| 00:00 | Really. So this is cellular in science lecture two. And we will |
|
| 00:07 | with a few lecture slides that were undiscussed, left undiscussed lost lecture. |
|
| 00:15 | in particular, we talked about the of neurons about 10 micrometers in |
|
| 00:21 | We talked about the sizes of the , 20 nanometers that space for chemical |
|
| 00:29 | . And you'll also learn that there electrical synopsis. And some of you |
|
| 00:33 | know that have that space much smaller space between neurons down to four |
|
| 00:40 | And to visualize this, we need powerful tools. We need microscopes to |
|
| 00:47 | synopsis. We need electron microscopes because can resolve at a below nanometer |
|
| 00:54 | And it's really important that we have full understanding from what a single dendritic |
|
| 01:02 | looks like from the shapes that those spines may have number densities of these |
|
| 01:07 | which are very important for normal communication plasticity in the brain down to uh |
|
| 01:14 | from those individual synopsis, if you to individual cells to the anatomy and |
|
| 01:21 | of these individual cells that we we about. And we'll continue talking about |
|
| 01:25 | about neurons to larger formations and the of these cells into into networks and |
|
| 01:33 | functions of individual cells as well as functions within the network. So all |
|
| 01:39 | these scales we understand now what is here is that we have, it |
|
| 01:47 | uh we have uh things that we looking at on a single dendritic spine |
|
| 01:55 | , single cell level. At a setting, you have a way to |
|
| 02:00 | at the brain activity noninvasively using positron tomography and functional magnetic resonance imaging. |
|
| 02:09 | it tells us that different parts of brain using this imaging techniques, different |
|
| 02:15 | are excited and are active and create we call these brain maps during different |
|
| 02:23 | tasks such as looking at the reading the words, thinking of the |
|
| 02:26 | and so on. Now a big that is important and we've talked about |
|
| 02:34 | you can stain the cells and you visualize the cells. We also talked |
|
| 02:37 | how you don't always have to stain cells. You can use this infrared |
|
| 02:42 | , especially in electrophysiology to visualize Uh but issue with these technologies or |
|
| 02:50 | techniques including in vitro electrophysiology is that even with infrared microscopy cannot visualize cells |
|
| 02:59 | within nutrition. So if you imagine have underneath this microscope objective here, |
|
| 03:10 | . You have a a slice and slice that your imaging activity and this |
|
| 03:16 | whatever shape or form it may be . This slice is about 300 |
|
| 03:23 | 350. Let's say 500 micrometers And so when we do infrared |
|
| 03:32 | a lot of microscopy imaging, light imaging, even fluorescent microscopy imaging, |
|
| 03:39 | still really just looking at the at very surface at about 50 to 100 |
|
| 03:48 | . What is happening on the very finding the cells on the surface? |
|
| 03:53 | the issue becomes is how deep can image the cells? And can you |
|
| 03:58 | it noninvasively? In other words, you do it in vivo? What |
|
| 04:04 | it mean noninvasively? Well, not the skull. But in this |
|
| 04:08 | if you want to image the cells image activity, you still have to |
|
| 04:12 | them with something. So you still this case, putting some sort of |
|
| 04:16 | fluorescent tag in order to now visualize cells that are in this case, |
|
| 04:22 | is a depth of 507 109 So we have and the width. |
|
| 04:33 | we're talking about 700 micrometers is is uh 700 micrometers and 700 micrometers |
|
| 04:45 | So now with this triple photon what it does is it allows for |
|
| 04:52 | lasers to point and we'll talk about in greater detail and we talk about |
|
| 04:57 | activity imaging. This is sort of introduction of this technique and that's why |
|
| 05:00 | just using one slide from here. one figure from this uh from the |
|
| 05:05 | material. But the point is that this, you point the lasers deep |
|
| 05:15 | the tissue and you typically have more one photon. So multi photon |
|
| 05:21 | this is three photon imaging and it us to visualize the cells deep within |
|
| 05:27 | tissue. And it also allows to activity of those cells such as calcium |
|
| 05:33 | , which is indicative of cellular It allows us image that uh |
|
| 05:40 | neuronal activity and for example, calcium which could sometimes equate to changes in |
|
| 05:47 | membrane potential all at the same So this is a a challenge |
|
| 05:55 | It's a challenge that we'll discuss again we talk about even clinical imaging that |
|
| 06:01 | still have certain clinical imaging techniques that place you at an advantage of imaging |
|
| 06:07 | tissues rather than more superficial tissues. an experimental neuroscience will still grapple with |
|
| 06:14 | , with this problem with this And these types of setups like three |
|
| 06:19 | microscope setups, you have to spend about a million or more to have |
|
| 06:27 | piece of equipment that functions at such optical resolution and has all the lasers |
|
| 06:33 | bells and whistles on it. I in my uh life, talk about |
|
| 06:42 | I and then classes too about augmented and visual reality. And just the |
|
| 06:49 | day, I heard a program on Public Radio, it was actually Houston |
|
| 06:54 | media and we have this wonderful radio that broadcasts right off campus and they |
|
| 06:59 | talking about the new headsets by Apple supposedly have like integration of either virtual |
|
| 07:11 | or augmented reality with, uh, reality, normal reality, I |
|
| 07:18 | And, uh, it's, I think they were discussing that it's |
|
| 07:23 | $4000. Unless you have glasses, have to get special size lenses inserted |
|
| 07:28 | with the purchase of this headset. then it's a lot of, |
|
| 07:33 | data usage, I guess because as using it, it, it sucks |
|
| 07:38 | lot of just like your phones and of the, uh plays by Apple |
|
| 07:43 | to Upsell you on data. All . Buy more data, buy |
|
| 07:47 | you know, you need more more subscriptions, more music. So |
|
| 07:51 | what the glasses is. They, run out of the data and you |
|
| 07:56 | to keep buying more megabytes or gigabytes , to supplement so that you can |
|
| 08:01 | it. So it's the usage plans are also very expensive. Ok. |
|
| 08:05 | you know, these things when they out, like cell phones, cell |
|
| 08:09 | first came out and they came out 30 years ago. Ok. |
