Imaging living systems

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So for example, 820.

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Um, it's a great pleasure to to be here today, um, talking to you.

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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,

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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.

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And I worked on the sort of active motor and tissue mechanics models.

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Julia. Julia told you about helium. So, talking about Cambridge, I want to be a bit mischievous and actually, like, willing to like that.

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You're not the first generation of physicists to be interested in biology.

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And I think the best example of that is the discovery of the structure of DNA by, um, Franklin,

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Watson and Crick and essentially like it was enabled by the development of the concepts and tools of physics.

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And, uh, well,

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by that I mean the developments of the quantum mechanics at the beginning of the 20th century and experimental development associated with that,

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like X-ray diffraction, first by, uh, Bragg, father and sons in Cambridge and then by Moseley,

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um, here in Oxford and then later on after World War two by parents and Kendrew,

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um, in Cambridge, where I actually started this, basically this little lab, um,

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in the shade of the back of the old Cavendish Laboratory and that said themselves to

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our young and motivated people to work out the structure of biological molecules,

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proteins and DNA. And here is the famous basically like E3 diffraction picture acquired by Rosalind Franklin,

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which basically helped Watson and Crick confirmed that their theoretical model of the structure of DNA was right.

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Um, 70 years on, like it seems, you know, like we are in the same position, um, not talking about molecules anymore,

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but cells and tissue, as Julia, um, so eloquently like, demonstrated it during a presentation earlier this morning.

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So I want to tell you a bit about myself, because that is exactly where I am at the moment.

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So I have moved like, you know, like from physics to biology.

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And what I'm really interested in is doing what people have done before me,

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trying to apply the tools and the concept of physics to answer biological questions, questions which have relevance to biology.

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In biomedical sciences, we're trying to find mechanism of diseases and to cure them.

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And the particular questions I'm interested in is the role of mechanical forces in selfish decision.

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So a selfish decision is a decision the cell is taking, um, during its life.

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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.

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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.

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And we find them in the embryo and in the adult tissues.

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But then they can also take another decision, which is like when they divide,

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they can decide to either remain stem cells or produce differentiated cells which do not have the same definitive potential to divide,

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but or specialise to accomplish with particular function.

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And this selfish decision between dividing and not dividing, moving and moving.

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Remaining the stem cell. Differentiating interacting with your neighbour and not interacting with your neighbours.

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They are the urging of all the biological processes like embryonic development, tissue mill stages and regeneration.

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Julia was like showing beautiful movie of the development of starfish and Drosophila embryo and then movie showing the example of regeneration,

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for example wound healing. But they also very much involve basically, um, in uh,

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the inception and physio pathology of a host of diseases like cancer or inflammatory diseases like we are interested in at the Kennedy Institute,

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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.

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So my presentation is entitled Imagining Living Systems.

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But really like in my lab, we are an interdisciplinary lab.

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And not only will using imaging, but we are using this imaging in comparison.

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Like. Like not in comparison, but actually in addition of further technologies brought by physics into biology.

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So micro fabrication technology, which allowed basically to grow and confine cells on patterns of a particular shape,

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and then basically just look at the impact of mechanical forces of them.

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So you can gross them in these basically macro fabricated patterns into a system, in vitro culture system.

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And then you merge them. But we also basically genetically modified these cells to make them fluorescent where they express a particular gene.

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For example, here is a gene them keratin 14.

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And then when this cells express these gene, so when they produce a protein associated to this gene that they become fluorescent.

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And all their daughter cells when they divide or also fluorescent the same colour.

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So we can track, you can start to track basically the division.

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And so think your pattern and then ask the question why some cells, you know,

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like divide and express to particular gene and commits to a particular effect.

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And when some other cells do not do that.

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And of course, basically to do that we need to use modern image analysis techniques so we can detect and track these cells.

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And for this we are using machine learning and the particular form of machine learning which is called deep learning.

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But we are also using basically the most recent methods in genomics to not be limited to look at a single genes,

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but look at basically the full basically genome and see what's happening when a particular

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cell commits to felons to a given state and see which genes it expresses or not.

