DNA CHIPS- Technology and Utility

Transcription

1 DNA CHIPS- Technology and Utility Yanal Alkuddsi Ph.D Student Dept. of Genetics and Plant Breeding University of Agricultural Sciences Dharwad, Karnataka, India,

2 1.INTRODUCTION CONTENT 2.MICROARRAYS: MAKING THEM AND USING THEM 3. SEQUENCE DATA BASES FOR MICROARRAY 4. BASIC DATA ANALYSIS 5. APPLICATIONS 6.CONCLUSION 2

3 A- INRODUCTION 3

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5 Analysis of gene expression at the single gene level. Methods for measuring a single gene: Northern Blots Measure RNA levels by hybridization of a labeled probe to total RNA. Reporter Genes Use of an enzyme to measure the amount of transcription from a promoter. Quantitative real-time RT-PCR. 5

6 Assaying the regulation of thousands of genes in a single experiment DNA microarrays DNA molecules printed at high density used to determine the level of RNA or DNA in a sample. Can be thought of a reverse Northern blots Other technologies SAGE Microbeads 6

7 What is a Microarray? An arrangement of DNA sequences on a solid support A surface (nylon, glass, or plastic). Containing hundreds to thousand pixels. Each pixel has copies of a sequence of single stranded DNA (ssdna). Each such sequence is called a probe Each microarray contains thousands of genes Able to simultaneously monitor gene expression levels in all these genes 7

8 Definitions: Target - the nucleic acid (cdna) sample who s identity and quantity are being measured. Probe an attached nucleic acid with a known sequence (the DNA chip). Fluorophore usually green and red labels attached to the target to enable visualizing expression. Array a set of DNA reagents for measuring the amount of sequence counterparts among the mrna of the sample. 8

9 B- Making Microarrays 9

10 The 6 steps of a DNA microarray experiment 1. Manufacturing of the microarray 2. Experimental design and choice of reference: what to compare to what? 3. Target preparation (labeling) and hybridization 4. Image acquisition (scanning) and quantification (signal intensity to numbers) 5. Database building, filtering and normalization 6. Statistical analysis and data mining 10

11 Workflow Biological sample of some sort Extract mrna Amplify Label and Fragment Analyze down to one number per gene Find features in scan Scan chip Hybridise to a chip 11

12 Different Types of DNA Microarrays Two types: Affymetrix or cdna glass slide arrays Oligonucleotide (Affymetrix) Greatly reduced crosshybridization Uniform Tm Requires knowledge of gene sequences Short Sequences Spot Known Sequences More reliable data Arrays typically more expensive How DNA sequences are laid down; In Situ synthesis cdna(complete sequences) Cross-hybridization possible Non-uniform Tm No gene sequence knowledge required Long Sequences Spot Unknown Sequences More variability Arrays cheaper How DNA sequences are laid down; Robotic spotting 12

13 Microarrays Types cont DNA probes attachment chemistry Covalent reaction 5 aliphatic amine group of probe + chemical linkers on glass Non Covalent reaction phosphate group + chemical linkers on glass 13

14 Microarrays Types cont DNA probes attachment chemistry Surface chemistry DNA probes Covalent Non cove lent Oligonucleot ides Yes No cdna Yes Yes Most oligonucleotides are smaller than cdna 14

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16 1- Manufacturing of the microarray A) Robotic Spotting Firstly developed at Stanford University. 16

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18 Pins collect cdna from wells Print-tip group 1 cdna clones Spotted in duplicate Glass Slide 384 well plate Contains cdna probes Array of bound cdna probes 4x4 blocks = 16 print-tip groups Print-tip group 6 18

19 Microarray of thousands of genes on a glass slide 19

20 One spot on a microarray contains many DNA strands of the same sequence One spot = one gene 20

21 Microarrays Types cont B) In Situ synthesis oligonucleotide arrays Oligonucleotides are built up base by base on the surface of the array by two methods: Affymetrix This technique consist of the following properties: One chip per sample One for control One for each experiment Each probe 25 bp long probes per gene Perfect Match (PM) as well as Mis Match (MM) probes are present 1. Affymetrix Microarrays Ink Jet Printing 21

22 Affymetrix photolitography Lipshutz et al.,

23 Why do we have mismatch probes? Mismatch probes (MM) are trying to detect background. The mismatch probes are supposed to detect things that are close but not an exact match. It is assumed that these things also bind to the perfect match (PM), erroneously. Lipshutz et al.,

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25 2. Inkjet Printed Microarrays Inkjet head 25

26 Ink Jet Printing Four cartridges are loaded with the four nucleotides: A, G, C,T As the printer head moves across the array, the nucleotides are deposited in pixels where they are needed. This way (many copies of) a base long oligo is deposited in each pixel. 26

