 Welcome to MOOC course on Introduction to Proteogenomics. Today we have scientist Dr. Suman Thakur from Center for Cellular and Molecular Biology, CCMB Hyderabad. Dr. Thakur will talk to you about mass spectrometry based quantitative proteomics and how it can help the cancer research. We will also talk about why quantitative proteomics is very important, in which way various tools available for doing quantitative proteomics have helped to understand different diseases. Dr. Thakur will focus on why we are not getting clinically relevant biomarkers for all the cancers and why one drug cannot cure all the cancers. He will talk about different specifications for mass spectrometry based columns, for example the gradients, the column length, another type of mass spectrometry parameters, how it could be optimized to obtain good results. He will also talk about why the number of proteins decreases in labelled analysis as compared to the labelled free quantitative analysis. He will then talk about anti-cancer compound screening and how the proteomics cell biology and animal studies together could only help to find out the clinically relevant information. So, let us welcome Dr. Suman Thakur for today's lecture. Mass spectrometry based quantitative proteomics in cancer. So, anyhow you are well trained and thanks to all the previous speaker who has set the stage and I should just move it. So, all you know proteogenomics, so genomics is here, proteomics is here, middle transcriptomics, metabolomics for us these all are different subject, but for body all are happening, billion year trillion year of evolution has made this. So, this is not a one day anything, maybe this is new subject for us, but what is here? So, biology change, thousand years before also question what is life, today is also question what is life, most probably thousand years later will be also question what is life, what is evolution, but what change that you have to understand, technology. So, technology drives biology, you get new technology in your hand and you try to answer different question, if you want to do something in life you have to ask question. What is the question? Find a specific anti-cancer compound for particular cancer without side effects, is it possible to find a drug without side effects that is the reason you ask question what is not easily possible. Second, find earliest stage and cancer biomarker, cancer is such a word till you discuss in symposium and lecture hall it is fine, if you see any patient your near and dear you will be second very badly. So, that is a bad word is a cancer basically is a disease, still after so many years hundred years of research we are not able to cure it, we are still in the middle, so what you need earliest stage, every doctor ask earliest stage, but what is the question common cause there are different organs in the body different types of cancer are there, if all are there then cancer should be common, this question you can ask and we try to ask this question and I thought take select eleven or ten cancer cell line, it is very nice to we have to get clear this is the cell line, same thing we are trying to repeat on the tissue also. So, you take Hila, cervical cancer, Jurgat, leukemia, Habg to liver cancer, Gamgee brain tumor, MCF breast cancer, A5-4-9 lung cancer, LNCP prostate, RKO colon, U2S bone and K5-6-2 again myeloid. So, different type of cancer are there, why not one drug is there for all the cancer, impossible, origin is different, metastatic is different, so that means it is very complex, but then you have to take if you want to do anything in the proteomics, you have to select one control and this is non-cancerous cell line. So, we thought we will take this ten cancerous cell line and very childish way you think you get 5000 protein, so then in L10 or L11 you will have almost whole human proteome practically it is not possible or you do different-different fractionation and you come to 10000 protein in each cell line, still you will not able to cross 11000 maximum 13000, so what is the matter in that means all proteins in all the cancer are same only quantitation is getting changed, some place someone is expressed, some place someone is expressed. So, that means what expression level, quantitation, so finally what came, quantitation is important in the experimental and it is happening inside the body, but how you will do this, this is very simple, we want cancer full proteome that is one or few proteins protein chemistry, you will see some lab is whole 20 years has been focus on one protein, but some lab will not like one protein, why one protein we should have 1000 protein, but what you will do is 1000 proteins, again what is miss whatever is your interest that you have miss, so finally if you want to make a drug or target you will target on one protein, so what protein mix is giving you, completely back cycle to find the target and come back on the one protein and make the drug, same things we are trying also to do, but before that I will go with little bit problem is there, there is nothing everything is good good good in life, but mostly we talk good good good thing, if you go to say the biomarker things, different disease same marker, same disease different marker, all you will get confused and you will end up with the nothing, so bottom line what we need, need unique biomarker, that is the whole thing and field should grow it or maybe you need set of biomarker, just like if someone is telling no no this is this disease, then again you go to cross