What is RNA Sequencing (RNA-Seq)?

In the past decade, RNA sequencing (RNA-seq) has evolved into an indispensable tool for scrutinizing differential gene expression across the entire transcriptome and investigating mRNA differential splicing. The advent of second-generation sequencing (NGS) technology has catalyzed the widespread adoption of RNA-seq, extending its utility to a plethora of RNA-centric investigations, including single-cell gene expression, RNA translation (translatome), and RNA structure. Excitingly, novel applications such as spatial omics are actively being explored. Moreover, the integration of emerging long-read and direct RNA-seq technologies with advanced computational analysis tools is ushering in a new era of RNA biology comprehension. This comprehensive approach enables researchers to unravel intricate details ranging from the timing and location of transcript transcription to RNA folding dynamics and molecular interactions critical for functional fulfillment.

Recommended reading: What is Translatomics? and The Potential and Applications of Translatomics.

RNA-seq is a sophisticated technique that integrates experimental methodologies with computational tools to unravel the identity and abundance of RNA sequences within biological samples. This encompasses discerning the compositional order of adenine, cytosine, guanine, and uracil ribonucleic acid residues present in individual single-stranded RNA molecules, a feat accomplished through RNA sequencing. Experimental procedures in RNA-seq encompass RNA extraction from cells, tissues, or entire organisms, the generation of diverse RNA libraries, and subsequent bioinformatic data analysis.

Recommended reading: How to Plan Your Next RNA Sequencing Experiment.

Key Steps in RNA Sequencing

Key steps involve RNA isolation from various sources, the creation of distinct RNA libraries, chemical sequencing of these libraries, and the subsequent analysis of bioinformatics data. A notable divergence from earlier techniques, such as microarrays, lies in the remarkable throughput and sensitivity of contemporary RNA-seq platforms. This heightened capability empowers researchers to uncover novel transcripts, elucidate models of gene regulation, and explore a plethora of non-coding small RNAs with unprecedented depth and precision.

Fact Sheet RNA Sequencing and Data Analysis is a helpful article for you to know the steps of Illumina RNA sequencing.

The History of RNA Sequencing Platforms

The evolution of RNA-seq methodologies is intricately linked to advancements in sequencing technology across generations. Initially, high-throughput sequencing began with the pioneering Sanger double deoxy chain termination technique. This method produced a series of fragments differing by one base, followed by capillary electrophoresis for fragment separation. Each run, employing 96 capillaries, yielded short sequences of 600 to 1,000 bases, totaling approximately 100,000 bases of sequence data.

The advent of next-generation sequencing (NGS), revolutionized the field by employing parallelized chemical synthesis of individual nucleotides. This massive parallelization enabled single runs to encompass millions of sequencing reactions. For instance, in an NGS run targeting an RNA sequence of 100 bases, an astounding 6 followed by 90 zeros bases of sequence information could be generated.

Long-read sequencing builds upon these innovations, primarily employing large-scale sequencing-while-synthesizing through chemical synthesis. Notably, it facilitates individual sequencing of each DNA or RNA molecule, significantly increasing the length of sequence reads, with some reaching up to 10,000 nucleotides.

8 Steps to Select the Right Platform for RNA Sequencing may be a helpful article to guide you in selecting the optimal platform for your research needs for RNA sequencing.

Recommended reading: What is the Difference Between Short and Long-read RNA-Seq?

The wealth of information gleaned from RNA-seq data is vast and transformative. It enables the identification of transcripts encoding new proteins in embryonic stem cells and the detection of overexpressed transcripts in skin cancer cells.

This data empowers researchers to pose and answer a myriad of questions: What disparities exist in gene expression levels between normal and cancer cells? How do changes in gene expression levels contribute to cancer cells evading the action of oncogenes? What alterations occur in gene expression following mutagenic treatments? Which genes are upregulated during brain development? Which transcripts are tissue-specific, distinguishing skin from muscle? How does oxidative stress impact gene splicing? What novel miRNAs are present in human embryonic stem cells? The versatility of RNA-seq facilitates the exploration and resolution of these and countless other inquiries, underscoring its profound impact on biological research.

If you want to know more about RNA sequencing technology, you can contact our technical team.

Different Types of RNA Sequencing

RNA-Seq methods offer a diverse array of advantages tailored to different research inquiries and experimental designs. Researchers can select the most appropriate method based on their specific objectives and available resources.

• mRNA Sequencing: This technique enables sensitive and precise quantification of gene expression. It identifies both known and novel isoforms in the coding transcriptome, detects gene fusions, and measures allele-specific expression.
• Targeted RNA Sequencing: Focused on analyzing gene expression in specific sets of genes, this approach can be achieved through enrichment methods or amplicon-based approaches, providing depth in examining particular genomic regions.
• Ultra-Low-Input and Single-Cell RNA-Seq: Ideal for investigating individual cell behavior within their microenvironment, this method is invaluable for studying cell differentiation, proliferation, and tumorigenesis. Ultra-low-input RNA-Seq facilitates analysis of minute starting materials, while single-cell RNA-Seq unveils gene expression profiles at a single-cell resolution.
• Total RNA Sequencing: Offering a comprehensive snapshot of the transcriptome, this method accurately measures gene and transcript abundance, detecting both known and novel features in coding and noncoding RNA.
• Small RNA Sequencing: Focused on sequencing small RNA species like microRNAs, this technique sheds light on the roles of noncoding RNA in gene silencing and posttranscriptional regulation.
• LncRNA Sequencing: Utilizing high-throughput sequencing and bioinformatics analysis, this method explores the functions and characteristics of long noncoding RNAs, providing insights into their regulatory roles in cellular processes.

Bioinformatics Workflow of RNA-Seq

• RNA-seq Analysis Methods

At the sample level, RNA-seq analysis delves into transcriptome similarity, elucidating the molecular landscapes across different biological samples. Gene-level analysis focuses on unraveling gene expression kinetics, providing insights into the dynamic regulation of gene expression. Transcript-level analysis entails reconstructing and quantifying transcripts, shedding light on the intricate splicing patterns and isoform diversity. Additionally, exon-level analysis scrutinizes exon inclusion in selective splicing events, unraveling the complexity of alternative splicing mechanisms.

Recommended reading: What is Alternative Splicing Analysis?

• Advanced RNA-seq Analyses

Advanced RNA-seq analyses encompass a spectrum of sophisticated techniques tailored to extract deeper biological insights. Weighted Gene Co-Expression Network Analysis (WGCNA) elucidates complex gene regulatory networks, uncovering functional modules within transcriptomic data. Gene Set Enrichment Analysis (GSEA) discerns biological pathways enriched in differentially expressed gene sets, providing context to transcriptional changes. Time Series Analysis dissects temporal gene expression patterns, unveiling dynamic regulatory processes. Gene Fusion Analysis identifies fusion transcripts, offering clues to oncogenic events and structural variations. RNA editing analysis unveils post-transcriptional modifications, illuminating regulatory mechanisms. Protein Interaction Network Analysis (PINA) maps protein-protein interactions, elucidating cellular signaling networks. Finally, metabolic pathway visualization analysis integrates transcriptomic data with metabolic pathways, offering a holistic view of cellular metabolism and function.

Recommended reading: Bioinformatics Workflow of RNA-Seq.