2D-DIGE: Applications

dr srinivasa from ge healthcare who is going
to talk to us about dige technology and give us a demonstration on software to perform
dige gel analysis the next one is differential in-gel analysis
shortly we call as dia in this you can see we can create a new project
new dia here like there is an option create workspace from here you can now it will take
you to where we already saved our gels in our database now from the database we can
select any particular project and from there i am selecting gel number 1 as we saved this
one then now you can see this gel where we have
uploaded so now after uploading here you can process these gels then during process you
have to give some number this is some threshold which you are giving here actually this may
be 2000 so that it will take care of background issues also then basically in decyder it co-detection
will be but i would like to explain you some more about what is co-detection this co-detection uses information of all
3 channels and will create the geometrically identical spot boundary for a spot across
all the channels that means there are 3 channels cy2 cy3 cy5 out of this 3 channels in cy2
image it creates a particular volume and the same area can be applicable for the remaining
2 gels also this is way it works in this way quantitative and qualitative results are much
more accurate than with a single detection in dia each image is co-detected with its
internal control producing 2 images pairs the ratio of standard sample is calculated
further or the ratio of standard sample is calculated for each protein in each image
so as we see here these are all the number of spots which has been detected in which
there are you can see some red color spots here this are all down regulated and compare
with control with treated and this blue color spots where you can see
these are all up regulated spots when comparing with control and treated in between this there
are some blue color spots these are all similarly regulated so this is what we can see in dia
now you can go through each and individual spot and you can see the 3d view of that particular
spot if suppose we can select anyone it can go to one by one and you can see whether it
is exactly spot or some background or if it is a background you have to remove that so suppose this is the background so there
is no spot at all still it is detected some background so you can exclude it from here
by clicking then confirm it so this protein has been removed from the gels so the same
way you can go each and individual then exclude it and confirm it by this way you can check
all spots and you can have more accurate data with you like i say you can see the how accuracy
would be there like the spot detection now you can see this is what we will get in
dia thus dia creation we finished this is the bva bva is nothing but biological variation
analysis one of the internal standard image is selected as a master image and all internal
standard image matched into this samples standard spot ratio for each protein each sample than
compared giving t test value fold changes anova values for each and individual protein how to create to create bva workspace can
open the bva and create a bva workspace and go to our dia workspace where we have our
dias from there you can create actually add it one dia i have added and this is another
dia i am adding here minimum 2 dias we require for the bva so we have 2 dias here and click
on create so it creates a new bva for you okay this is the new bva from here the first
all cy2 gels are automatically going to standard folder there is a standard folder you can
see and remaining all gels remains in the unassigned folder where we need to assign
these gels as according to the gel type or sample type then we just have to click on
add option now create a group may be control or treated this first one is the control and
apply some color draft confirm it then another you can create like treated then
give some color draft confirm it now we have 2 folders control and treated so as we have
in assign folder both control and treated this control gels we can transfer into control
folder by dragging those images and treated gels we can transfer to treated folder by
dragging them now we have our images here after shifting
the control to control and treated to treated we have to match all gels just click on match
and match all just match the matching process has been finished now as we discussed in out
of all standard gels 1 gel selected as a master image as you can see the number 24 gel has
been selected as a master and it will compares remaining other gels with this master gel so now we have this comparison data after
that we need to calculate statistical parameters so click on statistical parameter button see now we have some paired statistical parameters
like independent t test average ratio student’s t test one-way anova in between different
groups we are doing in between control and treated so calculate them so the statistical
parameters have been calculated now we can see exact visuals of statistical parameters if i can go to the table view here you can
see t test one-way anova this you can see so we can select from here which are all the
statistical significant and which are all not significant after analyzing the statistical data now we
can see the complete results here here we can see the 4 views like this is the image
view and this is the histogram view in histogram
view we can see clearly particular protein how it is behaving throughout control and
treated if you can see this is the standard gel that means this is the mixture of these
control and treated so it is somewhere 0 we can consider this one then the control is
