 Yeah, yeah, it's what the guy told me, because he's going, he's recording the presentation, it's a bit more just more difficult because you have to look there, I mean here, okay. Okay, I think we start, so we end at 9.45 if I'm correct and then we will have one more lecture on Monday and then there will be the three lectures by Professor Mario Nicodemi. So, do you want to say something, Matteo? What what? No, it doesn't work, but it's okay, I will use the mouse for the guy, the guy said that it's better. So, today I will tell you a few things, today and also on Monday, I will tell a few things about how you can use polymer physics in order, I mean in order to model chromosomes. So, it appears there in Milan because there are several lectures I gave last year in Milan, but it's actually, it's the same content that I wanted to tell you. I will be a bit fast because here I also, there are also notes on poly, I mean on the generic polymer stuff that I already told you so that those slides I will skip. So, let's, so the outline is this. And so first I will present the generic features of chromosomes, so you will learn how chromosomes are formed, the molecules that form chromosomes, just very basic stuff, I mean the kind of stuff that you can find in normal tests, I mean in normal biological tests, but even less because in general textbooks are much more detailed than this. And I also try to, I mean to transmit to you why this problem is so important and why it is relevant, and then there will be the rest which will mainly deal with the, let's say with the use of polymer physics trying to model chromosomes. So, first of all, this is something that I already mentioned to you briefly the very first day. So, this is, let's say, this picture is taken from the book by Albert Et al, which is called Molecular Biology of the Cell, which is an enormous book on biology that is basically all stuff. And actually, this picture is quite famous, I mean, you can find also on internet, and it gives you a, let's say, a cartoon-esque depiction of how, I mean, of what are the main ingredients to form a chromosome. So, on top, you have DNA, right? So, this is, this problem you probably know. And let's say, just to fix the idea, for instance, for the human genome and for deployed cells, you have 46 chromosomes, 23 and 23, I mean 23 pairs of chromosomes. Each chromosome contains, I mean, each one of it contains about one centimeter of DNA, and then it's written there that translates in about one millimeter or 30 nanometer fiber. So, what is the 30 nanometer fiber? Well, chromosomes are not, so the single DNA filament which forms each chromosome is not, I mean, it's not just pure DNA, but there are some proteins. Those proteins are mainly histones, and histones are proteins that are positively charged, and so, since the DNA is negatively charged, so DNA wraps around this histone in this way, as it is depicted here in the cartoon. And it forms a necklace, I mean, a sort of necklace-like structure, and this structure is called the 10 nanometer fiber. I mean, why is it called the 10 nanometer fiber? Because, I mean, it's very easy to understand because the diameter of this necklace, I mean, the complex histone plus DNA is about 10 nanometer. So, this is like a sort of disk, and DNA wraps around, okay? Okay, so, in this way DNA starts to be a bit more compact than in its, let's say in its native-like state, I mean, without, I mean, without histones. So, what people know is that also histones play a regular, regulatory role inside the nucleus. That means that sequences who are wrapped around histones basically are not accessible, so cannot be read, basically, by all the machinery of the cells, and that's because they are just folded around histones. So, this is a very, it's completely, I mean, it's a very, and it's an enormous subject also, this, I mean, it relates with the so-called histone code, so I won't talk about that. But just to keep you in mind, I mean, keep in mind that histones are very, very important. It's just not like DNA likes to wrap around those proteins, they have some, they have a functional role. So, then what people observe, so this is, I mean, the 10 nanometer fiber, I mean, it was reconstructed in vitro, so that means that if you have DNA filament in a buffer, and you have histones dispersed in this buffer, the DNA tends to form 10 nanometer fibers. Okay, so it was seen in vitro, and in some indirect form also in vitro, yeah, also in vivo, so namely inside the cells. So, in general, I think that biologists believe, I mean, on the existence of this 10 nanometer fiber, because, I mean, this is a cartoon, so what you have to, let's say, biologically experiment, you have to prove that these structures exist, I mean, apart from DNA on course. But in 10 nanometer fiber, I think it's a fact. Then there is something which is more controversial, and that's the following. So, always in vitro, I mean, if you put this compound of DNA and proteins in, always, let's say, in the same buffer, and you switch on condition, on ionic condition, which are supposedly close to the ones that you can find in the cells. The 10 nanometer, yes, the 10 nanometer fiber starts to fold. Basically, if you are familiar with proteins, similar to proteins, and forms a more compact structure. That's the third level of, let's say, of this cartoon. And so, in the kind of folding that, I mean, the kind of folding that it forms is sort of, let's say, it's a kind of solenoid. So, it goes this way, the 10 nanometer fiber, because it has to compact all these disks together. And because of its, let's say, of the measured diameter in the experiments, that fiber is called the 30 nanometer fiber. So, it's a more compact fiber. And, I mean, the effect of instance, plus the, let's say, the electrostatically induced compaction that forms the 30 nanometer fiber gives a compaction for DNA. So, naked DNA has, I mean, per each, so it's 10 base pair correspond roughly to 3.4 nanometer. So, I mean, 10 base pairing sequence of DNA are linearly stretched of about 3.4 nanometer. So, that means that you have about 3 base pair per nanometer. So, then, because, I mean, helped by the presence of instance and the electrostatic interaction that compact the fiber, you have a larger compaction. So, you have a compaction which is about, let me remember, it increases the factor of 30, so it's about 100 base pair per nanometer. And this is the 30 nanometer fiber. So, which means that now each nanometer contains 100 base pair of DNA because of this, let's say, of this compaction induced by instance and by the electrostatic compaction induced by the environmental conditions. Ah, sorry. So, DNA, sorry. So, DNA is a double helix, right? And so, base pair is the, I mean, one base pair is one single pairing between two different bases. So, DNA is formed by, it's like an alphabet formed by four letters, right? So, you have adenine, thymine, cytosine, guanine, okay? And so, it's, you have two filaments of the, adenine, cytosine is formed by two twin filaments, okay? So, each filament is a sequence of these letters. Oh, I'm sorry, not random, but in general means, but you can see like, it's like words, no? And each one of these pairs, so, sorry, each one of, I mean, for each letter, this single one of a filament, it always in general pairs with a corresponding letter in the other filaments. So, in a way that, A, it always pairs with T, okay? And C, it always pairs with G. So, this is the Watson and Crick base pairing, right? So, in this way DNA is, I mean, becomes very auto-mechanically stable because you have, let's say you have collective interaction. So, it becomes very stable and very stiff. It's much stiffer than a single filament. And that's why, I mean, base pair, because it's always, they always go in pairs, okay? So, adenine team, cytosine one, and then so on and so forth, okay? Yeah, sorry, I thought it was, you knew about it. Yeah, of course, interact with me if you, because sometimes I give things for granted, so. And so, I mean, what I mean by these? I mean that, I mean, in