1. Microarrays
Pauliina Munne

2. Biomedicum Functional Genomics Unit
FuGU
Established in 2006 as a center supporting functional genomics research in nation and internationwide
Comprehensive and state of the art functional genomics technology services (nonprofit)
Services include e.g. next-generation sequencing, microarrays, recombinant virus services and genome-scale reagents for gene knockdown

3. Microarrays & Next Generation Sequencing
NGS
Illumina MiSeq & HiSeq
NextSeq
Microarrays
Affymetrix
Illumina
Agilent
Yleisesti: kaksi osa-aluetta. Näihin liittyen tarjotaan palvelut ihan alusta (DNA:n/RNA:n eristys) ihan loppuun (data-analyysi) asti.

4. Recombinant Virus Services
Recombinant Viral Particles
for gene expression and knock-down studies (shRNA)
virus titering and biosafety analyses
BSL II facilities
Genome Scale TRC1 shRNA Libraries for RNAi
Q-RT-PCR Services for knock-down efficiency validation
LightCycler®480 Instrument II
Universal ProbeLibrary (UPL) probes (Roche)

5. Microarray Services Plan & design experiment Perform experiment + QA
Experimental planning and selection of the most suitable technology platform (based on project size, organism, number of samples and genes)
Fast and high quality service including full data analysis
Plan & design experiment
Perform experiment + QA
Analysis of the results
Biological interpretation

6. Applications of Microarrays
- gene, exon miRNA, epigenetics, aCGH etc.
Affymetrix:
HTA, Exon
Gene
3’ IVT
miRNA
CytoScan
Agilent:
Expression
Exon
CGH + SNP
Illumina:
Gene

7. Microarray Pipeline Design and perform experiment
Process and normalise data
Statistical analysis
Differentially expressed genes
Biological interpretation

8. Experimental Design & Replicates
Biological replicates: how many?
At least 3 per condition group
having more replicates increases sensitivity in detecting differential expression
=> Needed replicate number depends on:
Strength of the studied effect
Within group variation
Level of technical noise
Technical replicates:
not often used nowadays (except if comparing experiments between chips in Agilent and Illumina)

9. Experimental Design & Replicates
Treatment A
Treatment B
3 biological
replicates
1 sample
= 1 array
Treatment Treatment 2
compare

10. Experimental Design & Replicates
What kind of samples can be compared?
Do not try to compare apples and oranges:
If the samples are too different – all genes will be differentially expressed
=> no useful information can be gained
Two different tissues are usually too different to be compared directly
If several tissue samples (meant to represent the same tissue) contain varying amounts of different cell types this can also be a problem

11. Experimental Design & Replicates
Other Important Issues:
RNA sample quality
Standardize conditions for all samples in the experiment set
(e.g. age, gender, RNA extraction method etc.)
Choose the correct time point
Only pool samples when sample material is scarce
Be prepared to validate your microarray results with some other technique like RT-QPCR
Data analysis issues should always be considered when making experimental design
Experienced data analyst / bioinformatician should be consulted

12. cDNA microarray
Oligonucleotide microarrays

13. cDNA microarray (Agilent)
RNA from two different tissues or cell populations is used to synthesize single-stranded cDNA
in the presence of nucleotides labeled with two different fluorescent dyes (for example, green Cy3 labeled on sample A and red Cy5 labeled on sample B
Both samples are mixed in hybridization buffer and hybridized to the array surface
=> competitive binding of differentially labeled cDNAs to the
corresponding array elements
=> High-resolution confocal fluorescence scanning of the array with two different wavelengths corresponding to the dyes used provides relative
signal intensities and ratios of mRNA abundance for the genes represented on the array.
Green spots indicate the genes upregulated in sample A.
Red spots indicate the genes down-regulated in sample A.
Yellow spots indicate the equal expressions of those genes in sample A and sample B
Agilent: two-color gene expression analysis
=> Not recommended any more

14. Oligonucleotide Microarrays
(Illumina, Affymetrix)
RNA from different tissues or cell populations is used to generate double-stranded cDNA carrying a transcriptional start site for T7 DNA polymeras
biotin-labeled nucleotides are incorporated into the synthesized complementary RNA (cRNA) molecules, because the oligonucleotides sequence are in the sense direction and so one has to use antisense RNA which is cRNA
Each target sample is hybridized to a separate probe array
The arrays are stained with a streptavidin-phycoerythrin conjugate that binds to biotin tags and emits fluorescent light when exited with a laser
Automated image analysis software measures fluorescence by calculating signal intensity units at each discreet probe site or feature on the array
Signal intensities of probe array element sets on different arrays are used to calculate relative mRNA abundance for the genes represented on the array

