1 00:00:00,980 --> 00:00:06,260 So for example, 820. 2 00:00:12,710 --> 00:00:17,740 Um, it's a great pleasure to to be here today, um, talking to you. 3 00:00:17,750 --> 00:00:27,290 So, as you can see us, I'm not part of the physics department here, but I was actually, um, trained as a physicist, uh, first in Paris, 4 00:00:27,620 --> 00:00:36,349 where I did my undergrad, and then, um, in Cambridge, where I went to do my master's degree and my teaching degree, um, in biophysics. 5 00:00:36,350 --> 00:00:42,170 And I worked on the sort of active motor and tissue mechanics models. 6 00:00:42,190 --> 00:00:53,520 Julia. Julia told you about helium. So, talking about Cambridge, I want to be a bit mischievous and actually, like, willing to like that. 7 00:00:53,570 --> 00:00:59,510 You're not the first generation of physicists to be interested in biology. 8 00:01:00,050 --> 00:01:07,850 And I think the best example of that is the discovery of the structure of DNA by, um, Franklin, 9 00:01:08,090 --> 00:01:17,719 Watson and Crick and essentially like it was enabled by the development of the concepts and tools of physics. 10 00:01:17,720 --> 00:01:19,280 And, uh, well, 11 00:01:19,310 --> 00:01:28,580 by that I mean the developments of the quantum mechanics at the beginning of the 20th century and experimental development associated with that, 12 00:01:28,910 --> 00:01:36,800 like X-ray diffraction, first by, uh, Bragg, father and sons in Cambridge and then by Moseley, 13 00:01:37,070 --> 00:01:44,480 um, here in Oxford and then later on after World War two by parents and Kendrew, 14 00:01:44,720 --> 00:01:51,230 um, in Cambridge, where I actually started this, basically this little lab, um, 15 00:01:51,260 --> 00:01:56,089 in the shade of the back of the old Cavendish Laboratory and that said themselves to 16 00:01:56,090 --> 00:02:02,090 our young and motivated people to work out the structure of biological molecules, 17 00:02:02,420 --> 00:02:10,760 proteins and DNA. And here is the famous basically like E3 diffraction picture acquired by Rosalind Franklin, 18 00:02:11,000 --> 00:02:19,010 which basically helped Watson and Crick confirmed that their theoretical model of the structure of DNA was right. 19 00:02:19,460 --> 00:02:28,220 Um, 70 years on, like it seems, you know, like we are in the same position, um, not talking about molecules anymore, 20 00:02:28,310 --> 00:02:37,580 but cells and tissue, as Julia, um, so eloquently like, demonstrated it during a presentation earlier this morning. 21 00:02:38,030 --> 00:02:44,060 So I want to tell you a bit about myself, because that is exactly where I am at the moment. 22 00:02:44,270 --> 00:02:49,850 So I have moved like, you know, like from physics to biology. 23 00:02:50,060 --> 00:02:55,250 And what I'm really interested in is doing what people have done before me, 24 00:02:55,250 --> 00:03:03,470 trying to apply the tools and the concept of physics to answer biological questions, questions which have relevance to biology. 25 00:03:03,620 --> 00:03:09,890 In biomedical sciences, we're trying to find mechanism of diseases and to cure them. 26 00:03:10,190 --> 00:03:17,120 And the particular questions I'm interested in is the role of mechanical forces in selfish decision. 27 00:03:17,510 --> 00:03:24,620 So a selfish decision is a decision the cell is taking, um, during its life. 28 00:03:24,830 --> 00:03:32,990 So as Julia showed, this decision could be to move or not to move, to interact with your neighbours or to not interact with your neighbour. 29 00:03:33,260 --> 00:03:42,260 But it could be also decision to divide and only like a small fraction of cells on the which is called ability and they are called stem cells. 30 00:03:42,260 --> 00:03:45,680 And we find them in the embryo and in the adult tissues. 31 00:03:45,980 --> 00:03:50,300 But then they can also take another decision, which is like when they divide, 32 00:03:50,450 --> 00:03:58,610 they can decide to either remain stem cells or produce differentiated cells which do not have the same definitive potential to divide, 33 00:03:58,910 --> 00:04:03,050 but or specialise to accomplish with particular function. 34 00:04:03,380 --> 00:04:09,080 And this selfish decision between dividing and not dividing, moving and moving. 35 00:04:09,080 --> 00:04:14,180 Remaining the stem cell. Differentiating interacting with your neighbour and not interacting with your neighbours. 36 00:04:14,450 --> 00:04:22,459 They are the urging of all the biological processes like embryonic development, tissue mill stages and regeneration. 37 00:04:22,460 --> 00:04:31,370 Julia was like showing beautiful movie of the development of starfish and Drosophila embryo and then movie showing the example of regeneration, 38 00:04:31,370 --> 00:04:37,159 for example wound healing. But they also very much involve basically, um, in uh, 39 00:04:37,160 --> 00:04:48,260 the inception and physio pathology of a host of diseases like cancer or inflammatory diseases like we are interested in at the Kennedy Institute, 40 00:04:48,260 --> 00:04:59,780 for example, Crohn's disease or arthritis or um, so yeah, but today I going to tell you a bit, um, more about the kind of tools we can use. 41 00:04:59,780 --> 00:05:03,050 So my presentation is entitled Imagining Living Systems. 42 00:05:03,260 --> 00:05:07,400 But really like in my lab, we are an interdisciplinary lab. 43 00:05:07,580 --> 00:05:12,260 And not only will using imaging, but we are using this imaging in comparison. 44 00:05:12,440 --> 00:05:19,460 Like. Like not in comparison, but actually in addition of further technologies brought by physics into biology. 45 00:05:19,610 --> 00:05:27,649 So micro fabrication technology, which allowed basically to grow and confine cells on patterns of a particular shape, 46 00:05:27,650 --> 00:05:32,030 and then basically just look at the impact of mechanical forces of them. 47 00:05:32,030 --> 00:05:39,259 So you can gross them in these basically macro fabricated patterns into a system, in vitro culture system. 48 00:05:39,260 --> 00:05:48,020 And then you merge them. But we also basically genetically modified these cells to make them fluorescent where they express a particular gene. 49 00:05:48,020 --> 00:05:51,110 For example, here is a gene them keratin 14. 50 00:05:51,230 --> 00:05:58,730 And then when this cells express these gene, so when they produce a protein associated to this gene that they become fluorescent. 51 00:05:58,880 --> 00:06:02,900 And all their daughter cells when they divide or also fluorescent the same colour. 52 00:06:03,020 --> 00:06:07,219 So we can track, you can start to track basically the division. 53 00:06:07,220 --> 00:06:11,840 And so think your pattern and then ask the question why some cells, you know, 54 00:06:11,840 --> 00:06:16,700 like divide and express to particular gene and commits to a particular effect. 55 00:06:16,700 --> 00:06:18,860 And when some other cells do not do that. 56 00:06:19,130 --> 00:06:26,510 And of course, basically to do that we need to use modern image analysis techniques so we can detect and track these cells. 57 00:06:26,510 --> 00:06:32,120 And for this we are using machine learning and the particular form of machine learning which is called deep learning. 58 00:06:32,300 --> 00:06:40,370 But we are also using basically the most recent methods in genomics to not be limited to look at a single genes, 59 00:06:40,370 --> 00:06:46,159 but look at basically the full basically genome and see what's happening when a particular 60 00:06:46,160 --> 00:06:52,190 cell commits to felons to a given state and see which genes it expresses or not. 61 00:06:52,400 --> 00:06:58,250 And of course, we are still using models from theoretical physics trying to make sense of all these experiments. 62 00:06:58,400 --> 00:07:03,059 Data some. So. Today. 63 00:07:03,060 --> 00:07:14,490 What I going to try to focus on for the rest of the presentation is basically like looking at all recent advances in images allow us to image, 64 00:07:14,700 --> 00:07:19,920 you know, cells as a precedent for spatial and temporal resolution. 65 00:07:20,220 --> 00:07:28,260 And all we can use that's basically to infer mechanical forces, um, across cells in a tissue and all. 66 00:07:28,260 --> 00:07:37,140 We can also use similar images. Similarly imaging approaches to look at uh, the expression of genes in cells. 