Kavli Institute for Brain and Mind:Imaging the Brain-Colon-Ramos Lippincott-Schwartz Miyawaki

Kavli Institute for Brain and Mind:Imaging the Brain-Colon-Ramos Lippincott-Schwartz Miyawaki

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So. I'd like to start by sharing a. A. Personal, story so all scientists, have I think, memory. A, story, that they can go back to when they think about how they became interested in science and I wanted to share mine this. Is mine here, these, are baby leatherback, turtles that are hatching from a nest not far away from where I was born and raised in Puerto Rico and when. I was a child there I always found fascinating that, these animals are coming out of the nest and they haven't been to turtle school and they know exactly where to go their. Beeline and a towards the ocean, there's. Something really. Profound, happening, here also very important for the turtle but invisible to our eyes which. Is that these turtles are for me in a memory they're forming a memory of this Beach these are leatherback turtles they grow up to be about the size of a, Volkswagen about. Nine feet long and they, will travel the world's oceans, and decades later when they have to make what, is perhaps the most important, decision in a turtle's life which is where it's gonna lay its eggs it, will remember this Beach because that's where he was born and he'll, come back to Ana latex at that place so. With that I'd like to, coarse-grain. Behaviors, and essentially. Group them into, innate. Or hardwired, behaviors, like this animals capacity, to know to go towards the ocean when they're born an experiential. Memory Sider capacity, to remember this specific, vision come back to it now, innate, behaviors, are facilitated. By the developmental. Program that leads to the formation of the neural circuit architectures, and experiential. Memories, are facilitated. By a pretty, complex interplay between that, architecture. That develops, and the. Environment, that the animal is experiencing, what the animal is seen but, I want to emphasize that both of them are ultimately, facilitated. By, the architecture. Of the nervous system so what do I mean by the architecture, of the nervous system and this is essentially, what I mean if, you look, at bounced, hippocampus, for example you'll see this incredibly. An exquisite, organization. Of d neurons this is actually image, here using, a technique called brain bow by, Jeff Lichtman, Harvard and this. Organization raises, all sorts of interesting questions one. Of them which my lab is interested on, is how is it that this organization is actually established. And in. Thinking of how this organization is established if you take for example the case of the human brain you have about 80 billion, neurons us about that's, more neurons in the in a healthy human brain that you have stars. In the Milky Way and about 100 trillion synapses and during, development you have hundreds, of millions of neurons almost simultaneously.

During, Development specifying. Fade growing. Out axons, connecting, to each other finding each other in a very specific way to lay out this, amazing. Architecture, so how does that happen what are the organizing, principles, that, rules the, this, disorganization, that ultimately, underpin, human behaviors now, we look at this question and we don't do so in in. In, turtles or humans, or mice actually we do so in a worm it's, a tiny nematode, called C elegans it's over here one of the characteristics, that the. Elegans, has that we really like is that it's transparent so, it allows us to look at the organization of the nervous system which is very finely, organized as you can see here now, I want. To acknowledge that, C elegans is far simpler, than, the, healthy. Human brain so, instead of a hundred billion neurons C elegans has exactly. 302. Neurons and instead. Of 100, trillion synapses C, elegans has approximately, 7,000, synapses so, some of you might be wondering how is it that we can extract any useful, information out, of such a simple system that will tell us anything useful about a complex. Organ, like the human brain and to address this I like to use a quote by Diderot that, said a worm is only a worm but, that only means that the marvelous complexity, of its organization, is hidden from us by its extreme, smallness now I'm not as eloquent as Lederach so I'll say it in a worthier way when, nature, finds, a solution to a problem it. Recycles, that solution over and over again so the fundamental, principles, that allows. For, the proper functioning of the nervous system in this tiny worm are not unlike the fundamental, principles, that facilitate. The functioning, of our own brains and, what. And to exemplify that I'll just give a few examples, studies. Done in this worn by other groups ranging. From the development, of neurons like sulfate specific, knocks on guidance to. Fundamental. Physiology. Having to do with abiogenesis of neurotransmitters. Or neurotransmitter, release on stuck to each other to. Even, system's, levels, examination. Of sensory. Perception, and behavior all, done, in this new model have actually shed light, on, important. And fundamental aspects. About how our own brains and brains in higher metal swans work so it's actually it's, really true that evolution, kind of conserves these principles, so. Using. That as a foundation we, wanted to to, essentially examine, this question of how this nervous system comes together and both our studies and the studies that I just mentioned benefited. From the fact that this is also the only animal, for which we have a wiring, diagram now we're hoping this is going to change soon and you're gonna hear other talks today that are working very hard to make this you, know I think of the past now we're very happy that that that's the case but, today this is the only animal for which we have that wiring diagram so what, is the wiring diagram is the ability to know where each neuron is essentially. The morphology of the neuronal and what a neuron is connecting, to and we. Use this wiring diagram to examine two fundamental, questions one, of them is how is it that the brain of this animal, is organized, and the, second one is how how, does this organization, comes. Out during development now we've had the wiring diagram, for about 30 years the the, first wiring diagram was done in, 1986. And we. Still have these two questions that we, need to address and I'll explain what. We know and what we don't know in the next slide so the way that that wiring diagram, was originally, done was. That somebody took a group. Of scientists, led by John White took, this animal, and, essentially. Fixed it and sliced. It like a salami so in my angel you're slicing all over the animal here and then you get these these, cross, sections and, then, they did electron, microscopy, on those cross sections and by, segmenting, each of the neurons which we are putting here in different colors by. Hand this is 1986, they segmented, them by hand, they. Were able to recreate the connectivity. Map for every single cell now. This was this was incredibly, powerful for the field and it has benefited our studies, but, I just, want to emphasize that all of this was it. Wasn't digitalized, it was all done by hand so you could go narrow, by narrow and you can kind of tell the shape and who it connected, to but we didn't have a systems, level understanding, about how this happened, until, recently where, Scott, Edmonds at Albert Einstein University, and his students Steve cook actually, went in and did the same segmentation.