|
| 08:16 | 35 years ago, if you had cell phone, you were a wealthy |
|
| 08:22 | or you were a business person and cell phone was this huge bulky |
|
| 08:29 | you know, that weigh like Uh, there was before that there |
|
| 08:34 | satellite phones in the cars and, , when I went to college, |
|
| 08:39 | 11 of my peers had a satellite and everybody knew about it in |
|
| 08:45 | He had a satellite phone in his so he could order a pizza as |
|
| 08:48 | was driving to pick it up, know, and pay an enormous amount |
|
| 08:51 | money, like $10 for a minute charge. So he was spoiled. |
|
| 08:57 | anyways, uh, we like the and, um, now it's all |
|
| 09:04 | trade in all one, get one free. And again, they'll get |
|
| 09:07 | on the usage on the data. you're still paying a lot for, |
|
| 09:10 | know, a life time on the and what you put into it, |
|
| 09:13 | still a lot of money but it's . So a lot of these things |
|
| 09:17 | are coming out now, made me about $4000 all these usage plans. |
|
| 09:21 | look five years from now, 10 for them, it's gonna be probably |
|
| 09:26 | for, for that technology. It's be even better improved, you |
|
| 09:30 | usage plans are gonna go down and is becoming more and more a part |
|
| 09:35 | our lives. And I think we of have two camps about all of |
|
| 09:39 | realities. Uh artificial intelligence also. virtual realities. The two, the |
|
| 09:46 | thing is the virtual reality can distance from human intelligence from H I because |
|
| 09:52 | lose the human interaction, you can't people. I always talk about that |
|
| 09:57 | your phone or through even these fancy glasses, you know. But so |
|
| 10:03 | a part of human intelligence, how process each other too, including all |
|
| 10:09 | . Uh But it's all coming into . Artificial intelligence also has seems to |
|
| 10:14 | to the other people in virtual reality no, that's fine. It's just |
|
| 10:18 | my things. And you know, people were upset about kids playing video |
|
| 10:22 | and then it turns out that kids played video games are better three dimensional |
|
| 10:28 | of the structures in the world. when they play uh uh uh video |
|
| 10:33 | you move through spaces, you you know what's behind you kind |
|
| 10:37 | Uh so it, it trains you a certain way and uh artificial intelligence |
|
| 10:43 | the same way, you know, scared of it. I don't know |
|
| 10:45 | to do with it. It's especially older generation, you know, |
|
| 10:50 | have to do everything from scratch. know, I have to type my |
|
| 10:54 | on the typewriter. What A So, and then there's another |
|
| 10:59 | like I'll just, I'll just put in and get it out and I'll |
|
| 11:04 | , it sounds good to me. know, the truth lies somewhere in |
|
| 11:08 | . I think that we have to cautious, but we have to take |
|
| 11:12 | of these things too. I think philosophy is a little bit, let's |
|
| 11:16 | be so timid so that we don't A I. But also let's not |
|
| 11:21 | it because, you know, what is uh with a I, I |
|
| 11:27 | that the imagery is kind of a like when people generate images, it's |
|
| 11:33 | so many engines that are available for or for a nominal fee and they |
|
| 11:38 | only certain models across those several Therefore, so all images are gonna |
|
| 11:43 | like. So you don't want like artists painting all of the paintings in |
|
| 11:46 | world or thinking about how it should represented. Although of course, it's |
|
| 11:51 | broader than that. But you and you can recognize virtual reality images |
|
| 11:57 | . So we still have that, know, so it's not still |
|
| 12:00 | it's still can fool us, although can fool us in many instances, |
|
| 12:04 | becoming really, really good. I it in my personal case. I |
|
| 12:08 | it like this. I have an . I'm developing some idea. I've |
|
| 12:12 | a paragraph. I don't like the that paragraph sounds. I will ask |
|
| 12:15 | it to change the grammar, maybe or shorten that paragraph. Then I |
|
| 12:20 | take it, I'll review it again then maybe I'll publish it. But |
|
| 12:26 | I do that, I'll go back the A I engine and say was |
|
| 12:30 | written by A I? And it it's highly unlikely this was written by |
|
| 12:34 | OK. Then I'm good to go I had almost like a kind of |
|
| 12:38 | , a checker suggesting whether they did are right or wrong. Another way |
|
| 12:43 | use it is good ideas, good about projects about paper. Yeah, |
|
| 12:51 | quite good. Some answers from A are good. Some answers are crappy |
|
| 12:57 | you always have to double check, when you're talking about science or something |
|
| 13:00 | you're relying on. You still have double check the references, but some |
|
| 13:05 | are really good. And sometimes you just say what are the major parts |
|
| 13:08 | this presentation that I should have? we'll tell you, ok, |
|
| 13:12 | there, you know, these eight outlines and go like, oh, |
|
| 13:16 | pretty close to what I was but I didn't think about this and |
|
| 13:19 | would enhance my presentation and such. we know that once the brains get |
|
| 13:25 | within virtual reality, these are the maps without virtual reality when the person |
|
| 13:30 | doing the same task. And this a map of the brain with virtual |
|
| 13:33 | . We know that the brain maps therefore the physiology of the brain and |
|
| 13:38 | the physiology of the body or decision your motor out, but also changes |
|
| 13:43 | the presence of virtual reality. There emerging discussions on a topic that is |
|
| 13:50 | meta pseudos where patients that have, say burn marks or inflammation and pain |
|
| 13:58 | with some injury, they're placed within reality and within that virtual reality, |
|
| 14:03 | say they are in the snow, a very cold climate and there's a |
|
| 14:08 | dis difference in in their temperature, example, from this virtual reality. |
|
| 14:12 | it can influence the physiology, for . What we're using A I and |
|
| 14:20 | for is uh you know, for kind of uh enhancement things. But |
|
| 14:24 | really going to be an important I think when you think about its |
|
| 14:30 | in clinic and right now I'll give an example of this is arti artificial |
|
| 14:37 | in Parkinson's disease. But before we about Parkinson's disease, I'll give you |
|
| 14:42 | example that I have learned about uh hard way. About a year and |
|
| 14:48 | half ago that about 25 to 30% all of the pathological test results for |
|
| 14:57 | that come into MD Anderson are 25 to 30% of pathology results means |