a sequence, so, three bears pair, okay? I do it a bit larger here. So, just in naked DNA, three bears pairs, like this, occupy, I mean, our linearly space of about one nanometer, okay? While the linear, okay, I give you more landscape, the diameter, let's say, if you think of DNA as a cylinder, the diameter is about 2.5 nanometer. So, it's very thin. So, this is naked DNA. So, then this, well, I'll try to do it. So, this thing around this instance, you can think of instance like, it's like a cake, because it's formed by, actually eight proteins that really like a cake. I mean, this, more or less, I mean, so, yeah. And DNA wraps around this way, spools around this way. And so, because of this compaction becomes, I mean, within the same space, you can fold more base pairs, that's basically the kind of mechanism. So, the 13 nanometer fiber, because of this participation, let's say, of proteins plus electrostatic interaction, you attain a compaction of 100 base pairs per nanometer. So, it becomes 30 times more compact, which, as I mean, is quite remarkable. And this is at the level of the 13 nanometer fiber here, okay? Yes. So, this, but this fiber actually, so just, I think it's good that you know about it. It was mainly observed, or basically observed only in vitro, so, namely in a buffer, in very controlled situation in the laboratory. So, which means, I mean, because of course, I mean, biologists do also experiments in vitro to attain very controlled condition. In vivo is much more difficult to observe. And, in fact, it was, I mean, all the methods people have developed in the course of the years somehow have never observed a structure which is compatible with the 13 nanometer fiber in vivo, so, namely in a cell, in a living cell. So, which probably means that this structure does not, I mean, people, I think, now they tend to believe that it does not exist in vivo. And, in particular, if it doesn't exist, probably it has any functional role. So, what it's, yes, where, in experiments, so, you put DNA in a buffer, so, with disperse, with instance, reconstituted instance. And then you start observing, so, you put, of course, use the same condition that probably is, I mean, that are in the cell. And then these things start to form this first 10 nanometer fiber, because the distance starts to fall and then DNA starts to wrap around just because of the electrostatic interaction. And then if you move to condition, physiological condition which are closed, I mean, with ions which are closed to the ones of the cell, then these things form, tend to form these fibers. So, they are observed. In vivo, because in vivo is another story. So, in vivo means inside the cells. And you cannot use the same tools. You have to use other tools like things which are more similar to, like, NMR or such kind of stuff, or other tools actually I'm probably going to talk later. But, I mean, and the results of this experiment in vivo seems to tell that these things does not exist in vivo, which is, which does have to surprise you somehow, because experiments which are made in vivo, actually they remove a lot of ingredients. Because you have been in these experiments, you have basically very few ingredients. You have DNA, you have instant, which are proteins, basically, and you have ions. And this is, I mean, I won't expect this is the true cell environment. Cell environment is much more complicated. There are also other proteins there. In general, I mean, of course, in vivo experiments are very useful. Those experiments mean that were made a long time ago, I think maybe 40 or 50 years ago, so they were super-index, I mean, they were amazing, but they were done again in very controlled situation. So, inside the cell, the situation in the cell is much more complicated. But it's important that, nonetheless, you know about, I mean, this is somehow the kind of thing that you find in textbook anyhow. So, it's still a matter of debate amongst biologists. And then, so this is, let's say, the third level. So, you can see that, like, chromosomes are sort of hierarchical. And then you have, let's say, the mystery. So, from the 13 nanometer fiber up to the formation of all chromosomes, you are probably familiar with this picture. So, chromosomes are formed by two arms, which are sort of X-shaped, right? Which are attached here. I mean, at the central structure that I will name after. And so, you have to imagine that these, let's say, the 10 nanometer fiber or the 39 nanometer fiber, whatever it is, it has to stay very compact in these kind of X-shaped structures. You have to imagine that it's folded and folded and folded. And how it is folded, it's, of course, important because from that it depends how, not only how, not only we, but all the living beings live and evolve and behave in the course of their life. But it's a mystery. So, basically, people don't know how you can get, I mean, they don't know the intimate structure. Let's say, moving from the 39 nanometer fiber up to this very compact structure, which are the chromosomes. And that's where, let's say, biologists are interested in where they, I mean, they develop new experimental technique, which is also a bit the topic of these lectures. Okay. So, first of all, where chromosomes live. So, chromosomes for eukaryotes, so, namely, like us or budding yeast or animals, plants, chromosomes live inside the nucleus. So, the nucleus is another cartoon. The nucleus is an organelle, let's say. It's a compartment, actually, more than all. It's a compartment within the cell. It exists in almost all cells. Some cells, for instance, the red blood cell do not have nucleus, but those are exception. So, in general, cells have a nucleus. And the nucleus contains the DNA and contains, of course, the chromosomes. So, the nucleus is a very complex structure because it does not contain only chromosomes. But, I mean, as a matter of fact, one thing to it, like a sort of container where you have all the chromosomes distributed around. And, let's say, how the chromosomes are distributed. So, not only the structure of a single chromosome, but also how chromosomes are distributed within the nucleus and distributed with respect to each other. It's actually a very hot topic. It's a matter of studies. And it's important for understanding how cells work and then how the whole organism works. And even for the very simple budding yeast, this is a very complex, which is probably the most simple eukaryotic organism because it's made only one cell, but it's already a eukaryotic. So, it contains the cell and then you have the nucleus. It's already quite complicated. So, I mean, it's a very vast subject. Yes, for humans. No, it's very variable. Actually, this is also a quite interesting subject. So, how the number of chromosomes depends on the size of the genome. So, the genome is the total amount of DNA. So, we have a total amount of DNA of 6.5 for a deployed cell times 10 to the 9 base pair. So, DNA is always measured in terms of base pair. And this has to be divided by 46 chromosomes. Yes, this is for humans. This is in each nucleus. Except for the sperm cell and all sides, because they have half of it. But apart those, each cell has the same amount of DNA. So, you can see, I mean, it's a lot. Apparently, it's a lot. So, it's 6.5 billion of base pair. And all chromosomes are not equal, but roughly. So, if you divide, if you take this number and you divide by it, means that each chromosome contains roughly 10 to the 8 base pair. Or they are all magnitude. So, it's 100 million base pair. So, this is for humans. And, of course, I mean, you can imagine that we are not very different. I mean, in general, in mammals, it's more or less the same. Especially between us and apes. Or even between us and mice or rats. I mean, we are very similar. Yes. Now, what it changes a little bit, as far as I know, is what is called the telomere problem. So, telomeres are the ending, are the final part of chromosomes. And so, telomeres are shortening during the life. So, I become a bit shorter, shorter, shorter. And probably, I don't know