Simon, I'm very glad that you agreed to talk to me about chronos, because I was really not sure you had thought about this concept, you know, when we made programs back in the 90s. And at the very end of the last show we did on the radio in Israel, in Hebrew, from right to left, you mentioned the chrono, which was very surprising. So let's revisit the chrono, and we decided to call this talk its high time to define time. But I would also like to catch another concept, a small concept, the concept of God, which somehow seems to be linked to the question of time. And I'll open with a quotation from Sholamit HaLeven, who was a famous Israeli author, writer. And I once asked her what she thought about God, and she said, well, God is the other language. So, here I am. She almost anticipated my work, because my work is about language. Let's hear about chronons then. First of all, I hate to burst your bubble, but actually our talks were in the 80s. We are much older than that. In the 80s, you think? But late 80s, maybe. Actually mid 80s. But late 80s, I think you're right. And so my work, the chronon field theory, was first developed in my PhD, in my PhD in physics, in 1982, 1983. By the time we had our chats, it was already four years into its existence. But having tried to communicate it, even to physicists, even once to Richard Feynman, a Nobel Prize winner. Possibly the second brightest mind in physics after Einstein. Having tried to communicate the chronon field theory and having failed consistently, I gave up on it. I gave up on it until a group of very, very talented, I would say geniuses, Israeli geniuses actually, all of them Israeli, picked up a copy of my PhD in the Library of Congress. They didn't know what they were doing. They were searching for time and they found a copy of my PhD in the Library of Congress and they picked it up. And they fell in love with the theory. And ever since then, this group of physicists, all of them are physicists actually, by now, well over 50 physicists from seven countries, they've been developing this thing. Not only Israeli. Not on Israel anymore. Not on Israel anymore. Although the main contributor to this very day is in Israeli, his name is Eitan Suchard. So he's in Israel. He lives in the United States now. So they took the theory further, way further than, I mean, it's no longer my thing. You don't mean Eitan Suchard, the one that is on L-Alflights, the one who presents all kinds of mental tricks? Oh, I don't believe so. No, okay.  No, I don't believe he would be presenting tricks. Because his name is also Eitan Suchard. He didn't go that far. No, no.  He lives in the United States. And others, there's both of them, there's others. And they worked together to develop the theory. And they took it so far that it's no longer mine. It's no longer mine. I was kind of a catalyst for the impetus for the development. Still, most of, if not all, of my original ideas are still in the evolved form of the theory. And so to answer your question, after this very long introduction, which I wanted to put a bit in historical context, what's going on. To answer your question, my work, what I try to do in my work is two-fold. I try to introduce a new language to describe reality, in other words, to do physics. And the second thing I try to do is to deconstruct Einstein and to say that time is not a dimension, but a force. It's a force. The test was this. Can we take these two underlying assumptions? And of course, in this limited time frame, and I kind of delve into the equations and so on. But can we take these two principles? Which two principles? New language, with one word, time. Not mass, not momentum, no other words. No vector. Nothing, just time. Time. If we take this monosyllabic new language and we couple it with a force field, the way we describe forces, which we call fields. If we couple these two, can we then derive all the equations in all the fields of physics that had ever existed? You were trying to create a tau theory? Yes, a kind of tau theory. A theory of everything. Yes, but there's a test for this. And the test is if you take your assumptions and couple them, can you, based on these assumptions alone, derive the totality of physics? And what these physicists succeeded to do over the past six years is to derive all of physics. Now I'm comfortable to say this, because they had completed the work about two or three months ago. And they had derived all of physics. Cosmology, particle theory, chromodynamics, electrodynamics. As large as cosmology. Everything. Let's cut it short. Everything. Newtonian physics, Einsteinian physics. You name it, they succeeded from these two principles. So the question arises, of course, now that we know that these two principles give us the totality and the entirety of physics. What are these principles? What are we talking about? So the first thing I wanted to do is not to repeat Einstein's mistake. Einstein's mistake was not in developing relativity theories. Relativity theories are not mistaken. Of course. They're very good theories. They've been proven time and again and so on and so forth. Are we 18 minutes into the totality? No, no, no, we're 18 minutes left. We are still 18 minutes left. But what I consider to be his mistake is that he did not invent a new language. Essentially Einstein's language is Newton's language. They're using the same concepts. Yes, Einstein looked at it from the left side, while Newton was looking at it from the right side. It's like the blind man and the elephant, you know? Yes, yes. Trust me, the elephant. One of them is holding the totality. They each touch a different part of the elephant's body. But it's the same elephant. It's the same elephant. It's the same elephant. Einstein and Newton are working on the same elephant. And I wanted a new elephant. I wanted to introduce a new elephant into the room. Like Shulamita Revan's new language. Yes, that's what I'm saying. It's very apt, actually. It's very apt what you said. Because I introduced a totally new language. And I said I am not going to use the old language. I refuse to speak Hebrew. I'm going to speak English, you know? So I'm not going to introduce... And the new language consisted of one word, time. That's all. That's the language with one word. I said to myself, I must derive all the equations of physics using one word and one assumption. Now, this is what is known as Occam's razor. Parsimony. If there is a multiplication of words and a multiplication of entities, probably your theory is wrong. That's Occam's razor. And what had happened in physics over the decades, especially the last 100 years, is an explosion, a supernova of entities, new entities, and supernova of new words and new concepts. Which is a very bad sign. It's a sign that we have lost our way. Give me examples of... I don't know if there's well over 100 and something elementary particles. There are models for particles and models for gravity. No, but that's about particles, not about time. Nobody thought of bringing in particles into the theory of time. Everyone realizes that physics is in crisis because it has too many concepts. Too many entities. Too many laws and