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1.Introduction to RNA‐Seq and Differential Expression Analysis
RNA sequencing (RNA-Seq) is a high-throughput sequencing technique that captures a snapshot of gene expression by reading and quantifying mRNA molecules present in a sample. By sequencing the RNA content of cells or tissues, we can determine which genes are active and at what levels. RNA-Seq provides a global view of transcriptional activity, allowing us to measure gene expression comprehensively across the genome.
RNA-Seq is widely used in research for several reasons:
- High sensitivity: It can detect a broad range of expression levels, including low-abundance transcripts.
- Discovery power: RNA-Seq can reveal novel genes, splice variants, and gene fusions.
- Comparative analysis: It enables direct comparisons of gene expression under different conditions, such as treated vs. untreated, diseased vs. healthy, or mutant vs. wild-type samples.
Because of its versatility, RNA-Seq is valuable for studying diverse biological questions, from understanding disease mechanisms to identifying potential therapeutic targets.
Image source: Wikimedia Commons
RNA sequencing (RNA-seq) involves converting RNA molecules into complementary DNA (cDNA) and sequencing them, often using high-throughput platforms like Illumina. This process generates millions of short sequence fragments, or reads, that can be aligned to a reference genome and quantified to analyze gene expression.
RNA-Seq Analysis Workflow
- 1. Sample Preparation: Extract RNA, fragment it, synthesize cDNA, and prepare a library for sequencing. More about RNA-Seq experimental design.
- 2. Sequencing:High-throughput sequencing of cDNA libraries yields raw read data. Find out more about quality control.
- 3. Read Alignment: Map reads to a reference genome for accurate quantification. Discover how to do sequence alignment.
- 4. Quantification: Count mapped reads to estimate gene expression levels. Counting reads.
- 5. Differential Expression Analysis: Use statistical methods to detect significant expression differences between conditions.
Note
Know Your Data Type: Single-Cell vs. Bulk RNA-Seq
- Bulk RNA-Seq captures the average gene expression across a population of cells, providing a single measurement per gene for the entire sample. This approach is cost-effective for studying whole tissues or cell populations but cannot resolve differences between individual cells.
❌ Single-cell resolution
✅General view of gene expression across tissue or condition
- Single-cell RNA-Seq(scRNA-Seq) captures gene expression at the individual cell level, allowing us to detect cellular diversity and subpopulations within a sample, understanding cellular diversity, identifying subpopulations, or exploring cell-specific transcriptional profiles are key research goals.
For this workshop, we will focus on scRNA-Seq data analysis. For more information on scRNA-Seq analysis, consider reviewing this reference on scRNA-Seq methodologies and applications.
Differential Gene Expression (DGE) Analysis is a statistical approach used to identify genes whose expression levels vary significantly between conditions. This analysis helps us understand how different experimental conditions influence gene activity. For example, DGE can reveal genes that are upregulated or downregulated in a disease state compared to a control.
DGE analysis provides insights into biological mechanisms and can inform various applications, including:
- Identifying genes associated with disease or treatment response.
- Studying the effects of genetic mutations on gene expression.
- Characterizing regulatory networks and pathways involved in a biological process.
Proper experimental design is critical to ensure accurate and meaningful RNA-seq results.
Biological Replicates: Include replicates to capture natural variation across individuals or cell populations.
Sequencing Depth: Ensure sufficient depth to accurately detect low-expression genes and capture true biological differences.
Normalization: Apply normalization to correct for technical biases, including library size differences, and maintain comparability across samples.
Avoiding Bias:
- Biological Replicates: Capture natural variability and improve the statistical power of the analysis.
- Technical Replicates: Control for technical noise during library prep and sequencing.
- Experimental Controls: Help isolate the effects of experimental conditions and reduce confounding factors.
To accurately detect changes in gene expression due to experimental treatments, RNA-seq requires both biological and technical replicates. Biological replicates, representing distinct samples, capture natural biological variation, while technical replicates, produced from the same RNA sample, control for experimental noise.
Tip
Reference for Experimental Design Best Practices: Altman and Krzywinski, 2014.
1. Identify Research Objectives: Define the biological question driving the study.
2. Recognize Potential Nuisance Factors: Consider environmental, technical, or biological factors that may introduce unwanted variability.
3. Design to Minimize Bias: Structure experiments to limit the impact of nuisance factors on gene expression measurements.
4. Use Randomization: True randomization minimizes selection bias and ensures equal treatment of all samples.
Carefully planning these steps will help ensure that the RNA-seq data reflect true biological changes rather than artifacts from experimental setup or analysis.
Tip
Recommended Number of Replicates for reliable results:
- Three biological replicates for basic differential expression studies.
- Six replicates to capture a comprehensive profile of a condition's transcriptome.
- Up to twelve replicates for detecting subtle gene expression changes, especially for low-expression genes. Reference for Replicate Guidelines: Schurch et al., 2016.