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  • Methods for Investigating the Transcriptome

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    Ribonucleic acid (RNA) represents a range of diverse molecules that play a crucial role in many cellular processes. From serving as a protein template to regulating genes, the complex processes involving RNA make it a focal point of study for many scientists. This article will spotlight various methods scientists have developed to investigate different RNA subtypes and the broader transcriptome.

    Whole Transcriptome RNA-seq
    Whole transcriptome sequencing (WTS), as the name implies, is one of the most direct approaches to exploring the transcriptome of a target organism. “WTS is at the forefront of NGS technology, allowing all RNA molecules to be determined at the time of sampling,” explained Maša Ivin, Ph.D., Scientific Writer at Lexogen, and Yvonne Goepel Ph.D., Product Manager at Lexogen. “The transcriptome is a highly dynamic part of the cell that opens a world of potential discovery far exceeding the possibilities of genome sequencing.”

    They detailed that the changes in drug responses, disease states, post-transcriptional modifications, and alternatively spliced transcripts are a few of the many discoveries made possible using WTS. Some popular applications of WTS include investigating differentially expressed transcripts, identifying and quantifying transcript isoforms, analyzing alternative splicing, studying long non-coding RNAs, and performing transcriptome assembly.

    Cissy Jiang, Product Manager for RNA Sequencing Library Prep Solutions at Bio-Rad, also explained the benefits of utilizing WTS in RNA research. “Most commercial library preparation kits are limited to a certain type or size of RNA,” she explained. “To study the complete transcriptome, one would need to construct and sequence multiple libraries; choosing one for small RNA biotypes like miRNA, snRNA, tRNA, piRNA, etc., and a second for larger RNA biotypes like mRNA and lncRNA.” This means doubling the preparations, sequencing expenses, and datasets to analyze. However, by capturing and sequencing the entire transcriptome in a single library, Jiang emphasized that labor, time, and costs are significantly reduced.

    Many WTS library preparation kits can also be modified to further isolate any RNAs of interest. Using poly(A) enrichment or rRNA depletion can be beneficial based on the RNA type under study, although these steps aren’t necessary for studies involving the entire transcriptome. The drawback of WTS is that it typically costs more due to the increase in transcripts being sequenced and the necessary depth, adding to a more extensive analysis; but in return, researchers obtain a more comprehensive view of the transcriptome.

    While WTS experiments offer significant advantages, studies targeting a particular type of RNA, such as mRNA or those aiming to quantify transcripts, often benefit from an alternative technique. One such tactic, as the Lexogen team explained, is to utilize a technology like their QuantSeq that specifically targets 3' ends of mRNA molecules. This is ideally suited for gene expression profiling and can be coupled with other additions that are used for the determination of poly(A)-sites or targeted sequencing. Additional tools like unique molecular identifiers (UMIs) in conjunction with these library preps further improve read quantification by reducing PCR duplicates.

    Researchers utilizing this sequencing approach can also bypass typical pre-processing steps such as mRNA enrichment or rRNA depletion. Ivin and Goepel noted that this further streamlines the workflow and reduces the number of necessary consumables. In addition, mRNA-seq requires less sequencing depth compared to WTS (shown in Table 1), which simplifies the data analysis and lowers the costs.

    When deciding between methods, Ivin and Goepel explained that mRNA-seq is optimal for studying differential gene expression, whereas whole transcriptome RNA-seq is preferred for examining transcript properties like alternative splicing. “Whether one method is more or less advantageous than the other depends solely on the research question and the goal of the experiment,” they added.

    Table 1. Recommended read depth per RNA-seq application (courtesy of Lexogen)

    Recommended Read Depth per Sample
    Differential Expression Profiling (WTS) 10‒25 M Liu Y. et al., 2014; ENCODE 2011 RNA-seq
    Differential Expression Profiling (3' mRNA-seq) 3‒5 M Moll P. et al., 2014
    Alternative Splicing (WTS) 50‒100 M Liu Y. et al., 2013; ENCODE 2011 RNA-seq
    Allele-specific Expression (WTS) 50‒100 M Liu Y. et al., 2013; ENCODE 2011 RNA-seq
    De novo Assembly >100 M Liu Y. et al., 2013; ENCODE 2011 RNA-seq

    Single-Cell RNA-seq
    While traditional RNA sequencing remains an invaluable tool for researchers seeking to understand gene expression, this technique relies on bulk sampling, which masks the important differences between individual cells. The improved resolution provided by single-cell sequencing, Jiang noted, has enabled the discovery of important cellular differences and has become increasingly important in cancer research. This precision has empowered scientists to identify specific cellular abnormalities, deepening our understanding of a wide range of diseases.

    Despite the benefits of this improved view of heterogeneity, single-cell sequencing does come with certain challenges. As the Lexogen team pointed out, “The sensitivity of traditional single-cell RNA-seq methods is comprised of multiple library preparation steps, resulting in losses during reverse transcription, template switching, or ligation.” Due to this, only data from highly expressed genes are identified and reproducible. To reduce dropouts (undetected low-level transcripts) in single-cell experiments, researchers should employ techniques designed for single-cell sequencing, especially strong amplification methods capable of accurate transcript profiling from minimal RNA amounts.

