So for example, 820.
Um, it's a great pleasure to to be here today, um, talking to you.
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,
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.
And I worked on the sort of active motor and tissue mechanics models.
Julia. Julia told you about helium. So, talking about Cambridge, I want to be a bit mischievous and actually, like, willing to like that.
You're not the first generation of physicists to be interested in biology.
And I think the best example of that is the discovery of the structure of DNA by, um, Franklin,
Watson and Crick and essentially like it was enabled by the development of the concepts and tools of physics.
And, uh, well,
by that I mean the developments of the quantum mechanics at the beginning of the 20th century and experimental development associated with that,
like X-ray diffraction, first by, uh, Bragg, father and sons in Cambridge and then by Moseley,
um, here in Oxford and then later on after World War two by parents and Kendrew,
um, in Cambridge, where I actually started this, basically this little lab, um,
in the shade of the back of the old Cavendish Laboratory and that said themselves to
our young and motivated people to work out the structure of biological molecules,
proteins and DNA. And here is the famous basically like E3 diffraction picture acquired by Rosalind Franklin,
which basically helped Watson and Crick confirmed that their theoretical model of the structure of DNA was right.
Um, 70 years on, like it seems, you know, like we are in the same position, um, not talking about molecules anymore,
but cells and tissue, as Julia, um, so eloquently like, demonstrated it during a presentation earlier this morning.
So I want to tell you a bit about myself, because that is exactly where I am at the moment.
So I have moved like, you know, like from physics to biology.
And what I'm really interested in is doing what people have done before me,
trying to apply the tools and the concept of physics to answer biological questions, questions which have relevance to biology.
In biomedical sciences, we're trying to find mechanism of diseases and to cure them.
And the particular questions I'm interested in is the role of mechanical forces in selfish decision.
So a selfish decision is a decision the cell is taking, um, during its life.
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.
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.
And we find them in the embryo and in the adult tissues.
But then they can also take another decision, which is like when they divide,
they can decide to either remain stem cells or produce differentiated cells which do not have the same definitive potential to divide,
but or specialise to accomplish with particular function.
And this selfish decision between dividing and not dividing, moving and moving.
Remaining the stem cell. Differentiating interacting with your neighbour and not interacting with your neighbours.
They are the urging of all the biological processes like embryonic development, tissue mill stages and regeneration.
Julia was like showing beautiful movie of the development of starfish and Drosophila embryo and then movie showing the example of regeneration,
for example wound healing. But they also very much involve basically, um, in uh,
the inception and physio pathology of a host of diseases like cancer or inflammatory diseases like we are interested in at the Kennedy Institute,
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.
So my presentation is entitled Imagining Living Systems.
But really like in my lab, we are an interdisciplinary lab.
And not only will using imaging, but we are using this imaging in comparison.
Like. Like not in comparison, but actually in addition of further technologies brought by physics into biology.
So micro fabrication technology, which allowed basically to grow and confine cells on patterns of a particular shape,
and then basically just look at the impact of mechanical forces of them.
So you can gross them in these basically macro fabricated patterns into a system, in vitro culture system.
And then you merge them. But we also basically genetically modified these cells to make them fluorescent where they express a particular gene.
For example, here is a gene them keratin 14.
And then when this cells express these gene, so when they produce a protein associated to this gene that they become fluorescent.
And all their daughter cells when they divide or also fluorescent the same colour.
So we can track, you can start to track basically the division.
And so think your pattern and then ask the question why some cells, you know,
like divide and express to particular gene and commits to a particular effect.
And when some other cells do not do that.
And of course, basically to do that we need to use modern image analysis techniques so we can detect and track these cells.
And for this we are using machine learning and the particular form of machine learning which is called deep learning.
But we are also using basically the most recent methods in genomics to not be limited to look at a single genes,
but look at basically the full basically genome and see what's happening when a particular
cell commits to felons to a given state and see which genes it expresses or not.
And of course, we are still using models from theoretical physics trying to make sense of all these experiments.
