🧬Genomics Unit 5 – Transcriptomics and Gene Expression Analysis

Introduction to Transcriptomics

• Transcriptomics studies the complete set of RNA transcripts produced by the genome at a specific time or under specific conditions
• Provides a snapshot of the genes actively expressed in a cell or tissue at a given moment
• Includes coding RNA (mRNA) and non-coding RNA (ncRNA) such as rRNA, tRNA, miRNA, and lncRNA
• Enables understanding of the functional elements of the genome and reveals molecular constituents of cells and tissues
• Allows for quantification of changes in expression levels of each transcript under different conditions (development, disease, treatment)
• Complements genomics by focusing on the dynamic aspects of gene expression rather than static DNA sequence
• Has applications in biomarker discovery, disease diagnosis, drug development, and personalized medicine

Key Concepts in Gene Expression

• Gene expression is the process by which information from a gene is used to synthesize functional gene products (proteins or RNA)
• Involves two main stages: transcription (DNA to RNA) and translation (RNA to protein)
• Transcription is carried out by RNA polymerase enzymes (RNA Pol I, II, III) and regulated by transcription factors
  • RNA Pol I transcribes rRNA genes
  • RNA Pol II transcribes mRNA, miRNA, snRNA, and lncRNA genes
  • RNA Pol III transcribes tRNA, 5S rRNA, and other small RNA genes
• Translation occurs in the cytoplasm by ribosomes and involves tRNA molecules carrying amino acids
• Gene expression is tightly regulated at multiple levels (transcriptional, post-transcriptional, translational, post-translational) to ensure proper cell function
• Epigenetic modifications (DNA methylation, histone modifications) can influence gene expression without changing the DNA sequence
• Alternative splicing allows for the production of multiple mRNA isoforms from a single gene, increasing proteome diversity

RNA Sequencing Technologies

• RNA sequencing (RNA-seq) is a high-throughput method for transcriptome profiling using deep-sequencing technologies
• Involves converting RNA to cDNA, fragmenting, and sequencing millions of short reads in parallel
• Illumina sequencing is the most widely used platform, based on sequencing by synthesis chemistry
  • Includes library preparation, cluster generation, and sequencing steps
  • Generates paired-end reads typically 50-150 bp in length
• Long-read sequencing technologies (PacBio, Oxford Nanopore) allow for sequencing of full-length transcripts without fragmentation
• Single-cell RNA-seq (scRNA-seq) enables transcriptome profiling at the individual cell level, revealing cellular heterogeneity and rare cell types
• Spatial transcriptomics methods (FISSEQ, MERFISH) provide gene expression information while preserving spatial context of cells in tissues
• Targeted RNA-seq approaches (RASL-seq, CaptureSeq) focus on specific subsets of transcripts for cost-effective and sensitive quantification

Experimental Design and Sample Preparation

• Careful experimental design is crucial for successful RNA-seq studies to ensure reliable and reproducible results
• Biological replicates (multiple samples from different individuals or experiments) are necessary to account for biological variability
• Technical replicates (multiple sequencing runs of the same sample) can assess technical noise but are less important with modern sequencing platforms
• Sample size and power calculations should be performed to determine the number of replicates needed to detect significant differences in gene expression
• RNA extraction methods (TRIzol, column-based kits) should be chosen based on sample type and yield high-quality, intact RNA
• RNA quality assessment (RIN score, 28S/18S ratio) is essential to ensure sample integrity and comparability
• Ribosomal RNA depletion (Ribo-Zero) or poly(A) selection is often performed to enrich for mRNA and remove abundant rRNA
• Library preparation involves cDNA synthesis, fragmentation, adapter ligation, and PCR amplification steps

