http://gcat.davidson.edu/Pirelli/index.htm
Microarrays are used to measure the gene expression of cells in different conditions. When a cell becomes cancerous, for example, some genes are induced (transcription increases), other genes are repressed (transcription is decreased) and with other genes nothing changes. Cells respond to different conditions, some environmental (e.g. sun burn) and some chemically induced (e.g. taking heroin). Medicines and their affects on gene expression are prime candidates for micro-array analysis so they can find out why people respond differently to the same drug - which genes are up-regulated/down-regulated in the presence of the drug.
Part 1
We'll use the experiment mentioned in the above multimedia example... Yeast cells can grow with or without oxygen. But in order to survive-in or adapt-to these conditions they have to create new proteins and also stop the production of other proteins that are not so useful in that condition.
If we place some cells in one condition (with oxygen) and then extract the mRNA from them we can tell which proteins are being made. Cells in oxygen is our control condition.
If we extract the mRNA from the cells in the other condition (sans oxygen) then we know what proteins are being expressed for this specific state. Cells without oxygen is the experimental condition.
If we compare the proteins being made (or not being made) in the two conditions we can discover what transcription has started or stopped in the anaerobic state.
We use DNA microarrays to do find this out. The microarray chip (glass slide) contains the mRNA of the whole yeast genome attached to it. To make a microarray we need to get the mRNA strands from both samples and use them to make mRNA probes which are attached to the surface of the microarray. This is a long process which we won't go into. Often, a ready-to-use microarray chip is available to buy from a company like Affymetrix.
Part 2
In order to find out which proteins have been transcribed we have to attach the mRNA of the samples in each condition to the microarray and we have to label them in a way to identify which mRNA came from which sample.
Since both the microarray mRNA probes and the mRNA strands from the cells are the same they cannot combine so we have to make complimentary DNA (cDNA) strands from the mRNA of each sample.
The enzyme reverse transcriptase converts the mRNA to cDNA. The cDNA is made with flourescently-labelled nucleotides so under the correct light the cDNA will glow. The cDNA has the complementary sequence of the mRNA, so if the mRNA was as shown below, thecDNA would be:
CUUUUUAUCCCCCGGGC - mRNA
GAAAAATAGGGGGCCCG - cDNA
Sample 1, the control, in aerobic conditions is labelled green. The second sample, the experimental condition, anaerobic, is labelled with a red flourescent. The mRNA is dissolved using RNAse so we end up with pure cDNA.
The red and green cDNA is complementary to the mRNA of the microarray so when it is squirted on to the microarray slide from both samples they quickly bind to their complimentary strands. Anything that didn't attach is washed off.
Part 3
The microarray is scanned using a machine that has two lasers that iduce flourescence from red and green labelled strands. Pictures for each color are stored on the computer and processed to measure the intensity of the flourescence - the greater the intensity, the more cDNA is attached to the probes and this tells us that a particular gene is highly expressed. Or the intensity is really weak so we can tell that that particular gene is barely expressed.The pictures/data can be combined to compare both conditions:
If a gene was expressed only in the control cells then a spot on the microarray would glow green.
If a gene was expressed only in the experimental cells (anaerobic) then a spot on the microarray would glow red.
If the gene was expressed in both conditions the colors green and red would mix to form a yellowy shade.
Genes that aren't expressed in either condition show as black since no light is emitted.
Since each gene of the yeast genome is a spot on the microarray, we know what gene each color-spot represents on the microarray so we can easily find out which genes are induced or repressed after the scan.
Simple sort-of :) . Data retrieved from microarray analysis is usually processed using complex programs like R using the bioconductor module. There's a lot of statistics behind the analysis of the data so most people who deal with this stuff are specialists.
Part 1
We'll use the experiment mentioned in the above multimedia example... Yeast cells can grow with or without oxygen. But in order to survive-in or adapt-to these conditions they have to create new proteins and also stop the production of other proteins that are not so useful in that condition.
If we place some cells in one condition (with oxygen) and then extract the mRNA from them we can tell which proteins are being made. Cells in oxygen is our control condition.
If we extract the mRNA from the cells in the other condition (sans oxygen) then we know what proteins are being expressed for this specific state. Cells without oxygen is the experimental condition.
If we compare the proteins being made (or not being made) in the two conditions we can discover what transcription has started or stopped in the anaerobic state.
We use DNA microarrays to do find this out. The microarray chip (glass slide) contains the mRNA of the whole yeast genome attached to it. To make a microarray we need to get the mRNA strands from both samples and use them to make mRNA probes which are attached to the surface of the microarray. This is a long process which we won't go into. Often, a ready-to-use microarray chip is available to buy from a company like Affymetrix.
Part 2
In order to find out which proteins have been transcribed we have to attach the mRNA of the samples in each condition to the microarray and we have to label them in a way to identify which mRNA came from which sample.
Since both the microarray mRNA probes and the mRNA strands from the cells are the same they cannot combine so we have to make complimentary DNA (cDNA) strands from the mRNA of each sample.
The enzyme reverse transcriptase converts the mRNA to cDNA. The cDNA is made with flourescently-labelled nucleotides so under the correct light the cDNA will glow. The cDNA has the complementary sequence of the mRNA, so if the mRNA was as shown below, thecDNA would be:
CUUUUUAUCCCCCGGGC - mRNA
GAAAAATAGGGGGCCCG - cDNA
Sample 1, the control, in aerobic conditions is labelled green. The second sample, the experimental condition, anaerobic, is labelled with a red flourescent. The mRNA is dissolved using RNAse so we end up with pure cDNA.
The red and green cDNA is complementary to the mRNA of the microarray so when it is squirted on to the microarray slide from both samples they quickly bind to their complimentary strands. Anything that didn't attach is washed off.
Part 3
The microarray is scanned using a machine that has two lasers that iduce flourescence from red and green labelled strands. Pictures for each color are stored on the computer and processed to measure the intensity of the flourescence - the greater the intensity, the more cDNA is attached to the probes and this tells us that a particular gene is highly expressed. Or the intensity is really weak so we can tell that that particular gene is barely expressed.The pictures/data can be combined to compare both conditions:
If a gene was expressed only in the control cells then a spot on the microarray would glow green.
If a gene was expressed only in the experimental cells (anaerobic) then a spot on the microarray would glow red.
If the gene was expressed in both conditions the colors green and red would mix to form a yellowy shade.
Genes that aren't expressed in either condition show as black since no light is emitted.
Since each gene of the yeast genome is a spot on the microarray, we know what gene each color-spot represents on the microarray so we can easily find out which genes are induced or repressed after the scan.
Simple sort-of :) . Data retrieved from microarray analysis is usually processed using complex programs like R using the bioconductor module. There's a lot of statistics behind the analysis of the data so most people who deal with this stuff are specialists.
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