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And of course, we are still using models from theoretical physics trying to make sense of all these experiments.

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Data some. So. Today.

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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,

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you know, cells as a precedent for spatial and temporal resolution.

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And all we can use that's basically to infer mechanical forces, um, across cells in a tissue and all.

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We can also use similar images. Similarly imaging approaches to look at uh, the expression of genes in cells.

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So this is typically uh, the kind of modern microscope we have at the Kennedy.

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So this is like a very complex device. So it's not like microscopic to fluorescence microscope.

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So we are using it. We source injected or genetically modified to produce basically fluorescent markers.

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And then it's a light sheet microscope which is using advanced adaptive optics basically to correct focus and spherical aberrations.

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It's it's using basically a set of like two objectives at 95, uh, 45 degrees.

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And then those objective for illumination with a laser,

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which is basically going through a cylindrical lens to make a light sheet, which basically will be scanned across the specimen,

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which is contained in the box, which are allowed to maintain, like the specimen, either like a tissue section of cells in culture alive.

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So you need to control the oxygen, the CO2 levels.

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You need to pump them with culture medium.

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And actually when you do that, you can start studying not only cells in a dish, but more complex system like the developing unreal.

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So this cartoon here shows basically like the development of a mould, some real.

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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.

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And this basically initial cell is gonna divide and very progressively after two days, you will have a small bowl of cells,

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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.

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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,

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well the cells in the middle, they will develop and make the embryo.

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And as time goes on cells continue to divide.

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And the they also basically differentiate and assume different fates which show up as we pattern into space.

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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.

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And then the truffle blast, which is like a more differentiated precursor of the plus of the placenta and the primitive underarm,

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which is basically and those are from very unique lineage, um, like the placenta.

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Here. Quite interestingly, after almost two week we have like.

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Something which was initial your ball which is like normal longitude like a cylinder.

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And here we have this what we call the free gem layer, the AP blast, the mesodum and the under derm.

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And these free different groups of cells is free.

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Like like for two tissues, they will give rise to all the other tissue in the body of the animal.

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So for example like um, the brain or actually the skin and the rest of the nervous system will be derived from this.

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Like if people ask like and then you will have like other um, which is also called the ectodum,

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then you will have these other like organs derived from this other gym layer.

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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,

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this little group of cells which we call the under Durham. And here the earth's or the cardiovascular system will be derived.

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And the muscles, for example.

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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.

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And you can see here that here you have just like basically like three little groups of cells,

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you know, like with your patch on one on top of the other at 6.5 days,

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at 8.5 days, like two days later, you have something which looks like an animal,

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you have a brain, you have Hertz, you have like a goats, you have like a spinal cord.

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You have like a non-serious posterior axis. Um, you have like left or right polarity.

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So in a matter of two days, you transition from a ball of cell to something which looks like an adult animal, almost.

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And with the kind of microscope I've shown, you know,

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you can image that and see all the cells in the embryo undergoing this process, which we call embryonic morphogenesis.

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And here, like the most deeply, I have been modified genetically.

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So the nucleus of the cell is fluorescent. And you can see all these nucleus about dividing and then moving and the pattern,

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the of the embryo being established until posterior pattern, the left right pattern.

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And you can image this in total today in a microscope.

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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

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self-organisation process which involve like an very large number of different phenomena at different scales.

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So if we try basically to use the approach of a physicist,

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we would like to look at the different scales at play and find what other key players are these different scales.

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So of course we have the scale of the molecules and these. So the genes which are contained in the nucleus.

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But then we have all the molecules which are basically proteins which are coded by the gene and which

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are secreted by the cells like some the molecules which are called broad structures and morpho genes,

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and which will basically instruct the cells to divide or differentiate in one particular cell types.

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So they are the messenger which help the cells coordinate their action during embryogenesis.

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But we can also see what's happening. Hmm. Let's go on boys back.

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Okay, cool. And then we have also at the nucleus kill the cells.

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And then here we can start thinking about the shape of these cells, um, their contractility,

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but also all they divide and all they move, like Julia showed this neurone.

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But then if we go at one scale, we have the scale of the tissue.