Data Analysis Pipelines

• Raw sequencing data (FASTQ files) undergo quality control (QC) to assess sequencing quality, adapter contamination, and GC bias
  • Tools like FastQC and MultiQC are commonly used for QC
• Reads are aligned to a reference genome or transcriptome using splice-aware alignment tools (STAR, HISAT2, TopHat2)
  • Alignment generates BAM files containing mapped read information
• Read quantification is performed at the gene or transcript level using tools like featureCounts or HTSeq
  • Generates count matrices with raw read counts for each gene/transcript in each sample
• Normalization methods (RPKM, TPM, DESeq2, edgeR) are applied to correct for differences in library size and composition between samples
• Batch effect correction (ComBat, RUV) may be necessary if samples were processed in different batches or conditions
• Dimensionality reduction techniques (PCA, t-SNE, UMAP) are used to visualize sample relationships and identify outliers or confounding factors
• Clustering methods (hierarchical, k-means) can group samples or genes based on similarity in expression patterns

Differential Expression Analysis

• Differential expression analysis identifies genes that are significantly up- or down-regulated between conditions (e.g., disease vs. healthy, treatment vs. control)
• Statistical methods (DESeq2, edgeR, limma) model read count data and test for significant differences in expression while accounting for biological variability
  • Based on negative binomial distribution, which captures the mean-variance relationship in RNA-seq data
• Genes with adjusted p-value < 0.05 and fold change > 2 are typically considered differentially expressed
• False discovery rate (FDR) correction is applied to adjust for multiple testing and control the expected proportion of false positives
• Differentially expressed genes can be visualized using volcano plots, heatmaps, or MA plots
• Validation of differential expression results using orthogonal methods (qPCR, Western blot) is important to confirm findings
• Meta-analysis can be performed to combine results from multiple RNA-seq studies and increase statistical power

Functional Annotation and Pathway Analysis

• Functional annotation involves assigning biological functions or properties to differentially expressed genes
• Gene Ontology (GO) annotation categorizes genes into three domains: biological process, molecular function, and cellular component
• Pathway analysis identifies enriched biological pathways or networks among differentially expressed genes
  • Overrepresentation analysis (ORA) tests for significant overlap between gene sets and annotated pathways
  • Gene set enrichment analysis (GSEA) assesses whether a gene set is enriched at the top or bottom of a ranked list of genes
• Pathway databases (KEGG, Reactome, WikiPathways) curate and provide information on known biological pathways
• Protein-protein interaction (PPI) networks can be constructed to identify hub genes and key regulators in the context of differentially expressed genes
• Transcription factor binding site (TFBS) enrichment analysis can reveal potential upstream regulators of gene expression changes
• Integration with other omics data (proteomics, metabolomics) can provide a more comprehensive understanding of biological processes and mechanisms

Advanced Applications and Future Directions

• Single-cell RNA-seq (scRNA-seq) enables transcriptome profiling at the individual cell level, revealing cellular heterogeneity and rare cell types
  • Allows for identification of novel cell types and states, lineage tracing, and pseudotime analysis
• Spatial transcriptomics methods (FISSEQ, MERFISH) provide gene expression information while preserving spatial context of cells in tissues
  • Enables the study of tissue organization, cell-cell interactions, and spatial patterns of gene expression
• Long-read sequencing technologies (PacBio, Oxford Nanopore) allow for sequencing of full-length transcripts without fragmentation
  • Facilitates the discovery and characterization of novel isoforms, fusion transcripts, and long non-coding RNAs
• Multi-omics integration combines transcriptomics with other omics data (genomics, epigenomics, proteomics) for a holistic view of biological systems
  • Provides insights into the relationships between different molecular layers and their impact on phenotypes
• Translational applications of transcriptomics include biomarker discovery, disease subtyping, drug target identification, and personalized medicine
  • Transcriptomic signatures can serve as diagnostic or prognostic markers for disease
  • Identifying key pathways or genes dysregulated in disease can guide the development of targeted therapies
• Emerging technologies such as in situ sequencing and in vivo RNA labeling will enable real-time monitoring of gene expression in living cells and organisms
• Integrating transcriptomics with CRISPR-based perturbation screens can uncover functional roles of genes and regulatory elements in biological processes