Introduction to Microarray Analysis Methods Course: Gene Expression Data Analysis -Day One Rainer Spang
Microarrays Highly parallel measurement devices for gene expression levels 1. How does the microarray experiment work? 2. What is measured? 3. Analyzing gene expression with the eye
We want to measure mrna abundances in a tissue or cell culture DNA RNA Transcripts
How does the microarray experiment work?
Purify mrna and transform it to cdna clones Identical Information Figure 8-43 Molecular Biology of the Cell ( Garland Science 2008)
cdna vs. genomic DNA The molecule is DNA but the cdna abundances reflect RNA abundances Figure 8-44 Molecular Biology of the Cell ( Garland Science 2008)
Fluorescently label cdna DNA cdna clones from RNA Transcripts
The complex probe How can we sort the different cdna molecules?
Base Pairing Complementary single stranded DNA sequences bind to each other
DNA Hybridization DNA can be reversibly melted Labeled DNA can be hybridized to non labeled DNA
The concept of the array Target Probe
Oligonuleotide probes
The microarray is a cdna sorting device Heating up and cooling down Spot 1 Spot2 Spot 3. The probe catches the target
Transcriptome wide oligonucleotide library glued to a chip
Hybridizing an array
High expression high fluorescent intensity of the spot low expression low fluorescent intensity of the spot Spot 1 Spot2 Spot 3.
Gene expression is read out as fluorescent intensities spot by spot
Summary of the experiment
The Affymetrix Design
Background intensities Small amounts of target cdna stick to the array unspecifically
Cross Hybridization Some targets do not find their matching probe but a similar one Spot 1 Spot2 Spot 3. These targets will contribute to the intensity of the wrong gene
Image Analysis Let the Affymetrix software do it
The.cel file is load by R
What is measured?
mrna abundances DNA RNA Transcripts
Gene Regulation Figure 7-5 Molecular Biology of the Cell ( Garland Science 2008) Expression is not identical to transcription Expression = basal expression +transcription - degradation mrna expression poorly correlates with protein expression
Initiation of Transcription Binding sides for the same Transcription Factors recur over and over again in the genome Figure 7-44 Molecular Biology of the Cell ( Garland Science 2008)
A cell does not regulate its genes individually but in large transcriptional modules Rows: Genes Columns: Samples Color: Expression High Low Figure 7-3 Molecular Biology of the Cell ( Garland Science 2008)
Gene Interaction A second cause for the formation of gene clusters in heatmaps: The genes interact, the expression of one gene influences the expression of many other genes Heat map
Functional interaction vs. physical interaction The clusters reflect functional interactions of genes: If Genes A and B are in the same cluster this means that it is not possible to regulate them independently from each other or the cells chose not to do so. Functional interaction is different from physical interaction like the binding of two proteins.
Different cell types express different sets of genes Expression profile characterize different types of cells Figure 7-3 Molecular Biology of the Cell ( Garland Science 2008)
The Transcriptional Identity of a Cell Type Which genes a cell expresses depends on the proteins and RNAs already present in the cell These are different in different tissues The cells are all in a consistent functional state of molecule concentrations, however this state is different from cell type to cell type Different stained proteins in different embryonic tissues (mouse)
Combinatorial gene control creates many different cell types Vice Versa: The number of different functioning cell types limits the spectrum of existing expression profiles Figure 6-3 Molecular Biology of the Cell ( Garland Science 2008)
Discovering new types of cells Cells with different expression profiles are different types of cells Figure 7-3 Molecular Biology of the Cell ( Garland Science 2008)
Functional similarity of different cell types Most analysis addresses expression differences We can learn a lot, maybe even more, by marveling over the expression similarities of cells that we consider different Figure 7-3 Molecular Biology of the Cell ( Garland Science 2008)
The Spectrum of Cells Physiological cells: neurons, hepatocytes, B-cells (native, activated, in the germinal center, ), Pathological cells: tumor cells, infected cells, Experimental cells: Transfection, Knock out, sirna,
Synchronized cells in the cell cycle The expression of genes in a cell is a dynamic process
Genetic aberrations can reprogram the transcriptional identity of a cell Yeoh et al, Cancer Cell 2002
The IgH-Myc Translocation in Burkitts Lymphomas Myc gets under the influence of a IgH promoter When ever the cell wants to transcribe IgH it transcribes Myc Confused gene expression
Tissues Expression Profiles of tissues (tumor biopsies) average gene expression of heterogeneous cell types The profile characterizes the cell composition and the tumor cells
Cell Lines In cell lines or cell cultures we typically have a single cell type still the profile averages over the expression in many cells
Expression Noise Two reporter genes (red/green) controlled by the same promoter Figure 8-75 Molecular Biology of the Cell ( Garland Science 2008)
Analyzing gene expression with the eye
The Heat Map Rows: Genes Columns: Samples Color: Expression High Low
The color encodes expression levels, but not globally otherwise the heat map looks like this! The largest expression differences are between genes and not within a gene across samples
Using ranks gene by gene The highest value of a gene across samples is bright yellow, the lowest is bright blue
The colors suggest that these genes have two well separated expression levels It is low for the left half of patients (right) and high for the other half (left)
well this is not the case
Two classes of samples All genes differentially expressed
A continuum of samples Two groups of genes
Nothing but noise
Deceiving the eye It is all the same data just sorted differently
Questions?