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Where? Then the mechanical fancy, then the mechanical properties of the cells and the tissue of the wall will become important,

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as well as the structure and the geometry of the tissue.

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And these different biological objects which have these different chemical and physical attributes.

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They don't live in isolation for each other.

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They basically all interact with each other, feedback on each other, mixing, basically the comprehending the whole process.

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Very difficult. So what usually what people do is just they focus on the particular scale and set of objects.

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For example, looking at the scale of molecules,

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they will look at all basically the information contained in the genes in the DNA is transcribed and then translated into protein.

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So this is what is called the central dogma of the bio molecular biology.

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Essentially you the information is contained in the DNA.

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And this information is basically the genes which are chunk of DNA,

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which codes for particular end products, which is a protein which is made of amino acids,

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but in between the genes which is made of DNA, and the protein, which is the building block,

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which is made of amino acids, you have something which is called messenger RNA.

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And what's happening is just when this cell wants to decide to produce particular building blocks,

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popular protein, it will do what is called transcription of the gene.

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So you will like select some particular genes which codes for proteins of interest.

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And then these genes will be transcribed into, uh, another kind of nucleic acid RNA which is single stranded.

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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.

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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.

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So what you can do to study basically development is use the most recent genomics

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technique and the most recent genomics techniques that your single cell technique.

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So you can take a number, you dissociate all the cells, use a microfluidic device to isolate the single cells.

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And then you can basically of this cell like they just do with some enzymes.

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So you only keep these messenger RNA which all the transcribed genes.

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And then you can barcodes them with some particular chemical which will allow you to distinguish them individually.

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So you can take single cells. Then just basically all the different RNA molecules contained into the,

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into the cytoplasm barcodes, these RNA then amplify them and then sequence them.

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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.

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So it's going to make these 2000 particular proteins. But these those are cells.

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It does not express them and it express three thousands of other genes.

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And that way you can have a very precise idea of which protein also is going to produce.

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And as you will remember, like proteins are the building block.

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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.

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If you do not produce this protein, you won't be able to do that.

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So some or understanding what genes are expressed by your particular cell or cell type of

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interest is crucial to understand the selfish decision and the behaviour of this scale of this,

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of these cells from the molecular scale point of view.

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Okay. So people have actually used that to study embryonic development.

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So they have collected most embryo like every um 12 hour between 6.5 and 8.5.

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And they have like dissociated all the cells.

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And they have used this RNA sequencing technology to sequence basically their gene express in each cell of each embryo at that time.

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And then they have used advance machine learning analyses, technique to look at the pattern of gene expression.

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So you have around 20,000 genes in the genome. So for each cell you know which of these 20,000 genes or expressed.

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And you have like a few thousand of cells for each embryo at each time point.

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And you can basically like so it's a very basically complex and big data set.

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We are talking about multi-dimensional arrays.

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And then what you can use is basically machine learning tools to do things which are called dimensionality reduction and clustering.

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And then you can start to have these low level low dimensional representation which are basically cluster.

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And this cluster, each dot is a cell. And all cells in the cluster will have similar gene expression pattern.

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So they will express, you know, like the same genes, articulate the same genes and don't regulate don't express user genes.

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And then what you start to realise is that you can map basically these cells which express the same genes to particular cell type,

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particular tissues, particular organs in the embryo.

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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.

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Really cool. But then you can go back and say, okay, but I want to understand the process at the school.

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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.

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And you can again use machine learning techniques to basically track the movement and the behaviour of these cells over time.

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And then what you can do is just like you have no idea about which genes they express, but you track them.

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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.

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So on the basis of their final position, which is called selectively,

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you can tell you machine learning algorithm to assign them to a particular cell type.

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And know your being able to tell cells of this particular cell type, you know, range will divide at that rate while still.

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So these other particular cell types in blue will divide at that other rate.

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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.

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So that's another level of description. That's another level of phenomenology.

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And then you can start thinking about what's happening at these bigger scale, the scale of the tissue in the organs.

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And there's sees the scale where, you know,

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like the collective property of cells start to mature and start to dictate the shape of the tissue in the embryo.

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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,

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by these cells forming these tissue compartments.