showing up regulation and after giving the particular treatment it is showing the down
regulation so this kind of data we can see here than
in the table view as we can see the complete protein data where the t test value average
ratio value and anova value this all we can see here in the table view so the 4 views
at a time to see this is the 4 view we can see here so after this we can filter them
according to our interest so select the few parameters which are available restrict to
tangles which are spot maps which are present a particular protein should be there and student’s
t test as well as average ratio then one-way anova value then filter it so
there are 2299 spots are available in this all gels but 107 proteins only passed all
these parameters so these parameters we can select as a protein of interest and assign
pick list so that this protein can be saved in a file this file can given further to our
spot picker these are all the things we can able to identify
very easily in bva this is very user friendly there is no much more manual interference
this is what this helps you to analyze your dige gels so can you elaborate on what is
eda or extended data analysis what it can do which we are unable to do it in bva so
there is the layers here right one is the dia followed by bva and then ultimately eda
analysis professor – student conversation starts basically
what we can do here is we can compare 2 bva together there exactly here we are talking
about a particular disease or a particular set of data only there we can analyze different
bva together in eda there you will get a majorly differential expression again you will get
as well as pca and discriminate analysis even this kind of statistical data you will get
in eda very shortly i will just show you a briefly
regarding that professor – student conversation ends if i understood correctly probably the
statistical parameter will be more stringent towards the end in the eda and you can have
some better biological significance and formation from the data set because in lot of clinical
data or different type of treatments people like to do several gels and lot of treatments
so your number of sample to be analyzed is very large and really obtaining the meaningful information
is one of the major challenges in all the proteomics so i would like to see in a eda the thing which we can see here is differential
expression analysis in which you can see differences in between control as well as 2 experimental
data this is the different treatment were given here you can see how the particular
protein is expressing throughout these control as well as this kind of things and you can see this kind of data for each
and individual protein so that from here you can see which one is your interest and which
is not of interest you are actually analyzing the data spot wise now here exactly here the
spot by spot which we are seeing the number of events the index number which shows there
is a master gel from that master gel you can see exactly this number this is what which we are seeing here for
each individual spot here we can see the results than as well we can see principle component analysis for
this data here there are almost 18 and proteins so out of 18 and proteins you can see there
are inner the circle there are protein some proteins are present and out-layers are there
the inner circle they are similarly specially if i can say 95% statistically significant
there and out-layers which you can see are exactly this can be some non reproducible
spots or else what the major things is these are all very highly up regulated or highly
down regulated so this can be worked as also then we have
to go back to our bva data and we can check the protein how exactly it is regulating then
we can identify the protein and we can use it for further analysis so this is a powerful
statistical parameter by using which you can identify some outlets which could be the potential
discriminator between the control and treatment and once you identify those proteins then
actually you can go back to your original data from the bva and get all the analysis
done so this is very interesting professor – student conversation starts and
next pattern analysis we can see the whole proteome then how these are difference from
each other so this is the hit map of the total 82 proteins which we are taking into consideration
then how in which area they are up regulated if you can see the green area exactly we can
see is completely down regulated area and the red color portion where you can see that
is up regulated portion and the remaining black color which you can see those proteins
are similarly regulated this is what which you can see here this kind of data will help you to represent
your complete whole analysis professor – student conversation ends so dr srinivas it was very
useful to have you here and to get an overview of dige technology how people can use decyder
software and analyze the data by using dia bva and eda and although there was not enough
time but you have given a very good demonstration in a very short time to give a glimpse of
the processes involved for doing this analysis as well as how different type of statistical
parameters can be applied to get some very powerful statistical information from our
biological data so thank you very much for coming here and giving this very good introduction
about dige technology so i hope our discussion with dr srinivas was useful and now you can
perform this analysis by using a specialized software and obtain some very useful biological
information from your data set probably you must appreciate there are lot
of meticulous steps are involved in performing these experiments but at the end this provides
