Now, the analytical tool I wanna talk about here,
is actually used in the discipline of transcriptomics
and it's called a microarray analysis.
Now, microarray analysis is an amazingly
powerful technique that allows a researcher
to measure how much of every given
messenger RNA is being made,
okay? Now, microarray, in general, can analyze DNA and
analyze protein. It can analyze RNA and other things.
But this analysis of transcriptomics
that I am going to talk about
relates to messenger RNAs being made in a cell.
How does it work? Well, you see in front of you a
grid and that grid has little spots on it
and those little spots are targets
for molecules we are going
to make and attach there,
okay? So, we can see here that the grid is now
being defined a little bit more with color.
We know for example the sequence of the human genome.
We know every base in it. We know every gene in it.
And we would like to understand
well how much of every gene
in the human genome is being made in this
particular cell type, let's say muscle.
Well what would I do? I would
take the sequence that I know
and I would go to a chemist and I would
have the chemist chemically synthesize
and yes that's possible to do, chemically
synthesize the sequence of each gene.
So let's say I have a gene
for hemoglobin as an example
and I take that sequence for hemoglobin
and I tell the chemist
"Make me a few thousand copies
of that sequence of that gene."
And then take the sequence of
another gene let's say hexokinase
and make a few thousands copies of that.
Now I am keeping each
one of those separate.
So the hemoglobin I might take
for example and chemically bond it
to that first upper left
square that you see on the grid.
I might take the hexokinase
and link it to the other.
It doesn't really matter what order I put them in
as long as I keep track of which gene is in each place.
So gene 1 will have thousands of copies of the DNA
Gene 2 will have thousands of
copies of a different DNA
and gene 3 will have etc.
We could do this on a microarray
for every gene of 30,000
genes that human cells make,
okay? This is a power miniaturization.
What we do with that then, is we now have a grid that
contains the sequence of every gene in the human genome.
We wanna use this to study, let's say cancer.
So, we take a healthy cell so
let's again say we have got muscles.
We take a healthy set of muscle cells.
We isolate the messenger RNAs from that
and then we have got a cancer
that came from a muscle cell
and we isolate all the messenger RNAs from that. We
are keeping them separate at the moment.
So there we have a copy of all the messenger RNAs of
each cell type, normal and cancerous.
We then copy each messenger RNA using reverse transcriptase.
Now, reverse transcriptase I talked about
in another presentation but it's
an enzyme that copies
RNA and makes DNA from it.
Now the reason for using DNA is, it's a little
easier to handle DNA than it is to handle
messenger RNA so that's the reason that we do it.
We then take those cDNAs, a cDNA is
simply means its a copy of the RNA.
We take the cDNAs and we
add fluorescent tags to them.
So for all the cDNAs that are in the healthy
cells put a green tag on it. It's simply a chemical.
On the cancer cells we
taken we add a red tag to it.
So we have two batches of messenger RNA now that have
been flagged with each one with an appropriate color.
Then we do something surprising. We take these samples
that we have been so careful to keep apart and we mix them.
And the reason that we mix them is
we wanna give each one of these
an equal opportunity to mix on this plate.
So we take them and we pour the sample onto the
plate that now has every possible gene on it.
We allow hybridization to
occur and that simply means
finding the base pairs of each other like the
primers of DNA found the strands in the PCR process.
We allow hybridization to occur such that
only strands that pair properly
will pair with each other.
So if there are for example messenger RNAs in there for
hemoglobin they are gonna go to the spot
where the hemoglobin is and anneal with it.
We then take the plate and wash away anything that
hasn't hybridized; because, things that haven't hybridized
we are not really interested in.
And then we take the plate to a plate
reader and we analyze what's there.
Now remember that the healthy
cells had a green fluorescent tag
and the cancers sells had a red fluorescent tag.
This is what a microarray analysis might look like.
The intensity of the color of the spot that we see on here,
first of all, is a measure of the
quantity of messenger RNA that was in each cell.
The shade of the color tells us the relative
expression of that messenger RNA in each cell type.
So let's see what that means. Bright green
means for example bright
what indicate, it's abundant
and green would indicate it's in healthy
cells but it's not in cancers cells.
Bright red would mean a gene that is abundant
in cancer cells but not in healthy cells.
Bright yellow means it's abundant in both cell
types; because, in this kind of analysis when you mix
green and red, believe it or not, you get yellow.
Black would means it's absent in both cell types;
because, not every gene is made inside of every cell.
Now the beauty of this analysis is
you can very quickly on one plate
not only see what genes are being made but you can see
where the differences are between two different cell types.
You can understand that why is
that protein being made
in a cancer cell and not a regular
cell, maybe that's a protein
that I can design a drug for
and knock out a cancer cell.
So this technique is incredibly
powerful that allowing us to
use this information to design therapies and
better understand how it is the cell's function.