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And dipole physicists again have developed very clever way to look at mechanical forces in these embryonic tissues.

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They have created this small fluorescent droplet that you can inject in the embryo.

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And then by looking at there's change of shape of the droplet, you can basically,

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um, infer the, the stress, the mechanical stress tensor around the cells.

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I'm just a part of it. So essentially you can basically, uh, infer the non deviatoric part of the stress tensor.

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But it's enough basically to have like an idea of, for example, shear stress in that particular region of the street.

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But what they have also done is to again use but those are tools from physics magnetism by filling up, um,

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these fluorescent droplets with a solution of a, of fluid which is made of tiny colloidal silver magnetic particles.

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And then they can use a magnetic field like a big electromagnets to apply a force on this droplet and make them move.

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And then by doing that, they will deform the tissue around them.

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And by looking, when they stop to apply the magnetic field and the magnetic force, the droplet stops to move.

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And by looking up the droplet will change shape over time.

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They can measure the local strain of the tissue.

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In response of this mechanical stress, they have applied and they understand the mechanical property of the cell in the tissue.

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Things like, you know, like the young modulus of the Poisson ratio.

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So it's it's also like a very interesting like approach to understand the physics of these systems.

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But somehow we are still, you know, like looking at these different scaling installation.

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And in my research, what I've been trying to do is just try to find a way to reach different scale.

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And some will be able to of basically like an integrative vision at the tissue scale,

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but at the same time to be able to measure the mechanical forces acting on individual cells,

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but also be able to look at, um, the genes which are expressed by individual cells as well.

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So can we do that? So to do that you have to be able to measure the gene expression pattern in individual cells.

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But you can't dissociate the cells anymore because otherwise you losing the special information their position in the tissue.

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So people have been thinking very hard about that.

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And over the last five years they have developed a new methodology so that you can measure the gene expression,

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measure the expression of particular mRNA, not in dissociated cells anymore, but in tissue section.

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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.

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And then like what people in biology would traditionally do, like histology,

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this kind of new section where they use basically dyes which will label the nucleus and the cytoplasm of the cell.

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But here what you can do is use like small, um, western groups which basically have parts which are made of RNA.

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So. You have, like a throwaway molecule attached to a small strand of RNA, and you will make that small,

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trained strand of RNA complementary to a particular sequence of a given area in the cell.

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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.

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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.

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And each block little fluorescent dot you see here is a particular RNA protein in the cell.

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And you can use that for one to free RNA.

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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,

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like exactly like you will do with the barcode run sequences of hybridisation of different probes with different fluorophores,

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which will bind to the same and many and that will.

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And that way you will be able to image for androids, if not thousands of in a single cell in the tissue.

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So this is really amazing. And in collaboration with a group of Professor Longi at Caltech was pioneer these the development of this technology.

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We have been applying it to the really most some of you.

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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.

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And we have been taking section of the embryo and sagittal section of the embryo.

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And here you can see section of three different embryos. And these embryos look like you have a scale bar here.

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So like they are roughly like around like 1 or 2 millimetre in size.

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So you can have them on the cover slip of a microscope like the section is around 20 microns deep.

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So it's a very thin section. And then you can put your cover sleep um,

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into like a microfluidic chip which is mounted on one of these advanced micro fluorescence microscope I told you about.

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And then you have like a complex system of pumps, which allows you to pump like reagents in solution,

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which contain this fluorescent probe which can ebru dyes with RNA.

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And then you image basically your embryos.

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So you are taking dual like fluorescence images.

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And you do that for each probe cycle.

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And progressively you will be able to have all these different fluorescence notes which are particular mini molecules containing individual cell.

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So no, we have the capability to measure like the transcriptome.

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So the ensemble of gene expressed by yourself in a single cell within a tissue, you know, resection.

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So we can really have that integrated view about what's happening at the genetic level in single cell, in the tissue in context.

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But we are still missing like the mechanical forces part.

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So all consoles us.

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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.

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And actually, this man, Dustin Thompson, who was, uh, a zoologist and professor at the University of Saint Andrews,

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he wrote like an amazing book at the beginning of the 20th century, uh, which is, um, named On Growth and Form.

