Microarray technology and analysis of gene expression data Hillevi Lindroos.
Published byModified over 4 years ago
Presentation on theme: "Microarray technology and analysis of gene expression data Hillevi Lindroos."— Presentation transcript:
Microarray technology and analysis of gene expression data Hillevi Lindroos
Introduction to microarray technology Technique for studying gene expression for thousands of genes simultaneously. Study gene regulation, effects of treatments, differences between healthy and diseased cells... Comparative Genome Hybridization: - gene content in related strains/species - gene dosage in cancer cells Microarray: glass slide with spots, each containing DNA from one gene
Two-colour spotted microarrays Spot = PCR-product (~500 bp) from one gene or long oligonucleotide (~50 bp) Differential expression (two samples compared)
Experimental procedure: 1. Isolate RNA from 2 samples (experiment and control). 2. Reverse transcribe to cDNA with fluorescently labelled nucleotides, e.g. Cy3-dCTP (control) or Cy5-dCTP (experiment). 3. Mix and hybridize to microarray. 4. Laser scan: measure fluorescent intensities
In principle... Red spot: up-regulated gene, ratio >1 Green spot: down-regulated gene, ratio <1 Yellow spot: no differential expression, ratio =1 Red and green images superimposed:
Sample (e.g. heat shock) gene A RT + red dye mixing equal amounts of cDNA competitive hybridization Microarray Red dot in image Up-regulation
Why differential expression? Fluorescent intensities do not directly correspond to mRNA concentrations, due to: different shapes and densities of spots different hybridization properties between genes different amounts of dye incorporation between genes Compare intensities (expression) from two samples.
Data processing and analysis 1. Image analysis Locate spots in image Quantify fluorescence intensity (spot + background) Mean / median of pixel intensities
2. Background correction – local background for each spot, or global for whole array – assuming additive background: Spot intensity = True intensity + Background
Output Cy5 (R) and Cy3 (G) intensities Ratio = R/G ~ [mRNA_experiment] / [mRNA_control] Up-regulated genes: ratio >1 Down-regulated genes: ratio= 0-1 Assymetry!
Use logarithm! M = log 2 (ratio) is symmetrically distributed around 0 Upregulated 2 times: ratio= 2, M= 1 Downregulated 2 times: ratio= 0.5, M= -1
3. Normalization: correction of systematic errors (dye bias) different amounts of control and experiment samples different fluorescent intensities of Cy3 and Cy5 different labelling and detection efficiencies
Dye bias: Most genes seem to be upregulated (higher Cy5 than Cy3 intensity). Plot of Cy5 intensity (R) vs Cy3 intensity (G):
Corrected for by scaling Cy5 values with total_Cy3/total_Cy5. Assumes most genes unaffected by treatment.
Dye bias may depend on total spot intensity A (A =½(log 2 R+log 2 G)), position on array, print-tip… Intensity dependent dye bias
Identify differentially expressed genes Simple: cutoff (e.g. |M| > 1) Better: statistical test, e.g. t-test (replicate spots or repeated experiments) => Significance – Unstable mRNAs may have high ratios – and high variation! – Weak spots: small difference in signal may be big relative difference (high ratio).
Affymetrix genchips Spots = 25 bp oligonucleotides Pairs of perfectly matching probe + probe with 1 mismatch for each gene One sample per array Radioactive labelling Expression level computed from difference in intensity between matching and mis-matching probe
Expression profiles Plot expression over a series of experiment (e.g. time series)
Clustering expression profiles Analyze multiple experiments to identify common patterns of gene expression Similar function – similar expression (co-regulation) Goals: Identify regulatory motifs Infer function of unknown genes Distinguish cell types, e.g. tumors (cluster arrays)
Hierarchical clustering Expression profile -> vector Compute similarity between expression profiles (e.g. correlation coefficient) Successively join the most similar genes to clusters, and clusters to superclusters
Serum stimulation of human fibroblasts, time series. A: cholesterol biosynthesis B: cell cycle C: immediate-early response D: signaling and angiogenesis E: wound healing from: Eisen et al., 1998, PNAS 95(25): 14863-14868 Distance: correlation coefficient Agglomeration: average linkage
Clustering of arrays: classification of cancer cells. From Chen et al. (2002). Mol Biol Cell 13(6):1929-39
Exercise: Normalization (Excel): R-G plot M-A plot most up- and downregulated genes