rules. So everyone is trying to unify the theory. To reduce it, Einstein said we should have one equation. That's one equation, the God equation. Should have one equation. And I said rather than going from multiplicity to unity, I'm going to start with unity. Why do I have to go from multiplicity? I'm going to start with unity. And I said, what should I use? Maybe momentum. Should I use force? Should I use mass? Should I use energy? Should I use space? What would capture all of these? And so the only thing that captures all of these is time. Because in time, given time, all these things happen. If I give you... If I phrase out, had I decided on energy, energy doesn't give you time. Energy doesn't give you many elements in physics. Energy gives you mass. Energy is a form of mass. A form of mass gives you energy. Energy gives you momentum and force. But it doesn't give you many other things. Okay, so if I take space, space gives me gravity, curvature of space, gives me mass to some extent, but nothing else. So each of these concepts can give you some other concepts, can lead to some other concepts, but not to others. There's only one concept that leads to everything. Time. Given enough time, you have mass and you have space and you have force and you have momentum and you have everything else. Given enough time, everything happens in time, in due time. So I said, okay, so let me rewrite physics using time. But then I said, if I use time as a field of potentials, because it's a field of potentials where everything can happen, mass can happen, force can happen, momentum can happen. So it's a field. Field of potentials. Potentialities. But we know in physics that whenever we have a field, we have a force. There's no such thing in physics as filled without force. You have electromagnetic field. So whenever you have a field, you have a force. So I said, this should not be an exception. If time is a field of potentials, then it's a force. Because we don't have an exception. Okay. Okay, so it's a force. So time is a force. Time is a force, not a dimension. And then I went to Thrasda, I said, wait a minute. If time is a force, we know that all forces are mediated via particles. And or waves? Particles are described potentially as waves. Okay. Forces are mediated through particles. Particles. Even mass is mediated through a particle known as Viggs boson. So particles mediate the physical world. They are like couriers. They are like Federal Express. They have the small vans and they carry energy. Yes. They carry mass. They carry, you know. Right. They're like couriers. So I said time should not be an exception. If time is a field and time is a force, there should be a particle of time to mediate time. And then I came to Cronons. Cronons are these particles. But then I went a step further. I don't know if you're following me. I'm following you. So far I know that we've reached the, you know. I'm trying to make it as simple as possible. The particle called Cronon. Yes. And now I'm trying to understand what it does. But I'm showing you the heritage, the chain of thought. Of thought. The chain of thought. And then I said, okay, there's this particle. But time is a field of potentiality. So there must be a way for time to become mass. There must be a way for time to become momentum. There must be a way for time to become force and energy. There must be a way to realize these potentials in the field. Yes. How? So I said, well, the only solution is for the Cronon to be in different states. Not in a single state, but to change states. We call this process excitation. So the Cronon is excited. A little like me. The Cronon is excited. And when it passes through various excitations, and it disrupts the field. It disrupts the field. And it creates events. The disruption of the field is an event. This event could be, if we look at an event this way, it's mass. If we look at it the other way, it's energy. It depends on how you look at these events. They translate into the totality of physics. So Cronons are changed. You can think of it this way, if you want, in layman's terms. Cronons shape shift. They change shape. Okay. Cronons, so they disguise themselves. They wear clothes. If they wear a certain type of clothing, they become mass. If there were another type of clothing, they become energy. Another type, they become force. So the excitation states disrupt the field, literally disrupt it, and create what we call reality. Is there a specific number of excitation stages? This is a question that is excellent.  Because there is a Pauli principle that says that excitation states are quantum. In other words, they are not continuous, but they are disparate. So yes, the Cronon excitation states are disparate. They obey the Pauli exclusion principle. Disparate. But we can map each excitation state to the old language of physics. Like these excitation states were used to be called in physics mass. Mass, okay. And so on. So we have a dictionary. We have like a dictionary. Okay, so this excitation stage is mass and this excitation state is energy. This excitation state is force. If I may, there's another example. I learned many years ago that actually the Indian belief is a monotheistic belief. So I said, but you have Brahma, Shiva and Vishnu. And they said, well, there are only manifestations of the one Godhead. Holy Trinity. You don't need to go so far. Holy Trinity. You could even say that. Holy Trinity. It's one God, but it's three manifestations. So here we have... Exactly. It's one field of potential. Field. Which is where there is a single particle. Particle. And this single particle changes its shape. Where's different... Geises. Geises. And becomes what we used to call the old physics. Even Einsteinian physics becomes... So how does it become excited? Why would it become excited? And by what and by whom? Yeah. How would it... It acquires... It accelerates. It moves. The field of potential is a field of potential because it has kind of latent energy in it. So the particle... Think of it... The particle surfs the wave. Think of this field as an ocean. So the particle is surfing the waves. Okay. And he's accelerating. As it accelerates, it creates all these things. Mass and so on. So this is in a... In a natural. But here's the beauty. The beauty is that by using essentially two principles. Time as a force with a field. And a single particle in different excitation states. Which is infinitely simpler than current physics. Like infinitely. Simple. And current physics is an abomination. This is much simpler. If you use this, you can derive every theory ever invented. In physics. Including string theory. Including quantum field theory. The latest. The cutting edge theories in physics. If you use these two simple principles. You can derive them fully. You get them fully. So I give you an example to... Just to... I said that the chrono has excitation states. Yes. When the chrono gets excited, like me. It vibrates. It begins to vibrate normally. Yes. It accelerates. Of course, creates vibrations. These vibrations are what we call strings in string theory. Okay. Because strings in string theory are tiny vibrations. Vibrations. Tiny, tiny