too much about that, but I think people know that this is related to the aging process. So, actually protecting telomeres. So, the chromosomes develop strategies for the cell. They develop strategies in order to protect the shortening of telomeres. Because if they become short too soon, basically, you won't do that. People die. But even the organism dies. And this is a common mechanism for most, for sure for mammals, it exists. So, basically, there is a sort of cap on telomeres. But it's a very mild effect. I mean, from the point of view of all genomes, it shortens only the very final part. So, the DNA content remains always the same. Okay. What's an interesting question is do more complex organisms have a larger genomes? And the answer is no. I mean, there are organisms, I mean, living beings, which have more DNA, I mean, if we think that we are complex enough, they have much more larger genomes. So, for instance, I don't remember the number, but the largest genome known is a plant. Usually plants have very large genomes, much larger than animals. But, for instance, the largest genomes is a plant, it's a Japanese plant, it's called something Japanese. It has a genome, I think it's 100 times larger than the human genome. So, it's enormous. It's the largest known genome. So, let's say, there is no direct correlation between the complexity I mean, what you would call complexity of an organism and it's in the size of its genome. Unless, I mean, also, you can define complexity in many different ways. You can say that plants are more complex than us, than maybe, but I mean, it's very, there is no direct connection. While this seems to be, I don't have a plot for it, but there seems to be a connection between how, a linear connection actually, they are linearly correlated, how the size of the genome and the size of the nucleus. So, there is a sort of linear relationship, which is somehow, we are posteriorly, maybe can be somehow relatively expedient, that you want to maintain, so basically the room for DNA on average is always the same between different organ. That factor does not change too much. But apart from this, there is no direct correlation between genome size and the complexity of the organ. Seems to be a bit, there is some randomness in that. So, as I said, so this thing, I mean, this is the chromosome contained inside the nucleus. And the nice thing that nowadays, this is probably, I'll really show you this cartoon, nowadays we can actually, we can actually see them, so I mean, see chromosomes at work. So this is, well, okay, the first, this picture is, a picture done by using electron microscopy. And this is the typical shape of a generic chromosome. For instance, I don't know what it belongs, but I think it's a human chromosome. And this is another technique that I'm going to explain you, which is called the fluorescence in C2 hybridization, which allows to see, really to, it's a sort of, although you would call it, it's a kind of, not really like X-rays, but almost, to really see chromosomes inside the nuclei of the cell. But before saying that, you have to keep in mind that chromosomes are quite dynamic. Eukaryotic cells in general are quite dynamic, okay? And roughly speaking, there are two faces during the life of the cell. And these two faces are one, the first one is called mitosis, and the second one is called interface. And basically the cell goes on this cycle, I mean, continuously. And the reason why is because DNA has to duplicate. So the cell, each cell has to transmit to the daughters the genetic content. And so what it happens is the following. During the interface, the DNA starts duplicating. There is a very complex machine to duplicate the genomic content of each cell. Then, chromosomes, so then we are roughly here. Then what it happens is that the DNA content of each chromosome start to compact, become more and more compact, and start to assume this, to take this shape, the rod-like shape. Then there is a molecular machine which attaches to this central part of the chromosome that pulls chromosome, on one side of the cell and on the other side of the cell. And the cell starts to grow and to become more and more elongated this way. The nucleus breaks because then the two pairs have to separate. And you have a sort of structure of this kind. And this is the moment where proper mitosis start. Then the two chromosomes go apart this way. And the two new nucleus form. Starting from one cell you have two new cells. And then you go on and on forever. So this thing you can also find movies on YouTube, for instance the application of yeast. As I said, yeast are unicellular organisms, but they are eukaryotes. So you can really see how the cell cycle works in that. They are very simple, it's very fast. So yeast duplicate quite fast. I mean one day you get a lot of offsprings from a single cell. So that's the interesting part of let's say everything is interesting, but I would say that's the part that we are going to explore more in detail by using polymer physics. We are going to talk mainly just to fix the idea on this part, interface. Interface is the part of the cell cycle where chromosomes are more active. By more active it's not like in mitosis they are not active, but during interface is where chromosome actually do not look like this, they just have this rod-like shape. But the DNA which is inside the chromosome start to open to uncompact, let's say. By all this, how do we know that? They know that by different techniques, but mainly so you can also see here by this picture. This blue here is the DNA or let's say the chromatin fiber this third, let's say 10, 30 nanometer fiber, whatever. And so as you can see here DNA during interface looks more homogeneous than DNA during mitosis where you have different filament. If you make a zoom here so here basically you can distinguish more or less you can distinguish different chromosomes while here everything looks like sort of mixed. So biologists have gone really into the details let's say mixed structure and they have developed this technique which is called fluorescence in C to hybridization and this is a very powerful technique I think Mario will tell you a bit more details about that but just to give you a flower so fluorescence in hybridization is done in this way so conceptual is quite simple what you do is the following you construct probes of small probes of DNA single stranded probes of DNA so it's this way and here you attach some fluorescent protein fluorescent probes which can be a protein so some also let's say artificially constructed molecule but it's important that it's fluorescent so then what you do is the following you induce what is called denaturation of DNA so basically you warm the cell so first you attach the cell to some substrate artificial substrate then you warm the cell or you use some chemical agent that induces partial so you have the double strength here as I said these let's say warming or chemical modification induces an opening of DNA some everywhere because you cannot really control whether this thing happen then actually it looks like this sequence since it's just a single stranded is introduced a lot of them so you design a specific sequence that is precisely the twin some selective sequences that you want to mark on DNA so then this thing is introduced here like for instance this way and the result is that you have now a fluorescent probe attached to some specific sequences on the DNA then you revert your condition so you go back to normal condition so you for instance if you have used heat you cool your system or if you have used chemical agents basically you remove them you wash them hoping that you have not destroyed the 3d structure because it can happen or if you are too harsh you destroy everything and then what you at the end you use just normal so you put light on it and those fluorescent molecules react to the light so you can see them because then these things remain attached here so you can see where you have attached your sequence and in particular you can see different spots I