That John Waite did thirty years ago but with computers, what, that allows us to do is to be able to know whose, neighbor in who, in a quantitative, way in a way that we can actually analyze and. We can analyze using tools not unlike the tools that, are being used by companies like Twitter or Facebook to enter to understand, interactions, between human, beings so, our, collaboration, included, computational, scientists Smita and alex and what they did is that they took that data and, we. Worked together with them in, using, clustering, algorithms. Again, similar to a cluster in network algorithms, that are used to understand. Personal. Interactions, and cluster. Neurons with similar contact, profiles so essentially neurons that are contacting, each other as they're traveling together in the nervous system in fascicles, and indeed iterative, clustering, to understand, how how, that structure, of the brain is organized, and you, end up with with, essentially a flow diagram like, this where in one corner. You have all the neurons and this, cluster in algorithms, iteratively, bring, them together so this. Different, clustering, of the different subgroups of neurons that are ventually clustering to these larger families what. It represents, what underlies that is, real biology of how these neurons are interacting, with each other real biology dewitt that was inaccessible before. We were able to digitize these these, programs and we capable, of RNA in that on. Top, of the, original, en micrographs, each, of these pseudo. Colors here that we have placed, represent. A community of neurons that that. Are interacting. Together more than the other neurons and I'm gonna just walk you through the brain of the animal and, this is gonna be playing in a movie so, you can see how the different, communities, are actually sneaking, and how they relate to each other how they come together, as. This movie plays I'll explain that what I'm showing you here it's actually reproducible. Across the animals for which we have connectomes, so this has been a really fun project because, we were able to. Work, with the computational, biologist, and inform. The algorithms, with the biology and use the algorithms, to inform our own biology, we. Were able to find four, main bundles. That I come for 83% of the whole connect them so now, we have a pretty good idea about how these, different, brain regions are, organized, and these, neighborhoods, I'll just mention that they reveal important. Biological, insights so there's a reason why they're organized, this way that. Have to do with development, and function and I'm you. Know I could give a one-hour talk about this so I'm gonna use one example to present. This and I'll talk about how these neighborhoods, led, to insights, about how, this organization, is actually established, during development and this gets to a fundamental question that is a blind spot in the field which. Is how do you go from. A group, of cells in an embryo, to this organize connectome, in the one and this. Is so this is essentially a question that we wanted to understand, and the, reason that this has been a blind spot is that for many organisms this, part of the development, happens inside. Like for example in mammals it happens inside the uterus so the organisms. Are essentially inaccessible. In C elegans they lay eggs so the organism, is accessible, but there were a number of challenges that have prevented, in the past like four, or five decades to. Image embryonic, neural development so this is a pretty large blind spot we have this information but we didn't know how this was happening the, reason that imaginary. Development, in C elegans was, challenging, were, multi, fold one. Of them is that embryos, when they're inside the egg they're very quick they're moving very quickly so, if you're looking for example at the development, of a single neuron using, even the fastest microscopy, that we had available at the time which is spinning disk microscopy, for the officinalis in the audience this, is a single neuron and you get a motion blur where it looks like four neurons because, the animal is moving and you're taking different pictures I looked at you taking four neurons there the, other problem, that is hard to capture in an image is the animals just die so, when, you're imaging for prolonged periods of time which is what you want to do to be able to capture these different neural developmental events the.