67 00:07:37,290 --> 00:07:42,960 So this is typically uh, the kind of modern microscope we have at the Kennedy. 68 00:07:43,110 --> 00:07:48,930 So this is like a very complex device. So it's not like microscopic to fluorescence microscope. 69 00:07:49,170 --> 00:07:56,220 So we are using it. We source injected or genetically modified to produce basically fluorescent markers. 70 00:07:56,430 --> 00:08:05,460 And then it's a light sheet microscope which is using advanced adaptive optics basically to correct focus and spherical aberrations. 71 00:08:05,700 --> 00:08:13,410 It's it's using basically a set of like two objectives at 95, uh, 45 degrees. 72 00:08:13,620 --> 00:08:16,979 And then those objective for illumination with a laser, 73 00:08:16,980 --> 00:08:23,790 which is basically going through a cylindrical lens to make a light sheet, which basically will be scanned across the specimen, 74 00:08:24,030 --> 00:08:32,519 which is contained in the box, which are allowed to maintain, like the specimen, either like a tissue section of cells in culture alive. 75 00:08:32,520 --> 00:08:36,089 So you need to control the oxygen, the CO2 levels. 76 00:08:36,090 --> 00:08:38,310 You need to pump them with culture medium. 77 00:08:38,880 --> 00:08:48,480 And actually when you do that, you can start studying not only cells in a dish, but more complex system like the developing unreal. 78 00:08:48,750 --> 00:08:54,420 So this cartoon here shows basically like the development of a mould, some real. 79 00:08:54,780 --> 00:09:04,410 And it all starts basically with the fusion of, uh, the old sites and uh, just palmettos, which produce the first cell of the embryos or zygotes. 80 00:09:04,770 --> 00:09:13,589 And this basically initial cell is gonna divide and very progressively after two days, you will have a small bowl of cells, 81 00:09:13,590 --> 00:09:21,720 but already so cells in the middle or different from the cells on the shell of the embryo, they have started to take these self a decision. 82 00:09:21,960 --> 00:09:30,180 And then the cell basically will show on the outer part of the embryo they will make the placenta which is not part of the embryo, 83 00:09:30,180 --> 00:09:33,660 well the cells in the middle, they will develop and make the embryo. 84 00:09:33,990 --> 00:09:37,380 And as time goes on cells continue to divide. 85 00:09:37,680 --> 00:09:47,520 And the they also basically differentiate and assume different fates which show up as we pattern into space. 86 00:09:47,820 --> 00:09:57,930 So here for example, after 4.5 days we will have the IPS blast, which is basically the bowl of cell which will yield the final animal. 87 00:09:58,050 --> 00:10:06,000 And then the truffle blast, which is like a more differentiated precursor of the plus of the placenta and the primitive underarm, 88 00:10:06,150 --> 00:10:11,550 which is basically and those are from very unique lineage, um, like the placenta. 89 00:10:12,430 --> 00:10:17,380 Here. Quite interestingly, after almost two week we have like. 90 00:10:17,530 --> 00:10:22,270 Something which was initial your ball which is like normal longitude like a cylinder. 91 00:10:22,660 --> 00:10:30,310 And here we have this what we call the free gem layer, the AP blast, the mesodum and the under derm. 92 00:10:30,670 --> 00:10:34,780 And these free different groups of cells is free. 93 00:10:34,780 --> 00:10:44,740 Like like for two tissues, they will give rise to all the other tissue in the body of the animal. 94 00:10:44,830 --> 00:10:52,470 So for example like um, the brain or actually the skin and the rest of the nervous system will be derived from this. 95 00:10:52,480 --> 00:10:59,440 Like if people ask like and then you will have like other um, which is also called the ectodum, 96 00:10:59,680 --> 00:11:05,139 then you will have these other like organs derived from this other gym layer. 97 00:11:05,140 --> 00:11:12,980 So for example the gut tube is derived like and when I say the good tube is a full gastrointestinal tract is derived from this body, 98 00:11:13,180 --> 00:11:20,290 this little group of cells which we call the under Durham. And here the earth's or the cardiovascular system will be derived. 99 00:11:20,290 --> 00:11:21,760 And the muscles, for example. 100 00:11:21,940 --> 00:11:29,680 And this so much so the future like a new like backbone will be derived from this also group of cells which is called the middle Durham. 101 00:11:30,130 --> 00:11:36,010 And you can see here that here you have just like basically like three little groups of cells, 102 00:11:36,010 --> 00:11:40,180 you know, like with your patch on one on top of the other at 6.5 days, 103 00:11:40,540 --> 00:11:45,460 at 8.5 days, like two days later, you have something which looks like an animal, 104 00:11:45,760 --> 00:11:50,710 you have a brain, you have Hertz, you have like a goats, you have like a spinal cord. 105 00:11:50,920 --> 00:11:57,310 You have like a non-serious posterior axis. Um, you have like left or right polarity. 106 00:11:57,550 --> 00:12:04,120 So in a matter of two days, you transition from a ball of cell to something which looks like an adult animal, almost. 107 00:12:04,540 --> 00:12:07,239 And with the kind of microscope I've shown, you know, 108 00:12:07,240 --> 00:12:15,670 you can image that and see all the cells in the embryo undergoing this process, which we call embryonic morphogenesis. 109 00:12:16,030 --> 00:12:20,470 And here, like the most deeply, I have been modified genetically. 110 00:12:20,590 --> 00:12:30,340 So the nucleus of the cell is fluorescent. And you can see all these nucleus about dividing and then moving and the pattern, 111 00:12:30,820 --> 00:12:35,950 the of the embryo being established until posterior pattern, the left right pattern. 112 00:12:36,280 --> 00:12:40,960 And you can image this in total today in a microscope. 113 00:12:42,400 --> 00:12:52,270 So what can we do with this kind of song? How can we try to use the tools and concepts of physics to comprehend basically these very complex 114 00:12:52,480 --> 00:13:00,730 self-organisation process which involve like an very large number of different phenomena at different scales. 115 00:13:01,480 --> 00:13:06,120 So if we try basically to use the approach of a physicist, 116 00:13:06,130 --> 00:13:12,750 we would like to look at the different scales at play and find what other key players are these different scales. 117 00:13:12,760 --> 00:13:18,340 So of course we have the scale of the molecules and these. So the genes which are contained in the nucleus. 118 00:13:18,610 --> 00:13:24,519 But then we have all the molecules which are basically proteins which are coded by the gene and which 119 00:13:24,520 --> 00:13:30,669 are secreted by the cells like some the molecules which are called broad structures and morpho genes, 120 00:13:30,670 --> 00:13:36,639 and which will basically instruct the cells to divide or differentiate in one particular cell types. 121 00:13:36,640 --> 00:13:42,760 So they are the messenger which help the cells coordinate their action during embryogenesis. 122 00:13:42,970 --> 00:13:47,470 But we can also see what's happening. Hmm. Let's go on boys back. 123 00:13:47,500 --> 00:13:52,140 Okay, cool. And then we have also at the nucleus kill the cells. 124 00:13:52,180 --> 00:13:58,420 And then here we can start thinking about the shape of these cells, um, their contractility, 125 00:13:58,570 --> 00:14:04,270 but also all they divide and all they move, like Julia showed this neurone. 126 00:14:04,270 --> 00:14:07,870 But then if we go at one scale, we have the scale of the tissue. 127 00:14:07,870 --> 00:14:14,200 Where? Then the mechanical fancy, then the mechanical properties of the cells and the tissue of the wall will become important, 128 00:14:14,530 --> 00:14:17,200 as well as the structure and the geometry of the tissue. 129 00:14:17,590 --> 00:14:25,000 And these different biological objects which have these different chemical and physical attributes. 130 00:14:25,210 --> 00:14:27,460 They don't live in isolation for each other. 131 00:14:27,640 --> 00:14:36,030 They basically all interact with each other, feedback on each other, mixing, basically the comprehending the whole process. 132 00:14:36,070 --> 00:14:43,900 Very difficult. So what usually what people do is just they focus on the particular scale and set of objects. 