mean different spots on the chromosomes or different chromosomes and this is the result the kind of results you get so now we are able to design probes which responds to different frequencies so you can see light of different colors and this is the kind of picture you get the technique is also called I mean with different colors chromosome painting it's like you can paint a chromosome and the result is this so this is taken from this publication but actually the two guys they did not invent the technique but they really made it let's say a routine tool in biology of chromosome they are two brothers actually and so what is this so this is a nucleus of a chicken of a specific cell line of chicken it's called a chicken fibroblast and each color here corresponds to a single chromosome so for instance green is a chromosome chromosome 2 and the other green here is also chromosome 2 because chromosomes are always in pairs so of course this technique is not able because it attaches to a sequence and because twin chromosomes always have the same sequence it's not able to distinguish between two separate twins so we have always two signals coming from the same chromosome even if you design for each chromosome one single color you will always have two signals unless the cell is it contains only one copy like the let's say the sex cells like sperm cells or all sides apart from these details in general it goes always in pairs and as you can see here there are also other colors for instance chromosome 3 and chromosome 3 or chromosome 1 and chromosome 1 I don't know chicken how many chromosomes it has but it's not important the important result actually is quite interesting this kind of result is was really a landmark in biology is that chromosomes as you can see here so this is the old nucleus they are not like even after they open they do not spread I mean they just they do not spread like everywhere but they tend to occupy specific portions of the nucleus it's like so if you think they this in terms of like of a earth so each of this thing is a country this is really partition so each chromosome belongs to a specific portion of the nucleus and this kind of structure is called the organization this kind of specific organization chromosome is called each one of these structures is called the chromosome territory so in fact the title of this paper is chromosome territory some blah blah blah and this I mean the discovery actually of that chromosomes form territories so they namely they stay localized inside the nucleus of the cell was a real landmark was let's say was a there was an intuition for it in the 70s but it was only proven by using this actually this this technique fluorescence titibulation because you can see chromosome with this technique so it so they were funny I seen only I think in the beginning of the 80s and also the territorial organization chromosome is not something that it belongs only to chicken fibrobutt but belongs basically to all eukaryotes yes this is immortal no in sense that the cells are attached so you have to kill the cell fish but it's in vivo in sense that you are you hope that you are not too harsh in the technique you have used so next week Mary will present a new technique which was developed by with this collaboration also in the group of Anna Pombo in Berlin which is which is a sort of less harsh technique which is called which is basically what is called cryo fish so cryo fish it works this for the following it's a sort of fish but what you do first you freeze the cell and then you you apply fish but then you you can be a bit milder because chromosomes are fixed because you're frozen the system and then you start cutting you make a thin slices of the cell and because you can make these thin slices you improve resolution you can be less harsh and you still see the same pattern so this Mary will tell you a bit much more about that I think and but I mean this is now a fact so it's a very important feature of chromosome so the next question is is this thing a curiosity okay we have the readers about two cares and actually the answer no it's not only nice or I mean very interesting experimental result but it's important because always biologists have shown that the way these territories distribute inside the nucleus has functional relevance so the fact that you have for instance make a Jackson example close to the nuclear boundary is not a random so it's important because chromosome one has to perform some function some function close to the nuclear boundary or if you find chromosome one close to the center of the nucleus it's because chromosome one has to perform some function close to the center of the nucleus and it's also and actually it was always the same two guys published a very interesting papers a few years ago where they've studied the same cell line this is always a strategy that biologists adopt namely you for instance you analyze the same cell line but in different organism to see differences because then you can be comparative and they made I mean a lot of measurement using fish and especially they've concentrated on retina cells in mouse of course but between diurnal and nocturnal mammals so namely like bats and I think they were mice or rats for diurnal mammals or probably hamster and they've seen the following stunning they've demonstrated the following stunning results that so you can imagine that retina cells are not very different between these two animals but the this the arrangement of chromosome inside these two cells were completely reverted so namely you can find just so in the paper you can find more detail but just to give you an example so you can find that in these retina cells for instance for bats chromosome one is always find preferentially at the center while in diurnal mammals is always find close to the periphery and they proved that this is associated to how retina cells work so they proved somehow that just the evolutionary pressure has induced a different arrangement of chromosomes inside the nuclei of these cells so this proves that actually territories are not only a curiosity it was the final proof let's say that are not always that are not only curiosities but they have a functional relevance and another for instance example is in for instance the comparison between healthy cells and cancer cell so I don't know if you heard about so the most used cancer cell which are sort of stem cells which are used in biologies these ila cells it's a famous cell line and so if you look at chromosome territory of course ila cells are also polyplodes so they have much more copies of chromosome but if you look at chromosome territories in ila cells and compare to chromosome territories in let's say healthy cells you can see differences a lot of difference in the territorial organization of chromosomes like for instance how chromosome the morphology is the real shape of this chromosome in two more cells they have a lot of so they look like anormal so they are sometimes chromosomes are bigger so there is differences actually the idea was that you can use this technique or looking at chromosome territory it's a sort of markers for two more I don't know if they have done that but that was also a sort of idea because now this technique can be done I mean it's complicated but it's more and more a sort of routine yes no so you said again I lost the first part so what is the same for the both cells well I think I think so but in general as I said you can always study in pairs so you cannot really say this is to A and to B you don't know what is to A so they are basically the same for this technique they are the same but yes in general these pairs are found I mean there are some patterns so what I mean is the following if these two so chromosome one for instance it's always find preferentially the periphery or chromosome two for instance always at the center just an example it's not like a chromosome one just to give you an example but when they say that they always mean the pairs because of course I mean you can always study the pairs because of this technique