check, you go to verification that is thing has to be done, why all these things is happening, answer is here billion year of evolution you cannot easily search find in one day directly sought it, it has to be systematic study or if you are by luck you are hitting in the dark it got hit, both are happening together and both example I will show you how we fail, how we pass, we thought instead of 15 centimeter make half a meter column, think little bit weird everyone will tell you crazy it works you are best, you did not works you still carry with the tag crazy, so now 15 centimeter, 50 centimeter column it is long column make beat side everyone using 5 micrometer, 3 micrometer, half what will happen HPLC pressure will be very increase, to increase the pressure simple use school knowledge Charles law, Boyle's law, heat it down the temperature down that increase the temperature down the pressure and somehow you will manage with old HPLC this 50 centimeter column long run and 5000 protein came that time in one single shot this was just example same technique we have developed now recently we have published how long gradient we can use and after 12 hour if you are using the gradient recently published in 2018 there is no use, so it is saturated, so need of fixation how long gradient, how long this and that. Now this technique you should use in silak that I am going to tell about the cancerous cell, so this is just introduction now I show you label free quantitation 5000 protein comes in one run, but when you do silak you get less protein why because now 2 peak is needed to identify and quantify a protein, so you increase the complexity if you increase the complexity sensitivity has to be compromised or your protein number has to be compromised but we know all the way how to increase the number also what if in one hour you got 3700 make triplicate now and then quantify here bioinformatics you like or dislike bioinformatics you have to use, so now this is the place 1 into 8 hour you got 3000 now run triplicate you get 4222 because which one peak has come in this run another peak came in this run by bioinformatics you merge and you can increase that, so this way you are showing, but there is the way triplicate 4 5 times 6 times how many time this is showing almost in triplicate on 4 almost you are getting close, so there is no need of going to unnecessary too much you have to keep your temptation and limitation that is also very important otherwise you are doing only one thing, now same thing we did with all this 11 cell line and then we try to find is there any common thing is there or we have to stop on 11 first we did 1 then gone 5 then 11 now we are planning to have 25 or 50 cell line to get complete idea, but we cannot decide in one it is very costly affair and we have selected in the broad range, now by this if you do deep proteome you will get 10000 protein in each cell line by fractionation, but we thought we should go single thought also and we should see in one run there is no comparison between any fractionation 4 fractions and unite no bioinformatics one run direct result and see that both approach we have used now you see what happens here it is single thought one thought whatever came this is CID silent you know CID, HCD you would have heard high energy today is the era of HCD. Now you take hack 293 light plus heavy so hack is there light and heavy now you are comparing that there should be theoretically any difference in quantitation up regulation down regulation it should not that is the reason when you quantify identify 4200 quantified 91 percent in 4, but when you did in 1 I told you already concept here you quantified only 78 percent here 89 percent here 91 percent. So what happens when you do multiple run you are going to quantify more, but now I am increasing 5 cell line I am mixing 5 cell line together and then I am trying to see what is happening one thing you see if one cell line give me 4,299 protein mind tells 5 cell line will give more correct or not simple thinking do not think too much answer is no then we thought 11 cell line will give more answer is again no see this pattern is containing single run is giving less triplicate is giving more and here you are getting more, but when you are increasing the cell line nothing is changing that shows your technology has limitation of course 5 cell line when you put you have more protein 11 cell line when you put you have more protein, but technology has limitation that means still our so called well developed well costly mass spec need to develop less or more very more so still price will increase a development will continue. Now see now this is same CID here compare hack 293 versus hack 293 so there is theoretically there is no difference when no difference so what happen anyhow in quantitation we do not take less than 2 fold ok see majority of things is falling in this less than 2 fold and whatever it is showing 1 percent that is almost a error ok. Now I have taken 5 cell line in 5 cell line I am taking HeLa versus Jurgat HeLa HEPG to GamG FCF7 so this HeLa is inside that 5 cell line, but I am able to quantify and see 2 fold that is falling only 84 percent that means others are changing ok here is the very less change here you can see more change that means different cell line has different things correct. Now here I took 11 cell line it is coming almost same that means because my technology is also coming close and result is also coming close so I cannot comment on that clear. But that shows me 5 cell line