133 00:14:44,230 --> 00:14:47,800 For example, looking at the scale of molecules, 134 00:14:48,040 --> 00:14:57,400 they will look at all basically the information contained in the genes in the DNA is transcribed and then translated into protein. 135 00:14:57,520 --> 00:15:02,650 So this is what is called the central dogma of the bio molecular biology. 136 00:15:02,800 --> 00:15:06,550 Essentially you the information is contained in the DNA. 137 00:15:06,850 --> 00:15:11,860 And this information is basically the genes which are chunk of DNA, 138 00:15:11,950 --> 00:15:17,740 which codes for particular end products, which is a protein which is made of amino acids, 139 00:15:18,010 --> 00:15:23,260 but in between the genes which is made of DNA, and the protein, which is the building block, 140 00:15:23,260 --> 00:15:27,370 which is made of amino acids, you have something which is called messenger RNA. 141 00:15:27,580 --> 00:15:33,190 And what's happening is just when this cell wants to decide to produce particular building blocks, 142 00:15:33,190 --> 00:15:38,589 popular protein, it will do what is called transcription of the gene. 143 00:15:38,590 --> 00:15:42,940 So you will like select some particular genes which codes for proteins of interest. 144 00:15:43,270 --> 00:15:50,140 And then these genes will be transcribed into, uh, another kind of nucleic acid RNA which is single stranded. 145 00:15:50,410 --> 00:15:58,330 And this area, they will act as a messenger. You will go out of the nucleus of the cell, go into an organelle which is called the ribosome. 146 00:15:58,630 --> 00:16:08,200 And in this ribosome the ribosome will some will read basically the sequence of DNA and find the right amino acids to make the protein out of its. 147 00:16:09,410 --> 00:16:15,450 So what you can do to study basically development is use the most recent genomics 148 00:16:15,470 --> 00:16:20,230 technique and the most recent genomics techniques that your single cell technique. 149 00:16:20,240 --> 00:16:28,220 So you can take a number, you dissociate all the cells, use a microfluidic device to isolate the single cells. 150 00:16:28,400 --> 00:16:33,740 And then you can basically of this cell like they just do with some enzymes. 151 00:16:33,740 --> 00:16:37,970 So you only keep these messenger RNA which all the transcribed genes. 152 00:16:38,300 --> 00:16:46,550 And then you can barcodes them with some particular chemical which will allow you to distinguish them individually. 153 00:16:46,730 --> 00:16:53,780 So you can take single cells. Then just basically all the different RNA molecules contained into the, 154 00:16:54,190 --> 00:17:00,560 into the cytoplasm barcodes, these RNA then amplify them and then sequence them. 155 00:17:00,920 --> 00:17:10,010 And once you sequence them, because you have done that for single cell, you can tell, you know, like that particular cell, it expresses 2000 genes. 156 00:17:10,010 --> 00:17:14,060 So it's going to make these 2000 particular proteins. But these those are cells. 157 00:17:14,240 --> 00:17:18,920 It does not express them and it express three thousands of other genes. 158 00:17:19,370 --> 00:17:25,729 And that way you can have a very precise idea of which protein also is going to produce. 159 00:17:25,730 --> 00:17:29,030 And as you will remember, like proteins are the building block. 160 00:17:29,240 --> 00:17:38,389 And they characterise what a cell can do. So if you produce the right protein you will be able to basically, for example, to migrate or to divide. 161 00:17:38,390 --> 00:17:41,420 If you do not produce this protein, you won't be able to do that. 162 00:17:41,600 --> 00:17:47,959 So some or understanding what genes are expressed by your particular cell or cell type of 163 00:17:47,960 --> 00:17:54,500 interest is crucial to understand the selfish decision and the behaviour of this scale of this, 164 00:17:54,500 --> 00:17:57,770 of these cells from the molecular scale point of view. 165 00:17:58,850 --> 00:18:03,290 Okay. So people have actually used that to study embryonic development. 166 00:18:03,440 --> 00:18:11,210 So they have collected most embryo like every um 12 hour between 6.5 and 8.5. 167 00:18:11,390 --> 00:18:14,060 And they have like dissociated all the cells. 168 00:18:14,210 --> 00:18:24,020 And they have used this RNA sequencing technology to sequence basically their gene express in each cell of each embryo at that time. 169 00:18:24,230 --> 00:18:32,150 And then they have used advance machine learning analyses, technique to look at the pattern of gene expression. 170 00:18:32,150 --> 00:18:39,110 So you have around 20,000 genes in the genome. So for each cell you know which of these 20,000 genes or expressed. 171 00:18:39,110 --> 00:18:44,210 And you have like a few thousand of cells for each embryo at each time point. 172 00:18:44,330 --> 00:18:49,430 And you can basically like so it's a very basically complex and big data set. 173 00:18:49,430 --> 00:18:52,310 We are talking about multi-dimensional arrays. 174 00:18:52,610 --> 00:18:59,750 And then what you can use is basically machine learning tools to do things which are called dimensionality reduction and clustering. 175 00:18:59,930 --> 00:19:06,740 And then you can start to have these low level low dimensional representation which are basically cluster. 176 00:19:06,950 --> 00:19:14,410 And this cluster, each dot is a cell. And all cells in the cluster will have similar gene expression pattern. 177 00:19:14,420 --> 00:19:21,680 So they will express, you know, like the same genes, articulate the same genes and don't regulate don't express user genes. 178 00:19:21,950 --> 00:19:30,439 And then what you start to realise is that you can map basically these cells which express the same genes to particular cell type, 179 00:19:30,440 --> 00:19:33,770 particular tissues, particular organs in the embryo. 180 00:19:33,920 --> 00:19:43,430 So somehow by doing that you are able to read like a fingerprint, like you are able to decipher what is a density at molecular level. 181 00:19:44,480 --> 00:19:50,110 Really cool. But then you can go back and say, okay, but I want to understand the process at the school. 182 00:19:50,120 --> 00:19:57,200 We will ask you and you can take back this beautiful light microscopy imaging of the whole development of the embryo I was showing you. 183 00:19:57,410 --> 00:20:04,730 And you can again use machine learning techniques to basically track the movement and the behaviour of these cells over time. 184 00:20:05,150 --> 00:20:11,270 And then what you can do is just like you have no idea about which genes they express, but you track them. 185 00:20:11,270 --> 00:20:18,170 So, you know, if they divide or if they don't divide, if they move or if they do not move and you know where they end up in the embryo. 186 00:20:18,260 --> 00:20:21,860 So on the basis of their final position, which is called selectively, 187 00:20:22,130 --> 00:20:27,260 you can tell you machine learning algorithm to assign them to a particular cell type. 188 00:20:27,500 --> 00:20:34,610 And know your being able to tell cells of this particular cell type, you know, range will divide at that rate while still. 189 00:20:34,610 --> 00:20:39,140 So these other particular cell types in blue will divide at that other rate. 190 00:20:39,410 --> 00:20:48,260 And for example, you will save cells in blue here of having average six neighbours, whereas cells in green here have an average seven labels. 191 00:20:48,320 --> 00:20:52,550 So that's another level of description. That's another level of phenomenology. 192 00:20:53,270 --> 00:20:59,570 And then you can start thinking about what's happening at these bigger scale, the scale of the tissue in the organs. 193 00:20:59,750 --> 00:21:01,879 And there's sees the scale where, you know, 194 00:21:01,880 --> 00:21:09,650 like the collective property of cells start to mature and start to dictate the shape of the tissue in the embryo. 195 00:21:09,770 --> 00:21:19,280 And here the key player is the mechanics. So you want to try to understand what all the mechanical forces generated by these collective of cells, 196 00:21:19,280 --> 00:21:21,890 by these cells forming these tissue compartments. 197 00:21:22,160 --> 00:21:31,729 And dipole physicists again have developed very clever way to look at mechanical forces in these embryonic tissues. 198 00:21:31,730 --> 00:21:36,740 They have created this small fluorescent droplet that you can inject in the embryo. 