Microarrays Pauliina Munne 09.10.2014

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

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.

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 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

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

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

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)

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

cDNA microarray Oligonucleotide microarrays

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

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

Oligonucleotide Microarray

cDNA microarray Oligonucleotide microarrays

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 www.affymetrix.com

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

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

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

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

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

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 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

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

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