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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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.
Building the Chip:
PCR PURIFICATION and PREPARATION
Hybing the Chip:
CELL CULTURE AND HARVEST
PCR PURIFICATION and PREPARATION
Full yeast genome
= 6,500 reactions
IPA precipitation +EtOH washes + 384-well format
The arrayer: high precision spotting device capable of printing 10,000 products in 14 hrs, with a plate change every 25 mins
Polylysine coating for adhering
PCR products to glass slides
Chemically converting the positive polylysine surface to prevent non-specific hybridization
CELL CULTURE AND HARVEST
Designing experiments to profile conditions/perturbations/
mutations and carefully controlled growth conditions
RNA yield and purity are determined by system. PolyA isolation is preferable but total RNA is useable. Two RNA samples are hybridized/chip.
Single strand synthesis or amplification of RNA can be performed.
cDNA production includes incorporation of Aminoallyl-dUTP.
Cy3 and Cy5 RNA samples are simultaneously hybridized to chip. Hybs are performed for 5-12 hours and then chips are washed.
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.
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.
Non - Contact
Contact (using rigid pin tools, similar to filter array)
Protocol for Post Processing Microarrays
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.
Likely cause: too
during post -
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
3. Information Extraction
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
Misregistration of the red and green channels
Rotation of the array in the image
Skew in the array
Results from SRG
Take the average
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
Many challenges: transcriptional regulation involves redundancy, feedback, amplification, .. non-linearity
Data Analysis & Modeling
Microarray Life Cycle
Taken from Schena & Davis
→ To extract biological meaningful results we need:
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
Basic idea: Least square parameter estimation, iteratively fitting and
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:
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)
* Cope et al. Bioinformatics, 03 (Speed’s Lab)
M1 = X1 – X2A = (X1 + X2)/ 2 Where Xi is the log2 of expression measure
Test Statistics: 1. Median std. 2. Avg. R2 (squared corr. coef.) between two replicates
Test Statistics: Fit a linear curve and compute1. linear fit slope (should be 1) 2. R2 to the linear fit
Here actual TP, FP numbers are used for the axes
Test Statistic: AUC (area under the graph)
Same as before, but only for FC = 2 cases (harder)