vibrations that look like strings. So they are tiny vibrations. So when the chrono vibrates, gets excited, accelerates. Never mind how you call it. It becomes a string in effect. In other words, the two theories intermesh perfectly. Simulously. And my theory yields all the equations of string theory. Without any effort. I have another question. And this will prove that I am not a physicist. Has anybody ever seen a chrono? No. You don't need to see... No one has seen a quark. Okay. No one has seen a quark. We don't pretend in physics, at least good physicists, don't pretend that they are talking about reality. Physics deals with one subject matter. Physics. That's a subject matter of physics. So that's a language? It's a language that deals with language. That deals with language. And then there is a correspondence theory of truth. This language should somehow resonate with our observations. Okay. And we have processes, the scientific method, falsifiability. We have ways to test how close is the language to our observations. But we don't pretend to put our finger on quark, on a quark. A quark is a useful language element. It's a useful language, a useful metaphor. Okay. Why it's useful? Because it yields predictions that we can then falsify by observations. End of story. Same with my theory. It's not a question whether there is a chrono. A chrono is a language element. And if the chrono theory yields all the other theory in physics which are already tested, these theories are verified. Yes. For example, you can derive relativity theory totally from chrono theory. Okay. No problem whatsoever. That was actually one of the easiest tasks. But deriving string theory and quantum field theory was much more difficult. But it's also... All the theories of physics can be derived from this and because they are verified, chrono field theory is verified, actually. So it's like Esperanto. I would come with some language. And this language would live one word and one... Well, Esperanto has many words. One word and one grammatic principle. Imagine. I come with one word and one grammatic principle. You can't express... Right, exactly. This language is useless. And then I show you that through clever manipulation you can actually derive not only Hebrew but all the languages of the world. Okay, okay, okay. And not only all the existing languages of the world but every possible... The artificial languages. Every possible language. Including artificial languages. Including what we call formal languages. This is chrono field theory. That's the power of chrono field theory. Thank you.


In an effort to promote unity and social integration through sport, the chairman of the Bedouin Lecky LCD Honorable Abdullah Oluwa has kicked off the meeting edition of the chairman's group Under 21 Football. The chairman reiterated the commitment of his administration to promote grassroot sport development. More in this report. Let's see, let's see, let's see. Stop Fc. Stop Fc. One of a plant. In an effort to promote unity and social integration through sport, the chairman of the Bedouin Lecky LCD Honorable Engineer Abdullah Oluwa has kicked off the meeting edition of the chairman's group Under 21 Football. At the press briefing and draws, Honorable Oluwa reiterated the commitment of his administration to promoting the game of football while emphasizing the need for his people to invest in and promote the game of soccer and grassroot sports. The Bedouin Lecky Chairman's Corp. It's one of our ideas that we believe will help in youth development in our local government. And it is a paradigm shift from what we used to have. And in fact, one of what I consider one of our blue ocean strategies you know to build capacities for young people nurture their talents, identify them and support them to greatness. The Chairman's Corp. which is scheduled to take place at four different venues within the Bedouin Lecky will have 16 registered football clubs based within the local government. Present at the draws is Honorable Oladile Ajay Lego State House of Assembly Member representing the Bedouin Lecky Constituency II who commanded the Honorable Oluwa led administration for being innovative, unique and showing how passionate he is and the growth of his people and grassroot football. Honorable Oluwa in an interview with Newsmen stated that the long-term plan for the event is to have a sustainable sports development within the Bedouin Lecky local government area. Where you know the private sector will come into a hit I think what this private sector really need is you know the government providing the enabling environment. The government cannot every time intervene you know because of lack of resources but of course if we provide the enabling environment the private sector will support us. Notable dignitaries which include Royal Fathers, Management Staff of the Iberjuleki LCDA Executive Members and the Honorable Commissioner for Youth and Sports and other stakeholders in sports are expected to grace the event.


Welcome to MOOC course on Introduction to Proteogenomics. In the last lecture, Dr. Karl Klauser introduced you to the basics of mass spectrometry based proteomics. Today's lecture will focus on the crucial steps in sample preparation for mass spectrometry based proteomics and also to provide a glimpse of label based quantitative proteomic approaches. Further, the concepts of peptide spectrum match or PSMs and spectrum library matching will be covered. So, let us welcome Dr. Klauser for his second lecture. Alright, so given what I told you now, let us talk about what you are going to do in an automated setting because we are almost never these days going to collect one MS spectrum at a time. We are going to stick it in a machine and the machine is going to go and go generating a mass spectra as long as it can, alright. So, you start out with to make that automated workflow happen, you are going to start out with a source of material which could be tissue, could be cell lines, you are going to extract that into proteins, then we most often will digest the proteins in the peptides, the peptides then go into a mass spectrometer and then this automated system does three basic steps, ok. It is going to separate the peptides chromatographically, eluding them over time based on their hydrophobicity and so this in this description here that run time takes about 120 minutes, ok. At some given point in time a scan is going to happen, this takes about 10 to 100 milliseconds now. The first thing you would do in a cycle is take an MS scan, you measure the masses of all of the peptides that are present and then you will very quickly collect some number of MS MS spectrum, a common number to do now is 10 so and it will take the 10 biggest things that it was observed in this MS scan and do MS MS on them, ok. Today this a cycle like this can happen in a second, ok, alright. And then this huge collection of spectra that are generated automatically then we will get put into a software program and it will try to match up assigned peptide sequences to each of the mass spectra and then you will have some additional software that will try to take the peptides that belong