15. Oligonucleotide Microarray

16. cDNA microarray
Oligonucleotide microarrays

17. Affymetrix Microarrays
photolithographic synthesis of oligonucleotide on microarrays
RNA fragments with fluorescent tags
Affymetrix – 25 mers are in situ sythesized on a glass wafer nucleotide by nucleotide using photolitography
Target
= fluorescently labeled sample mRNA
probe
---
-more than one cell for each transcript
Millions of DNA strands
build up in each cell
500 thousand cells in each array
a probe, 25 base long

18. Principle of Microarray Hybridization
Probes are printed to the array base by base in a process that employs a combination of chemistry and photolithography

19. Affymetrix Microarray Formats
Probes per feature (median)
11 oligomers in 3' end
21 oligomers along the gene
4 oligomers per exon
3 different
transcripts
5’ end
Probes per feature:
3’ = 11 oligomers in 3’ end
Gene = 21 oligs along the gene
Exon = 4 oligs per EXON
3’ end

20. Illumina Expression BeadChips Probes are bound to magnetic beads randomly distributed across arrays
6 – 12 samples on one chip
15 – 30 replicate beads per array target on the average
Most genes are represented by a single probe, some by two probes for different isoforms of the gene

21. Extracting information from the image
Raw data file
Feature identifiers
Sample columns
Intensity measurements

22. Future?
Illumina
New versions of each array type are published roughly every other year
=> old arrays are not available for very long.
=> This may be a problem for large studies spanning over several years
=> impossible to add samples to the old sampleseries
Agilent
Older, Agilent will be more focused on other areas
Affymetrix
New array versions are published infrequently
Complete support for any old array is provided
Most widely used platform
NGS will mostly likely subside the microarrays in the future, but for now the prices are still quite high

23. Spotted Microarrays
Oligonucleotides, cDNA or small fragments of PCR products corresponding to specific genes are spotted on the chip
A robot spotter normally does the process and one or more probes can be used for each gene
Contrary to oligonucleotide arrays, spotted arrays are "customizable"; the user can choose the probes to be spotted according to specific experimental needs
These kinds of arrays are usually hybridized with labeled mRNA, cDNA or cRNA because both strands are used as probes on the microarray

24. General Outline of Expression Data Analysis
Design and perform experiment
Process and normalise data
Statistical analysis
Differentially expressed genes
Biological interpretation
Analysis software:
R/Bioconductor (free)
GeneSpring (commercial)
Lots of other free &
commercial tools

25. Normalization & Pre-processing
Quantile normalization is typically used to correct between-chip bias

26. Normalization & Pre-processing

27. Normalization & Pre-processing
Quality Inspection (for raw +normalized data)
Quality control tools and quality plots create outlier chips, which can easily be detected
Removal of such arrays can vastly improve results of statistical testing

28. Statistical Analysis Running statistical tests (t-test)
p-values and false discovery rates for the reliability of the change
fold-change (FC) for the size of the change in gene expression
Filtering differentially expressed (DE) genes
Genes that have similar behavior within each
sample group but the group means clearly
differ from each other
= To produce a reasonable sized list of the
most differentially expressed genes
Visualising the results

29. Functional Analysis Carrying out gene functional analysis
Focus in pathways or other functional categorizations rather than individual genes
Different approaches exist for this:
Detect functional enrichment in the DE target list
Detect functional enrichment towards the top of the list when all array targets have been ranked according to the evidence for being differentially expressed
Make the statistical test between sample groups not assuming independence between array targets (as usually) but taking the dependence between genes belonging to same functional categorization into account

30. Functional Analysis http://www.geneontology.org
Classifies genes into a hierarchy, placing gene products with similar functions together
Three main categories:
Biological process (BP)
Molecular function (MF)
Cellular component (CC)

31. Functional Analysis The Kyoto Encyclopaedia of Genes and Genomes
Provides searchable pathways for molecular interaction and reaction networks for metabolism, various cellular processes and human diseases
Manually entered from published materials

32. Functional Analysis Tools for functional analysis David
Pathway-Express
GSEA
GOrilla
GenMapp
Cytoscape

33. Publishing Microarray Data
GEO (Gene Expression Omnibus)
ArrayExpress
Most journals require the expression data to be submitted to
a public repository
some even before they will send the manuscript to referees for evaluation
The data can be hidden from others than the authors and the referees before the official publication of the article