|
| 15:07 | to 30% of diagnoses the wrong. ? Why is that? Who when |
|
| 15:16 | stain this tissue for pathology, we'll about pathology. You already know about |
|
| 15:20 | pathologies, Alzheimer's disease. Let's say pathology who decides that you have a |
|
| 15:25 | type of cancer use all sorts of . You have all sorts of tests |
|
| 15:30 | usually pathologists and another physician they decide . So, so it's a human |
|
| 15:37 | . Their decision is based on what have learned. It's not that they're |
|
| 15:41 | . It's what they have learned, they have seen the markers that they |
|
| 15:45 | the algorithms for analysis that their institution this Methodist versus Baylor, they might |
|
| 15:55 | competing modes of analyzing the same making one better over the other in |
|
| 16:00 | ways or at least different. So what if we took that human element |
|
| 16:07 | in that error and trained machines with microscopes and introduced a lot more detail |
|
| 16:15 | the molecular level on the sequencing trans trans transcription level transcripton level. But |
|
| 16:22 | we did that and allow artificial intelligence have its say, so we think |
|
| 16:29 | diagnosis XYZ artificial intelligence, what do think? And it comes back and |
|
| 16:35 | 80% probability it's XYZ, but 20% is you haven't thought of this, |
|
| 16:42 | know. So I think it can very, very useful and helpful |
|
| 16:46 | in clinical obligations. So now how it relates to Parkinson's? And we'll |
|
| 16:54 | about uh Parkinson's later in the course early diagnosis and treatment. So learn |
|
| 16:59 | data patterns to make predictions. Uh are the data patterns? You |
|
| 17:05 | Well, how does individual move? we take a three dimensional tracking software |
|
| 17:11 | the person moves and say uh there's something wrong with the hand, |
|
| 17:15 | little flickering here and there to the . That's early diagnosis of tremor, |
|
| 17:21 | is one of the symptoms for Parkinson's . You have Parkinson's tremors, your |
|
| 17:25 | have tremors or shaking. That's pretty because you may not be able to |
|
| 17:31 | it with a naked eye. But you had a device, that device |
|
| 17:34 | pick up the standard deviation, the in the movement. For example, |
|
| 17:39 | you don't pick up with a naked . So targeting brain homeostasis, focusing |
|
| 17:45 | blood by barrier permeability prediction that is intertwined with predicting novel biomarkers, increase |
|
| 17:53 | sensitivity and specificity of diagnosis for a of the things for a lot of |
|
| 17:57 | diseases really that we'll be learning about in this course. What's really interesting |
|
| 18:02 | we want to have markers as early possible as noninvasively as possible. You |
|
| 18:08 | , full body F MRI scan is expensive just out of nowhere, but |
|
| 18:14 | blood test to pick up 15 markers may indicate something if we know that |
|
| 18:20 | markers and then the are in the . And if we know that those |
|
| 18:25 | have a direct correlation that have shown have a direct correlation to, let's |
|
| 18:29 | Parkinson's disease, we are really in shape if we can do it early |
|
| 18:34 | with a prick of a needle rather a body scan or eeg electron a |
|
| 18:41 | gram recording um and predicting these novel . So you can use A I |
|
| 18:49 | now look at biomarkers across and see molecules are present in Parkinson's disease that |
|
| 18:55 | be present in pathology of Huntington's disease may be present in pathology of other |
|
| 19:01 | diseases or maybe even epilepsy. you have a more clever way, |
|
| 19:09 | uh inclusive way to really understand this on the biomarkers and how they may |
|
| 19:15 | to Parkinson's early detection of Parkinson's could a substantial effect on diagnostic services. |
|
| 19:22 | treatment, of course, informal and care harbors the potential for predictive analytics |
|
| 19:28 | integrate all relevant patient factors. in some instances, Parkinson's patients have |
|
| 19:33 | have a neurosurgery. And again, would be helpful to know a lot |
|
| 19:39 | information, enhance Parkinson's care and improve motor and non motor symptoms and |
|
| 19:48 | Parkinson's therapeutics to identify protective drugs through of large compound libraries to both increase |
|
| 19:55 | therapeutics that are available and also a matching of therapeutics to the condition the |
|
| 20:01 | might be having. Uh for it's ancestry.com 23 and me are quite |
|
| 20:09 | . Pretty good Christmas gifts actually. they do have panels that they address |
|
| 20:14 | certain genes and they have panels that may address even as susceptibility to some |
|
| 20:22 | disorders or metabolic processing or something like . Uh And that data is like |
|
| 20:30 | more and more available. So now can actually do a gene test at |
|
| 20:34 | Seabold. I believe it's a little more expensive. Uh But so it's |
|
| 20:41 | into play where this is gonna be too. You know, something about |
|
| 20:45 | genetics, epigenetics of the person, know something about the biomarkers in their |
|
| 20:50 | . If you can correlate it with that get picked up by sensors and |
|
| 20:56 | that is more sensitive than humans, you're gonna be in much better |
|
| 21:00 | And this, all of these technologies allow us to push this forward major |
|
| 21:06 | of the nervous system and major disorders we'll focus on in this course, |
|
| 21:11 | listed in the syllabus. But I want you to know this list. |
|
| 21:14 | an important list. Um We'll place lot of emphasis on epilepsy. And |
|
| 21:21 | the undergraduate course, we uh spend half an hour to 40 minutes talking |
|
| 21:27 | epilepsy. I think we'll spend about hours. So it's a, a |
|
| 21:31 | approach. Uh It's a more detailed and we will talk about therapies |
|
| 21:36 | So we'll go all the way from mechanisms to the therapies that are available |
|
| 21:42 | the challenges of today and what can done in the future. OK. |
|
| 21:48 | this is the first lecture and I now share this second lecture which is |
|
| 22:00 | neurons. It's neurons and glia, we're going to focus on neurons and |
|
| 22:06 | comprise about 10% of the total population the cns of the south naglia. |
|
| 22:14 | . You are like chips in the chip cookie le is like the |
|
| 22:20 | Uh But I always say, you that chocolate chip cookie or cookie is |
|
| 22:26 | without chocolate chips, but there is cookie without dough. So there is |
|
| 22:31 | brain without glia. And today, talk about neurons and maybe overlap a |