to the same protein and you get out a list of proteins that were observed and all of the peptides that you have observed with this, ok. This is one of the, I want to say most desirable instrument but that it is most desirable only if you can afford it, it is one of the most expensive instruments and it can do a whole lot of things, ok. The CPTAC program is currently generating almost all of its data on this kind of an instrument but we do not use the entire capability of the instrument, ok. So this instrument, a fusion lumos from thermal, you put ions in here, you then have a way of isolating precursor ions here and then you can do MS MS by three different techniques. You can do something called high energy, higher energy, higher energy collision dissociation, you can do collision induced dissociation or you can do electron transfer dissociation, ok. And then you measure things, the spectra in the orbit trap, it is also possible to measure them in the ion trap out here at lower resolution, you can go faster with lower resolution if you go out here, ok. In practice in the CPTAC program for generating proteogenomic data, we generate only HCD spectra and we collect mass spectra only in the orbit trap, ok. So we are not taking full advantage of the instrument. The reason we are using those instruments is because this instrument which does only the things that we really want to, did not become available until just earlier in this year and we started the grants that we were doing a year before that, ok. Alright, so this I think of as today the workhorse type of instrument that if one was setting up a lab to do proteogenomics in the way that we are going to describe having done it in the CPTAC program, this instrument would be the one that one would get today. Sample preparation, ok. So in proteomics these are some of the basic considerations that you have to do in designing your experiment, ok. We are quite often going to start with either cells, tissue, fluid, the fluid might be blood for example, and then there is going to be some set of separations that we are going to choose to do, ok. You have the choice of maybe you want to do some fractionation at the protein level, you might want to do some enrichment or depletion at the protein level. If you are working on cells and you care about mitochondria, you might do preparation that gives you an enrichment of that subcellular fraction that you are interested in, ok. From the standpoint of proteogenomics, we do not do any fractionation at the protein level, ok. The first thing we do is digest into peptides and then it is all about separation of peptides after that, ok. If you are going to do fractionation at the protein level, it is usually because you are after some particular subset of things or let us say you are doing plasma. The most abundant protein in plasma is albumin, right, and it is the least interesting protein, but it is the first thing you want to do is get rid of it, ok. So, you use a depletion step to get rid of albumin before you go to peptides, right. But for the purpose of doing cancer proteogenomics, we take our tissue, grind it up, go to peptides and then we are going to fractionate peptides. Typically, if you are going to do it offline before you go to the instrument, what you want to do is choose a methodology that is going to give you a different kind of separation than the one that is going into the instrument. So, two common ways of doing that are either ion exchange or what we most commonly do now, which is basic reverse phase, ok. So, that means we are running a reverse phase separation, but we run it at pH 10, ok. The separation that goes into the instrument goes at pH 3, ok. Another thing that you want to do is enrichment, ok. So, if you are after phosphopeptides, you do not have to sequence everything else to get to your phosphopeptides. So, you use something to pull them out, we use a mobilized metal affinity chromatography. If you are interested in lysine acetylated peptides, you can isolate them with a anti acetyl lysine antibody, ok. All right. So, in choosing what you want to do, you are looking to make a trade off among these criteria, ok. All right. Most proteomics today is done in a way where there is going to be a digestion step to peptides, ok. Trypsin is by far the most common enzyme to use and it gives you convenient lengths of peptides that are generally tend to work well in a mass spectrometer. They have the property that they have a basic amino acid at the C-terminus, which is going to give you somewhat better fragmentation than if it is not at the C-terminus, ok. Cysteines can be disulfide linked when they are in a protein. If you just reduce them, they are very hard to chemically maintain throughout your process. So, what we typically do is reduce the dis, break the disulfide bonds and then alkylate them with some agent like iota acetamide that then makes them readily detectable, ok. All right. So, when you are doing enrichment, here you want to think about whether you are doing enrichment or depletion at the protein level here, then there would be a digestion step and then consider fractionation or affinity enrichment. The reason that you would make all of these kinds of strategic decisions is probably with some goal of increasing your depth of coverage. So, if you want to start out with a complex sample and you are only interested in these things that are low abundance, there is going to be typically some form of infinity enrichment involved or depletion of more abundant components, all right. So, the ones that are the most common post translation modifications that are that people can work on that are do by large scale, methods phosphorylation of course is the most significant one. In our lab, we also do a lot of ubiquitination work. This happens this is done by having a glycine glycine which is the starts out on the ubiquitin. So, the ubiquitin is covalently bonded to a lysine in a protein. When you treat it with trypsin, it cuts off the ubiquitin, but leaves two glycines that were the C-terminus of the ubiquitin, okay. A-satellated lysines are something else that we also now are doing routinely in the CPTAC program, okay. So, and you can do these by using an anti-stial lysine antibody, all right. So, we also have done this in a way where we don't have to split up the sample and lose, require more sample and then dedicate only some of it to one modification, only some of it to another, some of it to a third instead, we do the enrichments one after the other, okay. So, supernatant of what comes through a IMAT column can then be used to, so the things that don't bind to the IMAT column come through, you can then do a next step of enrichment for something else and in our case we can, we've published work on doing those three things in cereal, okay. So, you start with less total sample and achieve each of those items, all right. Okay, quantitation and multiplexing. The almost anything that we do in our lab today in proteomics and quite a lot of labs are trying to do things that are quantitative, okay. And the basics of doing something quantitative and having some statistical power requires that you have