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Introduction Into The Gene Expression Platform of the IVM 1. Principles and important terminology 2. RNA Preparation and quality controls 3. Data handling 4. Costs 5. Protocols 6. Information for collaboration partners 7. Downloads 1. Principles and Terminology 1. Principles and Terminology The human, murine, and other genome projects plus the availability of robust hardware- and software platforms to produce and evaluate microarrays have enabled genome-wide gene expression analyses, i.e. to quantify all mRNAs (> 30 000) of a total RNA extract relative to another RNA extract, within 48 hours. The platform used by the IVM (Affymetrix) is equipped with a hybridization oven, a washing station, a scanner and advanced software. The latter allows for mathematical, statistical, and information technology-based evaluation of the arrays. 1. Principles and Terminology Available: Whole Genome Arrays of Several Species Affymetrix produces expressionsarrays of several species (human, mouse, C. elegans and others; test the link !). These are available in different formats. Dependent on format and protocol 0,5 - 5 µg total RNA is required per array. 1. Principles and Terminology Production of Arrays Through Photolithography 25mer socalled „Perfect Match“ (PM) oligonucleotides (ON) whose sequences are derived from the genome projects are synthesized on a glass slide. To subtract unspecific hybridizations a „Miss Match“ (MM) ON is also synthesized, that differs from the PM ON by a single nucleotide exchange at position 13. This results in PM – MM ON pairs, i.e. „probe pairs“. Signals of MM ONs are subtracted from the corresponding PM ON thereby enhancing sensitivity and specificity of each PM ON. Each mRNA sequence represented by a „Probe Set“ consists of 11 probe pairs. This allows for statistical analyses and thus quality assessment of each measurement. 1. Principles and Terminology The Principle: „Probe Set“ Miss Match (MM) Perfect Match (PM) Probe Pair Nucleotidaustausch an Pos. 13 Feature Probe Set 1. Principles and Terminology Synthesis of the „Probes“ 1. Principles and Terminology Washing and Scanning Fluidics Station 1. Principles and Terminology Internal „Built-In“ Controls on The Array •Percent present Probe quality and reproducibility •Background and „Noise“ Scanner electric and hybridization •3‘ - 5‘ Degradation Pattern of Housekeeping Genes Quality of cRNA probe (checks all procedures) •Spiked oligo controls Hybridization, Staining (Efficiency and Linearity) •poly(A)-RNA spikes Quality of cRNA Synthesis 2. RNA Preparation and Quality Control A high quality RNA preparation is critical to generate an array of high quality. Degradation and contamination need to be avoided. We recommend the Qiagen RNeasy Lipid Tissue Mini Kit. In addition, sample preps, storage conditions, and homogenization prior to RNA extraction are important. Protocols need to be worked out for each sample (cultured cell, tissue, type of organ). 2. RNA Preparation and Quality Controls RNA Preparation 2. RNA Preparation and Quality Controls RNA Integrity Using the “Agilent” System 22 . 5 No short RNA fragments should be visible here 20 . 0 28s - 18s ratio >1.8 is required F lu o re s c e n c e 17 . 5 15 . 0 12 . 5 10 . 0 7. 5 5. 0 0. 0 19 24 29 34 2 8 S 1 8 S 2. 