Pauliina Munne09.10.2014

Microarrays

• 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

Biomedicum Functional Genomics UnitFuGU

Microarrays & Next Generation Sequencing

• NGS– Illumina MiSeq &

HiSeq – NextSeq

• Microarrays– Affymetrix– Illumina– Agilent

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)

Microarray Services

• 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

Applications of Microarrays

• Illumina: • Gene

• Affymetrix: • HTA, Exon• Gene• 3’ IVT• miRNA• CytoScan

• Agilent:• Expression • Exon • CGH + SNP• miRNA

- gene, exon miRNA, epigenetics, aCGH etc.

Design and perform experiment

Process and normalise data

Statistical analysis

Differentially expressed genes

Biological interpretation

Microarray Pipeline

• 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)

Experimental Design & Replicates

Experimental Design & Replicates

Treatment 1 Treatment 2

3 biologicalreplicates

Treatment A Treatment B

1 sample= 1 array

compare

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

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

Oligonucleotide microarrayscDNA microarray

• 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 BAgilent: two-color gene expression analysis=> Not recommended any more

cDNA microarray(Agilent)

• 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

Oligonucleotide Microarrays(Illumina, Affymetrix)

Oligonucleotide Microarray

Oligonucleotide microarrayscDNA microarray

Affymetrix – 25 mers are in situ sythesized on a glass wafer nucleotide by nucleotide using photolitography

www.affymetrix.com

Affymetrix Microarraysphotolithographic synthesis of oligonucleotide on microarrays

probe Target= fluorescently labeled sample mRNA

500 thousand cells in each array

Millions of DNA strandsbuild up in each cell

a probe, 25 base long

RNA fragments with fluorescent tags

• Probes are printed to the array base by base in a process that employs a combination of chemistry and photolithography

Principle of Microarray Hybridization

Affymetrix Microarray Formats

3’ end5’ end

3 differenttranscripts

Probes per feature (median)

11 oligomers in 3' end

21 oligomers along the gene

4 oligomers per exon

Illumina Expression BeadChipsProbes 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

Extracting information from the image

Feature identifiers Sample columns

Intensity measurements

Raw data file

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 sampleseriesAgilent• Older, Agilent will be more focused on other areas

Affymetrix• New array versions are published infrequently

Þ Complete support for any old array is providedÞ Most widely used platform

Þ NGS will mostly likely subside the microarrays in the future, but for now the prices are still quite high

Future?

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

General Outline of Expression Data Analysis

Analysis software:• R/Bioconductor (free)• GeneSpring (commercial)• Lots of other free & commercial tools

Design and perform experiment

Process and normalise data

Statistical analysis

Differentially expressed genes

Biological interpretation

Normalization & Pre-processing

• Quantile normalization is typically used to correct between-chip bias

Normalization & Pre-processing

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

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

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

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)

Functional Analysis

• The Kyoto Encyclopaedia of Genes and Genomes• http://www.genome.jp/kegg/• Provides searchable pathways for molecular interaction and

reaction networks for metabolism, various cellular processes and human diseases

• Manually entered from published materials

Functional Analysis

• Tools for functional analysis– David

• http://david.abcc.ncifcrf.gov/home.jsp– Pathway-Express

• http://vortex.cs.wayne.edu/projects.htm#Pathway-Express– GSEA

• http://www.broad.mit.edu/gsea/– GOrilla

• http://cbl-gorilla.cs.technion.ac.il/– GenMapp

• http://www.genmapp.org/– Cytoscape

• http://www.cytoscape.org/

Publishing Microarray Data

• GEO (Gene Expression Omnibus)– www.ncbi.nlm.nih.gov/geo/

• ArrayExpress– http://www.ebi.ac.uk/microarray-as/ae/

• 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

fugu-support@helsinki.fi