Many, Of the embryos just die so you end, up taking snapshots at different points in development, that you have to stitch together later, and neurons, are actually pretty thin these are there, near the diffraction, limit of light so. And. They're traveling you, know they're traveling in ways that are not coming for Imogene they're not traveling in planes they don't care about our imagined needs they're actually sneaking, in through the whole nervous system so, we needed methods, that allow us to have good resolution, not just in two dimensions, but in all three dimensions so to, address this we establish a collaboration. With. With a fabulous, microscope. Is Harris Roth and his. Team of scientist and Harry, essentially, you know we had these discussions together, and we and he. Came up with the design of this microscope, I will go into the nuts and bolts about how these microscopes, work but, what I will tell you is the aspects of this microscope, that makes it usable, for our studies, this, microscope, is called a lysine microscope, which essentially, means that instead of image, in a single point of light like many other microscopes, do it creates a whole sheet of light so, it's much faster than other than, regular microscopes, because you're it's, like like in Star Trek when they scan you we're scanning you point by point so, it's so it's faster, it's it because it's faster it exposes the animal to less light so, let's photo photo voxi CT also the way that light is generated to be able to image these animals exposes, the animals to less light and the. Other aspect, of this is that for. For the microscope, is in the audience so people have done microscopy, that will recognize that when you take an a 3-dimensional, image of an object in a microscope, the. The, resolution, in in in X Y is always really good but when you turn it the resolution is C it's always bad and that. Has to do with just the physics of light, which I'm not gonna go into but, I will say that X Y and C is relative, to how you're standing so, you guys run our m.i.c but, if I was standing this way that would be my C so, if I were if I had two images where they see varied then. I could combine them to essentially have an isotropic image, so if I could if I look at this object like this this is my X Y and then I look at it this way this is my X Y and I combine those two images, it's. The same resolution in all three axes and. This is essentially what we're doing with this microscope because, we have two cameras, this one eat this one illuminates, this one image and then they take turns you, end up with two images that, you can then fuse and the DC of this axis, is different from this one so, again. I'm discussing. This superficially, because I know fill color we'll discuss this in more detail so. Have this microscope, working, in my lab and this is a slide that I put here just to acknowledge two institutions, the. Marine Biological Laboratory, and, the University, of Puerto Rico which, were meeting places where were. Technologies. Like like, a microscope, is like like people from Harris group and biologists, from my group were able to come together and exchange ideas and I think these places are gonna be increasingly more important, for. For. Interdisciplinary collaborations. Necessary, to address these problems so. What. Can we do this with this so this is actually, the, brain of the animal here in an embryo, detail, the head and, that's the brain that ring that you're seen there and I'm gonna play a movie where we can actually see the. Development, of this, animal, in, real time and you can add this is I mean this is as, a biologist this is spectacular that we can see this you're gonna see the animals it starts to move it starts to twitch as the nervous system comes online but.

Even, When it's twitching you can get very crisp images now if you look at subsets. Of neurons which, we're going to do here you see in subcellular markers, or, markers, that are more restricted, you can still see the you can see narrow it's specific neurons growing into the brain of the animal this is all taken again in live animals and, essentially. You can stop it at any time and you can rotate it so it so two-dimensional. Projections, because I'm using a screen but deserts these are three-dimensional datasets, you can see the resolution is very good in all angles that you look at it and, you can continually, moving so, this gives us unprecedented. Access, to the events that are leading to information of this nerve ring and, then we had another collaboration. That was really, enabling, which, is that is C elegans we know the lineage of every, single. Cell we've known that since, since the animal was developed, as a model. Organism that's, how the, original studies of program cell death were done and this. Person this is wrong well he societies, of sloan-kettering who's. A computational, biology is not a developmental, biologist and, what. I'm gonna be playing here is an embryo, that is in a four cell stage now. This is like playing a movie that you know what's gonna happen because we know the lineage so we know for each one of these cells we know who, their progeny is gonna be because those studies have been done before but what he did is that he trained a computer, to be able to recognize this in real time so, as the animal is developing, the. Computer, can keep track of all of these nuclei and he knows what each cell is going to become so, why is this important, to us is important for two reasons one. Of them is because when we're marking neurons as we're gonna be doing here we. Can tell their identity, because if we have nuclei, that we're labeling in the background in, red then. We can keep we can keep the identity of every every single one of these neurons that are emerging but, the other aspect is that we. Have an internal coordinate, system as the, neurons are growing out and crossing. The different nuclei we know where. The where each neuron is so if we take different embryos, and we're imaging different numbers we can overlay them because we essentially have a multi-point, internal, coordinate system does that make sense okay so, so, what can we do with that and here. I'm gonna summarize. The. Work of a postdoc in my lab Mark, Boyle this is five years of work summarized in one slide so. Essentially. What we can what Mark did is he was interested in identifying the pioneer in neurons that lead to the formation that trailblaze, deformation, of the brain of the animal so. He looked in embryos he labeled all membranes, and he found the first membranes, that are formed that are part of the brain he, traced them back to these cells here he were pseudo coloring here so you can see them then, if he looked at the nuclear he can actually identify the. By name and last name those specific cells, then. He can image as I mentioned before the development, of the brain so he can take on key these cells and kill them and the hypothesis, is if you kill them because they're the first cells if the first cells are important, then you shouldn't be able to develop a brain and, we get these brainless, animals, in which we cannot see essentially.

