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HWW Gene Expression Experiments: H ow? W hy? W hat’s the problem?

HWW Gene Expression Experiments: H ow? W hy? W hat’s the problem?. High Throughput Experiments. Functional Genomics. Bioinformatics. DNA Hybridization. The principle: have two denatured DNA strands bond together, then check double strand amount (florescent dye, radioactive label)

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HWW Gene Expression Experiments: H ow? W hy? W hat’s the problem?

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  1. HWW Gene Expression Experiments: How?Why?What’s the problem?

  2. High Throughput Experiments FunctionalGenomics Bioinformatics

  3. DNA Hybridization • The principle: have two denatured DNA strands bond together, then check double strand amount (florescent dye, radioactive label) • “Traditional”: Southern/Northern/Western Blot • The great advance: micro array DNA chips – automation, material eng., computer aided (including algorithmic solutions)

  4. History cDNA microarrays have evolved from Southern blots, with clone libraries gridded out on nylon membrane filters being an important and still widely used intermediate. Things took off with the introduction of non-porous solid supports, such as glass - these permitted miniaturization - and fluorescence based detection. Currently, about 20,000 cDNAs can be spotted onto a microscope slide. The other, Affymetrix technology can produce arrays of 100,000 oligonucleotides on a silicon chip.

  5. THE PROCESS Building the Chip: PCR PURIFICATION and PREPARATION MASSIVE PCR PREPARING SLIDES PRINTING Preparing RNA: Hybing the Chip: CELL CULTURE AND HARVEST POST PROCESSING ARRAY HYBRIDIZATION RNA ISOLATION DATA ANALYSIS PROBE LABELING cDNA PRODUCTION

  6. Building the Chip: PCR PURIFICATION and PREPARATION MASSIVE PCR Full yeast genome = 6,500 reactions IPA precipitation +EtOH washes + 384-well format PRINTING The arrayer: high precision spotting device capable of printing 10,000 products in 14 hrs, with a plate change every 25 mins PREPARING SLIDES Polylysine coating for adhering PCR products to glass slides POST PROCESSING Chemically converting the positive polylysine surface to prevent non-specific hybridization

  7. Preparing RNA: CELL CULTURE AND HARVEST Designing experiments to profile conditions/perturbations/ mutations and carefully controlled growth conditions RNA ISOLATION RNA yield and purity are determined by system. PolyA isolation is preferable but total RNA is useable. Two RNA samples are hybridized/chip. cDNA PRODUCTION Single strand synthesis or amplification of RNA can be performed. cDNA production includes incorporation of Aminoallyl-dUTP.

  8. Hybing the Chip: ARRAY HYBRIDIZATION Cy3 and Cy5 RNA samples are simultaneously hybridized to chip. Hybs are performed for 5-12 hours and then chips are washed. DATA ANALYSIS Ratio measurements are determined via quantification of 532 nm and 635 nm emission values. Data are uploaded to the appropriate database where statistical and other analyses can then be performed. PROBE LABELING Two RNA samples are labelled with Cy3 or Cy5 monofunctional dyes via a chemical coupling to AA-dUTP. Samples are purified using a PCR cleanup kit.

  9. Printing Microarrays • Print Head • Plate Handling • XYZ positioning • Repeatability & Accuracy • Resolution • Environmental Control • Humidity • Dust • Instrument Control • Sample Tracking Software

  10. Ngai Lab arrayer , UC Berkeley

  11. Microarray Gridder

  12. Printing Approaches Non - Contact • Piezoelectric dispenser • Syringe-solenoid ink-jet dispenser Contact (using rigid pin tools, similar to filter array) • Tweezer • Split pin • Micro spotting pin

  13. Micro Spotting pin

  14. Practical Problems • Surface chemistry: uneven surface may lead to high background. • Dipping the pin into large volume -> pre-printing to drain off excess sample. • Spot variation can be due to mechanical difference between pins. Pins could be clogged during the printing process. • Spot size and density depends on surface and solution properties. • Pins need good washing between samples to prevent sample carryover.

  15. Post Processing Arrays Protocol for Post Processing Microarrays Hydration/Heat Fixing 1. Pick out about 20-30 slides to be processed. 2. Determine the correct orientation of slide, and if necessary, etch label on lower left corner of array side 3. On back of slide, etch two lines above and below center of array to designate array area after processing 4. Pour 100 ml 1X SSC into hydration tray and warm on slide warmer at medium setting 5. Set slide array side down and observe spots until proper hydration is achieved. 6. Upon reaching proper hydration, immediately snap dry slide 7. Place slides in rack.

  16. Practical Problems 1 • Comet Tails • Likely caused by insufficiently rapid immersion of the slides in the succinic anhydride blocking solution.

  17. Practical Problems 2

  18. Practical Problems 3 High Background • 2 likely causes: • Insufficient blocking. • Precipitation of the labeled probe. Weak Signals

  19. Practical Problems 4 Spot overlap: Likely cause: too much rehydration during post - processing.

  20. Practical Problems 5 Dust

  21. Steps in Images Processing 1. Addressing: locate centers 2. Segmentation: classification of pixels either as signal or background. using seeded region growing). 3. Information extraction: for each spot of the array, calculates signal intensity pairs, background and quality measures.

