Lecture 4 MicroArray Gene expression

1. Lecture 4MicroArrayGene expression BIO454 Dr. Alaa eldin Abdallah yassin

2. Outline • MicroArray experiment • Gene expression. • Preprocessing gene expression data. • Representation of gene expressing data. • Analysis of gene expression data. • Applications of microarray • MicroArray advantages • MicroArray disadvantages. • MicroArray in future. • Summary.

3. Microarray phases of study • Genomics. • Gene expression. • Microarray experiments.

4. Microarray experiment

5. MicroArray experiment The two samples are combined

6. DNA samples binned to its complementary bases on the chip Put the chip on the laser scanner ( to electronically capture the data )

7. Green healthy cells. Red abnormal cells. Yellow : gene was neither strongly expressed

8. Microarray experiments. • Microarray experiment steps: • Collect tissue. • Isolate RNA. • Isolate mRNA. • Make labeled DNA copy. • Apply DNA. • Scan Microarray. • Analyze data.

9. Review • In each type of cell, like a muscle cell or a skin cell, different genes are expressed (turned on) or silenced (turned off). • If the cells that are turned on mutate, they could—depending on what role they play in the cell—trigger the cell to become abnormal and divide uncontrollably, causing cancer. • By identifying which genes in the cancer cells are working abnormally, doctors can better diagnose and treat cancer. • One way they do this is to use a DNA microarray to determine the expression levels of genes. • When a gene is expressed in a cell, it generates messenger RNA (mRNA).

10. 1. Microarray experiment steps: Collect tissue • The first step in using a microarray is to collect healthy and cancerous tissue samples from the patient. • This way, doctors can look at what genes are turned on and off in the healthy cells compared to the cancerous cells. • We do this step from NCBI , GEO database ( Gene Expression Omnibus )

11. 1- Collect tissue…GEO database ( Gene Expression Omnibus ) • type “ lung cancer “ in the search tab. 32301 database

12. 1- Collect tissue… • Only one result , have 225 samples ! (225 chips) • Chose the suitable sample to download it data

13. 1- Collect tissue: before download data , show data description

14. 1- Collect tissue: GEO2R analysis tool • Before we download samples, we want to filter data and select the top quality, we can use some tools to perform this process, like GEO2R tool, (p-value , logFC) • GEO2R is an interactive web tool that allows users to compare two or more groups of Samples in a GEO Series in order to identify genes that are differentially expressed across experimental conditions.

15. 1- Collect tissue: data format • Data for 2 samples : 1 2

16. 2. Microarray experiment steps: Isolate RNA • Once the tissues samples are obtained, RNA is isolated from the samples. • To gain good result from Microarray experiment, efficient method for obtaining sufficient amounts of high quality RNA is important (extracting high-quality RNA from your cells).

17. 3. Microarray experiment steps: Isolate mRNA • Once the tissues samples are obtained, the messenger RNA (mRNA) is isolated from the samples. • Gain mRNA from isolated RNA .

18. 4. Microarray experiment steps: make labeled DNA copy • The mRNA is color-coded with fluorescent tags (dye) and used to make a DNA copy: • The mRNA from the healthy cells is dyed green. • The mRNA from the abnormal cells is dyed red. • The DNA copy that is made, called complementary DNA (cDNA). • We put on the chip the complementary cDNA to the genes that I want to discover it.

19. 5. Microarray experiment steps: apply DNA • After DNA copy is made, then it is applied to the microarray. • The cDNA binds to complementary base pairs in each of the spots on the array, a process known as hybridization. • So hybridization: the property of complementary nucleic acid sequence is to specifically pair with each other by forming hydrogen bonds between complementary nucleotide base pair. • Using this technology the presence of one genomic or cDNA sequence in 100,000 or more sequences can be sequenced in a single hybridization.

20. 5. Microarray experiment steps: apply DNA…

21. 6. Microarray experiment steps: scan microarray • Based on how the DNA binds together, each spot will appear red, green, or yellow (a combination of red and green) when scanned with a laser: • A red spot indicates that that gene was strongly expressed in cancer cells. • A green spot indicates that that gene was strongly repressed in cancer cells. • If a spot turns yellow, it means that that gene was neither strongly expressed nor strongly repressed in cancer cells (the gene bind both in the normal cell and with up normal cell). • A black spot indicates that none of the patient's cDNA has bonded to the DNA in the gene located in that spot. This indicates that the gene is inactive. (All of the genes in your experiment are active.)

22. 6. Microarray experiment steps: scan microarray • After finishing the Microarray experiment, Preprocessing steps are involved in producing the Gene Expression image/profile of the sample in hand.