|
| 22:37 | bit into Thursday. Then we'll talk glia and you'll understand how important glia |
|
| 22:43 | in neural development in neuroplasticity. And glial perspective, we'll focus on microglia |
|
| 22:51 | astrocytes and interactions between these two cell , the gain in the brain is |
|
| 22:57 | in the stain because in order to neurons, in order to visualize their |
|
| 23:02 | , it's all going to be very . We have to have certain stains |
|
| 23:07 | as goldi stain. Again, goldi will get picked up by a fraction |
|
| 23:11 | neurons but will reveal their precise And this stain will get picked up |
|
| 23:16 | all of neurons and glia. But stain is focused on the RN A |
|
| 23:22 | poly poly ribosomes which are located in selma and around the somatic region. |
|
| 23:28 | it won't expose the processes like dendrite um and axons on, on on |
|
| 23:35 | cells. We go back to these giants, Ra Monica Ha and Camello |
|
| 23:41 | , Camello, Golgi invented the Golgi which stains these neurons in a very |
|
| 23:48 | fashion. And Ramonica Ha reconstructed using technique called the camera lucid. So |
|
| 23:57 | looking on a slide that has a of the brain that has a stain |
|
| 24:02 | these neurons unless he's looking into the pieces. And he, he has |
|
| 24:07 | mirror that extends to the right of microscope. And the mirror shows him |
|
| 24:13 | hand. So it merges what he with his eyes and he sees these |
|
| 24:17 | of cells with his eyes with a . Here it merges to where his |
|
| 24:22 | is. So these are called neuronal , also called neuronal tracings. They're |
|
| 24:28 | drawings. In other words, he's looking in a microscope and then going |
|
| 24:33 | a piece of paper and trying to it. So this is a technique |
|
| 24:37 | camera elucidative. He is a big of neuron doctrine which argues that neurons |
|
| 24:43 | individual discrete units that communicate with each through synapses. He proposes that the |
|
| 24:53 | have uh quite a unique structure and dendrites are likely to be the sites |
|
| 24:59 | information comes down. So you can these arrows from the dendrites and information |
|
| 25:05 | into dendrites and traveling into the And as this information reaches the |
|
| 25:12 | he postulated that that information gets processed the SOMA. And he added other |
|
| 25:19 | on these thinner processes which are axons he labeled them here darker color. |
|
| 25:25 | he said that this is how information transmitted to other neurons. So there |
|
| 25:31 | a directionality inputs in gets processed and gets sent down to axels to communicate |
|
| 25:37 | information to other neurons. He drew very extensive networks. He proposed that |
|
| 25:45 | connections of plastic. So over 100 ago, 100 30 years ago, |
|
| 25:52 | Cajal thought that these connections are not to be potentially permanent, that they |
|
| 25:58 | change over time or maybe even with . So he was really uh thinking |
|
| 26:04 | ahead of his time and this principle dynamic polarization refers to just that, |
|
| 26:09 | he thought that there is uh information communicated from SOMA only in one direction |
|
| 26:16 | from dendrites into SOMA and then from through axons into other neurons. And |
|
| 26:20 | know that this has been called into challenge in modern neuroscience. And that |
|
| 26:25 | flow of information can happen also from back into dendrites as what we call |
|
| 26:33 | back propagating spike. And we also that there's other complex synaptic interactions that |
|
| 26:40 | such as dendrodendritic synapses that are quite , but they also exist challenging this |
|
| 26:47 | of dynamic polarization neurons such as like organelles, like other cells that have |
|
| 26:54 | lot of the same organelles, the mitochondria, poly ribosomes, golgi |
|
| 27:01 | smooth endoplasmic reticulum, rough endoplasmic cyto skeletal elements, neurons suck up |
|
| 27:08 | lot of energy. So neurons generate lot of A TP and they also |
|
| 27:15 | up a lot of A TP from sources. And these are the dietary |
|
| 27:20 | sources. So we generate a lot A TP our bodies and 20% of |
|
| 27:27 | A TP gets used by the Although it's about 3% of the total |
|
| 27:34 | mass, it's about £3.5.04 pounds by the brain. But it uses about |
|
| 27:40 | of all of the energy that the uses. So it's a pretty, |
|
| 27:45 | , it's a system that is working a pretty high level of energy |
|
| 27:50 | Energy is produced and uh uh is by adenosine triphosphate A TP where you |
|
| 28:00 | protein sugars, fats, dietary and energy sources. Uh through pyruvic acid |
|
| 28:07 | oxidation and mitochondria forming a TP and CO2. And this is the main |
|
| 28:15 | of energy in the body. And brain, the phospholipid bilayer or the |
|
| 28:21 | membrane that surrounds these neurons contains channel . So these channel proteins would be |
|
| 28:29 | channel proteins. They're channels because they have a channel in the middle of |
|
| 28:33 | protein that will allow typically for ions pass into the cell and to exit |
|
| 28:39 | of the cell or other small Also, apart from ions, you |
|
| 28:45 | have uh receptors and proteins that are channels. But a lot of times |
|
| 28:50 | may be linked to other complexes such G protein complexes. So there, |
|
| 28:57 | is no opening here, there's no but binding of a molecule to this |
|
| 29:02 | type of protein can activate uh in . Uh uh the complex, the |
|
| 29:10 | protein, uh the G complex is to this protein cell. So you |
|
| 29:17 | proteins that are embedded in plasma membrane membrane is a phospholipid bilayer. It |
|
| 29:23 | the polar group. So the polar are hydrophilic and they're pointing to the |
|
| 29:31 | the extracellular fluid or the cytoplasmic fluid the side and the fatty acid tails |
|
| 29:41 | hydrophobic. So they turn into each and form this bilayer embedded. You |
|
| 29:48 | cholesterol molecules, glycoproteins, there's a of carbohydrates hanging around the south. |
|
| 29:56 | they refer to sugar coated and the of the plasma membranes is supported underneath |
|
| 30:03 | this cytoskeleton elements. And there are major subtypes of cytoskeleton elements. Um |
|
| 30:14 | are large microtubules. This is an of microtubules also sometimes referred to as |
|
| 30:22 | Tullar highways. This is an example an axon that has been cut in |
|
| 30:27 | . So the axons are wrapped around myelin sheet and in this case, |
|
| 30:34 | axon has been cut in half and looking at this half of the Axion |
