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Microarray technology and analysis of gene expression data

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24 diapositives · 582 Views · Jul 16, 2014

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...

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Microarray technology and analysis of gene expression data
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  1. Microarray technology and analysis of gene expression data Hillevi Lindroos

  2. 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

  3. Two-colour spotted microarrays Spot = PCR-product (~500 bp) from one gene or long oligonucleotide (~50 bp) Differential expression (two samples compared)

  4. 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

  5. Red and green images superimposed: In principle... Red spot: up-regulated gene, ratio >1 Green spot: down-regulated gene, ratio <1 Yellow spot: no differential expression, ratio =1

  6. mixing equal amounts of cDNA RT + red dye competitive hybridization RT + green dye Control gene A Sample (e.g. heat shock) Microarray Red dot in image Up-regulation

  7. 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.

  8. Data processing and analysis 1. Image analysis Locate spots in image Quantify fluorescence intensity (spot + background) Mean / median of pixel intensities

  9. 2. Background correction • local background for each spot, or global for whole array • assuming additive background: Spot intensity = True intensity + Background

  10. 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!

  11.  Use logarithm! M = log2(ratio) is symmetrically distributed around 0 Upregulated 2 times: ratio= 2, M= 1 Downregulated 2 times: ratio= 0.5, M= -1

  12. 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

  13. Plot of Cy5 intensity (R) vs Cy3 intensity (G): Dye bias:Most genes seem to be upregulated (higher Cy5 than Cy3 intensity).

  14. Corrected for by scaling Cy5 values with total_Cy3/total_Cy5. Assumes most genes unaffected by treatment.

  15. Intensity dependent dye bias Dye bias may depend on total spot intensityA (A=½(log2R+log2G)), position on array, print-tip…

  16. Correction: Mnormalized = M – Mtrend(A)

  17. 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).

  18. 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

  19. Expression profiles Plot expression over a series of experiment (e.g. time series)

  20. 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)

  21. 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

  22. Serum stimulation of human fibroblasts, time series. A: cholesterol biosynthesis B: cell cycle C: immediate-early response D: signaling and angiogenesis E: wound healing Distance: correlation coefficient Agglomeration: average linkage from: Eisen et al., 1998, PNAS 95(25): 14863-14868

  23. Clustering of arrays: classification of cancer cells. From Chen et al. (2002). Mol Biol Cell 13(6):1929-39

  24. Exercise: Normalization (Excel): R-G plot M-A plot most up- and downregulated genes