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1. Principles and important terminology 2. RNA Preparation and quality controls 3. Data handling 4. Costs 5. Protocols 6. Information for collaboration.

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Presentation on theme: "1. Principles and important terminology 2. RNA Preparation and quality controls 3. Data handling 4. Costs 5. Protocols 6. Information for collaboration."— Presentation transcript:

1 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 Introduction Into The Gene Expression Platform of the IVM

2 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 (> ) 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

3 Affymetrix produces expressionsarrays of several species (human, mouse, C. elegans and others; test the link !).expressionsarrays 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 Available: Whole Genome Arrays of Several Species

4 1. Principles and Terminology 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.Photolithography Production of Arrays

5 1. Principles and Terminology Probe Set Probe PairFeature Miss Match (MM) Perfect Match (PM) Nucleotidaustausch an Pos. 13 The Principle: „Probe Set“

6 Synthesis of the „Probes“ 1. Principles and Terminology

7 Fluidics Station 1. Principles and Terminology Washing and Scanning

8 Internal „Built-In“ Controls on The Array Background and „Noise“ Scanner electric and hybridization Percent present Probe quality and reproducibility Spiked oligo controls Hybridization, Staining (Efficiency and Linearity) 3‘ - 5‘ Degradation Pattern of Housekeeping Genes Quality of cRNA probe (checks all procedures) poly(A)-RNA spikes Quality of cRNA Synthesis 1. Principles and Terminology

9 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.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).

10 2. RNA Preparation and Quality Controls RNA Preparation

11 Example and Stages of RNA Degradation RNA Integrity Using the “Agilent” System 28s - 18s ratio >1.8 is required 2. RNA Preparation and Quality Controls No short RNA fragments should be visible here Short = degraded RNA fragments

12 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.

13 Raw Data Analysis GCOS Statistics Excel, GeneSpring Function Evaluation GeneSpring, NetAffx, Gene Ontology, GenMapp, RefSeq, Unigene Clustering GeneSpring, Connect Raw Data with Software Access, GeneSpring, Excel Display Results GeneSpring, Excel, Fatigo Qualitäty Control GCOS (Report) Data Bank GCOS Manager 3. Data Evaluation Tools we Use to Evaluate Data

14 3. Data Evaluation Raw Data Evalution Using GCOS DAT-File CEL-File 7 x 7 Pixel per Feature A Number per Feature Signal Intensity giving Detection p-value per Probeset CHP-File For Each Arrray there is a „Report“ Giving Quality Check on Entire Experiment

15 The „Call“ 3. Data Evaluation 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, ,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.

16 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

17 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 FoldChange lines, 2x, 3x, 10x, 30x > 30 fold differentially expressed gene 3. Data Evaluation

18 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

19 Reduced „Straying“ Through Generation of Means 3. Data Evaluation

20 Filter

21 3. Data Evaluation Combination of Filters: List of Genes

22 The „Annotation“ List of Affymetrix Numbers via Access, GeneSpring, NetAffxGeneSpringNetAffx Relate to Data Bank Terminology - PubmedPubmed - UniGeneUniGene - LocusLink / Entrez GeneLocusLinkEntrez Gene - OMIMOMIM - EnsemblEnsembl -... 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 ! 3. Data Evaluation

23 4. Cost The cost per array: € bis €. Depending on: Array type and reagent/work load/experiment.

24 6. Information for collaborating partners Contact per mail: 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)

25 7. Downloads Contract Excel scheme for evaluating data Manual for Excel scheme Sheet „Project form“


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