199 00:21:36,920 --> 00:21:42,200 And then by looking at there's change of shape of the droplet, you can basically, 200 00:21:42,200 --> 00:21:48,319 um, infer the, the stress, the mechanical stress tensor around the cells. 201 00:21:48,320 --> 00:21:54,800 I'm just a part of it. So essentially you can basically, uh, infer the non deviatoric part of the stress tensor. 202 00:21:55,160 --> 00:22:03,020 But it's enough basically to have like an idea of, for example, shear stress in that particular region of the street. 203 00:22:03,260 --> 00:22:11,150 But what they have also done is to again use but those are tools from physics magnetism by filling up, um, 204 00:22:11,210 --> 00:22:18,380 these fluorescent droplets with a solution of a, of fluid which is made of tiny colloidal silver magnetic particles. 205 00:22:18,530 --> 00:22:25,550 And then they can use a magnetic field like a big electromagnets to apply a force on this droplet and make them move. 206 00:22:25,910 --> 00:22:30,170 And then by doing that, they will deform the tissue around them. 207 00:22:30,380 --> 00:22:36,140 And by looking, when they stop to apply the magnetic field and the magnetic force, the droplet stops to move. 208 00:22:36,320 --> 00:22:41,060 And by looking up the droplet will change shape over time. 209 00:22:41,120 --> 00:22:44,180 They can measure the local strain of the tissue. 210 00:22:44,270 --> 00:22:51,530 In response of this mechanical stress, they have applied and they understand the mechanical property of the cell in the tissue. 211 00:22:51,530 --> 00:22:55,220 Things like, you know, like the young modulus of the Poisson ratio. 212 00:22:55,970 --> 00:23:04,550 So it's it's also like a very interesting like approach to understand the physics of these systems. 213 00:23:04,880 --> 00:23:10,310 But somehow we are still, you know, like looking at these different scaling installation. 214 00:23:10,820 --> 00:23:19,100 And in my research, what I've been trying to do is just try to find a way to reach different scale. 215 00:23:19,100 --> 00:23:24,830 And some will be able to of basically like an integrative vision at the tissue scale, 216 00:23:25,070 --> 00:23:32,450 but at the same time to be able to measure the mechanical forces acting on individual cells, 217 00:23:32,660 --> 00:23:40,430 but also be able to look at, um, the genes which are expressed by individual cells as well. 218 00:23:40,610 --> 00:23:49,009 So can we do that? So to do that you have to be able to measure the gene expression pattern in individual cells. 219 00:23:49,010 --> 00:23:56,360 But you can't dissociate the cells anymore because otherwise you losing the special information their position in the tissue. 220 00:23:56,750 --> 00:24:00,080 So people have been thinking very hard about that. 221 00:24:00,410 --> 00:24:08,389 And over the last five years they have developed a new methodology so that you can measure the gene expression, 222 00:24:08,390 --> 00:24:16,120 measure the expression of particular mRNA, not in dissociated cells anymore, but in tissue section. 223 00:24:16,130 --> 00:24:22,700 So you can take a section of a tissue like um, here, for example, I think it's a section of film of a tumour. 224 00:24:22,940 --> 00:24:27,500 And then like what people in biology would traditionally do, like histology, 225 00:24:27,710 --> 00:24:33,470 this kind of new section where they use basically dyes which will label the nucleus and the cytoplasm of the cell. 226 00:24:33,710 --> 00:24:43,190 But here what you can do is use like small, um, western groups which basically have parts which are made of RNA. 227 00:24:43,190 --> 00:24:48,790 So. You have, like a throwaway molecule attached to a small strand of RNA, and you will make that small, 228 00:24:48,790 --> 00:24:56,140 trained strand of RNA complementary to a particular sequence of a given area in the cell. 229 00:24:56,230 --> 00:25:06,820 So some of you can basically like load your tissue section with a solution containing the small strain of RNA conjugated with a fluorescent protein. 230 00:25:07,150 --> 00:25:15,250 You RNA will bind to your like your everything probe will bind to a target RNA, and then you will put it under a microscope. 231 00:25:15,430 --> 00:25:22,420 And each block little fluorescent dot you see here is a particular RNA protein in the cell. 232 00:25:23,200 --> 00:25:26,680 And you can use that for one to free RNA. 233 00:25:26,950 --> 00:25:35,649 And very soon you will run out of fluorescent probes. So people what they have done is develop like a multiplexing or barcoding system by, you know, 234 00:25:35,650 --> 00:25:43,900 like exactly like you will do with the barcode run sequences of hybridisation of different probes with different fluorophores, 235 00:25:43,900 --> 00:25:47,440 which will bind to the same and many and that will. 236 00:25:47,710 --> 00:25:55,390 And that way you will be able to image for androids, if not thousands of in a single cell in the tissue. 237 00:25:55,630 --> 00:26:05,410 So this is really amazing. And in collaboration with a group of Professor Longi at Caltech was pioneer these the development of this technology. 238 00:26:05,710 --> 00:26:09,820 We have been applying it to the really most some of you. 239 00:26:09,970 --> 00:26:19,180 So remember like we are visiting these uh 8.5 years old most of the year where all the tissues and organs of the adult animal are already there. 240 00:26:19,450 --> 00:26:24,340 And we have been taking section of the embryo and sagittal section of the embryo. 241 00:26:24,640 --> 00:26:30,250 And here you can see section of three different embryos. And these embryos look like you have a scale bar here. 242 00:26:30,250 --> 00:26:34,420 So like they are roughly like around like 1 or 2 millimetre in size. 243 00:26:34,420 --> 00:26:39,690 So you can have them on the cover slip of a microscope like the section is around 20 microns deep. 244 00:26:39,730 --> 00:26:44,290 So it's a very thin section. And then you can put your cover sleep um, 245 00:26:44,290 --> 00:26:51,760 into like a microfluidic chip which is mounted on one of these advanced micro fluorescence microscope I told you about. 246 00:26:51,940 --> 00:26:58,659 And then you have like a complex system of pumps, which allows you to pump like reagents in solution, 247 00:26:58,660 --> 00:27:02,980 which contain this fluorescent probe which can ebru dyes with RNA. 248 00:27:03,160 --> 00:27:06,250 And then you image basically your embryos. 249 00:27:06,250 --> 00:27:10,180 So you are taking dual like fluorescence images. 250 00:27:10,180 --> 00:27:12,550 And you do that for each probe cycle. 251 00:27:12,790 --> 00:27:21,100 And progressively you will be able to have all these different fluorescence notes which are particular mini molecules containing individual cell. 252 00:27:21,310 --> 00:27:26,980 So no, we have the capability to measure like the transcriptome. 253 00:27:27,280 --> 00:27:35,320 So the ensemble of gene expressed by yourself in a single cell within a tissue, you know, resection. 254 00:27:35,590 --> 00:27:43,930 So we can really have that integrated view about what's happening at the genetic level in single cell, in the tissue in context. 255 00:27:44,440 --> 00:27:48,580 But we are still missing like the mechanical forces part. 256 00:27:48,820 --> 00:27:50,560 So all consoles us. 257 00:27:51,640 --> 00:28:01,820 So again, like we have to remember that this kind of, you know, like idea that mechanical forces play an important role in living system is not new. 258 00:28:01,840 --> 00:28:08,800 And actually, this man, Dustin Thompson, who was, uh, a zoologist and professor at the University of Saint Andrews, 259 00:28:09,010 --> 00:28:15,850 he wrote like an amazing book at the beginning of the 20th century, uh, which is, um, named On Growth and Form. 260 00:28:16,090 --> 00:28:24,020 And he was the first one to articulate this idea that some of the shape of an organism is a diagram of force. 261 00:28:24,040 --> 00:28:24,729 So clearly, 262 00:28:24,730 --> 00:28:35,230 mechanical forces have been known for a very long time to be very important in dictating the shape and the behaviour of biological objects. 263 00:28:35,680 --> 00:28:43,480 And if we go back at the cellular scale, what are the main determinants of mechanical forces between two cell in the tissue? 