1. DNA Microarrays
M. Ahmad Chaudhry, Ph. D.

2. Outline of the lecture Overview of Micoarray Technology
Types of Microarrays
Manufacturing
Instrumentation and Softwares
Data analysis
Applications

3. Microarray Development
Mainly used in gene discovery
Widely adopted
Relatively young technology

4. Evolution & Industrialization
1994- First cDNAs arrays are developed at Stanford.
1995- Quantitative Monitoring of Gene Expression Patterns with a cDNA Microarray
1996- Commercialization of arrays
1996-Accessing Genetic Information with High Density DNA Arrays
1997-Genome-wide Expression Monitoring in S. cerevisiae

5. Approaches What genes are Present/Absent in a tissue?
What genes are Present/Absent in the experiment vs. control?
Which genes have increased/decreased expression in experiment vs. control?
Which genes have biological significance based on my knowledge of the biological system under investigation?

6. What are Microarrays?
Microarrays are simply small glass or silicon slides upon the surface of which are arrayed thousands of genes (usually between ,000)
Via a conventional DNA hybridization process, the level of expression/activity of those genes is measured
Data are read using laser-activated fluorescence readers
The process is “ultra-high throughput”

7. GENE EXPRESSION ANALYSIS WITH MICROARRAYS
DNA Chips
Miniaturized, high density arrays of oligos
(Affymetrix Inc.)
Printed cDNA or Oligonucleotide Arrays
Robotically spotted cDNAs or Oligonucleotides
Printed on Nylon, Plastic or Glass surface

8. Affymetrix Microarrays
Involves Fluorescently tagged cRNA
One chip per sample
One for control
One for each experiment
Glass Slide Microarrays
Involves two dyes/one chip
Red dye
Green dye
Control and experiment on same chip

9. Gene Chip Technology Affymetrix Inc
Miniaturized, high density arrays of oligos
1.28-cm by 1.28-cm (409,000 oligos)
Manufacturing Process
Solid-phase chemical synthesis and Photolithographic fabrication techniques employed in semiconductor industry

10. Selection of Expression Probes Set of oligos to be synthesized is defined, based on its ability to hybridize to the target genes of interest 5’ 3’
Sequence
Probes
Perfect Match
Mismatch
Chip
Computer algorithms are used to design photolithographic masks for use in manufacturing

11. Each gene is represented on the probe array by multiple probe pairs
Each probe pair consists of a perfect match and a mismatch oligonucleotide.

12. Photolithographic Synthesis Manufacturing Process Probe arrays are manufactured by light-directed chemical synthesis process which enables the synthesis of hundreds of thousands of discrete compounds in precise locations
Lamp
In light directed synthesis, chips are synthesized spatially, with holes in each successive mask design determining the sequences to be built up
Mask
Chip

13. Click here to launch the movie file

14. Affymetrix Wafer and Chip Format
Millions of identical oligonucleotide
probes per feature
chips/wafer
1.28cm
up to ~ 400,000 features/chip

15. RNA-DNA Hybridization
Targets
RNA
probe sets
DNA
(25 base oligonucleotides of known sequence)

16. Non-Hybridized Targets are Washed Away
(fluorescently tagged)
“probe sets” (oligo’s)
Non-bound ones are washed away

17. Target Preparation B B IVT mRNA Target Preparation Wash & Stain Scan
Fragmented cRNA B B
Biotin-labeled transcripts
Fragment
(heat, Mg2+)
IVT
(Biotin-UTP
Biotin-CTP)
AAAA
mRNA
Target Preparation
RNA isolation is the first step. 1-2 hours
The messenger RNA is then reverse transcribed into cDNA (we then go on to make the second strand of cDNA).
4 hours
An in vitro transcription reaction using biotinylated nucleotides is then done to both amplify and label the transcripts hours
These are then fragmented in order to get a more efficient hybridization ( bases pairs is the goal).
1.0 hours
The fragmented target is then hybridized overnight to a GeneChip expression array. 16 hours
When washing and staining of the array is complete it can then be scanned hours
Wash & Stain
cDNA
Scan
Hybridize
(16 hours)

18. GeneChip® Expression Analysis Hybridization and Staining
Array
cRNA Target
Hybridized Array
Streptravidin- phycoerythrin
conjugate

20. Instrumentation for Gene Chip

21. Affymetrix Gene Chips Human Genome U133 Chip Set
33,000 genes, 2 chip set
uses recent draft of human genome
Arabidopsis Genome Chip: 24,000 genes
Murine Genome Chip: 36,000 genes
E. coli Genome Chip: 4,200 genes
C. elegans Genome Chip: 22,500 genes

22. Affymetrix Gene Chips Rat Neurobiology Chip: 1,200 genes
Rat Toxicology Chip: 850 genes
CYP450’s, Heat Shock proteins
Drug transporters
Stress-activated kinases
Rat Neurobiology Chip: 1,200 genes
Synuclein 1, prion protein, Huntington’s disease
Syntaxin, Neurexin, neurotransmitters
Drosophila Genome Chip: 13,500 genes
Yeast Genome Chip: 6,400 genes

23. Quality Control Issues
RNA purity and integrity
cDNA synthesis efficiency
Efficient cRNA synthesis, labeling and fragmentation
Target evaluation with Test Chips

24. GENE EXPRESSION ANALYSIS WITH MICROARRAYS
DNA Chips
Miniaturized, high density arrays of oligos
(Affymetrix Inc.)
Printed cDNA or Oligonucleotide Arrays
Robotically spotted cDNAs or Oligonucleotides
Printed on Nylon, Plastic or Glass surface