you cannot distinguish between those yes quite a question yes otherwise okay I talk five more minutes and then yeah it's a good it's a good then we do a small break so that's not the only one technique there are many many many and I just want to mention to this presentation two more technique so one technique that was developed I mean now I think more than 20 years ago maybe 30 years ago is to study chromatin I told you yes this is relevant actually if you want to apply those concepts to the study of chromosome chromosome dynamics inside the cells and so this is the study of chromatin chromosome dynamics by using green fluorescent protein this is a technique actually made in vivo for cells in vivo and the idea is that you you prepare so use green fluorescent protein this is a product that was derived from some jellyfish and actually they deserve the Nobel Prize I mean it was awarded the Nobel Prize for this discovery and this green fluorescent protein is I mean it goes and attaches to a specific sequence so you can somehow design the problem in such a way that you choose to which sequence you want to attach so this is actually I don't have the movie here but this is taken from this paper this is a movie so you can see that the real fluorescence emitted by the protein here which corresponds to the and this thing moves this is a nucleus and this thing moves inside the nucleus and this is attached to a specific sequence of interest to some chromosome this I think is east because it's very small so it's east because it's somehow easier to do a thing to do it for east and so you can really see the motion of this object and for instance you can study you can apply this technique to see how the dynamics of the chromosome depend on the environmental condition for instance if you have east or other cells too you can starve the cell and look if starvation induces less or more motion in general it induces less motion and so becomes less dynamics and for instance you can increase I mean the the availability of food and you can see how the dynamics of this object depends on the on the food that you have around and then you can do for instance you study for instance the mean square displacement of this thing and you can apply all the concept from like say analysis tool that explain to you yes I mean you can find in the literature and the third technique and also this is I think it will be very central to the talk that Mario will tell you next week so this technique relatively new technique now it's I mean the prototype was developed in 2003 but the real one tool was developed in 2009 so next year they will celebrate 10 years of this technique it's called chromosome conformation capture so made the briefly 3C and let's say the major explosion was when in 2009 when people have developed high C this highest any specific meaning except that it was high throughput chromosome conformation capture that was called high C because that allows this technique to measure genome wide all possible contacts between chromosomes inside the cells and the technique works in this way so you maybe you can spot really from here but what to do is the following you create a sort of bath or pool of so you put your cell inside formaldehyde formaldehyde is very common and it's toxic why they use it because it induces sort of bridges it forms bridges between proteins inside I mean between proteins in general now as I told you DNA sorry the chromosomes are not formed only by DNA but there are DNA plus instance and these stones are proteins and so formaldehyde induces bridges between those instance within DNA so then you do some kind of things which means basically breaking so now after the formation of after sorry injection of formaldehyde so you have now these bridges so this is the DNA filament in general and now you have this kind of bridges inside the cells then you use some specific enzymes that chop DNA in pieces so basically they cut DNA in small pieces this way so you have all these fragments in your buffer and now with these fragments what it happens that you go and try to recognize the sequences these sequences here and these sequences here that that were cross linked by the formaldehyde and this is the main idea of this experimental tool so now the interesting thing is the following that of course now what you would expect of course I mean if DNA right if there are of course most of the contribution of the sequences which will which will found cross linked in the experiment will come from those pairs of DNA of pieces of DNA which are also called which are close in along the sequence because if they are close along the sequence they are also close in space because these cross links act in space now everything which is close in space cross link but actually there will be also a contribution smaller contribution but non-negligible that will come from sequences which are very far I mean whose genomic distance is very far sorry is very long but that are close in space because of the randomness of how DNA is organized inside the nucleus and then by this technique you can detect a lot of contacts and you can study the statistics of these contacts in particular you can study the statistics on these contacts in connection to the functionality of namely how DNA works will be a bit more specific later but how DNA works inside the cells and this technique so now it's very popular actually and also it allow to test a lot of polymer theory and to allow to test a lot of polymer theory and to see if actually polymer theory or polymer models can be useful to to describe let's say how chromosomes work inside the cells and I just do a maybe because it's a good time to do a small break like 2-3 minutes and then we go because I have to drink it works this thing so so okay this thing was a brief well not so brief overview but I think it was important that if I told you that overview on introduction to chromosomes, experimental methods there are much more many many many more experimental methods because basically it depends on which things which phenomenology which biology you want to detect you want to see cells are very complicated even one single cell is very very complicated it's a complex actually system and so we are only looking at some specific aspect here now why chromosome should be interesting we know that physicists are always interested in new systems and now they have studied for many years proteins for instance using basically what are more or less complicated polymer models and so now people are starting since it's a relatively new it's much newer subject compared to what people have done in proteins because this kind of technique experimental technique now they've attained a huge degree of precision but it's only in the latest years so that's why people are starting to become interested in using polymer physics in recent years because before there were no real data that you could use to compare to your let's say computational prediction but why people should worry I mean let's say physicists should worry about chromosome and one observation which maybe which was a bit probably underappreciated at the very beginning but that become very popular recently is the following so take the picture that I shown to you so the presence of territories as I said basically and forget I mean DNA so basically think to chromosomes just like very long polymer actually for instance just take the example of human chromosomes so as I said we have humans have 46 chromosomes each chromosome is about 100 million of base pairs so it's very long we have 46 of them so it's basically if you want it's like a polymer solution so if you think in terms of polymer solution now there is something which is quite interesting now I mean if you just think to chromosomes inside the nucleus is like a polymer solution it is the following so we know that from the experiments that chromosomes have to form territories so they are compact and actually you think okay but if I think to this in terms of polymer solution this is very different from what I would expect from a polymer