264 00:28:44,910 --> 00:28:49,110 These cells, as Julia showed us before. They can make cells adhesion. 265 00:28:49,260 --> 00:28:56,880 So actually they express some particular proteins which are called coverings, which allows them to create adhesive bonds between them. 266 00:28:57,420 --> 00:29:06,450 But they are also inside the cell, like a kind of protective layer which is made of two kind of protein. 267 00:29:06,690 --> 00:29:13,110 Like these red filaments, it's a protein called actin, which makes a polymer a biopolymer, which makes these long filaments. 268 00:29:13,440 --> 00:29:18,300 And then those are kind of proteins, which is called myosin, which is what is called a molecule armature. 269 00:29:18,780 --> 00:29:25,290 And by using an external source of energy which is called ATP, which is the fuel of the cells, 270 00:29:25,590 --> 00:29:32,370 this myosin, they can kind of contracts and pull on these actin filaments. 271 00:29:32,700 --> 00:29:36,990 And when they do that, somehow they come to a balance. 272 00:29:37,170 --> 00:29:41,850 The other forces creating by, uh, this scattering molecule, 273 00:29:41,850 --> 00:29:50,130 which tends to make the cells spread against each other by something which is similar to, uh, surface tension like waiting phenomena. 274 00:29:50,280 --> 00:29:55,470 And these contractions creating like a mechanical force which is opposing that. 275 00:29:55,770 --> 00:30:02,010 And then the results of these two kind of forces cortical tension and adhesion tension. 276 00:30:02,280 --> 00:30:08,970 And so cell junction create like a net basically tensile mechanical force of the junction between cells. 277 00:30:09,240 --> 00:30:12,330 So this is really the key mechanical ingredients here. 278 00:30:13,030 --> 00:30:21,500 So no like if you train like to me as a physicist, what you're used to is that you're given basically the forces. 279 00:30:21,510 --> 00:30:28,050 So here's the map of the mechanical tension of the cell. So junction and the map of the pressure inside the cell. 280 00:30:28,320 --> 00:30:34,559 And then you can work out using partial formula. What is the individual mechanical stress on. 281 00:30:34,560 --> 00:30:41,640 Solve for each cell. And with that by having a knowledge of the mechanical property of the tissue. 282 00:30:41,670 --> 00:30:47,640 Simply speaking, by knowing Hooke's law for the tissue, then you can basically infer what all the cell shapes. 283 00:30:48,090 --> 00:30:49,950 That's what a physicist will do. 284 00:30:50,400 --> 00:30:56,670 But the problem here is just like we don't have access to this information, this is the information we want to have access to. 285 00:30:57,030 --> 00:31:02,339 And once we have through basically microscopy imaging and machine learning and the like, 286 00:31:02,340 --> 00:31:05,910 this is the shape of the cell, the segment and mass of the cell. 287 00:31:06,240 --> 00:31:12,390 So can we use that information to infer back these mechanical quantities. 288 00:31:12,630 --> 00:31:14,010 And the answer is yes. 289 00:31:14,190 --> 00:31:21,959 And it's basically part of a family of problem which is, uh, very ubiquitous in physics and applying mathematics, which are called inverse problem. 290 00:31:21,960 --> 00:31:25,290 And here the inverse problem question we are asking is like, 291 00:31:25,470 --> 00:31:33,030 how can we from infer from the shape of the cells we measure from microscopy, what are the mechanical forces. 292 00:31:34,350 --> 00:31:42,959 So here I'm not gonna give the full theory behind it, but just give you a feeling for it when you have these like very good. 293 00:31:42,960 --> 00:31:48,650 Like basically segmentation mask for the cell, you can know very precisely their shape. 294 00:31:48,660 --> 00:31:49,560 What does it mean? 295 00:31:49,770 --> 00:31:57,720 Knowing precisely their shape means that you can find all the vertices in the tissue, which are points where at least three cells the junction meets. 296 00:31:58,260 --> 00:32:03,839 And then you can also very precisely parametrise the shape of each cell. 297 00:32:03,840 --> 00:32:06,420 So junction by arc of circle. 298 00:32:06,810 --> 00:32:15,480 Now if we go back to the mechanics of the tissue, I told you that as in each cell cell junction you have a, you have tensile mechanical stress. 299 00:32:15,720 --> 00:32:24,000 And this basically tensile mechanical stress you create is creating like a tension force which is acting on the vertex. 300 00:32:24,330 --> 00:32:30,960 And so if you make the potencies that you will basically at steady states or mechanical equilibrium, 301 00:32:31,110 --> 00:32:39,300 you can say that the mechanical tension acting on each vertex, you know, in the tissue or balance. 302 00:32:39,750 --> 00:32:43,950 Then like looking back at the question of the shape of the junction, 303 00:32:44,220 --> 00:32:50,340 you will realise that this basically junction, they are not straight lines, the off curve. 304 00:32:50,670 --> 00:32:56,340 And the reason why the curve is because you have difference of pressure inside neighbouring cells, 305 00:32:56,340 --> 00:33:03,870 like difference of pressure between like um soap bubble in a form and actually like you can use a 306 00:33:03,870 --> 00:33:09,359 law which was derived independently by young and lab last at the beginning of the 19th century, 307 00:33:09,360 --> 00:33:15,080 thinking about this problem of like soap bubbles in the form in which called the young lab plus no. 308 00:33:15,120 --> 00:33:20,699 And we say that the difference of pressure between two neighbouring cell is proportional to 309 00:33:20,700 --> 00:33:26,160 the tension at the junction between these two cells and the coefficient of proportionality, 310 00:33:26,280 --> 00:33:33,720 the radius of curvature of the junction, which you can access by fitting the social junction by your knuckle circle. 311 00:33:34,290 --> 00:33:36,780 And this is the radius of this arc of circle. 312 00:33:37,050 --> 00:33:43,860 So if you do that for all the cells in your tissue because a cell has an average six neighbour in the tissue. 313 00:33:44,260 --> 00:33:50,140 You end up with a lot more equation than variables, your variables or the tension and the pressure. 314 00:33:50,290 --> 00:33:56,440 And because you like the tension, and the pressure will come back every time you write this equation for a given cell and its label, 315 00:33:56,590 --> 00:34:01,090 you have a lot more equation done than than variables. 316 00:34:01,270 --> 00:34:08,800 And this is very interesting because like you have another determining system of equation, meaning that you can always find a solution to the system. 317 00:34:09,070 --> 00:34:18,070 And then once again you can use basically like machine learning like optimisation technique to find the most likely, 318 00:34:18,250 --> 00:34:21,940 ah, distribution of mechanical tension and pressure. And here we go. 319 00:34:22,150 --> 00:34:30,280 If we put together these two things. So spatial transcriptomics methods I told you about which allows you to measure a single 320 00:34:30,280 --> 00:34:34,600 cell resolution in your tissue section through gene expression in individual cell. 321 00:34:34,930 --> 00:34:44,050 And this forced interference modelling, which allowed to infer the mechanical forces on acting on individual cells in the tissue. 322 00:34:44,290 --> 00:34:49,900 Then we have a pipeline where we can measure at the same time the physics and the biology. 323 00:34:50,230 --> 00:34:54,910 So we can measure the mechanical forces acting on individual cells in the tissue. 324 00:34:55,150 --> 00:35:04,959 But we can also say which genes are expressed in which cell, for example, this gene twist one is not expressed in this particular cells, 325 00:35:04,960 --> 00:35:12,040 but is expressing these cells or this gene which is called wind five 5G, which is expressed here in these cells but not in these cells. 326 00:35:12,370 --> 00:35:18,489 And then it's an amazing opportunity to try to decipher what is the relationship between 327 00:35:18,490 --> 00:35:24,040 mechanical forces generated and acting on cells and the gene expression pattern, 328 00:35:24,250 --> 00:35:34,330 and try to decipher if mechanical forces can have an impact on the genetics of the cell and the selfish decision behaviour. 