The Brain is really, obligated. So these neurons are actually very important, and it turns out when we look back in the maps that we have created with, the computational, biologists, that this pioneer, cells are actually the cells that are, that. Are here in purple and they're, they're like sim cells they're like their cells that are holding together the whole nerve ring and, with that I'd like to essentially. Bring, him to the attention how we can both, look at the how the brain is organized, and how the organization is established, to in development I'll finish by saying that. What, we have been able to do with this and our aspiration, is to create what would be the, first map of. Marathon. Neural development for any animal so we're systematically, tracking all of the assistance of these, narrows and creating this virtual, embryo that allows examination. Of all of these decisions, in the context on the developing embryo which with we'll be enabling for Neuroscience and we did have finished by thanking, the people that did the work this, motley crew and you. For listening to me today thank you so. I'm. Going to be talking about the. Complexity. Of the. Single, cells that, make up the brain we've. Heard a lot about. Neuronal. Populations. And how they're organized, which is quite. Amazing but, if you go if you zoom in and look at. Any one of those cells, whether they're astrocytes. Or neurons or. Microglia. Within. The. Brain inside. Those cells is a, plethora of. Sub. Cellular organelles. That. Are. Playing. A, huge, role in how the brain is operating, I. Just want to draw your attention to some of these organelles, who's. Who. If they don't function properly can. Lead to many of the neurodegenerative, diseases that we're aware of so for instance mitochondria. That you can see here. Defects. In mitochondria, lead to Parkinson's many, of the Parkinson's, disorders. Lysosomes. Proteins. That comprise some of these lysosomes, are. Responsible. For frontal temporal disorders. And the, antipas reticulum, which you, will hear a lot about today is. Very. Much at play in. Where. Mutations. In proteins that, shape that organelle. Underlies. A variety. Of spastic, paraplegia. Disorders. So in, order for us to really get, I think at this neurodegenerative. Aspect. Of the. Brain and how it ages, and deteriorates. We, need to understand, how these organelles, that comprise. The, cellular, components. Which is the unit of, that. The whole system of the brain are. Organized. So. This. Is a classic, transmission, electron. Micrograph a 90 nanometer slice. Through. A cell to, really. Reinforce the. Complexity. Of these organelles. What. We want to know is. More. Information, about the, three-dimensional, organization. Of these. Organelles, and how, proteins, are dynamically, distributed. That's. One thing we want to know and I'm going to be talking, about other, layers, of information, that. We're now beginning to get in terms of the organization. Of this, system. Thanks. To high end microscopy. Technology. So. Let's start with the 3d organelle, shapes. This. Is as I shut as I mentioned, is a transmission, electron, micrograph. Slice, of 90, nanometer 's what, that means is within that nineteen animators, slab. You. Have no Z, information. Everything's, flattened. But, thanks to, technologies. Like the focused, ion beam scanning, knowing. System. That in combination, with, scanning. Electron microscopy we, can now slice, through, the cell at very, thin.

Sections. For a nanometer in this case and, we've. Used that on collaboration. With Harold's beautiful. Fib systems. At, Genelia to, begin, slicing, through parts, of the cell to, reconstruct. Particular, organelles, in order to understand, how these organelles, are shaped in 3d and how, they communicate, with other organelles, what. You're looking at here is just the fine complex. Architecture. Of the endoplasmic reticulum. In a, small the. Edge of the cell in, this, panel right here. What. You're looking at is the confocal, volume, that we've sliced, in. What, you would see if you were using a confocal microscope. So you can see there's a huge difference between the. Reality of this organelle, as revealed. By imaging, at this very fine, three-dimensional. Architecture. Versus. The. Typical, image that you would get if you are using conventional. Confocal. Microscopes. Now. We've been working. With perrault. To look, in more detail. These organelles. In, this case we're. Looking at plasma, membrane mitochondria. Er, endosomes, that. We can segment out as. You mill through these, slices. This. Is a 2 micron, volume, of the cell not. 4 nanometer, voxels, and, you, can color-code each of these different organelles, to, see how they're, arranged, relative, to each other, now, the. Mitochondria. That you can see here in green are intimately. Communicating. With the ER, which, is shown in red and there's, a lot of crosstalk, between these, two organelles, that's, absolutely, critical for calcium, handling, in the cell for. Reactive. Oxygen. Exchange. As well as lipid, and other types of communication well. How do we localize, proteins, on, these organelles, and the. Approach that we've been taking, really. Builds from really. The twenty years or more of work, that people done using fluorescent. Protein technology. Which, allows you to tag proteins, of interest and then, look at how they're distributed, now. In order for us to get high resolution. Protein. As well as lipid, distribution. To. Understand. The fine architecture in these. In. A three dimensional section, of the cell we've. Applied, lattice, light sheet microscopy developed.

By Eric Mexican. Colleagues where. Essentially, you, take ultra, thin vessel, beings to. Create a thin, 2d, optical, lattice which. Is then used as a sheet, to pass through, your. Cell, of interest. What, this allows is, ultra. Thin slicing. Through a cell. Which. Is at a much smaller scale than, big. Neuronal. Slices. Sections. And. Because. We're we have such a thin light sheet that's. Basically, almost the same dimension. As the XY, lateral, resolution that, you get with the microscope, you have isotropic, resolution. And again, it's low it's relatively, low to photo toxicity. Because it's. A lattice sheet. Light rather than a full, sheet so. We combined, the. Lattice light sheet microscope, with. A point. Localization. Or palm-like. Imaging. Where, individual. Molecules, in this case we're looking at lipid, molecules, that, bind and then dissociate. From. Membranes. And whenever they're bound they. Create. A spot, on the surface of that membrane that, we can fit with. Very high accuracy by. Point. PSF. Centroid. Fitting, and when, we do that we can reconstruct, the entire. Sort. Of organelle. Distribution. Map it out in 3d through. Lattice, light sheet imaging, with, a combination of. Plotting. Out all the individual, distributions of these, lipids, that have been using. Super-resolution. Imaging, docked in and and. Put, in place now. Now, that we have an image, of all these organelles, in. This you can see mitochondria. Here and this, web-like, structure. Represents. Anaplasma, reticulum, we. Can now come in and dock in particular. Proteins, of interest I'm, just gonna zoom in on this area here because what, we were particularly, interested. In or were interested in in this study was, how, Endicott. Proteins, that are part of the endoplasmic reticulum. Which. Is this. Large structure. That expands, throughout this the cytoplasm, as this tubular. Meshwork, how, its organized, and so, what we can do, with. This technology is. Superimpose. Our. Distributions. Of fluorescent, proteins, that we've specifically. Genetically. Engineered. And tagged onto our. Fluorescently. Acquired, image using. This lattice light sheet system to. Dock. In where these proteins are localized. And this, is an example where we, have again. All of the membranes of this of the cell that have been painted, out if you will using. The, lipid our lipid, probe and single, molecule super resolution imaging, and we're, now correlating. It with the diffraction limit image of sex, 61, betta tagged. With an M emerald of fluorescent, probe and, you can see how, they align, this. Is exciting, to us because it, really sets the stage for beginning. To investigate, a whole, slew of different proteins, and how they localize, within. This cellular, system. So. We. Think that fits in and lattis light cheap paint or palm-like. Correlative. Approaches. Will. Allow us to really, gain deeper information, about, how all of these organelles, are shaped. In three, dimensions, and how different proteins, might distribute on them but. One of the challenges, that we still face is how. Many. Organelles, are arranged. Relative, to each other in a, living. Cell context. In. Particular that's. Important, for understanding how. Different. Organelles, are, contacting. Each other and, communicating. With each other we. Know for instance the, endoplasmic. Reticulum, which is, rep. This. Sort of snake-like, structure. That you can see in this en image is, contacting. Virtually. Every other organelle. Within the cell and. Communicating. With those organelles, through lipid, trafficking, Ross. Essentially. Exchange. Of reactive oxygen species, calcium. Signaling, many. Other types of communication. Is going on between these organelles, and our, problem, with trying to understand, that is that, we haven't been able to look simultaneously. At all. Of these organelles, in a, living cell, context. We can see them clearly. With electron microscopy but. If we do fluorescent, time-lapse imaging, we're, limited to imaging.