27 Ink Jet Printing (Agilent) The array is a stack of images in the colors A, C, G, T. Entirely controlled by the computer High spot quality 98% of coupling efficiency A C T G 27

28 C- Using Microarrays 1.Sample preparation and labeling 2.Hybridisation 3.Washing 4.Image acquisition 28

29 1.Preparation of Samples Use oligo(dt) on a separation column to extract mrna from total cell populations. Use oligo(dt) initiated polymerase to reverse transcribe RNA into fluorescence labeled cdna. RNA is unstable because of environment RNA-digesting enzymes. Alternatively use random priming for this purpose, generating a population of transcript subsequences 29

30 How does a DNA microarray detects gene activity? Reverse Transcription makes cdna from gene sequence mrna AAAAAAAAAAAAAAAA...GCUACGAUUGCAACGCCCGAAUGGUUACCAAAAAAAAAAA... CGATG CTAACGTTGCGGG CTTACCAATGGTTTTTTTTTTT cdna dttp CGATGdCTPC TAACGTTGCGGG CTTACCAATGGTTTTTTTTTTT dgtp datp 30

31 Labeling the samples Affymetrix arrays Prepare biotin labeled crna for hybridizing the chip Protocol is prepared by affymetrix, so every affymetrix lab perform same steps. It make more easier to compare the results Other arrays Fluorescently labeled cdna is used. Cy3 dye excited by green laser Cy5 dye excited by red laser Two samples labelled with different dye. 31

32 2-Color System... RNA from Normal Tissue RNA from Cancer or Drug Treated Tissue dctp dctp Reverse Transcription 32

33 Labeled DNA copies of all mrna from one sample (i.e. from a tumor) is hybridized to array x Dye 33

34 2.Hybridization Hybridization chamber 3XSSC HYB CHAMBER ARRAY LIFTER SLIP LABEL SLIDE SLIDE LABEL Temperature Oc Time hr Formamide (Lowers the Tm) Na+- improve stringency Rept.DNA- block cross.hyb 34

35 The Key to Nucleic Acid Detection is Sequence-Specific Affinity 5 G T C A T T G C C A A 3 3 C A G T A A C G G T T 5 35

36 Microarray-Based Assays TARGET is the fluorescence labeled cdna representation of the mrna and is hybridized to the probe. PROBE is DNA spotted (attached) to the solid substrate (non-fluorescent glass slide). 36

37 Targets (fluorescently tagged) 3.Washing To remove the excess of solution left on the array after hybridization process To improve the stringency of the experiments probe sets (oligo s) Non-bound ones are washed away 38

38 4. Image acquisition ( Scanning ) Detector Duplicate spots Image 38

39 Shine laser light on array and labeled DNA fragments glow 39

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41 Sample 1 -red dye Sample2 green dye Spot Gene expression Red Sample 1 Green Yellow Black Sample2 Both No Coding scheme: Green = repressed (less mrna) gene in experiment Red = induced (more mrna) gene in experiment Black = no change (1:1 ratio) 41

42 Total process 42

43 D- Sequence data bases for Microarray What resources could we use to design our own custom array? Microarray to particular disease, tissue or organism Will need to identify the genes that might be expressed. 43

44 Data Bases Primary gene sequence databases (Gene Bank, EMBL, DDGJ) holds all published sequences and are basis for all other data bases Secondary gene sequence databases (Uni Gene, TIGR, RefSeq) are excellent resources for designing Microarrays Genomic databases (TIGR, SGD ) are excellent resources for designing arrays for small organisms and can also be used for more complex organisms 44

45 Designing of oligonucleotide probes An oligonucleotide probe is short piece of single stranded DNA complimentary to the target gene whose expression is measured on the microarray by that probe. Usually probes for microarray are designed within several hundred bases of 3 end of target sequences. Good oligonucleotide properties: 1. Sensitive 2. Specific 3. Isothermal 45

46 1.Sensitive probe Is one that returns strong signal. When the complimentary sequence present on the target The probes don t have internal secondary structure Example: Designing probes for gene Homo sapience alcohol dehydrogenase beta 2 sub unit (ADH2) These probes have low complexity sequences i.e. repetitive sequences 46

47 Probes secondary structures The probe don t self hybridise, either oligonucleotide could form a stem loop structure or dimerise with neighbouring identical oligonucleotide on the surface of the array. (exclusion of palindrome sequences) 47

48 2.Specific probe Is one that returns weak signals when complimentary sequence of target is absent. It doesn't cross hybridise to other targets Prediction of cross hybridisation to related genes by Homology search algorithms. BLAST FASTA Smith-Wterman algorithm 48

49 3.Isothermal probe Probes behave similarly under the hybridisation conditions of microarray experiment temperature, salt concentration and formamide concentration Base staking model is used in determining the melting temperature. It consider both base composition and order of bases in the sequence Mfold software 49