replicates, okay. So, not only do you want to have replicates, but you typically want to compare two condition, at least two conditions. In examples of this might include wild type versus mutant expression, treatment with a drug or without a drug or capturing something with a bait or not. And then most of what you detect is probably going to be unchanged between the conditions and you're looking to do statistics to recognize some subset of things which change between the conditions that you do, okay. All right, what sort of experimental design considerations did you put into this, okay. I'm going to show you three different techniques. One here is called label-free where you basically combine the samples at the end, okay, and then I'm going to show you two labeled techniques. One is SILAC, Stabilisotope Labeling of Amino Acids in Cell Culture. And then the third one is something called a TMT or ITRAC where you're using a chemical labeling agent. You then are going to combine the samples and then put them into a mass spectrometer. You do MSMS, the quantitation comes at the level of the MSM spectrum, okay. In this technique here, you combine earlier in the process and the quantitation comes at the MS level, okay. Multiplexing wise, you can do three things, three samples at a time pictured or only two, okay. You can do light and heavy. The third one would be medium, okay. All right. With a TMT-10 reagent, you can put together 10 samples. If you have an ITRAC reagent, there's actually two ITRAC reagents. One's called ITRAC-4 and the other one's called ITRAC-8, okay. So that tells you how many samples you can put together. There's also something called TMT-6, okay. And I will get into some of the differences in what you have to have to be able to do those kinds of experiments, okay. So here are some of the features about this. Takes a lot more time to do an experiment this way, a lot more instrument time. Here, there is some loss of accuracy in the quantitation due to compression that I'll talk to you about. The reagents can be expensive, okay. All right, here you have less potential to do flexing. And in order to get a heavy label, you have to be able to add that to the cell culture that is going on. So that means you can't label humans, okay. So you can, this is really suited to working with cells in cell culture, okay. All right, and the quality of the quantitation is shown here and would be highest over here, okay. Why is it highest over there, okay? Ideally, when you're going to mix things together, you'd like to mix them as early in the process as possible so that any of the experimental variable, variability that happens, happens to all the samples together, okay. But because of the way you do the experiment, you can't necessarily mix things until a later stage, okay. So in the case of chemical labeling, you have to mix after you have done digestion. But if you do it in cell culture, you get to add and do the combination way back when they just after the cells have grown, okay. All right, and so I think I've already said some of the pros and cons about this. So let's move on, all right. Let's go straight to what happens when you do chemical labeling approach, okay. So the idea here is this, you might, here I'm illustrating TMT6, okay. So you would have six samples. You lice each of those, the samples that gives you proteins. Then you reduce and alkylate, trypsin digest the peptides. After you have peptides, you use the TMT labeling agent. These are amine chemistry based reagents, so they're going to put a label on the side chain of lysine and on the end terminus of the peptide, okay. And so the reagent normally comes in six colors, okay. These are actually masses and the masses shown here are the reporter ion masses that are present in the MSMS spectrum, okay. So then after you do the labeling, you mix the samples and then you have six different things labeled. The purpose of doing it this way is the labeling reagent causes all of the samples to have the same mass, okay. And the label is going to have a different mass, but only after you do MSMS, okay. So the signal that you see in an MS1 scan is the sum of all of the six samples, okay. Which is good, right? It means you get more signal when you combine the samples, okay. And then after you fragment, you're going to have the reporter ions that allow you to get the peak height that is shown here is going to enable you to do the quantitation back to the samples that they came from. All right, this is the chemical structure of the label, right. And shown where the asterisks are is where you would put C13 or N15, okay. And in order to do the labeling, you're going to put in five labels. But depending upon where you put them, you can end up with a 126 ion. If you put, I have another slide that will show you where. But the idea is you're going to put them in different places so that you have different labeling capability, okay. Now, because I told you at the very beginning, you could tell the difference between N15 and C13, right. If you have high enough resolution, you can separate and you can get 10 different things, okay. And that is all going to go back to whether there was a label on this nitrogen or C13 in this position, okay. So now this slide, it's harder to see. But the dots show you where the labels are for each of the different reagents, okay. All right, and then the things here colored in black correspond to the reagents that are for TMT6. The additional things in red are the extra channels that you use for TMT10, okay. Unfortunately, it's a little bit complicated, okay. So you have some impurities that you have to deal with in this thing. And there are two types of impurities, okay. One sort of an impurity comes from how pure is the C13 that you start to put in the label, okay. You can get over 99% pure C13 to incorporate these days, but there's some level of impurity. Same is true of nitrogen 15, okay. But there's the second set of impurity which is, this is in the unlabeled positions, okay. So this is over here, there's these carbons over here that are naturally occurring levels of C13, okay. And so if you end up with a C13 in one of these positions, it's going to be one carbon higher in mass than it would be, okay. So when you obtain the reagents, they also give you a set of correction factors, okay. That software will apply to correct the intensities to account for the impurities present in the label, okay. If you obtain data from some public repository and you want to reprocess it all from scratch, make sure you get the correction factors that are provided by the people who generated the data. Unfortunately, they don't always remember to give you the correction factors when they deposit the data somewhere and you might have to send email asking for them and hopefully someone can write back and give them to you, okay. One of the things that we try to do from our lab is always provide these but I often have to chase down the people who did the experiment and say you need to provide these before we can put the data in a public repository, okay. Those correction factors are then used with an algorithm to apply them and correct the intensities. This