Data some. So. Today.
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,
you know, cells as a precedent for spatial and temporal resolution.
And all we can use that's basically to infer mechanical forces, um, across cells in a tissue and all.
We can also use similar images. Similarly imaging approaches to look at uh, the expression of genes in cells.
So this is typically uh, the kind of modern microscope we have at the Kennedy.
So this is like a very complex device. So it's not like microscopic to fluorescence microscope.
So we are using it. We source injected or genetically modified to produce basically fluorescent markers.
And then it's a light sheet microscope which is using advanced adaptive optics basically to correct focus and spherical aberrations.
It's it's using basically a set of like two objectives at 95, uh, 45 degrees.
And then those objective for illumination with a laser,
which is basically going through a cylindrical lens to make a light sheet, which basically will be scanned across the specimen,
which is contained in the box, which are allowed to maintain, like the specimen, either like a tissue section of cells in culture alive.
So you need to control the oxygen, the CO2 levels.
You need to pump them with culture medium.
And actually when you do that, you can start studying not only cells in a dish, but more complex system like the developing unreal.
So this cartoon here shows basically like the development of a mould, some real.
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.
And this basically initial cell is gonna divide and very progressively after two days, you will have a small bowl of cells,
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.
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,
well the cells in the middle, they will develop and make the embryo.
And as time goes on cells continue to divide.
And the they also basically differentiate and assume different fates which show up as we pattern into space.
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.
And then the truffle blast, which is like a more differentiated precursor of the plus of the placenta and the primitive underarm,
which is basically and those are from very unique lineage, um, like the placenta.
Here. Quite interestingly, after almost two week we have like.
Something which was initial your ball which is like normal longitude like a cylinder.
And here we have this what we call the free gem layer, the AP blast, the mesodum and the under derm.
And these free different groups of cells is free.
Like like for two tissues, they will give rise to all the other tissue in the body of the animal.
So for example like um, the brain or actually the skin and the rest of the nervous system will be derived from this.
Like if people ask like and then you will have like other um, which is also called the ectodum,
then you will have these other like organs derived from this other gym layer.
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,
this little group of cells which we call the under Durham. And here the earth's or the cardiovascular system will be derived.
And the muscles, for example.
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.
And you can see here that here you have just like basically like three little groups of cells,
you know, like with your patch on one on top of the other at 6.5 days,
at 8.5 days, like two days later, you have something which looks like an animal,
you have a brain, you have Hertz, you have like a goats, you have like a spinal cord.
You have like a non-serious posterior axis. Um, you have like left or right polarity.
So in a matter of two days, you transition from a ball of cell to something which looks like an adult animal, almost.
And with the kind of microscope I've shown, you know,
you can image that and see all the cells in the embryo undergoing this process, which we call embryonic morphogenesis.
And here, like the most deeply, I have been modified genetically.
So the nucleus of the cell is fluorescent. And you can see all these nucleus about dividing and then moving and the pattern,
the of the embryo being established until posterior pattern, the left right pattern.
And you can image this in total today in a microscope.
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
self-organisation process which involve like an very large number of different phenomena at different scales.
So if we try basically to use the approach of a physicist,
we would like to look at the different scales at play and find what other key players are these different scales.
So of course we have the scale of the molecules and these. So the genes which are contained in the nucleus.
But then we have all the molecules which are basically proteins which are coded by the gene and which
are secreted by the cells like some the molecules which are called broad structures and morpho genes,
and which will basically instruct the cells to divide or differentiate in one particular cell types.
So they are the messenger which help the cells coordinate their action during embryogenesis.
But we can also see what's happening. Hmm. Let's go on boys back.
Okay, cool. And then we have also at the nucleus kill the cells.
And then here we can start thinking about the shape of these cells, um, their contractility,
but also all they divide and all they move, like Julia showed this neurone.
But then if we go at one scale, we have the scale of the tissue.
Where? Then the mechanical fancy, then the mechanical properties of the cells and the tissue of the wall will become important,
as well as the structure and the geometry of the tissue.