    Ivin and Goepel explained that enhancing the sensitivity of RNA-seq techniques to the subcellular scale offers the potential for a more profound insight into transcriptome characteristics and behaviors. They pointed to recent research indicating we might need to reassess our understanding of transcriptomic "noise" and its limits, as expression activities once deemed noise could play a role in cellular health (Weidemann et al., 2023). For this reason, high sensitivity is anticipated to greatly enhance our comprehension of basic biological activities at the subcellular scale.

    Unique RNA-seq Technologies
    The range of available RNA sequencing methods is constantly expanding with many being designed to capture and sequence specific subsets of RNAs. Ivin and Goepel emphasized how advancements in isolation techniques and library preparations have enhanced the study and understanding of small RNAs (those with fewer than 200 bases). These improvements ensure the preservation of these delicate molecules, even when obtained from minimal RNA sources like liquid biopsies.

    Other unique RNA-sequencing techniques include capturing newly transcribed RNA to provide a snapshot of transcription in real time. Additionally, kinetic RNA sequencing methods have enabled the analysis of RNA synthesis and turnover. Jiang indicated that spatial sequencing is another emerging technology important for transcriptomics. This method fuses the capabilities of bulk RNA-seq and in situ hybridization, linking transcriptome analyses with the spatial context of gene expression events. Furthermore, innovations in nanopore sequencing now allow for the direct sequencing of RNA. By preserving the native RNA molecule, researchers can pinpoint RNA modifications, rare isoforms, and full-length transcripts, effectively avoiding amplification bias.

    qPCR vs RNA-seq
    “From an overall perspective, qPCR is a low-cost and sensitive technique that enables rapid detection of already known RNA targets but is limited in the number of targets that can be detected simultaneously in a single sample,” explained Jiang. “On the other side of the spectrum, RNA sequencing can be used to address hundreds to thousands of gene targets, without having pre-defined targets to detect, but the process is much lengthier and will cost about 10-fold more in terms of reagents compared to qPCR.” Jiang noted that for this reason, RNA sequencing is the ideal approach for determining the transcriptional status of multiple gene sets in a sample, while qPCR is better when confirming a select subset of expressed genes. Therefore, employing qPCR for RNA research offers a simplified and user-friendly way to validate sequencing results or explore smaller transcript sets and also requires fewer informatics skills than typical RNA-seq tools.

    Jiang noted that the output of qPCR can be enhanced for medium throughput by utilizing advanced control systems, such as the CFX Maestro’s Gene Study tool, which allows for data integration from multiple plates. “It’s a great way to use traditional and accessible technology for flexible transcriptome analysis when appropriate for your research, which complements our parallel sequencing tools,” she added.

    Tailoring Your RNA Experiment
    Besides the RNA study methods discussed, there are numerous other techniques that haven’t been touched upon, as well as ways to refine or integrate these approaches. In the case of RNA-seq experiments to query the whole transcriptome, Jiang stated how a post-library ribodepletion strategy is ideal as it enables the detection of non-polyadenylated transcripts and small RNAs, in addition to reducing sample loss and maintaining a high library complexity which is more representative of the complete transcriptome.

    Additionally, Ivin and Goepel detailed the utility of controls like SIRVs (Spike-in RNA Variants), which serve as valuable benchmarks for RNA-seq, aiding in the validation of algorithms and transcriptome assemblies. Ultimately, the optimal method for RNA research depends on the study's specific objectives and the type of RNA under investigation. Future breakthroughs are eagerly anticipated, where advancing techniques will further deepen our understanding of the intricate transcriptome.

    1. Liu Y, Zhou J, White KP. RNA-seq differential expression studies: more sequence or more replication? Bioinformatics. 2013;30(3):301-304. doi:
    2. Standards, Guidelines and Best Practices for RNA-seq.; 2011.
    3. Moll P, Ante M, Seitz A, Reda T. QuantSeq 3′ mRNA sequencing for RNA quantification. Nature Methods. 2014;11(12):i-iii. doi:
    4. Liu Y, Ferguson JF, Xue C, et al. Evaluating the Impact of Sequencing Depth on Transcriptome Profiling in Human Adipose. Liu Z, ed. PLoS ONE. 2013;8(6):e66883. doi:
    5. Weidemann DE, Holehouse J, Singh A, Grima R, Hauf S. The minimal intrinsic stochasticity of constitutively expressed eukaryotic genes is sub-Poissonian. Science Advances. 2023;9:eadh5138. doi:

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    About the Author


    seqadmin Benjamin Atha holds a B.A. in biology from Hood College and an M.S. in biological sciences from Towson University. With over 9 years of hands-on laboratory experience, he's well-versed in next-generation sequencing systems. Ben is currently the editor for SEQanswers. Find out more about seqadmin

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