a very useful quantitative multiplexing approach to separate proteins and to analyze different
type of variations i hope at the end of this module and lecture you will be able to perform
the gel based proteomics experiment but please keep in mind these protocols and methods are
only giving you a feel for performing these experiments each experiment each sample type each biological
questions brings its own unique challenges and depending upon those conditions and your
sample type you need to optimize these methods there is no one technology which can answer
all of your question but it is good idea for you to know that what are different methods
which are available for you to use so i hope by taking this module on gel based proteomics
now you are familiar with different type of gel based techniques these are only few there are many other methods
as available but these are the most commonly used methods which people are applying in
the field of proteomics so among 2-de and dige which of these 2 techniques
will be better to separate serum protein samples obtained from large number of patients in
a clinical trial so if you have a multiple serum samples from
patients two dimensional electrophoresis although a very useful technique but it may not be
the best option in this case to analyze serum proteins from large number of patients
in this case dige will be extremely available tool for analysis of large number of samples
simultaneously without having to overcome the problem of gel-to-gel variations in dye gels the controls and test samples
can be differentially labeled by using the cyanine dige and then run on the single gel
the pool internal standard for dige is prepared by mixing equal amounts of all the samples
that need to be run on the gel and this prevent the problem of gel-to-gel variations from the same gel 3 different images can be
obtained for cy2 cy3 and cy5 therefore there will be no reproducibility issue and various
artifacts can be eliminated for the clinical or large number of samples analysis so the
main aim for the development of difference in gel electrophoresis was to overcome the
inherently poor reproducibility of conventional two dimensional electrophoresis so dige is quite sensitive technique with
less than one of of proteins which can be detected and it can enable the linear detection
of very broad dynamic range of the proteins as you can see in this slide the proteins
samples let say you have a control and treatment those are labeled with 2 different dyes cy3
and cy5 but a small aliquot of both of the samples is mixed together to make a internal
pool that internal pool is labeled with cy2 now all these 3 protein samples are mixed
together in one tube which contains both control treatment as well as the reference spot from
the internal pool all these protein mixtures are separated in
the first dimension on the same strip and then the same gel can be scanned with the
3 different wavelength to obtain the images for the cy2 cy3 and cy5 so in the conventional
2-dimensional electrophoresis the gel-to-gel variations which comes from the acrylamide
polymerization electrical ph and thermal fluctuations in different gels that can be overcome in
the dye gels because all the protein separation is going
to happen on the same gel so all those artifacts can be minimized by using dige approach so
in the slide it is shown there is a 3 samples are mixed and then isoelectric focusing is
performed from the pool sample and then this strip is placed on the sds page gel for the
protein separation in the second dimension so overall dige provides very uniform scanning
from gel to gel and shows high sensitivity and linear dynamic range of detection for
the expression for filing of complex biological samples so if your aim to resolve 1000s of proteins
and cover comprehensive proteome coverage then dige is a very good platform especially
if you want to do the comparative or differential proteomic analysis because your gel-to-gel
variations and other variations will be minimized and dige will provide the very high sensitivity so once we have run these gels now from the
same gel the images can be obtained 3 images of your control and the treatment and these
can be analyzed from different software such as decyder software and then by looking at
3 dimensional views and the statistical data then these proteins can be considered as interesting
for further investigations once the spots are analyzed and exercise from
the gel then the same tradition you have to follow you can use an of the mass spectrometry
platform and then obtain the ms spectra for further analysis using different type of bio
and pharmatic tools such as mass spot so overall the dige method is far more superior in terms
of the reproducibility as compared to the conventional two-dimensional electrophoresis
and for the quantitative accuracy so therefore applications of 2d dige can be
found in virtually all major biological research areas if you see the recent publication you
will appreciate there are several papers on each of the biological system for different
different type of applications whether self signaling or looking at developmental biology
looking plan proteomic analysis neurosciences clinical studies different type of diseases
including cancer you will find there are hundreds of publications available which have employed
the power of 2d dige technique so let us talk now a new case study case study
3 on 2d dige as a strategy to identify serum markers for the progression of prostate cancer so this study by an byme et al published in
2009 so in this study author aim for identification of serum markers by depicting the progression