And these different biological objects which have these different chemical and physical attributes.
They don't live in isolation for each other.
They basically all interact with each other, feedback on each other, mixing, basically the comprehending the whole process.
Very difficult. So what usually what people do is just they focus on the particular scale and set of objects.
For example, looking at the scale of molecules,
they will look at all basically the information contained in the genes in the DNA is transcribed and then translated into protein.
So this is what is called the central dogma of the bio molecular biology.
Essentially you the information is contained in the DNA.
And this information is basically the genes which are chunk of DNA,
which codes for particular end products, which is a protein which is made of amino acids,
but in between the genes which is made of DNA, and the protein, which is the building block,
which is made of amino acids, you have something which is called messenger RNA.
And what's happening is just when this cell wants to decide to produce particular building blocks,
popular protein, it will do what is called transcription of the gene.
So you will like select some particular genes which codes for proteins of interest.
And then these genes will be transcribed into, uh, another kind of nucleic acid RNA which is single stranded.
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.
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.
So what you can do to study basically development is use the most recent genomics
technique and the most recent genomics techniques that your single cell technique.
So you can take a number, you dissociate all the cells, use a microfluidic device to isolate the single cells.
And then you can basically of this cell like they just do with some enzymes.
So you only keep these messenger RNA which all the transcribed genes.
And then you can barcodes them with some particular chemical which will allow you to distinguish them individually.
So you can take single cells. Then just basically all the different RNA molecules contained into the,
into the cytoplasm barcodes, these RNA then amplify them and then sequence them.
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.
So it's going to make these 2000 particular proteins. But these those are cells.
It does not express them and it express three thousands of other genes.
And that way you can have a very precise idea of which protein also is going to produce.
And as you will remember, like proteins are the building block.
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.
If you do not produce this protein, you won't be able to do that.
So some or understanding what genes are expressed by your particular cell or cell type of
interest is crucial to understand the selfish decision and the behaviour of this scale of this,
of these cells from the molecular scale point of view.
Okay. So people have actually used that to study embryonic development.
So they have collected most embryo like every um 12 hour between 6.5 and 8.5.
And they have like dissociated all the cells.
And they have used this RNA sequencing technology to sequence basically their gene express in each cell of each embryo at that time.
And then they have used advance machine learning analyses, technique to look at the pattern of gene expression.
So you have around 20,000 genes in the genome. So for each cell you know which of these 20,000 genes or expressed.
And you have like a few thousand of cells for each embryo at each time point.
And you can basically like so it's a very basically complex and big data set.
We are talking about multi-dimensional arrays.
And then what you can use is basically machine learning tools to do things which are called dimensionality reduction and clustering.
And then you can start to have these low level low dimensional representation which are basically cluster.
And this cluster, each dot is a cell. And all cells in the cluster will have similar gene expression pattern.
So they will express, you know, like the same genes, articulate the same genes and don't regulate don't express user genes.
And then what you start to realise is that you can map basically these cells which express the same genes to particular cell type,
particular tissues, particular organs in the embryo.
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.
Really cool. But then you can go back and say, okay, but I want to understand the process at the school.
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.
And you can again use machine learning techniques to basically track the movement and the behaviour of these cells over time.
And then what you can do is just like you have no idea about which genes they express, but you track them.
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.
So on the basis of their final position, which is called selectively,
you can tell you machine learning algorithm to assign them to a particular cell type.
And know your being able to tell cells of this particular cell type, you know, range will divide at that rate while still.
So these other particular cell types in blue will divide at that other rate.
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.
So that's another level of description. That's another level of phenomenology.
And then you can start thinking about what's happening at these bigger scale, the scale of the tissue in the organs.
And there's sees the scale where, you know,
like the collective property of cells start to mature and start to dictate the shape of the tissue in the embryo.
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,
by these cells forming these tissue compartments.
And dipole physicists again have developed very clever way to look at mechanical forces in these embryonic tissues.
