MBE RNA seq

1. Experimental Purpose

• Scientific Question: How do gene expression patterns vary across conditions, cell types, or spatial locations within tissues?
• Design Rationale: Transcriptome analysis techniques reveal comprehensive gene expression profiles, alternative splicing events, and spatial distribution of transcripts
• Follow-up Studies: Functional validation of differentially expressed genes, pathway analysis, biomarker identification, therapeutic target discovery

2. Model System

• Primary Systems: Cell lines, tissue samples, patient biopsies, single-cell suspensions
• Rationale: These systems provide RNA of sufficient quality and quantity while maintaining biological relevance
• Alternatives:
• In vitro cell models (pros: controlled conditions, homogeneity; cons: may not reflect in vivo complexity)
• Animal models (pros: system-level responses; cons: species differences, ethical considerations)
• Organoids (pros: 3D structure, cell-cell interactions; cons: incomplete tissue architecture)
• Ethical Considerations: Patient consent for clinical samples, minimizing animal use, responsible data sharing and privacy

3. Measurement Approach

• Common Elements:
• High-quality RNA extraction and preservation
• RNA integrity verification
• Library preparation optimization
• Inclusion of spike-in controls
• Technical Replicates: Multiple technical replicates for microarrays; sequencing depth considerations for RNA-seq approaches
• Potential Biases:
• RNA degradation (use RIN scores to assess quality)
• Batch effects (include batch controls)
• PCR amplification bias (UMIs for single-cell approaches)
• 3' bias in degraded samples (assess coverage uniformity)

4. Group Setting

• Experimental Groups:
• Test: Samples from experimental condition of interest
• Control 1: Matched untreated/baseline samples
• Control 2: Technical controls (spike-ins, housekeeping genes)
• Control 3: Biological reference standards when available
• Controlled Variables: RNA quality, batch processing, sequencing platform, analysis pipeline
• Biological Replicates: Minimum 3-5 biological replicates per condition; higher numbers for heterogeneous samples
• Modified Design: Time-course analysis, dose-response relationships, multiple tissue regions

5. Data Analysis & Presentation

• Common Analysis Elements:
• Quality control metrics (read depth, mapping rates, coverage)
• Normalization strategies appropriate to technique
• Differential expression analysis with statistical thresholds
• Pathway and functional enrichment analysis
• Presentation Approaches:
• Heatmaps for expression patterns
• Volcano plots for significance visualization
• PCA/t-SNE/UMAP for dimensionality reduction
• Spatial maps for regional expression patterns

6. Technique Comparison

Feature	Microarray	RNA-seq	Single-cell RNA-seq	Long-read Sequencing	Spatial Transcriptomics
Primary Use	Global gene expression profiling	Comprehensive transcriptome analysis	Cell-type specific expression patterns	Full-length transcript analysis	Spatial mapping of gene expression
Sensitivity	Moderate (limited dynamic range)	High (wide dynamic range)	Moderate (limited by dropout effects)	Moderate (lower throughput)	Moderate (depends on platform)
Specificity	Good for known transcripts	Excellent for known and novel transcripts	Good for abundant transcripts	Excellent for isoform discrimination	Good for targeted gene panels
Quantification	Relative abundance through hybridization	Digital counts of transcript abundance	Digital counts at single-cell resolution	Accurate isoform quantification	Regional expression quantification
Throughput	High (thousands of genes)	Very high (entire transcriptome)	High (thousands of cells)	Moderate (fewer reads, longer lengths)	Moderate (spatial resolution trade-off)
Resolution	Gene-level only	Gene and transcript-level	Single-cell	Full transcript structure	Tissue region/cell location
Cost	Low to moderate	Moderate	High	High	Very high
RNA Input Required	50-500 ng	10-1000 ng	Single-cell (pg range)	50-1000 ng	Tissue sections
Technical Expertise	Moderate	Moderate to advanced	Advanced	Advanced	Very advanced
Best For	• Well-characterized systems • Large sample comparisons • Cost-effective screening • Established gene sets	• Discovering novel transcripts • Detecting rare transcripts • Alternative splicing analysis • Comprehensive profiling	• Heterogeneous samples • Rare cell type identification • Developmental trajectories • Cellular diversity studies	• Isoform identification • Fusion transcript detection • Complex structural variants • Complete transcript sequences	• Tissue architecture analysis • Cell-cell communication • Niche-specific expression • Disease boundary mapping
Limitations	• Limited to known sequences • Cross-hybridization issues • Narrow dynamic range • No novel transcript discovery	• Complex data analysis • Computational requirements • Short-read assembly challenges • Batch effects	• Low RNA capture efficiency • High dropout rates • Expensive per sample • Limited splicing information	• Lower throughput • Higher error rates • More expensive • Specialized analysis required	• Limited gene coverage • Resolution constraints • Expensive technology • Complex data integration

7. Complementary Usage Strategy

• Initial Profiling: Use microarrays for cost-effective screening of known genes across many samples
• Comprehensive Analysis: Follow with RNA-seq for in-depth transcriptome characterization including novel transcripts
• Heterogeneity Assessment: Apply single-cell RNA-seq to dissect cellular subpopulations and rare cell types
• Structural Validation: Employ long-read sequencing to resolve complex isoforms and structural variants
• Contextual Understanding: Integrate spatial transcriptomics to map expression patterns within tissue architecture
• Integrated Approach: Design multi-platform studies for comprehensive characterization:
• RNA-seq for global transcriptome profiling
• Single-cell RNA-seq to resolve cellular heterogeneity
• Long-read sequencing to characterize full-length transcripts of interest
• Spatial transcriptomics to map key findings to tissue context

8. Technology-Specific Considerations

Microarray

• Design Optimization: Probe selection affects specificity and coverage
• Hybridization Conditions: Critical for signal-to-noise ratio
• Data Normalization: Essential for cross-array comparisons
• Legacy Data: Valuable historical datasets available for meta-analysis

RNA-seq

• Library Preparation: Stranded vs. unstranded, poly(A) selection vs. ribo-depletion
• Sequencing Depth: Tailored to research question (15-30M reads for differential expression)
• Read Length: Longer reads improve mapping and transcript assembly
• Spike-in Controls: Essential for absolute quantification

Single-cell RNA-seq

• Cell Isolation: Dissociation protocols affect cell representation
• Droplet vs. Well-based: Trade-off between cell number and coverage depth
• Doublet Rate: Quality control to remove multi-cell captures
• Computational Deconvolution: Advanced algorithms for cell type identification

Long-read Sequencing

• Platform Selection: PacBio (higher accuracy) vs. Oxford Nanopore (longer reads)
• Error Correction: Critical for accurate transcript annotation
• Hybrid Approaches: Combining with short-read data for error correction
• Direct RNA Sequencing: Captures native modifications but with lower throughput

Spatial Transcriptomics

• Resolution Range: From tissue regions to subcellular localization
• Coverage vs. Resolution: Trade-off between gene number and spatial detail
• Image Integration: Correlating expression with histological features
• Cell Type Deconvolution: Computational methods to resolve mixed signals

This integrated framework provides a comprehensive approach to transcriptome analysis, leveraging the strengths of each technology while addressing their individual limitations. The complementary use of these methods enables researchers to build a multi-dimensional understanding of gene expression across biological systems.