Two Or three of these of, these organelles, at one, time, because. Of this, problem of overlap. In emission, spectra, in, among. The different floor fluorescent, proteins that are available so. Here we have classic. Fluorescent. Proteins. That. Have different, emission spectrum, cfp GFP yfp, these. Are the emission, spectra, for each of these different fluorescent, probes and the. Problem, is their. Emission, spectrum, is overlapping. And what, that means is, that if, you, tagged a. Particular. Organelle, with. Different. Fluorescent probes, like, the ones that I just mentioned which are the most widely used fluorescent. Probes and then. You, image. Essentially. You go across the emission, spectrum, to, look at that at, any particular wavelength. Of light which. Population. Of organelle. You've you've. Looked. At you can see that, you have overlap. At any particular wavelength. So that means if you, were imaging, for instance at 4:38. You'd see three, different for. You you would not be able to distinguish mitochondria, or and lysosomes, from each other it would just be one big blur. And hence. You could not distinguish, how these different organelles are behaving, relative, to each other, so. To overcome that challenge two. Postdocs in the lab. Decided. To employ a technology. Called multi spectral imaging, to, try to unravel, this. Overlapping. Emission, spectrum, and the. Strategy. That they used was essentially. Take. If you know the the emission, spectra of each of these different fluorophores you. Can then query, an observe, pixel, spectrum, that's a combination of one. Or more of these fluorophores, and then use linear. Unmixing, to, decipher. What combination. Of and. In what abundance. Any. One. Or two, or more of these floors, would give rise to this particular spectrum, and, using. That we can at each pixel. Of our image, unmix. To, determine, which floor four is giving, rise to the signal that we're observing at that, pixel, and so, we combine that with lattice, light sheet microscopy to. Be able to image. Simultaneously. Six. Different organelles, within, the cell over time. In order to do that we've essentially, introduced, six different laser lines at that, cover this the visible, spectrum, that, allows us to excite, the, floor for that we've tagged on each one of these different organelles, and then we do our linear on mixing algorithms. To, determine. Each. The. Specific. Spectra, associated. With. Each, organelle. And this. Is what you can see in the case of these six different organelles, that we've, introduced. Fluorescent, tags, for now. This is in a single cell so we can superimpose all, of, these signals on top of each other to. Simultaneously. See. In a three-dimensional. Space because, we're using that lattice light sheet to, move through the whole volume of the cell how, all of these organelles, are distributed. Now. With this technique. We, can begin to, Zone. In on. Really. Specific, measurements. In terms of organelle. Distribution. Localization. Essentially. Connectivity. This, is just a, set. Of values for, the. Number of organelles that, each of these different. Populations, of, organelles represents so for instance on average. In the cell that we're looking at there's about 89. Lysosomes. 186. Peroxisomes. 157. Lipid, droplets. The. ER, occupies, by far the largest volume in this cell, among all of these different organelles it's about 30, times the size of the Golgi apparatus which. Is involved, in the, secretory pathway, eight. To nine times the size of the, of the mitochondria, which, is involved, in energy, production, within the cell now. We. Can also come in and segment, these, individual. Organelles, to, look. At how they, are connected. To each other how they're contacting, each other in order to begin, to understand. The, communication, the cross communication, or cross talk in, activity, that, we know is so important, for how cells, are operating and. Communicating. With other cells in their environment, for. Instance the oedipus reticulum, controls. The secretory pathway, together. With the Golgi apparatus, it's what's secreting. The pair the the paranormal, Network that we, heard earlier about so under, it's critical, for us to really understand, how these organelles, behave. Relative, to each other and from. These types of segments and images we, can create what we've, been able to describe, what we call the organelle, interactome. Where, we, just measure the pairwise contacts. Between, these, different organelles, in. In. Our images. Of these cells and from. That we can see the, frequency of. Communication. That different organelles have, with, each other you, can see here that the ER is by far the most communicative. Of all of the organelles it's contacting. Every one importantly. If you look at a single cell over time, you. Can see that this organelle, interactome, is conserved, over a fairly significant. Periods of time and that is. Despite. The fact that any, particular contact.