They have created this small fluorescent droplet that you can inject in the embryo.
And then by looking at there's change of shape of the droplet, you can basically,
um, infer the, the stress, the mechanical stress tensor around the cells.
I'm just a part of it. So essentially you can basically, uh, infer the non deviatoric part of the stress tensor.
But it's enough basically to have like an idea of, for example, shear stress in that particular region of the street.
But what they have also done is to again use but those are tools from physics magnetism by filling up, um,
these fluorescent droplets with a solution of a, of fluid which is made of tiny colloidal silver magnetic particles.
And then they can use a magnetic field like a big electromagnets to apply a force on this droplet and make them move.
And then by doing that, they will deform the tissue around them.
And by looking, when they stop to apply the magnetic field and the magnetic force, the droplet stops to move.
And by looking up the droplet will change shape over time.
They can measure the local strain of the tissue.
In response of this mechanical stress, they have applied and they understand the mechanical property of the cell in the tissue.
Things like, you know, like the young modulus of the Poisson ratio.
So it's it's also like a very interesting like approach to understand the physics of these systems.
But somehow we are still, you know, like looking at these different scaling installation.
And in my research, what I've been trying to do is just try to find a way to reach different scale.
And some will be able to of basically like an integrative vision at the tissue scale,
but at the same time to be able to measure the mechanical forces acting on individual cells,
but also be able to look at, um, the genes which are expressed by individual cells as well.
So can we do that? So to do that you have to be able to measure the gene expression pattern in individual cells.
But you can't dissociate the cells anymore because otherwise you losing the special information their position in the tissue.
So people have been thinking very hard about that.
And over the last five years they have developed a new methodology so that you can measure the gene expression,
measure the expression of particular mRNA, not in dissociated cells anymore, but in tissue section.
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.
And then like what people in biology would traditionally do, like histology,
this kind of new section where they use basically dyes which will label the nucleus and the cytoplasm of the cell.
But here what you can do is use like small, um, western groups which basically have parts which are made of RNA.
So. You have, like a throwaway molecule attached to a small strand of RNA, and you will make that small,
trained strand of RNA complementary to a particular sequence of a given area in the cell.
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.
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.
And each block little fluorescent dot you see here is a particular RNA protein in the cell.
And you can use that for one to free RNA.
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,
like exactly like you will do with the barcode run sequences of hybridisation of different probes with different fluorophores,
which will bind to the same and many and that will.
And that way you will be able to image for androids, if not thousands of in a single cell in the tissue.
So this is really amazing. And in collaboration with a group of Professor Longi at Caltech was pioneer these the development of this technology.
We have been applying it to the really most some of you.
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.
And we have been taking section of the embryo and sagittal section of the embryo.
And here you can see section of three different embryos. And these embryos look like you have a scale bar here.
So like they are roughly like around like 1 or 2 millimetre in size.
So you can have them on the cover slip of a microscope like the section is around 20 microns deep.
So it's a very thin section. And then you can put your cover sleep um,
into like a microfluidic chip which is mounted on one of these advanced micro fluorescence microscope I told you about.
And then you have like a complex system of pumps, which allows you to pump like reagents in solution,
which contain this fluorescent probe which can ebru dyes with RNA.
And then you image basically your embryos.
So you are taking dual like fluorescence images.
And you do that for each probe cycle.
And progressively you will be able to have all these different fluorescence notes which are particular mini molecules containing individual cell.
So no, we have the capability to measure like the transcriptome.
So the ensemble of gene expressed by yourself in a single cell within a tissue, you know, resection.
So we can really have that integrated view about what's happening at the genetic level in single cell, in the tissue in context.
But we are still missing like the mechanical forces part.
So all consoles us.
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.
And actually, this man, Dustin Thompson, who was, uh, a zoologist and professor at the University of Saint Andrews,
he wrote like an amazing book at the beginning of the 20th century, uh, which is, um, named On Growth and Form.