5 39 T im e (s ec ond s ) 44 49 54 59 Example and Stages of RNA Degradation 4 .0 30 1 7 .5 1 2 .5 3 .5 1 0 .0 7 .5 10 F lu o re s c e n c e 15 F lu o re s c e n c e F lu o re s c e n c e F lu o re s c e n c e 20 Agilent Lab on a Chip 1 2 .5 1 5 .0 25 1 0 .0 7 .5 5 .0 5 .0 2 .5 2 .0 1 .5 1 .0 2 .5 5 19 24 29 34 39 T i m e (s e c o n d s ) 9180 44 49 54 59 19 24 29 34 39 T i m e (s e c o n d s ) 2474 44 0 .0 49 54 59 19 24 29 34 39 T i m e (s e c o n d s ) 971 2 8 S 1 8 S 0 .0 2 8 S 0 .5 1 8 S 2 8 S 1 8 S 2 .5 0 GAPDH Transcripts / ng RNA 3 .0 44 0 .0 49 54 59 19 24 29 34 39 T i m e (s e c o n d s ) 44 49 54 145 Short = degraded RNA fragments 59 3. Data Evaluation There are numerous approaches. Which one to choose depends on the questions asked in the experiment. Data evaluation is principally done in three steps: Raw data screening including „report“ on quality parameters. Statistical evaluation and application of „filters“. Annotation of genes and functional evaluation. 3. Data Evaluation Tools we Use to Evaluate Data Qualitäty Raw Data Analysis Data Bank Control GCOS GCOS Manager GCOS (Report) Function Evaluation GeneSpring, NetAffx, Gene Ontology, GenMapp, RefSeq, Unigene Statistics Excel, GeneSpring Clustering GeneSpring, Connect Raw Data with Software Access, GeneSpring, Excel Display Results GeneSpring, Excel, Fatigo 3. Data Evaluation Raw Data Evalution Using GCOS DAT-File 7 x 7 Pixel per Feature CEL-File A Number per Feature CHP-File Signal Intensity giving Detection p-value per Probeset For Each Arrray there is a „Report“ Giving Quality Check on Entire Experiment 3. Data Evaluation The „Call“ The statistics of the probe pairs, i.e. of a gene/mRNA, are converted by GCOS into a „call“. „Absent“ call (not detectable): Detection p-value > 0,065 „Marginal“ call (maybe detectable): Detection p-value 0,065 - 0,05 „Present call (expressed): Detection p-value < 0,05 Present means that the gene is significantly expressed, absent means gene is not expressed or expression is < sensitivity of probeset. 3. Data Evaluation The „Normalization“ To compare data from different arrays, data need to be adjusted or „normalized“. There are several possibilities to do that. We use: Standard: Scaling to a target value of 500 at mean. If saturated: Scaling to a target of 500 at median. Tests in general: Logarithmization and Scaling per gene at the 50th percentile. 3. Data Evaluation The „Scatter Plot“ Easiest evaluation of a 2 array experiment (control versus experimental) is the Scatter Plot. Results are plotted against each other logarithmically. Red: Present - Present; Yellow: Absent - Absent: Blue: Absent - Present > 30 fold differentially expressed gene FoldChange lines, 2x, 3x, 10x, 30x 3. Data Evaluation Statistics and Filters To perform statistics 3 repeated measurements are needed. This yields a p value. Filters then reduce the amount of data. . Filter: 1. Signal intensity value 2. Detection p-Wert 3. Fold Change 4. p-Wert of experiment . This results in a list of candidate genes that are - most likely differentially expressed. The stringency of 1. to 4. determines the quality of the candidate list. 3. Data Evaluation Reduced „Straying“ Through Generation of Means 3. Data Evaluation Filter 3. Data Evaluation Combination of Filters: List of Genes 3. Data Evaluation The „Annotation“ Problem: The investigator gets a list of genes that he doesn´t know: Needed: Rapid procedure to identify the genes. Generate data banks and structure your gene lists. Test the links below ! List of Affymetrix Numbers via Access, GeneSpring, NetAffx Relate to Data Bank Terminology - Pubmed - UniGene - LocusLink / Entrez Gene - OMIM - Ensembl - ... 4. Cost The cost per array: 800.- € bis 1250.- €. Depending on: Array type and reagent/work load/experiment. 6. Information for collaborating partners Contact per mail: [email protected] Discussion and advice Sample transfer with „filled-in“ form (available at IVM) Generation of Microarrays Transfer of raw data files and Excel files (Software tools available at IVM) 7. Downloads •Contract •Excel scheme for evaluating data •Manual for Excel scheme •Sheet „Project form“