solution because a polymer solution actually will never form territories so these are linear polymers chromosomes are basically linear polymers and then a solution of linear polymers they will never form territories actually for one very simple reason that probably I already mentioned to you in the last day that linear polymers because of the entropy like to stay extended so to expand tend to occupy all the available volume as I told you actually linear polymers in solution in concentrated solution they want so they basically form a random walk so the conformation of one single polymer is a random walk and of course you know that a random walk is not compact it is a very standard structure so there is the first important observation here that from by using just naive polymer physics that does not predict the formation of territories so there is this what you accept from experimental point of view and what you expect from physics and the question is can we still use polymer physics in order to model the formation of territories if there is any new physics coming from it so that was actually it was the thing that motivated my interest in these kind of topics because I mean again I want to stress this generic polymer physics does not predict territory cannot describe at least we let's say the physics we know cannot describe this kind of structure and this what I want to say here yes this is this kind of papers that why they are at this place now but I mean this place of the presentation that were the two papers that I mentioned to you before the second one is nuclear architecture or road photoreceptor cell adapts to vision in mammalian evolution that was the paper that I mentioned to you before that about why the structure or chromosome is so important because in diurnal mammal you find a different structure than nocturnal mammals so I don't know why this right at this point so actually as I said this was meant for a presentation in Milan it was mostly for biologists so here I summarized the different features of polymers but this I mentioned to you already yes so we can skip it it's very generic it's a sort of summary so you have all these kind of things they explained that polymers are factors blah blah blah so I want to go to tell you now how physicists have sort of explained I mean I've introduced different models of let's say increasing complications in order to understand how chromosomes fold inside the nuclei out of the cell and I will adopt this sort of because I think it's quite interesting I will adopt an historical perspective so namely I will start from the very first models and then I will move to the more recent models and that helps a lot because you will see that people have in many cases have gone back to older models to explain so to explain the complexity of chromosomes I mean let's say in this sense models are not I mean if you want evolution has gone back to the past which sometimes happens also in for living in real evolution I mean for living beings so what yes so let's say the first models that were developed is something that I would call the changed loops model and the reason why I use this word is the following so as I told you normal polymer physics I mean ordinary polymer physics is not enough to describe so simply it cannot account for territories in particular there was an important experiment that was here that was produced in the 90s middle of 90s where people have actually have used this technique the fluorescence in situ ability technique to tag pairs of loci on human in some cell line of human chromosomes is some cell line of by using human cells I mean which cells is not important and what they observe is the following interesting feature so they tagged I mean many many pairs so what they did is the following experiment so you have let's say you have your chromosome they identified the specific pairs of sequences that can be reliably marked so they attach their fluorescence from here and here and they measure all the possible spatial distances between those pairs so they know let's say the genomic distance between these pairs along the chromosome and they measure the average spatial distance between those pairs of sequences and they have done that for different pairs so you have to spot here so on and so forth and as you can see this is very and after that they measure the mean square distances versus L how these things grows with this and as you can see this is a properties that actually it's the first properties that I mentioned when we introduced polymer models this is the same quantity that you can measure for polymers sorry L is this this is genomic distance between a different pair sorry so it's this for distance or this one so you can do it for different pairs and what you have is a plot yeah you can see that it's a plot like this so you measure R square L versus L and you have I don't know different points it's the thing that you can see here okay this and this so the difference between these two plots is the following they have done two sets of experiments first they have okay they have taken a very limited region of the chromosome which is about I don't remember I think it's about 10 to the 6 base pair so this is the first set of experiments so it's a very small region because as I said one single human chromosome is 100 sorry it's 100 times larger than this so this is a very small region of the chromosome and then they have extended the same technique to the whole chromosome and the reason to do that I don't really know but I suspect that is because I mean in the 90's these experiments were really expensive so you have to really design it carefully and the reason for that but I think it's simple to understand because here you can monitor a very small region so you can monitor local details by this you can monitor global details and they found a very interesting stuff which is summarized by these two plots that let's say for very short genomic distances they find so the data stay close on some slope so basically they are reproduced by sort of linear relationship so you have a linear relationship between this and this so between r square and l and you have another linear relationship at large scale so between this and this it's always linear but with two different slopes it's like so you can see actually you can see very well from these plots so the first one here is the first slope and this is the second slope and the first part of the data fall here so this line on panel C here it's this line here so what you can see it's like you have two slopes this way if you have like a log-log plot so one slope here and one slope here okay so now the way they so it's clear how the data looks like so the way they somehow model so these data they are still quite remarkable because in sense it's very difficult to get such good quality data they made also for different chromosomes and they always find more or less the same behavior so you have a sort of bi-fasic behavior and the way they model this thing is the following so first of all from a linear relationship here you maybe you know that no because I told you that this is like in a Gaussian polymer because the mean square displacement sorry the end-to-end square distances between personal or side growth linearly so this is like a Gaussian polymer but in order to be a real Gaussian I mean I told so for a simple Gaussian polymer this is the slope of this relationship actually it's so this is has to be equal to some b times l but this is so that this b is basically the slope of this relationship is the same as I mean it's always the same doesn't matter if you have I don't know 10 base pair 100 base pair, 1 million base pair the slope has to be the same I mean a total length scale but this is not what they observe here they observe actually two distinct two very distinct slopes so in order to summarize their results what they did they introduce a model where you have your polymer so this is the model they introduce your polymer and the polymer forms loops and let's say in the loops so they basically constructed so