And he was the first one to articulate this idea that some of the shape of an organism is a diagram of force.
So clearly,
mechanical forces have been known for a very long time to be very important in dictating the shape and the behaviour of biological objects.
And if we go back at the cellular scale, what are the main determinants of mechanical forces between two cell in the tissue?
These cells, as Julia showed us before. They can make cells adhesion.
So actually they express some particular proteins which are called coverings, which allows them to create adhesive bonds between them.
But they are also inside the cell, like a kind of protective layer which is made of two kind of protein.
Like these red filaments, it's a protein called actin, which makes a polymer a biopolymer, which makes these long filaments.
And then those are kind of proteins, which is called myosin, which is what is called a molecule armature.
And by using an external source of energy which is called ATP, which is the fuel of the cells,
this myosin, they can kind of contracts and pull on these actin filaments.
And when they do that, somehow they come to a balance.
The other forces creating by, uh, this scattering molecule,
which tends to make the cells spread against each other by something which is similar to, uh, surface tension like waiting phenomena.
And these contractions creating like a mechanical force which is opposing that.
And then the results of these two kind of forces cortical tension and adhesion tension.
And so cell junction create like a net basically tensile mechanical force of the junction between cells.
So this is really the key mechanical ingredients here.
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.
So here's the map of the mechanical tension of the cell. So junction and the map of the pressure inside the cell.
And then you can work out using partial formula. What is the individual mechanical stress on.
Solve for each cell. And with that by having a knowledge of the mechanical property of the tissue.
Simply speaking, by knowing Hooke's law for the tissue, then you can basically infer what all the cell shapes.
That's what a physicist will do.
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.
And once we have through basically microscopy imaging and machine learning and the like,
this is the shape of the cell, the segment and mass of the cell.
So can we use that information to infer back these mechanical quantities.
And the answer is yes.
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.
And here the inverse problem question we are asking is like,
how can we from infer from the shape of the cells we measure from microscopy, what are the mechanical forces.
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.
Like basically segmentation mask for the cell, you can know very precisely their shape.
What does it mean?
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.
And then you can also very precisely parametrise the shape of each cell.
So junction by arc of circle.
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.
And this basically tensile mechanical stress you create is creating like a tension force which is acting on the vertex.
And so if you make the potencies that you will basically at steady states or mechanical equilibrium,
you can say that the mechanical tension acting on each vertex, you know, in the tissue or balance.
Then like looking back at the question of the shape of the junction,
you will realise that this basically junction, they are not straight lines, the off curve.
And the reason why the curve is because you have difference of pressure inside neighbouring cells,
like difference of pressure between like um soap bubble in a form and actually like you can use a
law which was derived independently by young and lab last at the beginning of the 19th century,
thinking about this problem of like soap bubbles in the form in which called the young lab plus no.
And we say that the difference of pressure between two neighbouring cell is proportional to
the tension at the junction between these two cells and the coefficient of proportionality,
the radius of curvature of the junction, which you can access by fitting the social junction by your knuckle circle.
And this is the radius of this arc of circle.
So if you do that for all the cells in your tissue because a cell has an average six neighbour in the tissue.
You end up with a lot more equation than variables, your variables or the tension and the pressure.
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,
you have a lot more equation done than than variables.
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.
And then once again you can use basically like machine learning like optimisation technique to find the most likely,
ah, distribution of mechanical tension and pressure. And here we go.
If we put together these two things. So spatial transcriptomics methods I told you about which allows you to measure a single
cell resolution in your tissue section through gene expression in individual cell.
And this forced interference modelling, which allowed to infer the mechanical forces on acting on individual cells in the tissue.
Then we have a pipeline where we can measure at the same time the physics and the biology.
So we can measure the mechanical forces acting on individual cells in the tissue.
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,
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.
And then it's an amazing opportunity to try to decipher what is the relationship between
mechanical forces generated and acting on cells and the gene expression pattern,
and try to decipher if mechanical forces can have an impact on the genetics of the cell and the selfish decision behaviour.