That We see is relatively. Transient, I should, emphasize that this interactome. Changes. Dramatically, if we, perturb the cell in in different ways we can deeply mirai's microtubules, or starve. Cells in different ways and we dramatically, change. The way these organelles, are interacting. With each other, now. This is a movie where we've segmented, out the mitochondria. As. An example, of a. An. Organelle. That is intimately. Communicating. With, the endoplasmic reticulum. On the, right hand side represents. The. Surface, all. Of the surface, sites of mitochondria. Where, we see ER signal. So. The ER is wrapping, around the. Surface of the mitochondria. And intimately. Communicating. With virtually all of those, mitochondrial. Elements, that we see in the cell. This. Contact, we think is what's, up, we. Think there's calcium flux, between the ER and mitochondria. That calcium, is playing, an, important, role for mitochondrial. Output. How much energy. Is being produced by the mitochondria we. Also know there's lipid, and cholesterol being transferred. Across these, contact. Sites and importantly. Reactive. Oxygen. Reactive. Oxygen species as being trafficked. Across those contacts, which. Could, play a big role in the. Disulfide. Bond, formation and protein. Remodeling. Occurring, in the endoplasmic reticulum. Now. Let's focus in on the ER the ER for, a second it, occupies. 25%. Of the cell cytoplasm. We, can measure that using our light lattice light cheap 3 construct three-dimensional reconstruction. What. Is interesting is that if we look. At we plot out the position, of the ER over a 15-minute, time period, which you can see in this movie what, we find is that the ER has pretty much explored, the entire. Cytoplasm. Over, just 15, minutes so, it is a very, dynamic. Organelle. That has, lots. Of, capability. Of communicating, now. In my final 2 minutes or 1 minute I've. Got a I want to take you through how fast, these organelles, can. Move. We, know that. They. Can the, the ER has the capability, of exploring, that cytoplasm, let's. Look at its dynamics, at higher, resolution. We, can do this using a turf sim system, we, can see these, tubules. Matrices. Move, incredibly fast. Interestingly. The. Tubes themselves undergo. An oscillatory, activity. That's. ATP, and gtp, dependent. So it's not just thermally, driven, and. Finally. We can actually come in and start. Mapping. Out, individual. Proteins, that. Move, or diffuse, along, the surface of the antipas matricula that's what you're seeing here each, of these yellow spots, represent. A halo, tagged protein. That, is associated, with the mem in the ER and we can begin to map out the trajectories. Of these proteins, and that's shown here for. Another ER resident, protein sex 61, beta. If. You sum up all of these trajectories you. See that these proteins, and this is a transmembrane, protein, you're looking at we'll explore, the whole surface. Of that ER freely, now, in. My final. Movie. Here I just want to show here's an example where, we're. Mapping out a. Protein. That actually, interacts. It's, on the surface of the ER but it is part. Of a tethering, complex, that, brings, the ER close to the mitochondria, and what, we can see is when we track these, individual. Molecules, as they diffuse. Across the surface. Of the ER we, can see that as they move across the area of the ER that's in close proximity of mitochondria, they they, slowed down significantly. Consistent, with a transient, interaction. Of, of. This tethering, protein, with, the target, protein on. Mitochondria. So. With that I want to end and say that this. Field is really, being. Significantly. Impacted. By, the high, resolution technology. That's now available that. Scans, from you know electron, microscopy up to the fast. Imaging. Technology. People. In my lab have been greatly impacted, by the.