|
| 30:39 | has been cut and you have these tullar highways running through through this axon |
|
| 30:47 | . OK. Uh And that's important cellular transport from the SOMA and back |
|
| 30:56 | the SMA, the smaller elements and and the smallest elements comprise of acting |
|
| 31:03 | . So microfilament and these elements are static and neither is the shape of |
|
| 31:09 | plasma membrane, the shape of the spines that we discussed, for |
|
| 31:15 | can come in three different shapes. over its lifetime, this dendritic |
|
| 31:22 | they become much, much larger in . And that's going to be really |
|
| 31:30 | of this plasma membrane. And for shape to change from shape one outer |
|
| 31:36 | to shape two, you have to reorganization of cyto skeletal elements. So |
|
| 31:42 | skeletal elements are sort of the the and uh frame that holds the whole |
|
| 31:51 | . And so if you want to the second floor, you have to |
|
| 31:55 | sure that you rebuild the walls and beams that are holding it. And |
|
| 32:02 | cross beams going across and you can the ceiling from 10 ft to 16 |
|
| 32:07 | . But these are the c of on them. So you have to |
|
| 32:11 | them and they can get rearranged pretty . They can polymerize into longer chains |
|
| 32:21 | they can depolymerize or re polymerize into chains. So they have this ability |
|
| 32:30 | essentially assemble into longer or shorter And once again, they're very important |
|
| 32:38 | supporting the overall structure of the cell it is like overall structure of the |
|
| 32:46 | . And the smallest elements shown here blue are the acting molecules. And |
|
| 32:52 | can see that the smallest elements, smallest molecules are located on the outermost |
|
| 33:00 | , outermost boundaries of the plasma membrane they are the smallest, they have |
|
| 33:06 | ability to polymerize and form longer chain get broken up much easier than the |
|
| 33:14 | and more rigid cytoskeleton elements which are found around the SOMA. So |
|
| 33:23 | for example, around the SOMA or the processes. For example, you |
|
| 33:28 | see these highways running within the In this image, you have transport |
|
| 33:37 | goods, transport of molecules across these tullar highways. This is a motor |
|
| 33:45 | kinesin that for example, will transport from the. So along this micro |
|
| 33:52 | highway into the axon terminal down, axon axons have a certain morphology. |
|
| 34:01 | dendrites have dendritic spines, axons have morphology that they typically have one major |
|
| 34:10 | output. But they can also form we call collateral, so they can |
|
| 34:16 | or spread into collaterals. And some these collaterals may leave synopsis locally. |
|
| 34:22 | then the major axon may carry information major synapses a little bit further down |
|
| 34:28 | distance Axon Hillock is where you have first beginning of the axon. And |
|
| 34:35 | is where the action potential forms and have the axon proper and at the |
|
| 34:40 | terminal where the synapse is, it's the exon terminal at the end of |
|
| 34:44 | axon. Uh and the plasm reticulum not extend into axon typically. And |
|
| 34:52 | differences that you find in dendrites versus versus axon. A lot of times |
|
| 34:58 | to do with uh unique protein There. Uh Those could be uh |
|
| 35:05 | proteins, receptor, uh proteins and alike that are present in dendrites and |
|
| 35:11 | a lesser degree in SOMA and may be present in axons and vice |
|
| 35:15 | So there's a diversity and there's uniqueness distribution of the proteins even within a |
|
| 35:22 | cell for its specific functions. So synapse, since this is the |
|
| 35:29 | this is the external terminal, external is also loaded with mitochondria. You |
|
| 35:34 | a lot of energy. You have vesicles that are primed to the |
|
| 35:39 | what we call active zones and they're 20 nanometers across the postsynaptic density or |
|
| 35:48 | this case, po synoptic dendrites, we have chemical synaptic transmission. |
|
| 35:55 | neurotransmitter gets released from these vesicles and will bind to the receptors. We |
|
| 36:01 | have electrical neurotransmission that will uh talk a little bit later on. In |
|
| 36:06 | course, they called gab junctures, actually will talk about them briefly on |
|
| 36:12 | , but then later in the course well. But essentially, when the |
|
| 36:17 | potential gets generated here at the initial , it gets regenerated and reaches the |
|
| 36:24 | terminal. And this depolarization, this signal causes the release of the |
|
| 36:30 | So you have the electrical transformation into signal, the release of neurotransmitter. |
|
| 36:36 | the neurotransmitter binds to the receptors, causes a pos synoptic response. And |
|
| 36:42 | is po synoptic receptor depolarization or hyper . And that is again an electrical |
|
| 36:49 | . So you have electrical chemical to transformation, which was inevitably again, |
|
| 36:56 | chemical. Uh it's important that you precise synaptic connectivity and functionality for normal |
|
| 37:03 | function of the brain. And if don't have certain elements of the spines |
|
| 37:09 | certain elements of the connectivity, uh function gets compromised. So structure and |
|
| 37:17 | equals function and they're intertwined and inseparably interdependent dendrites and the cells you have |
|
| 37:29 | that can be apical. So they're called ICAL because a lot of |
|
| 37:35 | we talk about graal cells, android cells uh look like pyramids. And |
|
| 37:42 | this is the apex of the pyramid these are called the optical dens on |
|
| 37:49 | . And this right here, this is the base of the pyramid. |
|
| 37:56 | therefore, these dendrites at the base the axons are referred to as basal |
|
| 38:04 | . And a lot of dendrites will the dendritic spines that we already |
|
| 38:09 | But some of the neuronal dendrites will smooth dendrites. So there will be |
|
| 38:15 | spines. Neuron morphology is very So neurons can be small and can |
|
| 38:25 | large. They can have 5000 synopsis they can have 100,000 synopsis. They |
|
| 38:32 | have uh dendritic trees that are easy understand because they may contain only 20 |
|
| 38:40 | and then they have dendritic trees that very difficult to understand because they may |
|
| 38:45 | 1000 branches on the dendrites. So a a whole variety of different types |
|
| 38:51 | cells. And the example of two of stellate cell and Permal cells both |
|