is a publication that's about ten years old. This is, we use the same method that they describe. We don't use the exact same software because this publication is old enough that it only applied to iTrack4. We've modified it to be able to work with TMT6 and TMT10, okay. So there's another sort of complication in working with TMT quantitation and that comes down to interference which goes back to if you fragment more than one thing at a time, okay. And so what I've tried to do is draw a cartoon here to illustrate how this works, okay. If you had two peptides of very similar precursor mass that were present at the same time and let's say one of them is uninteresting, there's no one of the six samples that has either up or down regulated levels of protein. But in the sample that's right next to it, it has up regulated levels of the red, okay. So there's way more red in this one than there is in the other one. If you're using an older instrument, you might have to set the precursor mass window to be two daltons wide which would cause both of these things to be transmitted at the same time, okay. The labels produce the same reporter ion masses and you can't tell which one they came from, all right. So if you were to transmit this whole thing then you would have a reporter ion set that looked like this only when the data came off the instrument it wouldn't have this white line through it that allowed you to tell which one it was. You would just have the sum of these things, okay. If you were able to use an instrument that had a narrow precursor window, then what information you would get would be just derived from this one peptide, okay. All right, so if you put the quantitation together and you combine these things, the ratio that you would calculate, if you calculated the red divided by the pink, I'm sorry, let's call that orange, you would get a ratio of 2.5. If you had only the one together, you would get a ratio of 4, okay. So the ratio of 4 is what you wanted to observe but it is compressed to 2.5 because of this effect, okay. All right, so if you, so this is just an example of what might happen, okay. And so that there's a couple of things you can do to deal with this, right. The first is you could do a better experiment, right. If you have an instrument that allows you to do better transmission, all of the CPTAC work that is going to be presented later in the week and is already published, is all iTrack data run on a Q-exactive instrument that at the time had a window of two dolens for precursor transmission. What you can now routinely do on a Lumos instrument or a Q-exactive HFX is run a 0.7 MZ tolerance or window width. And so you would be able to, in this kind of case, transmit only the one thing, okay. The second thing you could do is you could have data analysis that would go back and look at all your MS scans and say, ah, if we've got this thing, let's throw away that data point, okay. And because we're expecting most of our proteins to be detected by multiple peptides, we have some way of taking and recognizing that some data points are better than others and so we can exclude those, okay. So a common thing to do, different people do this, but they don't all call it the same thing, which is to take some measure of whether, how many things are here and what's the relative abundance of those things that are there. And when the relative abundance of those things is high, then you throw away the data point, okay. Now that's an approximation, because although I have shown you in this cartoon example, that the ratios of the MS1 peaks as the same as the relative ratios of the reporter ions, that's not always what actually happens, okay. When an individual peptide fragments, you're gonna get some reporter ion signal and some sequence ion signal. But sometimes the balance is like this, sometimes it's like that, okay. And so even if this peak right here in the MS1 scan is taller, it doesn't necessarily mean it's gonna contribute more reporter ion signal, okay. Right, so that's some of the uncertainty that's present in this type of data and getting better at this is there's room for improving our data analysis, okay. All right, all right, scoring peptide spectrum matches. All right, so this slide I already showed you once. It was several equipment failures earlier. All right, but the idea is that you're gonna take a sequence database and your experimental spectrum, you have programs that are gonna approximate what the spectrum is expected to look like and then score them. These are some examples of some names of programs that do this, okay. If you are gonna design an algorithm to do this, these are the kinds of things that you would have to think about, okay. And when you start to just look at other programs, these are some of the things that you could think about in terms of evaluating or reading about what they do, right. So, but they're all gonna one way or another have to deal with these kinds of things, okay. So there is going to have to be some step, maybe it's not within the search program itself, might be a program that you can run ahead of time that will do peak detection, okay. And it's gonna do these kinds of things. It's gonna do deisotoping. It could assign fragment charge and do some sort of signal to noise processing so that you're hopefully trying to only use a signal peaks, all right. You have to have, when you design the algorithm, know what fragment ion types are possible, okay. And when you start to use a program, you often have to choose what instrument type it is that you use to generate that spectrum. And when you've done that, it's gonna behind the scenes be consulting a configuration file that's got appropriate things like what ion types are possible for that instrument and potentially some different scoring values for the different ion types, okay. All right, then when your algorithm also, not only does it have mass information, it has intensity information. Today, search programs generally make not very much use of intensity other than to say present or not present, okay. With some of the machine learning approaches that are starting to be imposed, one of the goals of those is to make better use of intensity information, okay. All right, you're gonna have to choose some fragment tolerance units, okay. I told you the resolution was different across the mass range in certain instruments. That is particularly true in orbit traps and time of flight instruments. The mass accuracy is also different across the measure, across the mass range. And so we use different units. If you use parts per million unit, a typical value of a good mass accuracy on a high res instrument ought to be plus or minus five parts per million, okay. And you would say that across the entire mass range. But when you convert that parts per million into Dalton's, that means it's a wider mass, I'm sorry, wider mass window at high mass and a narrower mass window in Dalton's at low mass, okay. So if your instrument data has your mass accuracy specifically in units of PPM, ideally you would like to use a search program that could also support mass accuracy in PPM units, okay. But it is actually quite common