has more protein or different protein compared to that and if theoretically same 2 cell line if you compare where is the up regulation down regulation if here there you get up regulation down regulation that means better to stop the experiment ok. So that shows your control things are going in right direction, but this HeLa is inside the 5 cell line and 11 cell line with this you plot you have to learn all the R and all these things today by plot you can see the 5 cell line is not that much sharp 11 cell line is more that much sharp that means quantitation is better when you have multiple cell line a chances to find that protein is very high same things you do with HCD 1, 5, 11 cell line and you will get different type of things and we are fine here, but this HeLa is again inside that. Now HeLa compare with Jurgat you will get only 70% no changes almost, but HeLa with 5 cell line HeLa with 11 cell line you are getting more to cover it. Now it is different you are comparing again you see that HeLa with Jurgat your line your graph is not density plot is not so sharp here it is better and 11 cell line better. So this is giving me indication that my 11 cell line are higher stock I will able to cover more protein of that if that mix is ready that proper things is ready that means I will have more chance to quantify because in human I cannot label HeLa ok it is only possible in cell culture ok. Now you see here here we have taken one cell line HCC 1, 5, 9, 9 breast cancer this is not mix present in the mix of 5 and 11 now you see here chances of getting quantification is more or less because it is not inside you will get different things. So this concept is coming clearly how you make the mix we do we need a master mix do you need a this and whole analysis idea is still we are doing that what common protein is there in all the type of cancer where it is getting click or where it is getting a start that is still under the way most probably next time I will show you more and better same things I am trying also to go with the tissue now when 11 cell line we can take now we know what is going to happen now 11 tissue or 20 tissue we are trying to do all cancerous tissue and we are looking what is the common and can we find one common place where all this get trigger that will help will we do not know what will happen with this you again see this density plot always 11 cell line is coming better ok you all have learned R and with this I will move towards the little bit cancer drug discovery that is the my favorite hard work of VHD student and it is somehow giving good result. So what is there development of anti-cancer compound use cell biology mouse model and human model you have to go finally clinical tried so that is the what simple cancer means tumor break the cluster induce cell death apoptosis and reduce cancer cell proliferation three things as a target different compound we have a screen there you would have heard a company a screen 10,000, 15,000 compound library and then come to one target we thought how can we do a smartly here this is the end childhood cancer mostly happen in childhood time before 5 year why it happens it is very tough to tell no one knows it can happen in one eye it can happen in 2 eye and both the eye can be affected there is the drug with very high side effect carboplatin, etopocyte and vincristin mostly natural towards ok so we thought nature may be have something with this now how to think about the cancer how much unfortunately this retinoblastoma is very high in India or developing country India, Africa and all this why there is no answer of that but if you look all these things you will see 10% of pediatric patients have retinoblastoma ok in that also highest incidence of retinoblastoma is in Africa and India why no one knows this is the cancer related with the gene because you are in proteogenomics and this cancer is related with the gene retinoblastoma RB you would have heard cancer suppressor gene this is directly linked with that ok so this is sometime hereditary sometime environmental but there sometime no one understand why it happen ok now people are still thinking which place it is just getting a start and recently one in 2014 paper came that they tell it is mostly a starting with the cone precursor cell ok so this is the little bit evidence has come so this is below 14 years is non hereditary is 60% hereditary is 40% so wonderful model system to study the cancer where genes genetics is also involved environment is also involved but no one has any answer ok now this is terrible make is not good to see too much ok in 6 months times this become terrible ok if you did not get treatment then wear it off and that is the problem happened in developing country when you get you see leukocarea you can characterize if a doctor will see your normal person will quickly figure out there is something in the eye and this is the step where you understand that there is something leukocarea step you will tell I have mouse retinoblastoma mouse in lab we see that directly it ok but if it get bigger they get tumor bigger than head size now you can understand how this happen if timely there is nothing inuculation is only that a chances to having metastasis is very very high so what you should do now this is the chain if and most of the time what happened children parents not able to take to the hospital no one takes care it gets different aggressive a stage and then it is almost nothing now so what we need we need a need for the development of safe better and