| 38:57 | in the neocortex, dendritic spines going to understand a little bit more about |
|
| 39:03 | detail. So you have dendritic spines here, den and then you have |
|
| 39:10 | topic densities and this is electron microscope and they're juxtaposed to these red |
|
| 39:17 | These are external terminals, these are vesicle that are filled with neurotransmitter. |
|
| 39:23 | you can see the vesicles are binding the pre synoptic terminal. And once |
|
| 39:28 | fuse with a plasma membrane, this nanometer distance here, they get released |
|
| 39:33 | this cleft. It's called syna synaptic and they will bind to the po |
|
| 39:40 | receptors located in the postsynaptic densities and have a variety of the shapes of |
|
| 39:47 | dendrites. Here, we have three thin and mushroom like that are shown |
|
| 39:54 | C and you can reconstruct them. it's important to do that because we |
|
| 40:02 | that there are certain neurodevelopmental disorders that be associated and they're linked with improper |
|
| 40:12 | densities, dendritic spine densities, distribution much at the top, not enough |
|
| 40:17 | the middle. Uh So it's an factor. You can imagine if this |
|
| 40:22 | where the communication happens between the If you don't have enough of the |
|
| 40:27 | responses, they're all shaped and proper a certain way that communication is going |
|
| 40:33 | be impaired. The other interesting thing dendritic spines is that right here, |
|
| 40:40 | is shown is that they have synoptic ribosomes, the synaptic polar ribosomes, |
|
| 40:50 | spines means that they can do some translational modifications right here at the level |
|
| 40:58 | the spine. It gives them a bit of biochemical independence. They have |
|
| 41:03 | energy through a TP. They have polar Russom complexes. They are somewhat |
|
| 41:10 | independent units. They can be the synopsis, they can grow in |
|
| 41:17 | . So they're malleable as the surface of a synapse increases in the cyto |
|
| 41:23 | elements underneath the rearrangement. You have lot more membrane, you can embed |
|
| 41:27 | lot more receptor channels in that membrane that particular synapse is gonna be more |
|
| 41:35 | . That's what that's what we call strengthened or stronger, more powerful. |
|
| 41:41 | , you can shrink that dendritic spine really small soars with just a few |
|
| 41:46 | . And it, it's not anymore strong, it becomes weaker. Uh |
|
| 41:52 | spines number can be changed, spine can be changed anatomy and it is |
|
| 42:00 | by activity and end genes. So a certain element in the early formation |
|
| 42:07 | the dendritic spine and in applic activity our brains develop um before we're born |
|
| 42:14 | after we're born early in life, that is dependent on genes but also |
|
| 42:21 | nurture. So this is classifying So we're looking neurons in the |
|
| 42:30 | How many different subtypes of cells do have? That number keeps changing? |
|
| 42:36 | what we consider as a gold standard identifying cellular subtypes is changing at some |
|
| 42:43 | , we thought the more the let's find another three subtypes of the |
|
| 42:48 | . Let's find another four subtypes of cells. Let's say that they are |
|
| 42:52 | different from one another. And uh happens in the last 5 to 10 |
|
| 42:59 | is that we have all of these cell RN A sequencers, for |
|
| 43:04 | that come online like whoa, they sequence RNAs and a lot of cells |
|
| 43:09 | the same time. That's something we do. Isn't that important? Determining |
|
| 43:15 | C subtype apart from morphology? it is. And we'll see how |
|
| 43:20 | it is. So as new techniques added, those numbers may change, |
|
| 43:25 | thought we have 100 50 subs. we really have 120. Then another |
|
| 43:30 | I gets introduced another model like, no, it's actually 2000 different C |
|
| 43:36 | types. You're all working, looking it wrong. We have a new |
|
| 43:39 | of analyzing all of this, you , so it's a moving target. |
|
| 43:45 | many different cell sub types we And also what is the gold |
|
| 43:49 | But the gold standard is moving with advancement of the techniques. In other |
|
| 43:53 | , it's not being, the techniques not being rejected. No, we'll |
|
| 43:55 | to this. They're being incorporated. as they're being incorporated, it changes |
|
| 44:01 | understanding. Very rudimentary morphologies, unit cells, bipolar cells that have both |
|
| 44:10 | , north and south, multipolar cells have a lot of different uh |
|
| 44:16 | We'll talk about um bipolar uh cells little bit later today. Also |
|
| 44:23 | this is a bipolar cell of Uh We'll discuss this a little bit |
|
| 44:30 | CS, this is unipolar south, unipolar South because it's kind of all |
|
| 44:35 | has two poles, but they're both . So the fake poles, it's |
|
| 44:40 | just pointed north arrow in both Three types of multipolar cell examples. |
|
| 44:46 | shows you the diversity of motor spinal motor neuron, about 10,000 synopsis |
|
| 44:53 | Kinji cell over here, over 100 50,000 synopsis, very complex and duty |
|
| 45:00 | . So this is great. So and this goes back to Ramona |
|
| 45:05 | So as soon as we could see , we wanted to know even Ramona |
|
| 45:10 | these subtype glial cells. He says there's a variety of glial cells and |
|
| 45:15 | look like one type subtype and these like another subtype. 3rd, |
|
| 45:20 | I think he had at least five of real cells that he uh |
|
| 45:26 | So that's morphology. Then in the of the whoa in the middle of |
|
| 45:31 | 20th century, we have uh electrophysiology develops and electron microscopy that develops. |
|
| 45:40 | now we can start looking at we can see how far the process |
|
| 45:45 | projected their projection. So this is we refer to as projection cells, |
|
| 45:52 | lot of you know, but some you may not know some cells if |
|
| 45:58 | have networks. So this is for , one brain network that have different |
|
| 46:05 | of cells in it, right? this is another brain network that also |
|
| 46:10 | different types of cells in it maybe looks very different. But some of |
|
| 46:15 | cells are gonna be in these networks going to be projection cells that means |
|
| 46:20 | they're going to project and they're going contact cells far distances and some of |
|
| 46:29 | cells will just stay locally within this that we typically refer to these as |
|
| 46:37 | neuron cells. So these that stay here are interneurons and those that project |
|
| 46:49 | projection cells. OK. So you two types and for the most |
|