they still have a random walk which I mean springs and the basis of those loops connect so sorry the basis of each loop are connected to the next loop in sequence one after the other and the let's say the springs these springs here also let's say arranged according a random walk so you have a sort of hierarchical random walk so the first one these loops describe the small length scale and the let's say and these springs describe the second the second slope because it's also a random walk yes so you have two slopes here two linear they are linear two regime if you want okay yeah just so you have if you are square of L versus these experiments I want to stress this thing it's not model so what they observe they observe such kind of regime I mean I'm making more so you have a linear regime here right and a linear regime here for the data the data is here okay and this breaks at about one megabase pair if I remember correctly maybe a few megabase pair sorry 10 megabase means of course 10 to the 6 something like that so the way they model this data is by the following model so they introduce a sort of random walk but then you I mean you force this model to form loops and these loops are formed one after the other like this so now the springs that form these loops they also are also arranged in a random walk so on this level it's a random walk and now you can see that the local loops they are the size and the way they are constructed they want to describe this regime while these loops here sorry these springs here they describe this large regime because that will be also I mean on such large landscape that will be also linear because this is still a random walk this is locally it's a random walk so you have somehow invented a model with two regime okay and they call it random loop model I think sorry random walk giant loop model giant loop because so as you can see now basically they use this model it can be solved more or less exactly basically it's a generalized random walk so it can be solved almost exactly and but of course I mean it contains parameters to fit so you have the slope the parameters are three you have the slope the first slope here the second slope so it's two parameters plus the crossover where I mean so this is a three parameters model and they fit it to it and they call it giant loop model because they saw that the model is compatible with a fit where this crossover happens at around 100 about 10 to the 6 base pair I mean one mega base pair so these loops are quite giant actually I think they found three mega base pair it's in the range of a million of base pair that's why they call it giant loop random walk giant loop model now as far as I know that was the first serious attempt to apply polymer physics in order to understand chromosome conformation no it's not really a problem but because also this is larger so you can just basically you match the two but anyway this is what you find they agree with you that's smaller but that's described the behavior of this loop relatively smaller but this level of semiflexibility sorry it depends because if you have semiflexibility it tends to expand it tends to swell the polymer because it becomes locally more rigid and then it inflates the polymer but at this level as I said semiflexibility is not really an issue because the persistence length of well it would be not really the persistence length of DNA it's the order of it's of the order of well it's about 1000 of base pair which is much smaller than this landscape so it's here basically persistence length so you can really see the effect sorry it's inside this is a fish as I said you will kill the cell but it's presumably what you would see in vivo yes so you see it's a normal cell line it's fibroblast specific cell line but it's normal cell line nothing to actually fibroblast are kind of they're kind of stem cells but otherwise they are not much different than others so then ok so this is a bit if you are in this you can look directly into this paper you can see it was a very good publication as I said the experimental data are still quite I mean even after 20 years are still quite remarkable because they really mapped not only one chromosome they really mapped many chromosomes and this is very difficult to achieve even nowadays I mean with such a good quality so these data are still let's say used in the literature to compare to polymer models especially because they have done this let's say chromosome wide they have not limited their analysis to a small region of the chromosome which is in general what people do but they have analyzed all chromosomes so you can see here they really went up to maybe it's not really visible but they went up to in this case 200 megabase pair because this specific chromosome is 200 megabase pair it's one of the largest human chromosomes so they really went up to the full to the full chromosome this is very difficult to do experimentally I mean fluorescence civilization is really a pain as far as I know as biologists explain to me so now what's interesting is that there were I mean some guys in the group of Jörg Langoski in Heidelberg who reanalyzed actually the same data and they made a very simple analysis nothing very profound but what they did they plotted the same data but in a different way so basically these guys I mean the guys of the original paper they used a linear linear plot of their data so they plotted linearly r squared versus l while in the group this group here they said okay no if you wanted to and I agree with them if you wanted to see power loss you don't have to use lin lin plot but you have to use log log plot now it happens something let's say interesting so they made a more accurate fit of this data they found that the short time regime which is here, this one it's described effectively by an exponent which is compatible with the linear behavior you find here 05 this is the exponent 05 because they did not use r squared but they used the square root so okay that's true okay so that's why you find 05 and then the second part is not described by an exponent which is again 05 but with a different slope but it is described by an exponent which is close to 0 it's basically 032 so they made their fit and they found that this exponent is best described by a different actually and that's quite interesting a different power law behavior which is actually 033 which is so 032 or 033 whatever it's very close to 1 over 3 which is the exponent you would expect for a compact polymer because it's basically 1 over d where d 1 over d equals 3 where d is the dimension of the space and this is the let's say the scaling exponent for a compact object so now how they understood the model this data they did it in this way they basically adapted a little bit this random work giant loop model but they adapted into a model called multi loop sub compartment model so people like names fancy names where now loops are smaller are much smaller than this mega you still have springs between loops and they forced because you observe a sort of compact regime which is very different from a random work of the regime they forced their polymer to stay in a sort so they confined the polymer so here it's not so the confinement is not seen but you can find the details in their paper so it was published so now 20 years ago in physical review it's a computational model if I have time I will tell you about the kind of numerical technique that you can apply for these models but if someone is familiar with that it's just molecular dynamics it's nothing too complicated so this is a polymer model with small loops and the size of these loops now sorry I don't remember the size of these loops and because you have so these two ingredients you have loops plus confinement which is induced by some it's just spherical confinement by fine tuning the size of these loops and the size of the extent of the confinement they were able to describe let's say they were able to fit the to these experimental data of course what's what's hidden in this modeling is one thing and this what's called coarse