329 00:35:35,600 --> 00:35:38,780 So, um, but there is a paper way. 330 00:35:38,780 --> 00:35:45,670 I explain all that in more details and you will be like, be able to find it online and to download the preprint. 331 00:35:45,680 --> 00:35:49,010 We though it's not behind a paywall so everybody has access to it. 332 00:35:49,370 --> 00:35:54,950 And for the rest of the presentation, I want really to focus on the particular example, 333 00:35:55,250 --> 00:36:03,739 which is of basically like the development of the brain of the embryo as this 8.5 day stage of embryonic development. 334 00:36:03,740 --> 00:36:11,090 And look at different region of the of the brain and see what can physics tell us about the development of the brain of the embryo. 335 00:36:12,080 --> 00:36:20,990 So as I mentioned before, we can we have a data set and we have like the capability to segment delineate the contour of every individual cell. 336 00:36:21,170 --> 00:36:29,299 And we can use or mechanical force inference algorithm to infer the spatial pattern of tension at each cell. 337 00:36:29,300 --> 00:36:38,070 So junction and the pressure inside the cell. But because we have also for each cell in this data set, the gene expression pattern, 338 00:36:38,280 --> 00:36:43,620 then we can run the kind of bioinformatics genomics analyses I was telling you about. 339 00:36:43,920 --> 00:36:47,790 And on the basis of which genes are expressed by your cells. 340 00:36:48,000 --> 00:36:56,190 We can start to say, you know, like these cells, all of these particular cell types, you know, like in purple and these like light blue cells. 341 00:36:56,340 --> 00:36:57,300 And those are cell type. 342 00:36:58,020 --> 00:37:06,059 And if you take these three regions and you take the dominant cell type, you will observe that already at this stage, as I told you, 343 00:37:06,060 --> 00:37:13,560 it's a bit like in the nodules, like the tissue, the organelle starting to be patterned and they all will segregated from each other in space. 344 00:37:13,800 --> 00:37:17,340 So here you have that the brain of the animal in orange. 345 00:37:17,490 --> 00:37:23,910 And it's separated by, you know, the like nerve cell type which are called the neural crest here in red here, 346 00:37:23,910 --> 00:37:26,670 like in another region of the brain of another tissue, 347 00:37:26,670 --> 00:37:33,960 which is called the cranial mesodum, which will give you the bones and the muscle of the face later on in development, 348 00:37:34,230 --> 00:37:37,980 again surrounded by brain tissue, the full brimming brain and breadth. 349 00:37:38,190 --> 00:37:42,410 And here, in another region of the brain, you have pure brain tissue. 350 00:37:42,420 --> 00:37:50,940 So it's a new epithelium where you have like two compartments of the brain which are starting to form and separate from each other, 351 00:37:51,180 --> 00:38:00,060 the midbrain and the brain. And remember, like we have for this dataset, the map of the mechanical forces. 352 00:38:00,450 --> 00:38:09,719 So we can start by asking a very simple physics question, which is like all all these compartments between different cell types, 353 00:38:09,720 --> 00:38:17,580 different tissue forms, and maintain either some form of like mechanical patterning happening. 354 00:38:18,060 --> 00:38:27,420 And what you can do to answer this question with our approach is to look at cells which teeter at the boundary between the different cell compartment, 355 00:38:27,690 --> 00:38:31,560 and look at the particular social junction which sit on the boundary. 356 00:38:31,920 --> 00:38:37,620 And then what? You can measure the mechanical tension for these junctions sitting on the boundary. 357 00:38:37,860 --> 00:38:43,920 And what you will see that the mechanical tension for social junction at the boundary between two tissue compartments, 358 00:38:44,190 --> 00:38:52,830 the mechanical tension is always higher and even sometimes much higher than the average mechanical tension in the bulk of each compartment. 359 00:38:52,980 --> 00:39:01,410 So it is like a defined biophysical phenotype, like we observed that in the embryo when you have different tissues, 360 00:39:01,410 --> 00:39:08,160 which are partition in space at the boundary between these different tissues, you have a high mechanical tension. 361 00:39:08,790 --> 00:39:18,840 But here remember not only we have the like the physics and mechanics information, but we have also basically the genetic information. 362 00:39:19,020 --> 00:39:27,240 So we can start asking what is the biological the molecular underpinning of this biophysical phenotype we have characterised. 363 00:39:27,480 --> 00:39:36,090 And we can do that. But first I would like to prove you that's basically like mechanical forces and IO. 364 00:39:36,120 --> 00:39:40,380 Mechanical tension at the interface between these two compartments is enough to 365 00:39:40,440 --> 00:39:44,490 warrant the maintenance of the boundary and even the formation of this compartment. 366 00:39:44,700 --> 00:39:52,530 And then I going back to my background in theoretical physics and the family of active matter physics model I developed during my PhD, 367 00:39:52,950 --> 00:40:00,530 which are called self-propelled cellular Pots model. So the modification of the original model was actually developed here in Oxford 368 00:40:00,540 --> 00:40:05,939 in the department by an Australian physicist who was named Renfrew Potts. 369 00:40:05,940 --> 00:40:13,320 And he was um, um, a Rhodes Scholar at Queen's College here in the 60s during his PhD in theoretical physics with a rhythm. 370 00:40:13,590 --> 00:40:22,440 And at that time they were not interested in cells at all. They were basically interested in generalising the easing model to n parameter. 371 00:40:22,860 --> 00:40:30,749 But somehow in the 1990s, two of the theoretical physicists of French nautical physicists, clockwork Renault and the American one, James Blustered, 372 00:40:30,750 --> 00:40:38,100 during their postdoc in Japan at the same time and the same group realised that they could use this model to act to actually, 373 00:40:38,100 --> 00:40:41,220 like, model the shape of the cell in this tissue. 374 00:40:41,640 --> 00:40:47,640 And they took again the Hamiltonian, um, which was written by Potts for the model. 375 00:40:47,820 --> 00:40:52,290 And they assign biological meaning to the different system of a Newtonian. 376 00:40:52,560 --> 00:40:58,320 And they show that one of the term model, the bulk elastic energy of the cell, and the other model, 377 00:40:58,530 --> 00:41:02,580 the interfacial tension, which account for the addition to the cell contractility. 378 00:41:02,790 --> 00:41:10,379 In my PhD, I expanded this model to integrate the active cell motility and the active force. 379 00:41:10,380 --> 00:41:16,950 These these cells basically, uh, apply on the surrounding to be able to move like the Julia showed. 380 00:41:17,610 --> 00:41:23,790 And here I have used this model to confirm the experimental observation we have made. 381 00:41:23,970 --> 00:41:32,240 I think the experimental values I have measured for the mechanical tension at the boundary and in the bulk of each compartment, and I. 382 00:41:32,340 --> 00:41:37,440 Fed them as the input values for my model for the three different system. 383 00:41:37,590 --> 00:41:44,580 And you can see that if I take the experimental value, the high mechanical tension at the boundary and the lower mechanical tension in the bulk, 384 00:41:44,760 --> 00:41:51,120 and I run simulation of the dynamics of the system, it's enough to show that the boundary is maintained. 385 00:41:51,120 --> 00:41:56,220 So yes, with the I mechanical tension at the boundary, you can maintain the boundary between compartments. 386 00:41:56,490 --> 00:42:02,310 If you make the tension of the boundary equal to the tension in the bulk of each compartment, 387 00:42:02,520 --> 00:42:08,579 then basically the boundary is not maintained and the two compartment starts to mix, which is not good. 388 00:42:08,580 --> 00:42:13,230 Like you won't be able to progress in development because to do that you want to have a 389 00:42:13,260 --> 00:42:17,970 tissue compartment which will or will organise to make organ and the body to develop. 390 00:42:18,510 --> 00:42:27,060 So the next question is like all these are your mechanical tension at the interface between two different cell types, 391 00:42:27,240 --> 00:42:32,850 enough to explain the formation of these compartments in first instance. 