| 46:56 | the inter neurons are inhibitor, interneurons projection cells are excitatory for the most |
|
| 47:02 | , like 90% or so cases with exceptions, like in any uh scientific |
|
| 47:09 | . And then we understand that these have different neurotransmitters and that, that's |
|
| 47:14 | we call them excitatory versus inhibitory. , excitatory cells are the ones that |
|
| 47:19 | have glutamate, inhibitory cells are the that are going to release gaba, |
|
| 47:25 | are the chemicals. And it's not say that interneurons are not excitable interneurons |
|
| 47:32 | produce action potentials. Projection cells will action potentials except that the projection cells |
|
| 47:38 | release glutamate which is going to excite depolarize these networks. And the interneurons |
|
| 47:46 | release Gaba which is going to control subdue or hyperpolarize these networks. They |
|
| 47:54 | also apart from Gabba and glutamate neuropeptides many different neurotransmitters. And then |
|
| 48:01 | in the middle of the 20th we are capable of recording activity from |
|
| 48:07 | neurons using electrophysiology and that uh ushers understanding of functional subtypes of the cells |
|
| 48:16 | than morphological or chemical excited or inhibitory they look functional. And in this |
|
| 48:23 | , we're looking at the patterns of potential. So as you can |
|
| 48:27 | this is 1939 was the first action that was captured and reported published |
|
| 48:34 | then we have genetic expression, unique of proteins, receptors, neurotransmitters and |
|
| 48:39 | molecules that doesn't come into play until and quick discovers DNA in 1 |
|
| 48:47 | So all of this genetic studies on RN A studies and epigenetic studies. |
|
| 48:54 | is all stuff that comes out in last like 30 years, really 3040 |
|
| 49:01 | uh when I went to graduate school I wa and I was doing my |
|
| 49:04 | doc. Also the gold standard was if you could patch the cells, |
|
| 49:09 | you could use infrared microscopy, identify , place a microelectrode on them, |
|
| 49:17 | what firing signatures they have and reconstruct morphology. You were pretty good, |
|
| 49:25 | were really good. In fact, you had an opportunity to suck out |
|
| 49:30 | internal content of the cell, you potentially do single cell RN A |
|
| 49:37 | Bye. So this was pretty good this is we're talking about early |
|
| 49:42 | So the state of the art, could do two cells at the same |
|
| 49:45 | , three cells at the same you were, you know those docs |
|
| 49:50 | to be your friends. Uh So uh immuno is the chemistry. So |
|
| 49:57 | specific markers. So we have a of antibodies antibody labeling that comes out |
|
| 50:03 | , that comes out at the end the 20th century, all of these |
|
| 50:07 | labeling phosphor fluorescent tags on the microscopes with different filters for different color |
|
| 50:16 | imaging. And such, this is example of what we call a eclectic |
|
| 50:23 | behavior diversity of electrical behaviors of neocortical . So this is a patch of |
|
| 50:31 | and you have different cells and if do these electrophysiological recordings, you can |
|
| 50:36 | these cells the exact same stimulus, same input. But this cell on |
|
| 50:42 | right, you can see it produces pattern of action potentials. And it |
|
| 50:46 | a specific morphology, the parameter sedentary petal cell. And I have |
|
| 50:53 | the morphology of this cell that I on the right and this cell on |
|
| 50:56 | left responds to the same stimulus in much different pattern of action potential is |
|
| 51:03 | , much faster firing and it has much different morphology. This is definitively |
|
| 51:12 | we are trying to tell what sub of cells we are studying. And |
|
| 51:20 | you present the same stimulus to all these different cell subtypes. And you |
|
| 51:24 | see that this is the output that out of these different cells. Some |
|
| 51:29 | them produce trains or sequences of action that stutter that are not continuous, |
|
| 51:37 | that can be completely continuous and sustained very high frequencies, others that are |
|
| 51:44 | , but their frequency reduces over it's called accommodating others that produce bursts |
|
| 51:52 | activity. So they have these There's a burst, there is a |
|
| 51:57 | polarization, another depolarization, there's a . All of these cells are receiving |
|
| 52:02 | same stimuli from these electrodes, but responding with their own unique electrical |
|
| 52:11 | OK. Is that clear, I'm especially looking at the uh at, |
|
| 52:18 | U three that haven't taken my new is everybody with me so far |
|
| 52:23 | and everything today. Excellent. So a lot, you know, and |
|
| 52:29 | is the kind of a work that takes, you know, it's 8 |
|
| 52:33 | 10 hours under a microscope, go , eat shower repeat until you have |
|
| 52:42 | data, enough c that you can or show something. Uh It's quite |
|
| 52:49 | . Uh But there's a time for and everybody will get challenged, especially |
|
| 52:54 | you pursue like professional careers and do postdoc or residency or like advanced nursing |
|
| 53:01 | or something. So, OK. what is this? This is the |
|
| 53:08 | and the hippocampus. It turns out if you look at this excitatory projection |
|
| 53:14 | , you have excitatory parameter cells. not very diverse in their morphology and |
|
| 53:20 | have one in intercellular mark, it's kinin. So some of them will |
|
| 53:25 | kinin, they'll be CD positive and will be CB negative. And that's |
|
| 53:31 | the structure called the hippocampus. And learn more and more about the structure |
|
| 53:35 | the semester. But it's structure that predominantly three layers. This is stratum |
|
| 53:42 | , stratum thala it's called stratum thida most of the petal cells sous will |
|
| 53:48 | located in this white layer in this here and then you have stratum |
|
| 53:56 | Uh And these other elements marked here through 25 or 21 sorry are different |
|
| 54:04 | of local network into neurons. So are, these are projection cells and |
|
| 54:12 | don't see much morphological diversity or functional and only one marker difference, but |
|
| 54:20 | surrounded by a variety of these inhibitory network neurons. And you can see |
|
| 54:28 | they have their somos, some of in Oreos, some of them in |
|
| 54:36 | , some of them in Raia, can see that they d rides which |
|
| 54:42 | in orange or red. Some of are projecting vertically, others are projecting |
|
| 54:51 | . And finally, these yellow morphologically, these yellow cups and these |
