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Genomic Arrays – an overview Dr. Colin Campbell. Transcription Genome AAAAA Regulation DNA mRNA Protein The Central Dogma TranscriptionTranslation.

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Presentation on theme: "Genomic Arrays – an overview Dr. Colin Campbell. Transcription Genome AAAAA Regulation DNA mRNA Protein The Central Dogma TranscriptionTranslation."— Presentation transcript:

1 Genomic Arrays – an overview Dr. Colin Campbell

2 Transcription Genome AAAAA Regulation DNA mRNA Protein The Central Dogma TranscriptionTranslation

3 Genomics in perspective

4 Sequences available for hundreds of genomes viruses/plasmids >> mammalian genomes Genome sequence only the start Need to understand: genomic structure, replication, expression Problem of scale, complexity and diversity Advent of HTS functional genomic technologies: microarray, Si RNA, mutagenesis, proteomics, imaging Post Genomic Challenges

5 Functional genomics toolbox all genes used to assemble an organism ClassifyIdentifySequence ascribe function Monitor expression Post genomic approaches

6 Microarrays – a post genomic technology Gene Expression/Genotyping Proteomics Fundamental and applied biomedical research Supporting Technologies Statistics/Bioinformatics HTS Technology Developments: Arraying/ Scanning/ Lab-on-a-chip Computing/ Databases Mammalian Genome Database Database

7 Traditional method: taking gene by gene approach Insufficient to meet magnitude of problem Array technology Developed to provide a systematic way of studying RNA expression, genotyping, DNA/ RNA interactions and numerous other applications Array = A regular or uniform arrangement e.g. of DNA probes or other elements such as proteins or tissue sections arranged on glass slides or nylon membranes Evolution of array technology

8 Transcription Genome AAAAA Regulation DNA mRNA Protein The Central Dogma TranscriptionTranslation

9 RNA transcription analysis Expression of RNA assessed by Northern blotting, RNAase protection, RT-PCR methods Low to medium throughput approaches. Do not easily accommodate scale, complexity and diversity challenges e.g. Northern Blot DNA mRNA proteins Denature Gel electrophoresis, RNA separated by Size and blotted on filter Filters exposed to labelled DNA probe and subject DNA probe and subject to radiography to radiography RNA transcripts anlysed singly. Definiton of transcriptome would take thousands of blots Cell

10 DNAGENOME cDNA(s) or oligonucleotide(s) representative of genes representative of genes spotted on slide spotted on slide 1 2 3 4 1 2 3 4 genes DNA mRNA proteins Reverse transcribe RNA Using Cy3 (test RNA) or Cy5 (control) dCTP or Cy5 (control) dCTP Test cDNA control cDNA Intensityvalue Intensityvalue Hybridise to array = RelativeValue +ve = upreg Array Relative expression of RNA defined at whole genome level The microarray solution

11 First attempts at exploiting array approaches involved filter based screening of clone libraries involved filter based screening of clone libraries Basic genomic and RNA expression studies Two key innovations have enhanced the utility of genomic microarrays 1. Use of glass substrates to construct miniaturised arrays DIRECT DEPOSITION: Using automated printers: ~30-40K DNA probe elements deposited on a glass slide IN SITU SYNTHESIS: several million individual DNA probe elements defined by photolithography on silicon wafers 2. The use of fluorescence for detection Microarray options

12 Method 1. Array of 5,000 mouse genes - direct deposition method

13 DNAGENOME cDNA(s) or oligonucleotide(s) representative of genes representative of genes spotted on slide spotted on slide 1 2 3 4 1 2 3 4 genes DNA mRNA proteins Reverse transcribe RNA Using Cy3 (test RNA) or Cy5 (control) dCTP or Cy5 (control) dCTP Test cDNA control cDNA Intensityvalue Intensityvalue Hybridise to array = RelativeValue +ve = upreg Array Relative expression of RNA defined at whole genome level The microarray solution

14 Direct deposition DNA microarray scanner image

15 LT Method 2. In situ synthesised oligo array - Affymetrix GeneChip® system GAAAGGGCCCTTTAGCTAGCTAGT3’ Gene Sequence 25 mer Many million fold bound in specific feature representative DNA sequences derived from 3’ end of gene representative DNA sequences derived from 3’ end of gene 20 features used to represent one gene 400,000 features per array representing ~ 12,000 genes

16 DNA Cell/ Tissue of interest AAA AAA AAA AAA Isolation of total RNA AAAAAA AAA AAATTT TTT TTTTTT 1st strand cDNA synthesis T7 Promoter incorporated in first strand synthesis in first strand synthesis TTTTTT TTT TTT 2nd strand cDNA synthesis ds cDNA Affymetrix target labelling

17 b b b bTTT TTT TTT TTT In vitro transcription using Biotinylated dNTPs Affymetrix labelling and hybridisation Biotinylated cRNA b b b b L L L Lbb b b Hybridise to Array SA SA SA SA

18 Affymetrix Gene Chip results Expression of 10K genes – but what is the result ? Statistics and Bioinformatics essential

19 advantages Scale - true global analyses possible Semi-quantitative High throughput Emerging standards for analysis Scale demands stringent QC and analytical routines disadvantages Sensitivity Precision Relative cost/logistics Context independent Microarray technology - pros and cons

20 Microarrays in cancer biology RNA Expression profiling arrays: Targets > pathways Genotyping arrays: HTS SNP analysis > gene association studies Protein arrays: marker sets Expression based classification to detect dominant patterns of expression in heterogeneous tumours Can identify: Tumour markersTumour markers Origin of tumourOrigin of tumour Developmental stageDevelopmental stage Metastatic potentialMetastatic potential Therapeutic response profileTherapeutic response profile Fundamental insights >> definition of cancer pathways and controlFundamental insights >> definition of cancer pathways and control Contribute to diagnosis, prognosis and therapy.Contribute to diagnosis, prognosis and therapy.

21 Interferon related Clustered gene sets Breast luminal cell profile Basal epithelial cell profile Lung adenocarcinoma enriched profile enriched profile Proliferation gene set

22 DNA microarrays now extensively employed for RNA expression profiling studies in biomedical research. Crucial role for statistics, bioinfomatics and computational science to turn HTS data into useful information (gene targets and pathway definition) for the biologist to interpret Provides a critical approach to a thorough understanding of fundamental biological processes. Also contributing to applied areas such as disease diagnosis and definition. DNA microarrays providing a HTS and global platform technology for numerous biomedical and genomic research applications - splicing - sequencing and SNP analysis (v. high density oligo arrays under development) - CGH, BAC clones - epigenetic studies e.g. DNA methylation - Also, platforms developing for: proteins, cells and tissues DNA microarray approaches will ultimately replace many of the standard methods genetic analysis. DNA microarrays – a platform technology DNA microarrays – a platform technology

23 Full definition of biological processes requires additional contextual inforrmation (e.g. spatial, temporal, modification) Methods for precise micro sampling of complex cell populations and tissues can be combined with microarray readouts. Initial step involves precise sampling via cell sorting/enrichment or micro-dissection techniques Combine with target sample (micro RNA sample) amplification methods to enable readout on standard DNA microarray platforms readout on standard DNA microarray platforms Increases power of analysis and biological interpretation Biological context

24 Array technology will continue to develop for DNA, RNA, protein and various other physiological measurements. Developments will require increasing interface of biology with physical sciences and technology. Allow new questions to be asked at the whole genome/proteome level. Integration of HTS genomic, proteomic and cellular readouts will be required to define biological complexity and approach systems level understanding Key to this is input from bioinformatics and computational science to analyse, store and visualise data Future potential in biology and medicine


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