So, um, but there is a paper way.
I explain all that in more details and you will be like, be able to find it online and to download the preprint.
We though it's not behind a paywall so everybody has access to it.
And for the rest of the presentation, I want really to focus on the particular example,
which is of basically like the development of the brain of the embryo as this 8.5 day stage of embryonic development.
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.
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.
And we can use or mechanical force inference algorithm to infer the spatial pattern of tension at each cell.
So junction and the pressure inside the cell. But because we have also for each cell in this data set, the gene expression pattern,
then we can run the kind of bioinformatics genomics analyses I was telling you about.
And on the basis of which genes are expressed by your cells.
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.
And those are cell type.
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,
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.
So here you have that the brain of the animal in orange.
And it's separated by, you know, the like nerve cell type which are called the neural crest here in red here,
like in another region of the brain of another tissue,
which is called the cranial mesodum, which will give you the bones and the muscle of the face later on in development,
again surrounded by brain tissue, the full brimming brain and breadth.
And here, in another region of the brain, you have pure brain tissue.
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,
the midbrain and the brain. And remember, like we have for this dataset, the map of the mechanical forces.
So we can start by asking a very simple physics question, which is like all all these compartments between different cell types,
different tissue forms, and maintain either some form of like mechanical patterning happening.
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,
and look at the particular social junction which sit on the boundary.
And then what? You can measure the mechanical tension for these junctions sitting on the boundary.
And what you will see that the mechanical tension for social junction at the boundary between two tissue compartments,
the mechanical tension is always higher and even sometimes much higher than the average mechanical tension in the bulk of each compartment.
So it is like a defined biophysical phenotype, like we observed that in the embryo when you have different tissues,
which are partition in space at the boundary between these different tissues, you have a high mechanical tension.
But here remember not only we have the like the physics and mechanics information, but we have also basically the genetic information.
So we can start asking what is the biological the molecular underpinning of this biophysical phenotype we have characterised.
And we can do that. But first I would like to prove you that's basically like mechanical forces and IO.
Mechanical tension at the interface between these two compartments is enough to
warrant the maintenance of the boundary and even the formation of this compartment.
And then I going back to my background in theoretical physics and the family of active matter physics model I developed during my PhD,
which are called self-propelled cellular Pots model. So the modification of the original model was actually developed here in Oxford
in the department by an Australian physicist who was named Renfrew Potts.
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.
And at that time they were not interested in cells at all. They were basically interested in generalising the easing model to n parameter.
But somehow in the 1990s, two of the theoretical physicists of French nautical physicists, clockwork Renault and the American one, James Blustered,
during their postdoc in Japan at the same time and the same group realised that they could use this model to act to actually,
like, model the shape of the cell in this tissue.
And they took again the Hamiltonian, um, which was written by Potts for the model.
And they assign biological meaning to the different system of a Newtonian.
And they show that one of the term model, the bulk elastic energy of the cell, and the other model,
the interfacial tension, which account for the addition to the cell contractility.
In my PhD, I expanded this model to integrate the active cell motility and the active force.
These these cells basically, uh, apply on the surrounding to be able to move like the Julia showed.
And here I have used this model to confirm the experimental observation we have made.
I think the experimental values I have measured for the mechanical tension at the boundary and in the bulk of each compartment, and I.
Fed them as the input values for my model for the three different system.
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,
and I run simulation of the dynamics of the system, it's enough to show that the boundary is maintained.
So yes, with the I mechanical tension at the boundary, you can maintain the boundary between compartments.
If you make the tension of the boundary equal to the tension in the bulk of each compartment,
then basically the boundary is not maintained and the two compartment starts to mix, which is not good.
Like you won't be able to progress in development because to do that you want to have a
tissue compartment which will or will organise to make organ and the body to develop.
So the next question is like all these are your mechanical tension at the interface between two different cell types,
enough to explain the formation of these compartments in first instance.
And like so that we don't have experimental evidence, but we can use theoretical physics simulation to look at that.
