RNA sequencing, or RNA-Seq, is a laboratory technique used to measure the quantity and sequences of RNA molecules within a biological sample. This method provides a comprehensive snapshot of the transcriptome, representing all active genes and their expression levels at a given moment. By analyzing the transcriptome, researchers gain insights into cellular function, understand developmental processes, and investigate the molecular basis of various diseases.
Sample Preparation and RNA Isolation
The initial step in an RNA-Seq experiment involves obtaining high-quality RNA from biological source material, such as cells or tissues. Preventing RNA degradation is important, as RNA molecules are inherently less stable than DNA. Researchers work in environments free of RNases, enzymes that break down RNA, to protect sample integrity.
Sample collection is followed by lysis, where cells are broken open to release RNA. Specific reagents are used during lysis to inactivate RNase enzymes, safeguarding the RNA. Following cell disruption, RNA is isolated from other cellular components like DNA, proteins, and lipids. Common methods include phenol-chloroform extraction, which separates molecules based on solubility, or silica column-based kits that selectively bind RNA while impurities are washed away.
After isolation, the purity and integrity of the extracted RNA must be assessed through quality control (QC). A spectrophotometer, such as a NanoDrop, measures RNA concentration and purity by assessing absorbance ratios (e.g., A260/280 and A260/230). Microfluidic electrophoresis instruments, like a Bioanalyzer or TapeStation, verify RNA integrity by generating an RNA Integrity Number (RIN score), with scores above 7.0 indicating high-quality RNA suitable for downstream applications. Low-quality or degraded RNA can lead to unreliable sequencing results and compromise gene expression measurement accuracy.
Library Preparation for Sequencing
Converting isolated RNA into a format readable by a sequencing machine is a multi-step process known as library preparation. This procedure transforms unstable RNA fragments into stable complementary DNA (cDNA) molecules with attached adapters. The first stage involves selecting or depleting specific RNA types from the total RNA pool. Since ribosomal RNA (rRNA) constitutes a large percentage of total RNA and is not of interest for gene expression studies, it is either removed using rRNA-specific probes and bead capture, or messenger RNA (mRNA) is enriched by targeting its poly(A) tail.
After the target RNA population is isolated, RNA molecules are fragmented into smaller pieces, ranging from 200 to 500 nucleotides in length. This fragmentation is necessary because sequencing machines have limitations on the length of molecules they can efficiently read. These RNA fragments then undergo reverse transcription, where the enzyme reverse transcriptase synthesizes a stable complementary DNA (cDNA) strand from the RNA template. This conversion is important because DNA is more robust and compatible with subsequent library preparation and sequencing steps.
Following cDNA synthesis, short DNA sequences called “adapters” are ligated, or attached, to both ends of the cDNA fragments. These adapters allow cDNA molecules to bind to the sequencing flow cell and contain sequences for PCR amplification and unique “barcodes” or “indexes” that enable multiplexing, allowing multiple samples to be sequenced together in one run. The adapter-ligated cDNA fragments are then amplified using polymerase chain reaction (PCR), creating millions of identical copies. A final quality control step is performed on the prepared library to confirm fragment size distribution and concentration, ensuring it is ready for the sequencing instrument.
Running the Sequencer
Once the sequencing library is prepared, it is loaded onto an instrument, commonly an Illumina sequencer, to generate digital data. The process begins by introducing the prepared library into a flow cell, a glass slide containing millions of microscopic channels coated with oligonucleotides.
Each bound library fragment undergoes bridge amplification, which creates a localized, dense cluster of identical copies. The sequencing occurs through a method called Sequencing-by-Synthesis (SBS).
In this technique, the machine sequentially adds one fluorescently-labeled nucleotide (adenine, cytosine, guanine, or thymine) at a time to the growing DNA strands within each cluster.
After each nucleotide addition, a high-resolution camera captures an image of the flow cell. The color of fluorescence emitted by each cluster indicates which base was incorporated. This cycle of nucleotide addition, imaging, and cleavage of the fluorescent tag is repeated hundreds of times, allowing the instrument to read the sequence of each fragment, base by base. The output from the sequencing run is a data file in FASTQ format, containing raw sequence reads and associated quality scores, providing a measure of confidence for each base call.
Bioinformatic Data Analysis
The final phase of an RNA-Seq experiment involves computational analysis of raw sequence data to extract biological insights. This process transforms raw reads into meaningful information about gene expression. The first step in this workflow is raw data quality control, where software tools assess the quality of sequence reads generated by the sequencer. This involves identifying and trimming low-quality bases from the ends of reads and removing any remaining adapter sequences ligated during library preparation.
Following quality trimming, short sequence reads are aligned, or “mapped,” to a reference genome or transcriptome corresponding to the organism being studied. This step is analogous to reassembling a shredded document using an intact copy as a guide, identifying the original genomic location for each sequenced fragment. Algorithms efficiently match millions of short reads to their precise positions within the genetic blueprint.
Once reads are aligned, the next step is quantification, where the number of reads that successfully map to each gene is counted. This read count is directly proportional to the expression level of that gene in the original biological sample; a higher number of reads mapping to a gene indicates the gene was more active or highly expressed. These raw counts are normalized to account for differences in library size and gene length.
The goal of RNA-Seq experiments is differential expression analysis. Statistical methods compare normalized gene counts between different experimental groups, such as treated versus untreated cells, or diseased versus healthy tissues. This analysis identifies genes that show statistically significant changes in expression, meaning they are either “up-regulated” (more active) or “down-regulated” (less active) under specific conditions. Visualizations like heatmaps or volcano plots represent these differential expression patterns, providing clear insights into biological processes affected by experimental conditions.