graining so I will tell you if I have time I will tell you a bit later but that means that so you have your polymer model the polymer model basically contains one important parameter you have to decide on which is the minimal length scale you want to describe you want and you can describe because it's also a matter of computational resources so these chromosomes are enormous polymer you will never I mean with the current experiment with the current sorry computational tools you can never describe chromosomes on the level even one on the level of a single base pair so you need to do what is called coarse graining by coarse graining I mean the following you decide so you have a polymer model you polymer model you have single monomers each single monomer describe not one single base pair so it's like an effective description and you hope that let's say on large length scale these these these let's say it's not an artifact but these hypotheses of your model does not affect your results so these kind of stuff anytime you do polymer model and computational techniques for polymers you do this kind of assumption but in this case I mean I can anticipate it works quite well and so they adopted a model which is coarse grained model where each monomer their model describe a certain amount of base pair now I think it was about 1000 maybe more 10,000 of base pair but you can find the details there in this paper if you are in this the important thing is that is the physics they adopt namely the presence of loops the presence of confinement this is really critical and because of the confinement basically they are able to describe a let's say a compact object so they were happy they describe few things because they use molecular dynamics they were also able to describe the dynamics a bit of this object which is interesting because in these experiments dynamics fluorescence is in situabilization experiment so you don't have access to dynamics you don't have access to statics ok and then I talk about this and then I will stop because I think my time is over so yeah this is important I think this is an important point so in these models loops are quenched by quenched I mean the following that once you have loops you define your loops only they don't do not change in time ok even I mean it's like a static structure it's like a starting underlying structure of your model and even here even if they use the polymers sorry they use molecular dynamics so it's a dynamic model you can really simulate the dynamics of your polymers you do not so loops are for once they are they are established at the very beginning in your model and they are never disrupted so they always stay there they are real play an active role in your in your model so is this true is it not true we don't know but it sounds a bit unrealistic because I mean the genomes are very active systems and the chromosomes are active systems loops are not quenched in such that during during their behavior loops form if they exist loops but if they form they will probably also disruptive during the course of the set and that ingredient let's say was used I mean the fact that you don't have quenched loops but you have annealed loops so loops can change in time that was I mean a different series of models but they basically describe very similar physics so this the first one was the so-called random loop model where the ensemble of your polymer is so you have a sort of distribution probability distribution of the formation of the I lost my heart so okay so you have so basically loops so you have loops on your polymer structure but these loops are generated according to a probability distribution okay so you have two you have another one more ingredient and let's say the other what you establish is only the average type of a loop but then loops can form between monomers inside your polymer model and of course anytime you introduce loops as before in a polymer model you compact the polymer and they were able by this model to describe a kind of biological data which actually were always derived by this experiment, I mean this biological this experimental data by using fluorescence digitalization basically what they did they observed that there are regions in the genome experimentally I mean which are more compact regions which are more open and by fine tuning the presence of let's say the frequency of the loops inside their model they were able to reproduce let's say they were able to reproduce the magnitude of their data in particular if you want they were also able to introduce a sort of by fitting to this experimental data they were able to somehow predict which would be the frequency of these loops according to the chromosomes to the chromosomes they studied then there will be and I don't spend too much time in this because this model was introduced by Mario Nicodemio years ago so Mario will tell me more about that so let's say the phenomenology of chromosome was described by introducing a model where you have so you want your chromosome, your polymer to stay compact because this is what you saw in the experiment so how it works is the following so you have your polymer chain your polymer chain is described as a co-polymer so you have monomers of different species let's say A and B and it happens that you have let's say proteins fluctuating around which is very similar to very close to what you have in cells and these proteins attach to specific sequences on the polymer and according to the to the concentration of the protein or the model proteins and the affinity which means how likely these proteins will interact with specific sequences on your polymer model then you can have more or less folded polymer conformation in particular they were able to devise a sort of phase diagram so basically if you have a lot of binders namely a lot of proteins a sufficient concentration your polymer starts to become a very compact and if you have not that many the polymer will stay a bit open more open and you have a sort of phase transition if you want you have a sort of critical concentration of proteins where the polymer looks a bit in between so between a compact conformation and a swollen conformation but I want to talk too much about that because Mario for sure will tell you much much more and we will also explain that in connection to experiments what I want to just to stress is that the positions of the loops here is dynamic so it's a sort of you have a sort of tradeoff between the fact that the polymer likes to be expanded and the fact that you are forcing the polymer to stay compact because of the presence of the loops so and I think of course I think that this is a kind of model it's much more realistic because for sure I mean the presence of loops changes during time so this model as you can see also they were introduced much later also because I mean they require more computation so it's logical that they were studied more recently and that was okay the effect of depletion I mean if you want to deepen into diesel you can just look into this paper I will send you the transparencies anyway and I think I stop here because my time is over and next so Monday so for my last lesson I will tell you how you can which is closer to what to what I am interested in you can still use polymer models by different class of polymer models where you need to take into account topological constraint namely the fact that polymer like to stay confined because you have many chains so these are multi-chain effects because you have many chromosomes inside the cells and the fact that you have the presence of topological constraint means that two fibers cannot pass through each other but they start to be entangled so they do this way so since they cannot pass through each other they start to form different kind of structure in time and in space and I will explain bit more on these kind of effects that according to me are very important in order to understand chromosomes folding inside the cells and I stop here and I leave you to the next lesson yes they wanted transparencies I send them to Erica