So we can use with the same experimental volume for the like interfacial mechanical tension between cells of a different type,
and then taking the experimental values and using them as an input for the simulation.
We can see if the cells will sort out and segregate spatially.
And this is exactly the kind of like, uh, cell sorting experiments Julia was showing for the stockfish on real.
And here we do. We are doing simulation of that which are based on mechanical parameters, which we measure on most on real.
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.
If you take again the interfacial tension between two cells of the different
type being equivalent to interfacial tension of two cells of the same type.
Then you never have segregation and you stay with this salt and pepper pattern.
So by using this simulation, we can even predict that, uh,
having your interfacial tension between two different cell types might be enough in
first instance for these cell types to segregate spatially and partition in the embryo.
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
use basically models to predict biological behaviour and then go back to experiment,
to try to like, falsify or verify them depending which region of science you have.
But it's a question for philosophers, not for us today.
Okay. But let's go back to the system because like like my pipeline is very rich.
You give me both the mechanics but also the genetics.
And here I told you like the addition of the mechanical information for each single cell.
I have also the genomic information. I'm able to know which genes are expressed by each cell.
So what I can ask know as a question is what is the molecular underpinning of this mechanical patterning phenomenon?
I observe. And then like what I can do is exactly what I've done before with mechanics.
I can look at cells, which are the boundary between my tissue compartment, and I can ask, know from the standpoint of molecular biology,
all these cells sitting in the boundary between tissue compartments, difference in terms of the gene they express.
And the answer is yes. The cells which sit at the boundary between the compartment.
They are they express different genes. Now you can ask me what all these different genes they express.
We look at that.
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.
And these are basically genes which are coding for proteins, which sits on the cell membrane and these proteins on the cell membrane.
Basically, they have a rule for cell cell communication. They also need both cell to communicate with each other.
So one cell we love the ligands and this label.
So we'll have the receptor. They will come in contact together.
The ligands will basically bind with the receptor.
It's the biochemical interaction. And this will trigger something inside the cell what we call the signalling cascade.
And this signalling cascade will propagate down to the nucleus and basically drive the expression of all the particle agents.
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,
the energies between these ligand and receptors, and from basically the expression data,
you know, almost basically everything you produce for a given ligand and receptor.
So you can approximate. Almost 14. You will make for each slide gun and receptor.
Then basically you can compute like the equivalent of an interaction potential
between the legend and the receptor at the face of neighbour will sales.
And then you can use like a statistical approach which will give you basically the
likelihood of interaction between a given ligand and receptor expressed by normal cells.
And then you can look at basically at the genes, the particular ligand and receptor,
which has the highest likelihood of interaction amongst label cells.
For cells which are the boundary between compartments and cells, we showing the bulk of each compartment.
And then you will pick up a few ligand receptor genes.
And as I told you, there are functions to all those cells to communicate and to tell them to express particular genes.
So we can go back to all genomics information for each cell and ask what all these genes which are downstream,
these messengers and the genes which are those things, these messengers, they are cells that are adhesion proteins.
The coverings I was telling you about, and essentially what's happening is that these cells at the boundary,
they upregulate one particular type of gathering, like for example, the gathering 11.
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.
And then if you look at this also colouring the colouring to here is expressing these tissue compartments.
But it's not very much expressed here in these other tissue compartments.
So for cells which are the boundary between the two tissue compartment they
have different adhesion protein and tethering two cannot bind to tethering 11.
So some cells which are the boundary they cannot bind to each other.
And remember what I was saying before what condition the mechanical tension between two neighbouring cell is partly the cell cell adhesion.
So here by expressing different adhesion molecules for cells on the boundary they cannot bind to each other.
And then basically you decreasing the adhesion and you increasing the tension.
That's why we are able to express to explain the molecular mechanism which is conditioning or biophysical phenotypes.
And I think this is something really exciting and interesting,
because we can start bridging physics to biology and really understand the interplay between physical forces and the genomics.