50 Experimental Error Sample contamination Poor quality/insufficient mrna Reverse transcription bias Fluorescent labeling bias Background from non-specific hybridization Hybridization bias Cross-linking of DNA (double strands) Poor probe design (cross-hybridization) Defective chips (scratches, degradation) 50

51 E- DNA Microarrays Data Analysis Feature extraction software is convert the image into the numerical information that quantifies the gene expression 51

52 Problems with microarray images 52

53 Examples of spot imperfections. A. donut shape; B. oval or pear shape; C. holey heterogeneous interior; D. high-intensity artifact; E. sickle shape; F. scratches. 53

54 Image Analysis 1. Gridding: identify spots (automatic, semiautomatic, manual) 2. Segmentation: separate spots from background. (A), Fixed circle (B), Adaptive circle (C), Adaptive shape (D), Histogram 3. Intensity extraction: mean or median of pixels in spot 4. Background correction: local or global 54

55 Normalization 55

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58 Raw data Centered Distribution normalised 4/7/2010 Scaled DNA Chips 58

59 Analysis of differentially expressed genes Null hypothesis Reject Accept True Type I error False Type II error 59

60 Analysis of differentially expressed genes Example1- Samples are taken from 20 breast cancer patients, before and after 16 week course of doxorubicin chemotherapy, and analyzed using microarrays. To wish to identify gene (acetyl-coenzyme Aacetyltransferase 2 (ACAT2)) that are up or down regulated in breast cancer following that treatment tcal = 3.22 ttab (at 19 df) = 2.39 significant cal >tab Conclude that this gene has been sig. down regulated following chemotherapy at the 1%lvel. 60

61 Analysis of differentially expressed genes Example 2- Bone marrow samples are taken from 27 patients suffering from acute lymphoblastic leukemia(all) and 11 patients suffering from acute myeloid leukemia (AML) and analysed using Affymetrix arrays. To wish to identify the gene (Metallothionein IB ) that are up or down regulated in ALL relative to AML. tcal = 4.35 ttab (at 36 df) = significant cal >tab Conclude that this gene has been sig. high expression in AML than ALL at the 1%lvel. 61

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63 Hierarchical Clustering Example Samples were taken from 39 patients suffering from diffuse large B cell lymphomas Which genes are co regulated in this disease? Whether the groups of patients with similar gene expression? Correlation data 63

64 Hierarchical Clustering 64

65 Hierarchical Clustering 65

66 Hierarchical Clustering 66

67 Hierarchical Clustering 67

68 Single Linkage Shortest link between two clusters Complete Linkage Longest link between two clusters Average Linkage Hierarchical Clustering Linkage analysis Average of distances between all pairs of objects Average Group Linkage Groups once formed are represented by their mean values, and then those are averaged 68

69 Biological question Differentially expressed genes Sample class prediction etc. Experimental design Microarray experiment Image analysis 16-bit TIFF files Estimation Normalization R, G Testing Clustering Discrimination Biological verification DNA Chips and interpretation 4/7/

70 Differing expression of genes over time, between tissues, and disease states Identification of complex genetic diseases Drug discovery and toxicology studies Mutation/polymorphism detection Pathogen analysis Applications What problems can it solve? 70

71 Example:1 microarrays for disease diagnosis: Microarrays may help guide doctors in determining effective breast cancer therapy Current methods including pathology exam (loooking at cells) and molecular markers (examining few, specific genes) only give hints. Microarrays of human genome used to: detect patterns of genetic activity in a tumor Test for chance of developing metastases (cancer that spreads)

72 Example:2 microarrays to study biological processes Microarrays may help discover ways to prevent or stop the spread of SARS SARS chip: New microarray with entire genome of SARS virus Contains all 29,700 base pairs of the SARS virus Could help detect differences in particular strains of the virus and study the virus evolution over time. Further studies may determine better ways to contain the spread of SARS. U.S. National Inst. of Allergy and Infectious Diseases (NIAID), Affymetrix, Inc, through Pathogen Functional Genomics Resource Center (TIGR).

73 Example:3 Microarray analyses of pathogen treated vs. mock treated Solanaceae +pathogen -pathogen mrna from two treatments Hybridization to microarray Reverse transcription and fluorescent labeling of cdna Quantification of expression levels

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75 References Basic microarray analysis: grouping and feature reduction by Soumya Raychaudhuri, Patrick D. Sutphin, Jeffery T. Chang and Russ B. Altman; Trends in Biotechnology Vol. 19 No. 5 May 2001 Self Organizing Maps, Tom Germano, Data Analysis Tools for DNA Microarrays by Sorin Draghici; Chapman & Hall/CRC 2003 Self-Organizing-Feature-Maps versus Statistical Clustering Methods: A Benchmark by A. Ultsh, C. Vetter; FG Neuroinformatik & Kunstliche Intelligenz Research Report