to use a program where it only has Dalton mass accuracy. And so what you have to do is compromise and set the tolerance to only use the high mass one when you should be able to, in principle use it at lower mass and have a narrower tolerance, okay. All right, most search engines produce a score that is the primary score that's used to make most decisions, but along the way they might calculate extra things and that might be possible to use in reducing your list to the confidently assigned peptides, okay. Some scoring systems are gonna be dependent upon the size of the database. Others are gonna be only dependent upon the scoring of the ions and a particular sequence. And if you take that sequence and put it in a big database or a little debated database, the score is gonna be the same, okay. Some search engines will, however, take the size of the database into account, all right. So that's what you have to do if you're designing an algorithm, you consider all those things. If you're gonna use one, you have to consider these kinds of things, okay. You have to choose a database, okay. Most of the time today, there's also an opportunity to somehow do a decoy database that is used to calculate false discovery rate, okay. As you read literature, you will find that there are certain groups that always allow for partial enzyme specificity, okay. While other people may require that fully specific. So the trypsin had to cleave on both ends of the peptide, okay. If you're using a partial enzyme specificity, that increases the search space that the spectra are gonna be matched against. The program is gonna run slower and you usually have to have higher score thresholds to meet your FDR, okay. When you're gonna choose fixed and variable modifications, you wanna choose things that you can expect to find in your sample. And if you are interested in these things that are rare, especially if you choose many of them, it's gonna slow down the search and I have a slide that a little bit we'll talk about expansion of search space, okay. Then you have to choose, like I said, precursor ion tolerance and fragment ion tolerance, okay. All right, this is how the spectrum is scored in my software called Spectromel, right. And this up here is shown with the, all of the peaks that are present in the spectrum as it's generated from the instrument. Instrument doesn't have it colored blue, red and green though, it's all black, okay. All right, there's a preprocessing step that does peak detection that does these three, these several things, deisotoping, signal noise thresholding, removes apparent reliance, neutral peaks. So these are the only peaks that are left, that are subject to the scoring, okay. The scoring has three components to it. There is a positive component that means the mass matches a fragment ion type. That match, the score of that is independent of the intensity, but it is weighted by what ion type it is, okay. There is a bonus for having composition information like ammonium ions. And then there is a negative portion of the score that is for peaks that are not assigned, okay. And so basically a tall peak in a spectrum that's unassigned, that's bad, right. That suggests that you have an incorrect interpretation or you've got multiple things that are being fragmented at the same time, okay. The different ion types have different scores, B and Y have the highest score, they have score of one.  All right, so you do all of those things and you end up with a score. In this particular case, the score is 12. The peak detection will produce no more than 25 peaks. The maximum score is 25, okay. All right, now something that's quite a bit more different and less intuitive is something like one of these scores that is used a probability-based approach and this is the binomial probability equation. It is the basis for scoring in the Andromeda search engine that's part of MaxQuant. Roughly the same approach is used in Mascot and the way this works is that all ion types are given the same weight, okay. And in order to calculate the probability, you have to account for the chance of there being a randomly matched peak, okay. And the way that this is put into the binomial probability essentially comes down to breaking up the mass range into 100, I mean 100 Dalton chunks and you say if we're going to look for say six peaks then the chance of randomly matching would be six out of 100, okay. It may not be immediately obvious but that also suggests that the mass tolerance you are allowing was plus or minus a half a Dalton, okay. All right, now in practice, MaxQuant has allowed you to specify a fragment ion tolerance, okay. But that is not used as part of the scoring, okay. And up until about one or two years ago, Mascot did not allow you to use part per million as a fragment tolerance, you had to use Dalton's, okay. And it's because of the way that the scoring is built into the probability, okay. So from my point of view, the probability is not true probability but the scores are still effective. Okay, and the reason that the probability is not true are for the reasons that I listed here and I've already talked through, okay. All right, now what I wanna do to show you here is a contrast and this is what you would do if you knew what that peptide fragmentation was gonna look like, okay.  The particular case that I'm showing actually is one of these things where somebody is trying to demonstrate that the thing that they observed in a complicated experiment, they made the synthetic peptide, the spectrum looks almost the same, you can calculate a spectral similarity metric and it passes the threshold and they can say, see, this is what we set up was, okay. All right, so the equation that gets used here is a dot product score, there are a few different variations on this and I'm not gonna go through the math but the point is that you're really taking advantage of the intensities and you're not allowing for all possible fragment ion types that could occur to a peptide, you're only allowing for the ones that actually occurred to generate the reference spectrum, okay. So software programs that do this kind of spectral library search are listed right here, okay. The FDR method that's calculated is sort of, today not thought of as being as statistically rigorous as what it's used for database searching and as I would characterize that as a work in progress to be able to do good false discovery rate calculations, okay. Now it's also the case that in order to do this effectively, you have to have a good set of reference spectra to match to. And one of the things we found in our lab is that once we've got just a good reference library, somebody came up with a new chemical labeling agent and we switched it over it and they fragment all differently and now we have to start over, okay, all right. But after we've done a lot of work, somebody can collect all of our spectra and then use that as the basis for creating a library. All right, now let's talk a little bit about localizing and post-translational modifications. All right, so what I've got here is a MS-MS spectrum of a phospho-peptide. This is not two spectra, this is one spectra. It's just