  22. Steps in Image Processing 3. Information Extraction • Spot Intensities • mean (pixel intensities). • median (pixel intensities). • Pixel variation (IQR of log (pixel intensities). • Background values • Local • Morphological opening • Constant (global) • None • Quality Information Signal Background

  23. Addressing This is the process of assigning coordinates to each of the spots. Automating this part of the procedure permits high throughput analysis. 4 by 4 grids 19 by 21 spots per grid

  24. Addressing Registration Registration

  25. Problems in automatic addressing Misregistration of the red and green channels Rotation of the array in the image Skew in the array Rotation

  26. Segmentation methods • Fixed circles • Adaptive Circle • Adaptive Shape • Edge detection. • Seeded Region Growing. (R. Adams and L. Bishof (1994) :Regions grow outwards from the seed points preferentially according to the difference between a pixel’s value and the running mean of values in an adjoining region. • Histogram Methods • Adaptive threshold.

  27. Examples of algorithms and software implementation

  28. Limitation of fixed circle method SRG Fixed Circle

  29. Limitation of circular segmentation • Small spot • Not circular Results from SRG

  30. Information Extraction • Spot Intensities • mean (pixel intensities). • median (pixel intensities). • Background values • Local • Morphological opening • Constant (global) • None • Quality Information Take the average

  31. Local Backgrounds

  32. Summary of analysis possibilities Determine genes which are differentially expressed (this task can take many forms depending on replication, etc) Connect differentially expressed genes to sequence databases and perhaps carry out further analyses, e.g. searching for common upstream motifs Overlay differentially expressed genes on pathway diagrams Relate expression levels to other information on cells, e.g. known tumour types Define subclasses (clusters) in sets of samples (e.g. tumours) Identify temporal or spatial trends in gene expression Seek roles for genes on the basis of patterns of co-expression ……..much more Many challenges: transcriptional regulation involves redundancy, feedback, amplification, .. non-linearity

  33. Biological Question Data Analysis & Modeling Samplepreparation Microarray Life Cycle MicroarrayDetection Microarray Reaction Taken from Schena & Davis

  34. Oligonucleotide Arrays

  35. Schadt et al., Journal of Cellular Biochemistry, 2000

  36. Oligonucleotide Arrays Tech. • ~20 probes per “gene”, 25bases each* • Probe size: 24x24 micron (contain ~106 copies of the probe) • Probe is either a Perfect Match (PP) or a Miss Match (MM) • MM: • usually at the center of the probe • Aim: to give estimate on the random hybrd.

  37. Motivation • Data is noisy, missing values. • Each array is scanned separately, in different settings → To extract biological meaningful results we need: • Good expression estimations • Scale/Normalize across arrays

  38. What we need • Image segmentation • Background/Gradient correction • Artifact detection • Allow array to array comparison (scale/normalize) • Assess gene presence (quantitative “Measure”) • Find differentially expressed genes

  39. Why isn’t “Normalization” Easy? • No ability to read mRNA level directly • Various noise factors → hard to model exactly. • Variable biological settings, experiment dependent. • Need to differentiate between changes caused by biological signal from noise artifacts.

  40. Variability Sources • Real Biology – • Biological noise • Biological Signal • Sample preparation related • Technical dependent

  41. dChip MBEI • Based on several papers by Li & Wong (PNAS, 2001 vol 98 no.1 and others) • Implemented on their freely available dChip software • Model based: The estimation is based on a model of how the probe intensity values respond to changes of the expression levels of the gene

  42. dChip Model i is the array indexj is the probe index is the baseline response of the probe due to non specific hybridization is the rate of increase of the MM response is the additional rate of increase of the PM response

  43. dChip “Reduced” Model Basic idea: Least square parameter estimation, iteratively fitting and

  44. dChip “Reduced” Model For one array, assume that the set has been learned from a large number of arrays, and therefore known and fixed Given this set, the linear least square estimate for theta is An approx. Std. can be computed for this estimator:

  45. dChip “Reduced” Model • Similarly, we regard the set as known, and compute std. for each phi • We use these estimated Std. to find outlier and exclude them from the computation:

  46. Dchip – Array outliers detection

  47. Dchip – Probe outliers detection

  48. Normalization/Scaling • We saw how to get MBEI from dchip, i.e measure “quantitation “ • We still need to scale the different arrays: • Arrays usually differ in overall image brightness (differ in time, place, exper. Cond….) • This is usually done PRIOR to the “measure quantitation” manipulations (as dChip’s MBEI we just described).

  49. Global – Normalization/Scaling • Suppose we have two arrays X,Y with values x1…xM and y1 .. yM • “Global” normalization (MAS 5): find the constant “a” such that Which means: When we have multiple arrays then we choose Y to be the avg. of all arrays or compute a such that sum_i (x_i) = constant Better way: a(x) i.e adopt the fit parameter as a function of expression level ( as by dChip)

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