effective manner what you need at this time you need some drug ok whatever drug is there that is also not able to cure it has very side effect then this one of the student kamachi phd student has done this work how now we thought to break the cluster ok and now idea is that find a drug and find mode of action in science mode of action is more important drug needs patient mode of action will give you patient help and to make the another drug better ok so now simple concept break the cluster you see this is cluster forming cell ok so now when you are started giving this control cluster is there you give we have screens few compounds several compounds and then we came to this compound this break the cluster but this is not a big deal you put surf excel wheel anything it will break the cluster so this is nothing conclusion but indication is there ok now if you got what the biologist will do quickly go and do cytotoxicity ok so we did cytotoxicity and somehow we found why 79 cancer retinoblastoma cancer cell line is giving 18 micro molar IC 50 but if you got 18 micro molar in the cancerous cell it is also possible this will kill your normal cell ok if it is killing normal cell then how can this will be drug first argument you ask yourself and you confirm here now what we did we took ARP 19 it is also retinal epithelial cell and here you see IC 50 is 165 micro molar that means there is it is not killing normal cell we are looking for this we are looking a compound which has potential to kill only cancerous cell not to normal cell that is the whole point came here so now you see this is our slide anyhow there is nothing hard to tell fact this experiment we do did very late first we did because we do not have cell line we try to arrange cell line it took lot of time later we did but somehow it worked now here proteomics played the role now I told you you put surf excel also it will work ok but when surf excel concentration will go it will come again cell closure ok now when this compound this never comes close so that was one benefit now you took the give the treatment with this compound to different cell ok both now you do proteomics I have shown you how to do it ok now you do proteomics now you start comparing what happened after doing proteomics when you look the proteomics what you found cell adhesion related proteins are down regulated in e4 e4 is nothing it is my lab number room number ok so that is from it came so now all cell adhesion related protein so cell is not getting attached it is getting detached and cell adhesion protein is getting down regulated that means something is going towards the cell adhesion no one is going to believe proteomics if you are not doing antibody or some validation at least reviewer ok we all believe ok reviewer will ask you now we have done antibody experiment and we have moved forward now if it is really anti cancer all oncogen and cancer related protein should be down regulated perfectly hit ok so that means it is anti cancer now you have to talk about the mechanism what happens if all mitochondrial protein you will see that is going up regulated in the treatment so mechanism is somewhere related to mitochondria who is telling proteomics now this much indication is enough for biologist to kill the or make the project work ok that is the proteomics plate the room now you do whole analysis cluster networking all you have learned you know works of must do this is very useful when you do you saw tumor suppression protein tusk 3 is getting up regulated that is the logic no suppression protein should go up cancer related protein should go down but we found this transcription factor and caspase 14 came up so that we do not have anything ok clue about that you quickly look this compound is binding with DNA or not control take ethereum bromide and just you look it is not binding quickly next experiment TM oh TM is changing somehow no need to find quickly mechanism ok one by one one by one third you are in CCMB all are well trained here ok now third quickly look does E4 after altered cell cycle here we found a little bit very different and strange result you see this sub G1 population is getting very high with the concentration dose dependency ok that means you change the dose IC50 is 18 if you increase this dose and here you find the mechanism sub G1 population is getting very high here you see in same constant thing in 24 and 48 hour result is wonderful something confusion is there this pattern this pattern is same this pattern this pattern is changing in the S phase no answer we have ok this happens that means different dose is working at different different time point at different things ok but this gives sub G1 population is altering the circuit so what is the conclusion E4 perturbs the cell cycle and cause the cell death if this we know then next what we should do quickly do apoptosis and quickly we are showing again with the concentration increasing the concentration what you are doing you are able to go to late apoptosis more so this is working in the time and dose dependent manner that is we come you cannot conclude anything more than this now E4 cause cell death by apoptosis now you go another experiment DNA fragmentation run the gel quickly do it or do tunnel as a little bit fancy and you can see that DNA fragmentation is also going up by higher concentration so we are trying to find the mode of action mode of mechanism and question answer is here E4 cause apoptosis by DNA fragmentation dose and time dependent manner even not binding with