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And he was the first one to articulate this idea that some of the shape of an organism is a diagram of force.

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So clearly,

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mechanical forces have been known for a very long time to be very important in dictating the shape and the behaviour of biological objects.

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And if we go back at the cellular scale, what are the main determinants of mechanical forces between two cell in the tissue?

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These cells, as Julia showed us before. They can make cells adhesion.

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So actually they express some particular proteins which are called coverings, which allows them to create adhesive bonds between them.

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But they are also inside the cell, like a kind of protective layer which is made of two kind of protein.

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Like these red filaments, it's a protein called actin, which makes a polymer a biopolymer, which makes these long filaments.

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And then those are kind of proteins, which is called myosin, which is what is called a molecule armature.

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And by using an external source of energy which is called ATP, which is the fuel of the cells,

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this myosin, they can kind of contracts and pull on these actin filaments.

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And when they do that, somehow they come to a balance.

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The other forces creating by, uh, this scattering molecule,

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which tends to make the cells spread against each other by something which is similar to, uh, surface tension like waiting phenomena.

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And these contractions creating like a mechanical force which is opposing that.

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And then the results of these two kind of forces cortical tension and adhesion tension.

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And so cell junction create like a net basically tensile mechanical force of the junction between cells.

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So this is really the key mechanical ingredients here.

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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.

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So here's the map of the mechanical tension of the cell. So junction and the map of the pressure inside the cell.

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And then you can work out using partial formula. What is the individual mechanical stress on.

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Solve for each cell. And with that by having a knowledge of the mechanical property of the tissue.

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Simply speaking, by knowing Hooke's law for the tissue, then you can basically infer what all the cell shapes.

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That's what a physicist will do.

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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.

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And once we have through basically microscopy imaging and machine learning and the like,

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this is the shape of the cell, the segment and mass of the cell.

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So can we use that information to infer back these mechanical quantities.

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And the answer is yes.

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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.

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And here the inverse problem question we are asking is like,

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how can we from infer from the shape of the cells we measure from microscopy, what are the mechanical forces.

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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.

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Like basically segmentation mask for the cell, you can know very precisely their shape.

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What does it mean?

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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.

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And then you can also very precisely parametrise the shape of each cell.

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So junction by arc of circle.

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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.

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And this basically tensile mechanical stress you create is creating like a tension force which is acting on the vertex.

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And so if you make the potencies that you will basically at steady states or mechanical equilibrium,

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you can say that the mechanical tension acting on each vertex, you know, in the tissue or balance.

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Then like looking back at the question of the shape of the junction,

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you will realise that this basically junction, they are not straight lines, the off curve.

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And the reason why the curve is because you have difference of pressure inside neighbouring cells,

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like difference of pressure between like um soap bubble in a form and actually like you can use a

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law which was derived independently by young and lab last at the beginning of the 19th century,

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thinking about this problem of like soap bubbles in the form in which called the young lab plus no.

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And we say that the difference of pressure between two neighbouring cell is proportional to

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the tension at the junction between these two cells and the coefficient of proportionality,

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the radius of curvature of the junction, which you can access by fitting the social junction by your knuckle circle.

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And this is the radius of this arc of circle.

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So if you do that for all the cells in your tissue because a cell has an average six neighbour in the tissue.

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You end up with a lot more equation than variables, your variables or the tension and the pressure.

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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,

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you have a lot more equation done than than variables.

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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.

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And then once again you can use basically like machine learning like optimisation technique to find the most likely,

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ah, distribution of mechanical tension and pressure. And here we go.

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If we put together these two things. So spatial transcriptomics methods I told you about which allows you to measure a single

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cell resolution in your tissue section through gene expression in individual cell.

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And this forced interference modelling, which allowed to infer the mechanical forces on acting on individual cells in the tissue.

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Then we have a pipeline where we can measure at the same time the physics and the biology.

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So we can measure the mechanical forces acting on individual cells in the tissue.

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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,

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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.

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And then it's an amazing opportunity to try to decipher what is the relationship between

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mechanical forces generated and acting on cells and the gene expression pattern,

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and try to decipher if mechanical forces can have an impact on the genetics of the cell and the selfish decision behaviour.