23. In presentation , Step 1: data collection

24. 7. Microarray experiment steps: analyze databenefits • Microarray analysis techniques are used in interpreting the data generated from experiments on DNA (Gene chip analysis), RNA, and protein microarrays. • Genes that expressed in the cancer cells only (red) that is mean = this genes can used it in diagnose. • GREEN represents Control DNA, where either DNA or cDNA derived from normal tissue is hybridized to the target DNA. • RED represents Sample DNA, where either DNA or cDNA is derived from diseased tissue hybridized to the target DNA. • YELLOW represents a combination of Control and Sample DNA, where both hybridized equally to the target DNA • BLACK represents areas where neither the Control nor Sample DNA hybridized to the target DNA.

25. 7. Microarray experiment steps: analyze databenefits

26. 7. Microarray experiment steps: analyze dataimage analysis and data visualization

27. Preprocessing the Gene Expression data • What is gene expression data ? • Gene expression is the process by which information from a gene is used in the synthesis of a functional gene product. • Image Processing • Transformation/Normalization

28. Preprocessing : expression values • Gene Expression Ratio: = amount of red light w.r.t. amount of green light • For any gene K, the expression ratio for some sample is • Gene Expression Matrix that contains: • Rows representing genes • Columns representing particular conditions/samples. • Each cell contains a value that reflects the expression level of a gene.

29. Preprocessing : transformation • Absolute Measurement. • Relative Measurement: • Condition C4 is used as a reference. • All other conditions are normalized with respect to C4 to obtain expression ratios.

30. Preprocessing : transformation • Log2 (Relative Measurement): • Expression ratios converted into log2 scale • Treating up-regulation and down-regulation

31. Preprocessing : transformation • Discrete Values: • Log2(V) > 1 = 1 • Log2(V) < -1 = -1 • -1 ≤ Log2(V) ≤ 1= 0 > 20,000 samples

32. Organizing and Viewing MicroArray Data

33. MicroArray Data: Repositories and Analysis Tools • NCBI – GEO: Gene Expression Omnibus (NIH) • MSigDB/GSEA (Broad Institute) • Oncomine (U. Michigan) • XenaBrowser • FirebBrowse

34. Some questions for the age of genomics • How gene expression differs in different cells type. • How gene expression differs in a normal and diseased ( e.g., cancerous ) cell? • How gene expression changes when a cell is treated by a drug ? • How gene expression changes when the organism develops and cells are differentiating ?

35. applications • In cancer : • Tumor formation involves simultaneous changes in hundred of cells and variation of genes. • Identification of single – nucleotide polymorphisms (SNPs) and mutations, classification of tumors, identification of target genes of tumor suppressor. • Identification of cancer biomarkers, identification of genes associated with chemoresistance. • Early detection of precancerous lesions • Identification of gene expression profile or “ genomic fingerprints “ will allow clinicians to differentiate harmless lesions from precancerous lesions or from very early cancer.

36. Applications… • Antibiotic treatment. • Gene expression profiling: • In different cells / or tissues. • Under different environmental or chemical stimuli (motif). • In disease state versus healthy. • Molecule diagnose. • Molecular classification of diseases. • Drug development. • Identification of new targets. • Pharmacogenomics • Individualize medicine.

37. Advantages of Microarray • Provide data for thousand of genes. • One experiment instated of many. • Fast and easy to obtain results. • Huge step closer to discovering drug for disease and cancer. • Different parts of DNA can be used to study gene expression.

38. limitation of Microarray (next) • Array provide an indirect measure of relative concentration. • However, due to the kinetic of hybridization, the signal level at a given location on the array is not linearly proportional to concentration of the species hybridizing to the array. • For complex mammalian genomes, its often difficult to design arrays in which multiple related DNA/RNA sequences will not bind to the same probe on the array.

39. disadvantages of Microarray • Expensive to create : ( $100 per sample for standard gene-expression analysis and $300 per sample for more-complex analyses). • The production of too many results at a time require long time for analysis, which is quite complex in nature. • The DNA chips don’t have very long shelf life, which proves to be another major disadvantage of the technology.

40. The future of Microarray • When the cost is similar, sequencing has many advantages relative to microarray. • Sequencing is direct measurement of nucleic acids present in solution. One need only count the number of a given type of sequences present to determine its abundance. • Unlike DAN array, sequencing is not dependent on prior knowledge of which nucleic acids may be present. • Sequencing is also able to independency detect closely related gene sequence, novel splice forms or RNA editing that may be missed due to cross hybridization on DNA microarray. • As a result of sequencing advantages, and the decreasing cost of sequencing, DNA arrays rapidly replaced by sequencing for nearly every assay.

41. Summary • Microarrays are a powerful tool and holds much promise for the analysis of diseases. • Classifications of disease by DNA, RNA, or protein profiles will greatly enhance our ability to diagnose, prevent, monitor and treat our patients. • Microarrays promise a more biologically based, individualized treatment