So is this only me we seeing this kind of thing, or is it something you like experimentally confirmed by others?
You know the system and the answer is yes is consumed by others.
So you have this beautiful work by Francois Fargo.
Tool was been studying the development not of the most embryo but of the Xenopus, some real, which is like a frog.
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,
the Ectodum and the middle Durham here, and you will see that you have a boundary between these red here,
which is the middle term, and here the blue here, which is the ectodum.
And they looked basically at the cells, the junction sitting on the boundary.
They applied a similar basically mechanical force inference method.
And they were able to show that the mechanical tension is higher for these junctions sitting at the boundary between these two tissue compartments.
And then they did beautiful functional experiments where they made frog embryo which were unable to express these effing ligand receptors signalling
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.
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,
which were telling the south of the boundary to express less cell cell adhesion molecule and increased attention.
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.
And you're looking much later in development when basically like the spinal cord of the young real is form.
And then here what you are looking so at that stage is called the neural tube.
And this neural tube is starting to be divided in different tissue compartments.
So you can see that as the embryo develop the level of complexity is increasing like organs are forming.
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.
And then you know here what you can see that you have difference.
Um, you know, like, like tissue layers. We.
So starting to be pattern in the like the neural tube of these fish embryo and shears.
They have done like a very brave experiment.
Like um, the person inside with no like, um, a professor, um, at, um, um, Washington University in the US.
It took two basically to dissect this embryo and dissociated individual cells of these different tissue
layers and to take them and to come with a system where you would have like a micro manipulator,
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,
put them in contact, let them make an adhesion between them, and then even through them with a given force.
So we could directly measure basically to sell, sell seltzer adhesion that we were inferring from the gene expression.
There is the direct physics mechanical measurements of these social forces.
And he was able to show exactly that, the same thing.
We were showing that actually these cells, by playing the differential expression of two, um, like, uh, cells, that adhesion molecule,
the colouring twin, the colouring 11, they you able to tune the mechanical tension of the boundary and create these tissue compartments.
Quite interestingly, they show that, that this was driven by um like a particular other gene which is called sonic etch,
which is a molecule which is secreted by the cells and which can diffuse.
And like we were not, you know, like aware of that.
But then when we saw the paper, we thought we should go back.
And indeed we see exactly like going back to these, you know, like special transcriptomics information.
We could like see that we have this beautiful gradient of this gene, Sonic at the boundary.
So you can see that with this approach we can start drawing very powerful,
you know, like, uh, you know like relationship between mechanics and biology.
And I go natural foods. My, my talk with these two slides by so in knew that you know like with this type of
like experimental approach we knew of the two to look in an unbiased manner,
a correlation between gene expression pattern and mechanical quantity, like the pressure inside the cell,
or basically the magnitude of the stress tensor to noise flux results basically turns out compressive forces.
And we can know pinpoints genes, which are what we would call the kind of sensitive genes.
They are able to adjust their expression level in the cells in response to mechanical forces.
And we can investigate that using basically, again, like, uh, tools from applied statistics and machine learning.
And not only we can do that at the linear older, but we can look at nonlinear correlation.
And then we can start to see that for example some gene they will be upregulated more expressed
by the cell when the pressure is low but completely unregulated when the pressure inside the.
So I are you can see the opposite behaviour like the same switch like function here.
These genes they are not expressed when the cells under low pressure.
But the you express outside pressure. And the same for like the tensile and the compressive forces.
So no really we are starting to develop the tools to understand because of the
relationship between mechanics and biology and understand all mechanical forces,
shape the embryo and understand you like the idea.
Understood. Thompson thought that, um, an organism is a diagram of force and result.
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,
professor Ben Simons was, like, initially also theoretical physicist.
He was the head of condensed matter at the Cavendish Laboratory and decided to to shift to biology some 20 years ago.
And he was very influential, you know, like, in my way,
to see the biological questions at the interface between disciplines and to approach a question.
And, of course, the funding which has enabled this research and you for your attention.
Thank you very much.