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So, um, but there is a paper way.

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I explain all that in more details and you will be like, be able to find it online and to download the preprint.

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We though it's not behind a paywall so everybody has access to it.

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And for the rest of the presentation, I want really to focus on the particular example,

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which is of basically like the development of the brain of the embryo as this 8.5 day stage of embryonic development.

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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.

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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.

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And we can use or mechanical force inference algorithm to infer the spatial pattern of tension at each cell.

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So junction and the pressure inside the cell. But because we have also for each cell in this data set, the gene expression pattern,

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then we can run the kind of bioinformatics genomics analyses I was telling you about.

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And on the basis of which genes are expressed by your cells.

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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.

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And those are cell type.

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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,

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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.

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So here you have that the brain of the animal in orange.

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And it's separated by, you know, the like nerve cell type which are called the neural crest here in red here,

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like in another region of the brain of another tissue,

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which is called the cranial mesodum, which will give you the bones and the muscle of the face later on in development,

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again surrounded by brain tissue, the full brimming brain and breadth.

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And here, in another region of the brain, you have pure brain tissue.

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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,

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the midbrain and the brain. And remember, like we have for this dataset, the map of the mechanical forces.

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So we can start by asking a very simple physics question, which is like all all these compartments between different cell types,

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different tissue forms, and maintain either some form of like mechanical patterning happening.

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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,

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and look at the particular social junction which sit on the boundary.

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And then what? You can measure the mechanical tension for these junctions sitting on the boundary.

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And what you will see that the mechanical tension for social junction at the boundary between two tissue compartments,

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the mechanical tension is always higher and even sometimes much higher than the average mechanical tension in the bulk of each compartment.

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So it is like a defined biophysical phenotype, like we observed that in the embryo when you have different tissues,

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which are partition in space at the boundary between these different tissues, you have a high mechanical tension.

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But here remember not only we have the like the physics and mechanics information, but we have also basically the genetic information.

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So we can start asking what is the biological the molecular underpinning of this biophysical phenotype we have characterised.

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And we can do that. But first I would like to prove you that's basically like mechanical forces and IO.

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Mechanical tension at the interface between these two compartments is enough to

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warrant the maintenance of the boundary and even the formation of this compartment.

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And then I going back to my background in theoretical physics and the family of active matter physics model I developed during my PhD,

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which are called self-propelled cellular Pots model. So the modification of the original model was actually developed here in Oxford

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in the department by an Australian physicist who was named Renfrew Potts.

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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.

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And at that time they were not interested in cells at all. They were basically interested in generalising the easing model to n parameter.

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But somehow in the 1990s, two of the theoretical physicists of French nautical physicists, clockwork Renault and the American one, James Blustered,

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during their postdoc in Japan at the same time and the same group realised that they could use this model to act to actually,

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like, model the shape of the cell in this tissue.

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And they took again the Hamiltonian, um, which was written by Potts for the model.

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And they assign biological meaning to the different system of a Newtonian.

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And they show that one of the term model, the bulk elastic energy of the cell, and the other model,

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the interfacial tension, which account for the addition to the cell contractility.

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In my PhD, I expanded this model to integrate the active cell motility and the active force.

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These these cells basically, uh, apply on the surrounding to be able to move like the Julia showed.

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And here I have used this model to confirm the experimental observation we have made.

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I think the experimental values I have measured for the mechanical tension at the boundary and in the bulk of each compartment, and I.

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Fed them as the input values for my model for the three different system.

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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,

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and I run simulation of the dynamics of the system, it's enough to show that the boundary is maintained.

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So yes, with the I mechanical tension at the boundary, you can maintain the boundary between compartments.

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If you make the tension of the boundary equal to the tension in the bulk of each compartment,

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then basically the boundary is not maintained and the two compartment starts to mix, which is not good.

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Like you won't be able to progress in development because to do that you want to have a

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tissue compartment which will or will organise to make organ and the body to develop.

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So the next question is like all these are your mechanical tension at the interface between two different cell types,

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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.