1. Introduction to Gene Expression Analysis Phillip Lord & Cedric Simillion

2. Resources http://www.ebi.ac.uk/microarray/biology_intro.html http://www.mged.org/ http://www.tigr.org/tdb/microarray/ Microarray Bioinformatics Dov Stekel Product Details: Paperback 280 pages (September 8, 2003) Publisher: Cambridge University Press Language: English ISBN: 052152587X

3. How do we measure gene expression? Oldest technique is to look at a phenotype. In this case, the ura4 + gene from S.pombe Most other techniques based on hybridisation. –Northern Blot –Quantative RT-PCR

4. Microarray analysis Whole genome sequencing makes it possible to predict the entire gene complement Various technologies have built on this knowledge to produce systems that will monitor the expression (usually transcription) at the whole genome level –Measurement of global transcription is called transcriptomics Come by a variety of names – gene chips, arrays, DNA arrays. Can be somewhat confusing what is actually being described. Not to be confused with Genotyping Microarrays

5. Generating Microarrays There are many different systems for generating microarrays –spotting original technology, now rather old good for “one off” arrays –in-situ synthesis newer, more reproducible expensive first time around, then cheaper

6. Spotting Synthesize DNA, spot onto glass slides, fix. A Spotting Robot The head A Spotting Pin taken from Stekel, 2003

7. In-situ synthesis Uses chemically protected nucleotides Specific spots are “de-protected” Can then extend these oligos Different techniques for deprotection

8. Masked Synthesis Uses masks much like silicon chip production Masks are expensive Good for bulk production, standard arrays

9. Photo deprotection from Stekel, 2003 A light source is used to deprotect oligos Essentially, this is much the same as an LCD projector.

10. InkJet Synthesis An InkJet head is used to place nucleotides at the appropriate place on the array

11. An experiment Two Samples RT with Cy3 dCTP RT with Cy5 dCTP Combine into single sample Hybridise to Microarray

12. Hybridisation from Stekel, 2003

13. Detection Finally, the hybridisation extract must be detected The technology is related to desktop scanners, but more sensitive. Usually produces a TIFF file from Stekel, 2003

14. The end result from Stekel, 2003

15. Problems We are looking for variability between the expression of different genes. There are many (many!) other sources of variability Most microarray analysis is about trying to normalise these sources of variability, leaving biological variability

16. The Jolly Green Giant The Yellow Splodge Peril Space Invaders Artifacts

17. Solutions Removing Sections Background Subtraction Start Again

18. Feature Recognition Not all spots are equal – different sizes, different shapes. Identifying the exact scope of the spot on an array can therefore be hard. Often solved in the initial detection of spots.

19. Spot Detection The Doughnut A general disaster The basic solution to this is to not use circular spots for detection. There are a variety of edge detection algorithms, or manual tools which work.

20. An experiment Two Samples RT with Cy3 dCTP RT with Cy5 dCTP Combine into single sample Hybridise to Microarray

21. Channel Variability Cy3/Cy5 dyes have different properties. So do the lasers at different frequencies. So do the photomultipliers which detect them.

22. Within Slide Variability Slides often have imperfections, either from spots, or background Gaps are not uncommon, neither are chromatic effects

23. Inslide Normalisation

24. Between slide variability Results between different slides are not directly comparable. Results between different experiments are not directly comparable.

25. Further work Please read Cho RJ, Campbell MJ, Winzeler EA, Steinmetz L, Conway A, Wodicka L, Wolfsberg TG, Gabrielian AE, Landsman D, Lockhart DJ, Davis RW "A genome-wide transcriptional analysis of the mitotic cell cycle." Mol Cell. 1998; 2: 1: 65-73.