Physicists. At Genelia Eric Betts ik and Harold in particular, who, really provided, the technology, that allows us to do this type of work thank, you. Life, sciences, are always trying, to make, best use of light for many different different, purposes, for. Instance, we. Use, x-ray. Beam for, crystallography. And infrared. Light for. Vibrational. Spectroscopy, but, most. Biologists. Use visible, right. And. Chromophore. Is the structure, you need to so that and. Of certain, visible, right and, it is responsible, for Cara, and in, many cases it has the, PI conjugation. System, and single. And double bonds appear. Ultimately. So. That PI electrons, can be delocalized, so. Electrons, oscillate. Or. Sing on the chromophore and that's, quite. Important, for the interaction with the visible right and the. My laboratory, is engaged, in technological innovation, in bow imaging, and principally. Using fluorescent proteins and I'd, like to introduce to you so the most classical. Fluorescent. Protein quality of P and, long. Time ago so in 1962. Or some amaura discovered, protein, and from, the light-emitting organ, of, the tree fish and just 30, years later in 92, it see the neighbors cloned by that, pressure and. In. 94. So, the hetero expression. Ecology. He was achieved, by Marty shortly, at, Columbia, okay and. This is the primary structure, be curative page. 238. Amino, acid, so. There is no chromophore that I define. A moment, ago in, the peptide, but. From this three amino acid, can, still interest, in glycine, so, three reactions occur spontaneously, okay, and. Cyclisation. Dehydration. And oxidation, reactions. To make a pie conjugation. System, so this is the GPS chromophore. And which, preserves blue right okay, but, this ride doesn't say anything about its fluorescence. So. Now so, this is a crystal. Structure they call a GP, and ABI, rabaah Roseland, and the, enable new stranded the beta bottom, which, one Arab helix, inside, and the chromophore is formed on the helix, and that, the bar is very robust. And the, chromophore is packed inside and, it takes, very, rigid of structure, so that explains, why, GFP. Fluorescence, quantum yield, is very high. And. The mutagen studies, get, done by and took the rotor turns. Produced. Quite a few car. Abhorrent and the more usable, and brighter. And. I stayed, in roses robbed from 95, to 98. And I, used, CP. And a YP and as, the donor and acceptor for, Fred so, to create is that the. Genetically. Encoded an indicator for calcium, ion, comedians, as Mark, mentioned, kindly. Okay, so. Now I'd, like to discuss why, color so many images so interesting, and appealing, so. There should be two major reasons so, fast and carlson, signaling. Is very so dynamic, okay, so the concentration. Of the free calcium ion a change, so greatly so. It should be fun so, to. Observe. For, instance, here calcium, oscillations. The. Second. Due. To the indigenous and the very abundant, cotton buffering, systems, we can express a huge, amount of calcium drops so. Without affecting, interest, recursion with dynamics very much okay, so we can increase the signal. Signal. Signal to, noise ratio, yeah. So. Here we expressed. A large quantity of comedian, in excitatory, neurons, of the forebrain mass. And. We sign the skin its head with excitation, right and to, get these cars from DDOT and through. This into, the scowl and video rate and for more than 30 minute, so. This readout, refresh. So spontaneous, neuron activities. And which, are composed of multiple, versions. Of different frequencies and, very, symmetrical. And. The mass was, half awake so, when we gave a visual. Stimulation, so, we saw an able to response in the path in. The visual. Cortex. Now. Back to so, this is right so GPS chromophore, and it. Has a phenyl. Ring so which comes from Paterson resisted, okay. Well. Almost. All, through. Some proteins now available, so, do you have phenyl, rings in there chromophores, an. Exceptions. The seek in what are BP, so they have in the ring my, middle, ring coming, from, TripIt pan and histidine but, you know being containing, chromophores. Most uncommon. And.

There Is an, equilibrium. Between. Protonated. Ionized. State, of, phenol, hydroxyl, group and. They, an absorbed. 404. 80 nanometer. Light and, in, many cases that the ionized, form. Fluorescent. Okay. So. Therefore. GFP. Basically, has, bimodal. Absorption. Spectrum. And. Now a circular, permutation, okay. And. The long terminal just a serendipitously. Age if beard and induces. Rub found, so, this side 144. 145. So the midpoint of the barrister, on number seven the turret, the circular permutation. The cycle permitted GFP, can synthesize, kunafa, very well and, that, there now is that the promote war also, they the equilibrium. Is. Quite, sensitive, to, neighboring. Apparent such as a Christian, dependent, protein protein, fraction, okay, so this is the. Operational. Principle, of gcamp. The. Pole very important point is that so, that he Carson, binding changes, our absorption spectrum. Not, fluorescence, quantum yield, okay, so as a kind. Of a future perspective so. The G camp should be well combined, with, photoacoustic. Imaging. Now. I have to say and not only the jellyfish, but some other NIDA in animals, or marine, animals so, can produce quite. Similar, frozen, proteins. And. Where. We have Cologne many, new, frozen proteins, from those animals and. By. Using this so these green, red, proteins, we. Some. Time ago we developed, cell, cycle Pro, and. We, used an. Oscillator. Yes sir psycho depend the proteolysis so. To create the, cell cycle probe which enables individual, nuclei in Jerome face red and those in HD, to Empress's green. That's. Food and, the, green and the red indicate. So that go and stop the. Estate's entry, so like a traffic signal and, when, food was introduced, in tashera cells so, and derived. Malignant. Tumor, we. Observed. A set operation, in the other time at a single Siravo but when we did the same thing using, benign. Tumor derived saline, we. Saw. Very clear contact, inhibition and. Reaching, convertible. Area all, of the cells became red and then stopped. So their proliferation, and. We, introduced, garage, so. If some of the cells or what went in green up to some rate and time so, they reenter the cell cycle and to build, the gap, and. We, prepare, food, strands of mice so, this is a coronal, section, of an, embryo and in, the brain so, neural stem cells at the greenery with nuclei, in the bin to do zone but, post modern neurons, had only nuclei, in the code cooperate, and the. Green and the red signifies. Cell. A preparation, and the differentiation, and. During. The embryo genesis with, time so, green to red racial decreased, that, earlier the more, preparation. The later the more differentiation. And after the person so the red signal became, predominant but even, in adult tissue so, we can identify very, few good in nuclei and the, cells with green nuke like responded, in tissue stem cells like, a neural stem cells found in the pentateuch. Aras. And. When we. Used. Zebrafish. Embryo, very transparently, so we, can obtain cell cycle profile in four dimensions, so this is a segmentation. Period, of embryo, and a, side view and hidden here and again. With time I'm going to weigh the racial decreased but, some other organs, like, related. And brains are very green and, in. Top view in, addition, to two eyes two, eyes so, we saw and, notable, so, differentiate. In Northwood, and the, in not what we. Discovered. So, the cell cycle transitions. And moved trouble, from, head to tail. Like. This so, Jiwon this transition, will be twelve here and. We. Think, so, the cell. Cycle regulation in, the North code is linked to somewhat Genesis, which is also an characterized, by head to tail progression. Of differentiation. But. You. Know it's, quite, very amazing that, that we can visualize, that the, middle with the body from, our side so that's just because so. The fish embryo is a bit transparent. Okay, and by contrast, mouse, embryos, well my money my money animals were including us a bit yeah. Yes. Visible. Right scatters very much inside. So our body so, like na mentioned. Okay, so, now at each catification. And. Long time ago we. Discover that the, membrane for wisdom brought in analysis, it became transparent when, so in for moral real and we. Knew that so, the GP, barabar oh the brave a tough, so, rigid and the, tolerant, of, high. Concentration. Of rare. So. We. Started. So the creating, devolution, into, a ribbon so, with our paper on scale a 2 which, is the first and rebase, rear based eQuest. Solution-focused, gratification. Okay, and then, it was followed by many. Explosion. Of many new techniques, and. Also two years ago and we reported. Some modification, scary-ass. SKS. Line. Contains three Beatle in this in addition to Korea and, we, verify that scatter. S can. Preserve. So. The fluorescence. Signal also. Outer. Structures. Outer. Structures, like a snaps again. So, I think, so. Researchers. Such, as you should be aware with a critical, trade-off between, effective. Creating, and the, teach signal, preservation.

