Seq Experiments
The high abundance of rRNA presents significant challenges when conducting RNA-Seq experiments. Firstly, it restricts the dynamic range of gene expression measurements, making it difficult to accurately capture subtle differences in gene expression levels. Consequently, low-abundance transcripts of interest, including rare transcripts or those involved in specific cellular processes, may be overshadowed and inadequately detected.
Furthermore, the presence of rRNA necessitates higher sequencing depths to achieve satisfactory coverage of the entire transcriptome. This increased sequencing depth can be time-consuming and costly, particularly when studying samples with limited RNA availability or when processing large-scale experiments. Additionally, rRNA contamination in the sequencing data further compromises the accuracy and specificity of RNA-Seq results.
rRNA Depletion Techniques in RNA-Seq
Poly(A) Selection
Poly(A) selection is a widely adopted rRNA depletion technique that leverages oligo-dT beads to selectively capture messenger RNA (mRNA) molecules based on the presence of polyadenylated tails. This method effectively removes rRNA from the sample, ensuring enriched representation of non-rRNA transcripts. However, it should be noted that poly(A) selection may introduce bias against non-polyadenylated RNAs, limiting its application in certain contexts.
However, it is important to acknowledge that this method is restricted to eukaryotic organisms possessing polyA tails and is not applicable to prokaryotic organisms for mRNA extraction. In cases where the polyA tails of mRNAs have been degraded, it becomes challenging to enrich them due to the strict requirement for RNA integrity. Additionally, the enrichment of lncRNAs and circRNAs lacking polyA tails is not feasible through this approach. Furthermore, removing the highly abundant globin mRNA from blood samples using the polyA tail-based method is not possible.
Learn more about Poly(A) Tail in RNA Sequencing.
CD Genomics offers Poly(A)-seq service allowing for transcriptome-wide profiling of polyadenylation sites. By selectively sequencing the 3' ends of mRNA molecules that have poly(A) tails, Poly(A)-seq provides a comprehensive view of mRNA stability and translation efficiency.
Library Construction of Poly(A)-Seq - CD Genomics
Ribosomal RNA Removal Kits
Commercially available ribosomal RNA removal kits offer a highly specific and customizable approach to rRNA depletion. These kits utilize target-specific probes or capture beads that selectively hybridize with rRNA molecules, enabling their subsequent removal from total RNA samples. The flexibility of these kits allows researchers to tailor the depletion process to different species or RNA types, enhancing the accuracy and reliability of downstream analyses.
Probe-based rRNA depletion strategies. (Nature Research Custom Media)
Other Targeted rRNA Depletion Approaches
To address specific experimental requirements, researchers have developed alternative targeted rRNA depletion approaches. One such method involves RNase H-mediated depletion, where specific hybridization of RNase H to rRNA guides enzymatic degradation, resulting in rRNA removal. Additionally, the use of capture probes designed to specifically target rRNA sequences provides an effective means of selective depletion. Furthermore, selective degradation of rRNA by incorporating modified nucleotides in the depletion process has shown promising results.
Comparison of Different rRNA Depletion Methods
- Efficiency and Specificity
The efficiency of rRNA depletion methods varies, and it is crucial to assess their ability to selectively remove rRNA while minimizing the loss of non-rRNA molecules. Rigorous evaluation ensures reliable and accurate downstream analyses.
- Impact on Transcriptome Coverage
The choice of rRNA depletion method can influence the coverage and representation of different RNA species in the transcriptome. Evaluating the impact on transcriptome coverage helps ensure comprehensive analysis and minimizes bias introduced by the depletion method.
- Cost and Time Considerations
Researchers should consider the cost and time requirements associated with each rRNA depletion method, including reagent costs, labor, and compatibility with downstream library preparation protocols and sequencing platforms.
Advantages of rRNA Depletion
Increased Sensitivity for Low-Abundance Transcripts
By depleting rRNA, the sequencing depth can be focused on capturing rare and low-abundance transcripts. This enhances the sensitivity of RNA-Seq experiments, enabling the detection of genes with low expression levels that may have important biological functions.
Enhanced Coverage of Non-coding RNAs
Non-coding RNAs (ncRNAs) play critical roles in gene regulation and various cellular processes. rRNA depletion allows for improved detection and quantification of ncRNAs, enabling a more comprehensive understanding of their functions and involvement in biological pathways.
Improved Detection of Alternative Splicing Events
Alternative splicing generates multiple transcript isoforms from a single gene, increasing the complexity of the transcriptome. rRNA depletion facilitates the identification and characterization of alternative splicing events, enabling a more detailed analysis of gene expression regulation.
Limitations of rRNA Depletion
Potential Loss of Non-rRNA Molecules
Although rRNA depletion techniques are designed to specifically remove rRNA, there is a possibility of unintentional loss of non-rRNA molecules. Careful optimization and validation of the depletion method are necessary to minimize the loss of valuable RNA species.
Influence of rRNA Depletion on Downstream Analyses
The depletion of rRNA can impact downstream analyses, such as gene expression quantification and differential expression analysis. It is important to consider the potential biases introduced by rRNA depletion and adjust the analysis pipeline accordingly.
Considerations for Successful rRNA Depletion
RNA Quality and Integrity
The attainment of high-quality RNA samples containing intact molecules is crucial for the successful execution of rRNA depletion. Prior to initiating the depletion process, it is imperative to assess the purity and integrity of the RNA samples to ensure accurate and reliable results while minimizing any potential experimental artifacts.
Experimental Design Considerations
When devising a strategy for rRNA depletion, various factors related to the biological system, sample type, and research objectives should be taken into account. By optimizing the experimental design, one can enhance the specificity, efficiency, and coverage of RNA-Seq experiments, thus increasing the likelihood of achieving desired outcomes.
Selection of an Appropriate rRNA Depletion Method
Choosing the most suitable rRNA depletion method necessitates a comprehensive evaluation of its efficiency, specificity, impact on transcriptome coverage, cost-effectiveness, and compatibility with downstream analysis techniques. Thoroughly considering these factors guarantees the selection of a method that aligns with the research objectives and maximizes the chances of success.
Reference:
- Chung, Betty Y., et al. "The use of duplex-specific nuclease in ribosome profiling and a user-friendly software package for Ribo-seq data analysis." Rna 21.10 (2015): 1731-1745.