392 00:42:33,450 --> 00:42:40,140 And like so that we don't have experimental evidence, but we can use theoretical physics simulation to look at that. 393 00:42:40,320 --> 00:42:47,550 So we can use with the same experimental volume for the like interfacial mechanical tension between cells of a different type, 394 00:42:47,790 --> 00:42:52,380 and then taking the experimental values and using them as an input for the simulation. 395 00:42:52,620 --> 00:42:57,420 We can see if the cells will sort out and segregate spatially. 396 00:42:57,570 --> 00:43:04,440 And this is exactly the kind of like, uh, cell sorting experiments Julia was showing for the stockfish on real. 397 00:43:04,740 --> 00:43:11,160 And here we do. We are doing simulation of that which are based on mechanical parameters, which we measure on most on real. 398 00:43:11,400 --> 00:43:19,890 And here you can see that with the experimental value of the tension, we can indeed like show that cells will segregate into special compartments. 399 00:43:20,190 --> 00:43:25,709 If you take again the interfacial tension between two cells of the different 400 00:43:25,710 --> 00:43:30,720 type being equivalent to interfacial tension of two cells of the same type. 401 00:43:30,870 --> 00:43:34,620 Then you never have segregation and you stay with this salt and pepper pattern. 402 00:43:34,950 --> 00:43:39,419 So by using this simulation, we can even predict that, uh, 403 00:43:39,420 --> 00:43:44,520 having your interfacial tension between two different cell types might be enough in 404 00:43:44,520 --> 00:43:49,709 first instance for these cell types to segregate spatially and partition in the embryo. 405 00:43:49,710 --> 00:43:58,800 But we will need to do future experiments to prove that. So here again you can see this virtuous dialogue between physics and biology where we can 406 00:43:58,800 --> 00:44:04,680 use basically models to predict biological behaviour and then go back to experiment, 407 00:44:04,680 --> 00:44:11,250 to try to like, falsify or verify them depending which region of science you have. 408 00:44:11,850 --> 00:44:16,170 But it's a question for philosophers, not for us today. 409 00:44:16,440 --> 00:44:21,599 Okay. But let's go back to the system because like like my pipeline is very rich. 410 00:44:21,600 --> 00:44:25,200 You give me both the mechanics but also the genetics. 411 00:44:25,470 --> 00:44:31,560 And here I told you like the addition of the mechanical information for each single cell. 412 00:44:31,680 --> 00:44:37,440 I have also the genomic information. I'm able to know which genes are expressed by each cell. 413 00:44:37,710 --> 00:44:45,840 So what I can ask know as a question is what is the molecular underpinning of this mechanical patterning phenomenon? 414 00:44:45,840 --> 00:44:51,959 I observe. And then like what I can do is exactly what I've done before with mechanics. 415 00:44:51,960 --> 00:44:59,250 I can look at cells, which are the boundary between my tissue compartment, and I can ask, know from the standpoint of molecular biology, 416 00:44:59,550 --> 00:45:06,570 all these cells sitting in the boundary between tissue compartments, difference in terms of the gene they express. 417 00:45:06,750 --> 00:45:11,460 And the answer is yes. The cells which sit at the boundary between the compartment. 418 00:45:11,670 --> 00:45:17,790 They are they express different genes. Now you can ask me what all these different genes they express. 419 00:45:18,150 --> 00:45:19,170 We look at that. 420 00:45:19,380 --> 00:45:27,480 And when you look at these genes, you realise that they are from a family of genes which are called the aifread ligand receptor genes. 421 00:45:27,660 --> 00:45:37,500 And these are basically genes which are coding for proteins, which sits on the cell membrane and these proteins on the cell membrane. 422 00:45:37,740 --> 00:45:43,890 Basically, they have a rule for cell cell communication. They also need both cell to communicate with each other. 423 00:45:44,040 --> 00:45:47,729 So one cell we love the ligands and this label. 424 00:45:47,730 --> 00:45:51,270 So we'll have the receptor. They will come in contact together. 425 00:45:51,630 --> 00:45:55,050 The ligands will basically bind with the receptor. 426 00:45:55,050 --> 00:46:01,950 It's the biochemical interaction. And this will trigger something inside the cell what we call the signalling cascade. 427 00:46:02,250 --> 00:46:11,010 And this signalling cascade will propagate down to the nucleus and basically drive the expression of all the particle agents. 428 00:46:11,520 --> 00:46:20,129 And here like what we have been doing is what we have been working with colleagues who are biochemists and who have mapped out the interaction, 429 00:46:20,130 --> 00:46:25,800 the energies between these ligand and receptors, and from basically the expression data, 430 00:46:25,800 --> 00:46:30,000 you know, almost basically everything you produce for a given ligand and receptor. 431 00:46:30,090 --> 00:46:34,940 So you can approximate. Almost 14. You will make for each slide gun and receptor. 432 00:46:35,210 --> 00:46:40,130 Then basically you can compute like the equivalent of an interaction potential 433 00:46:40,220 --> 00:46:44,300 between the legend and the receptor at the face of neighbour will sales. 434 00:46:44,600 --> 00:46:49,549 And then you can use like a statistical approach which will give you basically the 435 00:46:49,550 --> 00:46:55,790 likelihood of interaction between a given ligand and receptor expressed by normal cells. 436 00:46:56,030 --> 00:47:01,490 And then you can look at basically at the genes, the particular ligand and receptor, 437 00:47:01,550 --> 00:47:05,960 which has the highest likelihood of interaction amongst label cells. 438 00:47:06,140 --> 00:47:11,120 For cells which are the boundary between compartments and cells, we showing the bulk of each compartment. 439 00:47:11,390 --> 00:47:14,810 And then you will pick up a few ligand receptor genes. 440 00:47:15,170 --> 00:47:22,610 And as I told you, there are functions to all those cells to communicate and to tell them to express particular genes. 441 00:47:22,850 --> 00:47:30,320 So we can go back to all genomics information for each cell and ask what all these genes which are downstream, 442 00:47:30,530 --> 00:47:37,100 these messengers and the genes which are those things, these messengers, they are cells that are adhesion proteins. 443 00:47:37,370 --> 00:47:42,860 The coverings I was telling you about, and essentially what's happening is that these cells at the boundary, 444 00:47:43,130 --> 00:47:49,400 they upregulate one particular type of gathering, like for example, the gathering 11. 445 00:47:49,520 --> 00:47:58,130 So here it's a calculated tissue compartment. And then it's completely not expressing this other tissue compartment on the other side of the boundary. 446 00:47:58,310 --> 00:48:05,270 And then if you look at this also colouring the colouring to here is expressing these tissue compartments. 447 00:48:05,270 --> 00:48:08,600 But it's not very much expressed here in these other tissue compartments. 448 00:48:08,930 --> 00:48:13,009 So for cells which are the boundary between the two tissue compartment they 449 00:48:13,010 --> 00:48:17,540 have different adhesion protein and tethering two cannot bind to tethering 11. 450 00:48:17,750 --> 00:48:21,560 So some cells which are the boundary they cannot bind to each other. 451 00:48:21,800 --> 00:48:29,960 And remember what I was saying before what condition the mechanical tension between two neighbouring cell is partly the cell cell adhesion. 452 00:48:30,110 --> 00:48:36,979 So here by expressing different adhesion molecules for cells on the boundary they cannot bind to each other. 453 00:48:36,980 --> 00:48:41,480 And then basically you decreasing the adhesion and you increasing the tension. 454 00:48:41,750 --> 00:48:49,760 That's why we are able to express to explain the molecular mechanism which is conditioning or biophysical phenotypes. 455 00:48:50,030 --> 00:48:53,839 And I think this is something really exciting and interesting, 456 00:48:53,840 --> 00:49:02,240 because we can start bridging physics to biology and really understand the interplay between physical forces and the genomics. 