the that so now detection of intracellular ROS we have some mitochondrial thing we have saw ROS protein so with this we found that this is also making sense and mitochondrial membrane potential SA we have done with this we came to that it is something is happening we are not sure ok something is happening towards the mitochondrial cell so compound E4 induce ROS generation mechanism and leads to mitochondrial membrane depolarization this indicates that ok so what proteomics told mitochondria ok result came by other biological technique same or different that is we need so with this now no one is going to believe you cell culture and only publication ok if you want to move mouse experiment you have to do so this is genograph mouse you take the cell line inject in the mouse make the tumor leave a month time to stabilize inject the compound and if it reduce supervisor will move next stage and a student will go happy another year for move ok so this happens what is the most important thing if you give take control because after 1200 ethical doesn't permit you to keep the tumor size and if you give treatment it is completely demolished this results gives very nice even I like this is first experiment you see because experiment you have to plan very well give expert injection only on Monday a student will not feel bad Monday come inject nicely wait for next Monday and you see the once how dose dependency go so this proves clear that don't give too much pressure then they will not think ok so this gives clear that how things are working if it is really working what is the next stage again use your proteomics now we are sitting here we have this tools technique knowledge we took the tumor from both after treatment that do full proteomics do histological staining quickly so where is the apoptosis is happening and histology of tumor thus you can see treatment with just this and now I told you one word side effect there should not be a side effect ok so now when histology of liver we do we don't see any change only compound when you inject when tumor form when tumor form any compound injected we don't see in histology any change and no mouse has died that is the biggest with this with this you see similar thing in spleen you saw similar thing in lung heart and kidney that gives the now here 2 and half years after hard work of system and paperwork finally we got knockout mouse retinoblastoma where gene is knockout RB minus P53 minus so mouse will get automatically retinoblastoma here we have started treating and we are getting potential very good result and I will show you most probably next time whenever we meet with this I showed you how things will go similar concept we have used for leukemia very nice IC 50 1 1 minute I will show you the concept is repeating that you got late apoptosis what is the interesting thing here got when you injected this is another compound but similar line in 5 minute it reach after injection to brain and if you leave for 45 days this compound get from body out also so this is another compound working for leukemia this is very nice third is melanoma all you know what I saw how to come the mechanism this is the last here there is no cluster if it is working there is no cluster and when cluster is there still compound is working you find mode of action and mechanism which signaling pathway very simple all the known target drug you take make the slide and when you find this is the target and then you do all your experiment and you will find by antibody this is happening ok I told you in biology antibody is very important now you see BCL 2 is the downregulated PAKT is downregulated and if this is really apoptosis all cast pages should be downregulated or upregulated upregulated biologist like this only mass spec is not enough you need mass spec and parallel this when you reach this by this anyone can tell there is the apoptosis cast pages up there is something is going if you have this data you make your models how mechanism is working when you go to this mechanism you saw we saw cast page 8, 3, 7 poly adipure apogylation cast page 9, FF, BCL 2 backs so this is the mode of that and mostly it is going towards the AKT signaling pathway ok now use your knowledge here experience is experience ok then you see it is AKT, FOXO 1, BCL 2, P53 backs apoptosis this is the chain it is coming downregulated and upregulated so finally we are coming it is towards the AKT signaling pathway with this thank you all of you your patience and for time. From today's lecture by Dr. Suman Thakur I hope you got a complete image understanding about why getting a single biomarker for cancer is tough why many biomarkers have not been able to reach to the transitional work so far in the clinics Dr. Suman Thakur showed you how multiple cancer cell lines having same protein but different amount this is reason a labeling technique like Syrac or eye track can be helpful. He also showed a complete overview how cell biology can be used to get the drug screening strategy and unable model experiment can help in validation. I hope you are gathering different facts from different scientists clinicians and trying to understand that what are the latest advancement in the field and also what are the gaps from the clinical as well as its successful translation in which way by integration of cell biology various type of omic based technologies and clinical strategies together only we can make some success in this area. The next lecture is by Dr. David Fanu who will talk about predictive analysis thank you.