of prostate cancer by using difference in gel electrophoresis techniques the prostate
cancer is recognized as a significant problem in older male population the prostate cancer
screening rely heavily upon testing for the higher level of prostate specific antigen
also known as psa within the peripheral circulations so psa is very sensitive marker but there
are lot of discussion on reliability and the specificity of psa for the prostate cancer
reason being that the level of psa is also high in benign prostatic hyperplasia or prostatitis
so therefore there are lot of discussion whether one should rely on only psa for detection
of the prostate cancer so this study aims to identify some new markers in the prostate
cancer by studying the serum proteome analysis so as you are aware and in fact we have discussed
the protein preparation from the serum earlier so each of the biological samples posses lot
of technical challenges and serum is one among them where presence of highly abundant proteins
such as albumin and immunoglobulin they result into the masking of low abundant proteins
so to eliminate those high abundant proteins authors used multiple affinity removal system
from the agilent technology and they removed most highly abundant proteins
from the serum sample including albumin igg antitrypsin iga transferrin and haptoglobin
after the abundant proteins were depleted from the serum sample then authors moves for
the protein extraction and further analysis so the differential proteomics analysis was
performed in the 2 different cohorts of histologically confirmed prostate cancer with different grades
of the disease so they used the patient with 2 different
grading system based on the lysine grading so the lysine grading system that is used
to help and evaluate the prognosis of men with the prostate cancer so depleted serum
samples obtained from the patients with lysine score 5 and lysine score 7 were used for comparison
and further analysis as you can see in the slide these samples
were first labeled with the cy3 cy5 and also the internal reference pools were made which
were labeled with the cy2 dyes these samples were then further mixed the depleted cancer serum from first cohort
of lysine score 5 and second cohort of lysine score 7 those were mixed separated in the
first dimension and followed by protein in the second dimension when authors analyzed these dige images they
found that 63 protein spots were differentially expressed between the lysine score 5 and 7
cohorts and 13 of these proteins were statistically significant among these 2 population so as
you know analysis of these gels is always challenging especially if you are looking
at the conventional 2d gel where you have separate gels obtained from each of these
groups but analysis in the dye gel is more automated
so if you remember our previous discussion in the dye gel analysis this analysis is more
automated and more straight forward but still we have to go through individual spots and
you have to look for the how real how significant those changes are and you have to look at
the 3d view the whole spots to ensure that it will reproducible among various control
and treatment groups so that the different label of analysis is
performed which we have talked earlier but this just shows you the final output that
63 spots after all the analysis steps were considered significant after 2d dige gel image analysis authors exercise
those spots and subject it for the mass spectrometry identification of proteins so the proteins
exercise from the gels were analyzed by using finnigan ltq mass spectrometer and data from
these ms experiments were analyzed by using bio-works browser by using program for validation authors employed various techniques
including western blots and enzyme-linked immunosorbent assay or elisa and also immunohistochemistry
so the pigment epithelium derived factor pedf and zinc alpha 2- glycoprotein also known
as zag those proteins were further validated by elisa technique so the pedf levels were
quantified by using elisa kit and results demonstrated as you can see in the slide that
statistically significant decrease in the pedf in the gleason score 7 depleted serum
group whereas the results for zinc alpha-2-glycoprotein
elisa which is shown in the red in the bottom panel that indicated 144 increase in the zinc
alpha 2 glycoprotein absorbance in the gleason score 7 group so these studies this elisa
validation confirm their findings from the 2d dige experiments authors also employed immunohistochemistry
or ihc for validating the pigment epithelium derived factor pedf and zinc alpha 2 glycoprotein
so that they are very confident that the protein which they have identified from the proteomic
profiling those are real and those are tested on the independent tissue samples so from this paper the major conclusions were
that serum markers which are reflective of the pathological grade and stage could be
beneficial for the identification of appropriate treatment strategies authors confirm that
differential expression of pedf and zag can be performed by using various techniques such
western blots elsa and immunohistochemistry based on the std and the follow up experiment
they concluded that pedf could be a potential marker of early stage prostate cancer prediction however more studies and follow up required
on the large number of patients before it can be established as a good biomarker you
may appreciate that there are lot of power of these techniques and these can be employed
for any biological application you pick an application of your choice and i am sure you
will be able to answer those by employing 2d dige techniques thank you

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