457 00:49:03,410 --> 00:49:11,420 So is this only me we seeing this kind of thing, or is it something you like experimentally confirmed by others? 458 00:49:11,420 --> 00:49:15,079 You know the system and the answer is yes is consumed by others. 459 00:49:15,080 --> 00:49:18,409 So you have this beautiful work by Francois Fargo. 460 00:49:18,410 --> 00:49:25,640 Tool was been studying the development not of the most embryo but of the Xenopus, some real, which is like a frog. 461 00:49:26,000 --> 00:49:35,180 And then again, looking at the development of this animal, you have like a period of development where you make this different kind of tissue layer, 462 00:49:35,420 --> 00:49:40,610 the Ectodum and the middle Durham here, and you will see that you have a boundary between these red here, 463 00:49:40,610 --> 00:49:44,060 which is the middle term, and here the blue here, which is the ectodum. 464 00:49:44,300 --> 00:49:49,520 And they looked basically at the cells, the junction sitting on the boundary. 465 00:49:49,820 --> 00:49:54,920 They applied a similar basically mechanical force inference method. 466 00:49:55,130 --> 00:50:02,240 And they were able to show that the mechanical tension is higher for these junctions sitting at the boundary between these two tissue compartments. 467 00:50:02,510 --> 00:50:12,889 And then they did beautiful functional experiments where they made frog embryo which were unable to express these effing ligand receptors signalling 468 00:50:12,890 --> 00:50:22,400 messenger at the surface of the cells or which were unable to express a particular colour in molecules which also lesion I was telling you about. 469 00:50:22,790 --> 00:50:32,209 And then they were able to exactly confirm that they were seeing the same thing, and that it was indeed this effing signalling messenger molecule, 470 00:50:32,210 --> 00:50:38,690 which were telling the south of the boundary to express less cell cell adhesion molecule and increased attention. 471 00:50:39,080 --> 00:50:48,170 This was also observed in another system. So this time is not the embryo for frog, it's an embryo for little fish, which is called the zebrafish. 472 00:50:48,470 --> 00:50:56,240 And you're looking much later in development when basically like the spinal cord of the young real is form. 473 00:50:56,660 --> 00:51:01,490 And then here what you are looking so at that stage is called the neural tube. 474 00:51:01,820 --> 00:51:07,040 And this neural tube is starting to be divided in different tissue compartments. 475 00:51:07,190 --> 00:51:14,870 So you can see that as the embryo develop the level of complexity is increasing like organs are forming. 476 00:51:14,870 --> 00:51:24,800 And then these organs are starting to be patterned and be made of different tissue layers which need to be compartmentalised, um, from each other. 477 00:51:25,010 --> 00:51:28,159 And then you know here what you can see that you have difference. 478 00:51:28,160 --> 00:51:31,340 Um, you know, like, like tissue layers. We. 479 00:51:31,380 --> 00:51:38,300 So starting to be pattern in the like the neural tube of these fish embryo and shears. 480 00:51:38,300 --> 00:51:40,820 They have done like a very brave experiment. 481 00:51:41,090 --> 00:51:50,120 Like um, the person inside with no like, um, a professor, um, at, um, um, Washington University in the US. 482 00:51:50,450 --> 00:51:56,959 It took two basically to dissect this embryo and dissociated individual cells of these different tissue 483 00:51:56,960 --> 00:52:02,810 layers and to take them and to come with a system where you would have like a micro manipulator, 484 00:52:02,930 --> 00:52:10,520 which is under a microscope, and it could take a cell from one side, for example, the red cell here and the green cell here, 485 00:52:10,790 --> 00:52:17,090 put them in contact, let them make an adhesion between them, and then even through them with a given force. 486 00:52:17,270 --> 00:52:24,440 So we could directly measure basically to sell, sell seltzer adhesion that we were inferring from the gene expression. 487 00:52:24,440 --> 00:52:29,810 There is the direct physics mechanical measurements of these social forces. 488 00:52:30,140 --> 00:52:32,510 And he was able to show exactly that, the same thing. 489 00:52:32,510 --> 00:52:41,749 We were showing that actually these cells, by playing the differential expression of two, um, like, uh, cells, that adhesion molecule, 490 00:52:41,750 --> 00:52:49,100 the colouring twin, the colouring 11, they you able to tune the mechanical tension of the boundary and create these tissue compartments. 491 00:52:49,550 --> 00:52:58,730 Quite interestingly, they show that, that this was driven by um like a particular other gene which is called sonic etch, 492 00:52:59,120 --> 00:53:03,170 which is a molecule which is secreted by the cells and which can diffuse. 493 00:53:03,560 --> 00:53:06,830 And like we were not, you know, like aware of that. 494 00:53:06,830 --> 00:53:10,100 But then when we saw the paper, we thought we should go back. 495 00:53:10,520 --> 00:53:16,340 And indeed we see exactly like going back to these, you know, like special transcriptomics information. 496 00:53:16,550 --> 00:53:22,070 We could like see that we have this beautiful gradient of this gene, Sonic at the boundary. 497 00:53:22,520 --> 00:53:26,690 So you can see that with this approach we can start drawing very powerful, 498 00:53:26,690 --> 00:53:31,879 you know, like, uh, you know like relationship between mechanics and biology. 499 00:53:31,880 --> 00:53:39,050 And I go natural foods. My, my talk with these two slides by so in knew that you know like with this type of 500 00:53:39,050 --> 00:53:43,970 like experimental approach we knew of the two to look in an unbiased manner, 501 00:53:44,130 --> 00:53:50,240 a correlation between gene expression pattern and mechanical quantity, like the pressure inside the cell, 502 00:53:50,510 --> 00:53:56,550 or basically the magnitude of the stress tensor to noise flux results basically turns out compressive forces. 503 00:53:56,800 --> 00:54:03,080 And we can know pinpoints genes, which are what we would call the kind of sensitive genes. 504 00:54:03,260 --> 00:54:08,960 They are able to adjust their expression level in the cells in response to mechanical forces. 505 00:54:09,200 --> 00:54:16,910 And we can investigate that using basically, again, like, uh, tools from applied statistics and machine learning. 506 00:54:17,330 --> 00:54:23,450 And not only we can do that at the linear older, but we can look at nonlinear correlation. 507 00:54:23,690 --> 00:54:29,360 And then we can start to see that for example some gene they will be upregulated more expressed 508 00:54:29,360 --> 00:54:35,300 by the cell when the pressure is low but completely unregulated when the pressure inside the. 509 00:54:35,320 --> 00:54:40,850 So I are you can see the opposite behaviour like the same switch like function here. 510 00:54:40,850 --> 00:54:44,540 These genes they are not expressed when the cells under low pressure. 511 00:54:44,720 --> 00:54:50,960 But the you express outside pressure. And the same for like the tensile and the compressive forces. 512 00:54:51,170 --> 00:54:55,909 So no really we are starting to develop the tools to understand because of the 513 00:54:55,910 --> 00:55:02,600 relationship between mechanics and biology and understand all mechanical forces, 514 00:55:02,870 --> 00:55:07,219 shape the embryo and understand you like the idea. 515 00:55:07,220 --> 00:55:13,940 Understood. Thompson thought that, um, an organism is a diagram of force and result. 516 00:55:13,940 --> 00:55:21,799 I couldn't have stopped. Uh, thanks, the colleagues. I've done my research recently, especially, uh, my mentor, uh, when I was in Cambridge, 517 00:55:21,800 --> 00:55:26,480 professor Ben Simons was, like, initially also theoretical physicist. 518 00:55:26,480 --> 00:55:33,770 He was the head of condensed matter at the Cavendish Laboratory and decided to to shift to biology some 20 years ago. 519 00:55:33,770 --> 00:55:37,579 And he was very influential, you know, like, in my way, 520 00:55:37,580 --> 00:55:43,190 to see the biological questions at the interface between disciplines and to approach a question. 521 00:55:43,430 --> 00:55:47,450 And, of course, the funding which has enabled this research and you for your attention. 522 00:55:47,450 --> 00:55:48,320 Thank you very much.