25. Microarray of thousands of genes on a glass slide

26. Spotted arrays steel spotting pin chemically modified slides
384 well source plate
chemically modified slides
1 nanolitre spots
um diameter

28. Spotted cDNA microarrays
Advantages
Lower price and flexibility
Simultaneous comparison of two related biological samples (tumor versus normal, treated versus untreated cells)
ESTs allow discovery of new genes
Disadvantages
Needs sequence verification
Measures the relative level of expression between 2 samples

30. Gene D
Over-expressed
in normal tissue
Gene E
Over-
expressed
in tumour
• Biomarkers
of prognosis
• Genes
affecting
Treatment
Response

32. The challenges of microarrays
Acquisition of high quality clinical samples, tumor and normal tissues
High Quality RNA
Experimental design: what to compare to what?
Data analysis -1: what to do with the data?
Data analysis -2: How to do it?
Very large number of data points
Size of data files
Choice of data analysis strategy/algorithm/software

33. Experimental Design
Choice of reference: Common (non-biologically relevant) reference, or paired samples?
Number of replicates: How many are needed? (How many are affordable?).
Are the replicate results going to be averaged or treated independently?
Choice of data base: Where and how to store the data?

34. Data Pre-processing Filtering Background subtraction
Low intensity spots
Saturated spots
Low quality spots (ghost spots, dust spots etc)
Normalization
Housekeeping genes/ control genes

35. Affymetrix Software for Microarray Data Analysis
Microarray Suite 5
Micro DB
Data Mining Tool (DMT)
NetAffx

36. Affymetrix Microarray Suite - Data Analysis
Absolute Analysis – used to determine whether transcripts represented on the probe array are detected or not within one sample (uses data from one probe array experiment).
Comparison Analysis – used to determine the relative change in abundance for each transcript between a baseline and an experimental sample (uses data from two probe array experiments). Intensities for each experiment are compared to a baseline/control.

37. Microarray data analysis
Scatter plots
Intensities of experimental samples versus normal samples
Quick look at the changes and overall quality of microarray

38. UP
log/log
scatter plot
DOWN

39. Normal ovary #1 versus normal ovary #2
Tumor ovary versus normal ovary #1

40. Microarray data analysis
Supervised versus unsupervised analysis
Clustering: organization of genes that are similar to each other and samples that are similar to each other using clustering algorithms
Statistical analysis: how significant are the results?

41. Two dimensional hierarchical clustering (Eisen et al, PNAS (1998) 95, p. 14863)
Unsupervised: no assumption on samples
The algorithm successively joins gene expression profiles to form a dendrogram based on their pair-wise similarities.
Two-dimensional hierarchical clustering first reorders genes and then reorders tumors based on similarities of gene expression between samples.

42. Two dimensional hierarchical (“Eisen”) Clustering

43. Cluster analysis of genes in G1 and G2
Chaudhry et. al., 2002

44. Publicly Available Softwares CLUSTER and TREEVIEW
Hierarchical Clustering
K means Clustering
Self Organizing Maps

45. Publicly Available Softwares
GenMAPP
Visualize gene expression data on maps representing biological pathways and groupings of genes.

47. Genomatix Software GmbH
Other Softwares
Extraction of information from DNA-chip with the technology of promoter analysis
Genomatix Software GmbH

48. Microarray Applications (some)
Identify new genes implicated in disease progression and treatment response (90% of our genes have yet to be ascribed a function)
Assess side-effects or drug reaction profiles
Extract prognostic information, e.g. classify tumors based on hundreds of parameters rather than 2 or 3.
Detect gene copy number changes in cancer (array CGH)
Identify new drug targets and accelerate drug discovery and testing
???

49. Applications Genotyping ADE Screens Toxicology Optimization Screening
Clinical
PreClinical
Leads
Genotyping
ADE Screens
Discovery
Toxicology
Optimization
Screening
Validation
Optimization
Target Discovery
Target Validation

50. Microarray Technology - Applications
Gene Discovery-
Assigning function to sequence
Discovery of disease genes and drug targets
Target validation
Genotyping
Patient stratification (pharmacogenomics)
Adverse drug effects (ADE)
Microbial ID
The List Continues To Grow….

51. Profiling Gene Expression
Kidney
Tumor
Lung
Tumor
Liver
Tumor

52. Normal vs. Normal

53. Normal vs. Tumor

54. Lung Tumor: Up-Regulated

55. Lung Tumor: Down-Regulated

56. Microarray Future
Must go beyond describing differentially expressed genes
Inexpensive, high-throughput, genome- wide scan is the end game for research applications
Protein microarrays beginning to be used
Fundamentally change experimental design
Will enhance protein dB construction

57. Microarray Future
Publications are now being focused on biology rather than technology
SNP analysis
Faster, cheaper, as accurate as sequencing
Disease association studies
Population surveys
Chemicogenomics
Dissection of pathways by compound application
Fundamental change to lead validation

58. Microarray Future Diagnostics Tumor classification
Patient stratification
Intervention therapeutics

59. Conclusion Technology is evolving rapidly.
Blending of biology, automation, and informatics.
New applications are being pursued
Beyond gene discovery into screening, validation, clinical genotyping, etc.
Microarrays are becoming more broadly available and accepted.
Protein Arrays
Diagnostic Applications