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This can be a short section of a gene or other DNA element that are used to hybridize a cDNA or cRNA sample (called target) under high-stringency conditions. Probe-target hybridization is usually detected and quantified by detection of fluorophore-, silver-, or chemiluminescence-labeled targets to determine relative abundance of nucleic acid sequences in the target. Probe Attachment : Probe Attachment In standard microarrays, the probes are attached via surface engineering to a solid surface by a covalent bond to a chemical matrix (via epoxy-silane, amino-silane, lysine, polyacrylamide or others). The solid surface can be glass or a silicon chip, in which case they are colloquially known as an Affy chip when an Affymetrix chip is used. Other microarray platforms, such as Illumina, use microscopic beads, instead of the large solid support. Nucleic acid hybridization : Nucleic acid hybridization The core principle behind microarrays is hybridization between two DNA strands, the property of complementary nucleic acid sequences to specifically pair with each other by forming hydrogen bonds between complementary nucleotide base pairs. A high number of complementary base pairs in a nucleotide sequence means tighter non-covalent bonding between the two strands. After washing off of non-specific bonding sequences, only strongly paired strands will remain hybridized. Hybridization : Hybridization AT and GC baseparing Affected by temperature, pH, and ion concentration Higher temperature higher stringency Lower temperature more non-specific binding Slide 7: DNA Microarray E. coli chromosome PCR Gene A Gene C Arrayer Gene A Gene B Glass Slide Purification of mRNA Labeling during RT Gene A Gene B 16 hr, 42C Wash Scan Image Analysis: (Probe) (Target) Organism-1 Organism-2 Gene C Gene C Gene B Organism-2 Organisms-1,2 Organism-1 Microarray On the surface : On the surface A G T C array T C A G probe hybridization T C A G A G T C Target Fluorescent tag In Affymetrix system the meaning for probe and target are reversed Comparison : Comparison GeneChip: expensive, high density, absolute value measurement, fixed design cDNA microarray: cheap, low density, relative value measurement, free design Oligoarray: cheap, low density, relative value measurement, free design Slide 11: cDNA Spotted Microarrays Slide 12: DNA microarrays can be used to measure changes in expression levels, to detect single nucleotide polymorphisms (SNPs) , to genotype or resequence mutant genomes (see uses and types section). Microarrays also differ in fabrication, workings, accuracy, efficiency, and cost (see fabrication section). Additional factors for microarray experiments are the experimental design and the methods of analyzing the data Data Acquisition : Data Acquisition Scan the arrays Quantitate each spot Subtract background Normalize Export a table of fluorescent intensities for each gene in the array Basic Data Analysis : Basic Data Analysis Fold change (relative increase or decrease in intensity for each gene) Set cutoff filter for low values (background +noise) Cluster genes by similar changes - only really meaningful across multiple treatments or time points Cluster samples by similar gene expression profiles Slide 16: Scatter plot of all genes in a simple comparison of two control (A) and two treatments (B: high vs. low glucose) showing changes in expression greater than 2.2 and 3 fold. Cluster by color difference : Cluster by color difference Microarry Data Variablity : Microarry Data Variablity Microarray data are inherently highly variable - you are measuring mRNA levels Any measurement of thousands of values will find some large differences due to chance (normal distribution) Must have replication and statistics to show that differences are real Use REAL replicas (different patients, different experiments), don’t just split samples. Sources of Variability : Sources of Variability Image analysis (identifying and quantitating each spot on the array) Scanning (laser and detector, chemistry of the flourescent label)) Hybridization (temperature, time, mixing, etc.) Probe labeling RNA extraction Biological variability Normalization : Normalization Can control for many of the experimental sources of variability (systematic, not random or gene specific) Bring each image to the same average brightness Can use simple math or fancy - divide by the mean (whole chip or by sectors) LOESS (locally weighted regression) No sure biological standards Normalization : Normalization Normalization in the same experiment due to the efficiency differences of fluorescent protein (cy3, cy5) by using house-keeping gene expression Global normalization for different experiments by using total expression or by using certain external chemicals for every experiment Are the Treatments Different? : Are the Treatments Different? Analysis of microarray data has tended to focus on making lists of genes that are up or down regulated between treatments Before making these lists, ask the question: "Are the treatments different?" Use standard statistical methods to evaluate expression profiles for each treatment (t-test or f-test) If there are differences, find the genes most responsible If there are not significant overall differences, then lists of genes with large fold changes may only reflect random variability. Sample Variability : Sample Variability Use paired samples - normal & cancer or before & after treatment from the same patient, 6 & 24 hours from same cell culture What is the variability of two samples from the same patient any two surgical samples have different amounts of various cell types different day,different environmental and metabolic factors Multiple Comparisons : Multiple Comparisons In a microarray experiment, each gene (each probe or probe set) is really a separate experiment Yet if you treat each gene as an independent comparison, you will always find some with significant differences (the tails of a normal distribution) False Discovery : False Discovery Statisticians call false positives a "type 1 error" or a "False Discovery" False Discovey Rate (FDR) is equal to the p-value of the t-test X the number of genes in the array For a p-value of 0.01 X 10,000 genes = 100 false “different” genes You cannot eliminate false positives, but by choosing a more stringent p-value, you can keep them manageable (try p=0.001) The FDR must be smaller than the number of real differences that you find - which in turn depends on the size of the differences and varability of the measured expression values Gene-Specific Variability : Gene-Specific Variability Different probes will hybridize to mRNAs with different efficiency microarrays can only measure relative change of expression, not absolute levels Cross-hybridization Gene families Chance similarity of short oligo sequence Affy mis-match >> perfect match for many probes Different Affy probes for the same gene show huge differences in hybridization intensity Alternative splicing!! Statistics : Statistics When you have variability in measurements, you need replication and statistics to find real differences It’s not just the genes with 2 fold increase, but those with a significant p-value across replicates Non-parametric (i.e. rank) or paired value statistics may be more appropriate Experimental Design : Experimental Design Real replicates! (same treatment, same biological source, different RNA prep, labeling, hybridization, and scanning) Dye reversal for two color hybs. Block design (don’t do exp. on one day and control on another) Work with a Statistician!! Higher LevelMicroarray data analysis : Higher LevelMicroarray data analysis Clustering and pattern detection Data mining and visualization Controls and normalization of results Statistical validatation Linkage between gene expression data and gene sequence/function/metabolic pathways databases Discovery of common sequences in co-regulated genes Meta-studies using data from multiple experiments Types of Clustering : Types of Clustering Herarchical Link similar genes, build up to a tree of all Self Organizing Maps (SOM) Split all genes into similar sub-groups Finds its own groups (machine learning) Principle Component and SVD every gene is a dimension (vector), find a single dimension that best represents the differences in the data Public Databases : Public Databases Gene Expression data is an essential aspect of annotating the genome Publication and data exchange for microarray experiments Data mining/Meta-studies Common data format - XML MIAME (Minimal Information About a Microarray Experiment) Send your suggetion to me at my firstname.lastname@example.orgWhole matter of this power point to access form internet. : Send your suggetion to me at my email@example.comWhole matter of this power point to access form internet. 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