labeled two different ways, okay. The same, you can see it's the same peptide. The only difference is whether the phospho site is on this serine or the phospho site is on this serine. So the fundamental premise of being able to pick where the thing is, is you have to have fragmentation between the possibilities, all right. So in this case, you have this single ion right here in the spectrum, which can be interpreted as the Y7 ion for cleavage right there where 101 would be the mass of serine in its unmodified form, 167 is the mass of serine, which is 87 plus 80, which is the phosphate, okay. All right, so if you were instead to allow 87 for the serine, that would stick this in here in a sort of messy part of the spectrum and then the gap out here would be shown as this ion to this ion and then that would leave that unexplained, okay. All right, but because those two residues are right next to each other, you're not gonna get much information to work with in order to make your decision, okay. So in cases like this, and it's gonna be often the case that if you have to determine two choices that are right next to each other, you're gonna have to make that decision on maybe one or two peaks in the spectrum, okay. All right, let's talk about the range of possibilities now that could happen here, okay. If you have, if you're looking for phosphorylation sites, the precursor mass is 80 Dalton's higher, so you know you've got phosphate, and then you look at the sequence, the quality of the information that gave those score distinctions, okay. I said that I want the ion type that you allow to make the decision has to be one of the highest information ion types. It's gotta be a B or Y ion. You're not gonna make the decision based on one ion that's a B minus water ion. You're also not gonna make the decision based on a tiny little peak that could be mistaken for noise, okay. So what I sought to do is say that it's gonna be a B or Y ion, and the relative intensity has to be at least 10% of the base peak, so it's a solid peak, it's not noise. That works out to giving you a score threshold that's of 1.1. In conclusion, we hope that this lecture and the series of five lectures so far has helped you to appreciate the importance of sample preparation for mass spectrometry based identification of peptides. The need for enrichment of post-translational modified forms of peptide prior to MS analysis. The direction has also provided you the glimpse of how impurities in the sample can lead to the errors during the identification of peptides. And additionally, you were introduced to the concepts of PSMs and how a specific software like Spectra Mill uses PSM to score the hits. Lastly, you were explained the concepts of phosphor site localization and scoring using suitable examples. In the next lecture, Dr. Carl Clouser will conduct hands-on sessions to help you interpret the MSMS spectra manually. Thank you. So, here's the 15W charge right here. Allows us to fragment between the second and third serine. So we know that the third serine now is not phosphorylated, but there is no fragmentation between the first two, okay? So we can't tell where that is. All right, here's the complicated one where there is two phosphorylation sites. The precursor mass is 160 greater than the unmodified version for this sequence. We have a fragment ion, Y9 and 10. That gap there is 187, which is gonna say that that's phospho-serine, and then Y5 and 9 here. There's not very good fragmentation between those. And so we can't tell where the localization is, okay? All right, so when you're gonna write something, so I tried to show you graphically, this is when you look at the spectrum, can you have the information? If you're gonna write a program to do this, right? These are some of the things that you got to put into the design of your experiment. You're gonna think about all of these things, okay? I think the most important of those things are shown here. The choice about how you decide what peaks are gonna be used to make your decision, and then how do you clearly represent the certainty or ambiguity in the localization decisions that the program has made, okay? There will be different choices made by people who write the programs about how to deal with the rest of these issues, okay? And then today, there is not a universally applied way of determining a false localization rate from these scoring things, whereas the target decoy calculation for identification is practiced throughout the field, okay? All right, this is one of the first automated scoring approaches and it is, again, using this binomial probability theorem, but instead of using the calculation based on all the possible fragmentation of the peptide, it's limited to just the fragmentation between the sites that you're trying to distinguish which have the localization, okay? But otherwise it uses the same framework, the same mass accuracy assumptions, and when you get down to the, what score threshold you're gonna use, it comes down to essentially saying that there has to be two good peaks that meet the scoring threshold, okay? At the time that this was published, the authors used a particular score threshold, I forget exactly what the value was, and then like a year or two later, they decided they could say they have more identifications if they made the threshold lower, okay? And it was essentially by saying instead of two peaks, you would allow one peak to make the decision, okay? All right, but you have this nice descriptive way of using a mathematical calculation, okay? When I wrote the calculation, I've tried to think of it more intuitively and I calculate the score difference in the identification scores given various possible places and the decisions I made were on the quality of the information that gave those score distinctions, okay? I said that I want the ion type that you allow to make the decision has to be one of the highest information ion types. It's gotta be a B or Y, I mean. You're not gonna make the decision based on one ion that's a B minus water ion. You're also not gonna make the decision based on a tiny little peak that could be mistaken for noise, okay? So what I sought to do is say that it's gonna be a B or Y ion and the relative intensity has to be at least 10% of the base peak, so it's a solid peak, it's not noise. That works out to giving a score threshold that's of 1.1. In conclusion, we hope that this lecture and the series of five lectures so far has helped you to appreciate the importance of sample preparation for mass spectrometry-based identification of peptides. The need for enrichment of post-translational modified forms of peptide prior to MS analysis. The lecture has also provided you the glimpse of how impurities in the sample can lead to the errors during the identification of peptides. And additionally, you were introduced to the concepts of PSMs and how a specific software like SpectraMill uses PSM to score the hits. Lastly, you were explained the concepts of phosphoside localization and scoring using suitable examples. In the next lecture, Dr. Karl Klauser will conduct hands-on sessions to help you interpret the MSMS spectra manually. Thank you.