And. Also we invented, a serviceable. Scale method which enables, three-dimensional. Immunohistochemistry. And. Some. Time ago and thus idol group and BSI. Generated. A single a between nuclear mouse models, of Alzheimer's. Disease and. We. Applied. Obscure, method so, to the brain of, aged, a PP, knock in mice and to, visualize a better price we, used Erickson, 488, labeled, an antibody. And there. Was a brain. Prepared. From nine month old and a, PP no key mouse and very sparse okay, but in 18-month. Old so, we saw we. Observed, so many immune. Or able to a better products, and. Yes. Another, experiment. Now we trans cuddly perfused, entire basket, with, accessible electing, to rebel, Barabbas sauce with red fluorescence. And then, we immunity, and a better prize with green and to. Obtain so, the 3d perspective very, comprehensive. Perspective of. How, a better perhaps interface, with blood vessels. So. Then we took interest in a special, association. Of a better price with, microglial, cells, and. We, applied so the doer cara am, scale so, to a brain so as prepared, from a PP no cream mouse and, we analyzed. So the interaction, between these two object. Light, sheet microscopy the patient, and, we. Measure the. Distance. From each microglia. Centers, to, the nearest, clock edge, in. The 3d, space and that. These those. Measurements. Should provided, the information about, stability. Of track, neuritis. Inflammatory. State for. Instance so, two neighboring, tracks, with. Similar, size okay. But different association. With MacDougall is Ellsbury and. Direct. Contact process. Very. Isolated, okay, so we think, so. They suggest, acute. Analytic, state versus, absolute. And. In, a similar way we. 3d, imaged. Track. Microgram. Association. In, post-mortem. Brain samples of ADA patient. More. Than 50 years old and in. These three brain. Samples we, observed. Detected, very clear a quad, perhaps grievant. And. Quad perhaps, quad, perhaps without and, with micro bio granule. Association. But. In the, remaining six to the brain samples samples. We. Didn't see any, not. We didn't see any, quadrats. So. Then we became interested. Diffused, racks so. The few spreads have basically, undetectable. Cannot. Detectable. In the two-dimensional. Image. But. Our new software can, this rice, so, the 3d reconstruction. Systematically. And with different. Size different. Thickness. And. That. Helped. Us to identify. Diffuse. Pipes of different sizes, and the. Diffuse products are usually. Assumed. To be very primitive, and, very isolated. From, any inflammatory, cells. But, we found so, most of them can, show the sign of, neuritis. With. Considerable. My, gross association. With microbial cells, okay. So clinically. Used, products, are ambiguous, but, the potentially, very very important, okay, and. Scale, press food, we. Studied, Basque, Renisha, care, for neural stem cells in the vendetta Chavez and, the turrible. So. Neural stem cells are proliferating, neural. Stem cells we used food. SDTM. Face mother and. Brothers. Were labeled, with red. And. Green. Eco lies at the neural, stem cells. Indeed localized, inside, that entered the Dara's not outside, bit and, we, again. Perform. Distance, measurement, in the two-dimensional, space we, meet the distance. From each green, nucleus, so, to the nearest bristle, and. Then. We, came to a conclusion that the neural stem cells are more closely. Associated. With, promises, so, damn mature neurons. And. The rusty has been saying so, a, doubt. Began. Poor neurogenesis, is up regulated, by exposure. To enriched. Environment, and we. Performed, the comparative, experiment. Comparative. Experiment, and, large-scale. 3d, reconstruction. And. Our data sold, and. The. Enriched environment. Increased. So the number hua density, of neural. Stem cells but, did not change, so, that their association. With brother. Sauce so this is a comprehensive. Data. I think I hope okay.

All. Right so, this, slide is an. Interplay. Between enlightened. Life. Well. I remember that so the Richardson, which, I used, to remind us nature, is very kind. To us to. Researchers. The. Nature still, remains the best source of bio. Imaging, tools. And. This is my. Last slide and only. A scientist, with a respect. For nature would. Have been permitted to, surpass. It in. English. Japanese, and, Chinese. Thank. You very much for attention. You.

2018-02-07 17:12

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Comments:

Fascinating, but Miyawaki really needs some coaching in articulating his obviously high-level command of English if he is to present in that language.

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