|
| 54:58 | process, these are axons and it that some cells will target the sous |
|
| 55:06 | the barometer cells and other cells like axons. Here, these cups will |
|
| 55:13 | the optical dendrites of the petal So they will target these cells at |
|
| 55:21 | um spatial what we call axosomatic axodendritic axis. So a lot of different |
|
| 55:31 | and all of these things AO so PV, baskets, eck, VIP |
|
| 55:37 | , eck, I don't want you memorize it. But what I want |
|
| 55:40 | to know is that these are all cellular markers. So how do we |
|
| 55:45 | it's 21 different sub types of cells we can distinguish them morphologically, they |
|
| 55:51 | a different appearance in morphology. We say that maybe chemically they're different because |
|
| 55:57 | release glutamate versus Gaba. They have that target certain locations of prominent cells |
|
| 56:08 | have cells, specific markers that's kind uh in, in, in |
|
| 56:17 | It was a gold standard. So example, if you go to this |
|
| 56:25 | should open neuronal diversity and temporal the unity of hippocampal circuit operations. |
|
| 56:34 | somebody we were all uh scientists, know, discover things and then we |
|
| 56:38 | to somehow create categories for these things taxonomy for these things that we all |
|
| 56:43 | to agree. And then a new comes out and it calls them to |
|
| 56:47 | the subtyping uh of the cells and , no, it's it should be |
|
| 56:52 | different. So 2008, this is figure right here. OK. So |
|
| 56:58 | can find it through the links. , I'm also showing you how you |
|
| 57:01 | find the supplementary material you can read , you can enlarge it. But |
|
| 57:07 | think about this. Uh 2008 is 20 years ago, 2024. So |
|
| 57:16 | 14 years ago. That's a long . Uh s So nonetheless, this |
|
| 57:31 | still very good way of doing And if you go into your folder |
|
| 57:38 | the uh neuron folder, there is couple of articles that talk about this |
|
| 57:45 | approaches and understanding of neuronal classifications and lot of things that we've discussed molecular |
|
| 57:53 | , self specific markers, morphology, output in the uh patterns of action |
|
| 58:00 | . It's still completely relevant, but somewhat shifting about how we define what's |
|
| 58:07 | same subtype and how closely the different may be interrelated to each other. |
|
| 58:13 | we have again, these techniques that morphological descriptions of different cells. This |
|
| 58:21 | physiology. So, electrophysiology. So , we're looking at these types of |
|
| 58:27 | that have this morphology produce these patterns action potentials. This is their |
|
| 58:34 | This is what I call it neurons . Uh they use, they all |
|
| 58:39 | language, they all use words like are action potentials, but they use |
|
| 58:46 | differently. So they all have dialects this is their signature of action |
|
| 58:51 | This dialect here of the purple cell different from the dialect of the yellow |
|
| 58:57 | . Yeah. But now we have ability to look at the molecular markers |
|
| 59:01 | molecular signature in the precise detail. instead of using immuno histochemistry, which |
|
| 59:09 | antibodies that target molecular markers inside the , cellular markers and neurotransmitters or |
|
| 59:17 | And you can use maybe two or of those markers. Instead of |
|
| 59:21 | we can do array of molecular studies on single cells. So there's some |
|
| 59:31 | interesting techniques. For example, you take the tissue like neuronal tissue in |
|
| 59:38 | example. And you can put them microfluidics where they get essentially uh separated |
|
| 59:46 | single cells and they go through this column and you can have this bar |
|
| 59:55 | beads, they are bar coded. as the cell is going through the |
|
| 60:01 | sort through this microfluidics, it can tagged with a bead and we know |
|
| 60:08 | bead it is exactly. So we that this bed with this cell. |
|
| 60:13 | you have droplets are pulled for DNA , amplification and sequencing. So, |
|
| 60:21 | transcription and sequencing. So now we instead of just patching like, you |
|
| 60:30 | , five cells a day and sucking the fluids, the cytoplasm and MRN |
|
| 60:37 | with these electrodes. So it spent hours, we put the tissue through |
|
| 60:42 | cell. So and within minutes, hours, you can have information on |
|
| 60:48 | , if not thousands of cells. as you sort them through the, |
|
| 60:52 | will then have their distinct molecular What kind of molecular uh molecules they're |
|
| 61:00 | or uh the transcription. This is is what you would do. So |
|
| 61:05 | would patch a cell report firing That's what we would do. If |
|
| 61:11 | can extract RN A from a single , suck up. Basically the inside |
|
| 61:16 | the cytoplasm, put it through the DNA synthesis and RN AC, that's |
|
| 61:22 | one cell. It may take you hour to do one cell with |
|
| 61:26 | You're talking about thousands of cell in hour going through disorder. Hold on |
|
| 61:32 | second. Now, the other thing that you also based on the firing |
|
| 61:38 | , you can now train the classifiers the analyzers of those firing patterns. |
|
| 61:45 | let's say in this experiment, you reconstruct the morphology of the cell, |
|
| 61:50 | you inferred the morphology of the cell all of the cells you train to |
|
| 61:54 | that have this pattern will likely have morphological appearance. Again, this is |
|
| 62:01 | things like A I can, you , come into play and uh help |
|
| 62:05 | make things more and more specific. is really cool tagging almost in a |
|
| 62:11 | fashion of all of the RN A . But I already see people outside |
|
| 62:17 | hallway. So my recommendation is if guys before Thursday want to review this |
|
| 62:27 | on these slides in the uh folder your folder in your page, we're |
|
| 62:32 | keep talking about this molecular sorting and it plays into it and start talking |
|
| 62:38 | the configuration of the networks and eventually we could apply that information for tissue |
|
| 62:46 | also. Ok. So there's, two attachments there, uh PDF S |
|
| 62:53 | those figures are from those PDF S there. If you want to review |
|
| 62:57 | , we're gonna then go through it faster on Thursday. Thank you very |
|
| 63:02 | for being here. I know it's . I think we have one more |
|
| 63:05 | of rain. Sorry, I can't to your question. Can we do |
|
| 63:08 | next lecture? Ok. Excellent. they're gonna rush the, we're gonna |
|
| 63:13 | in the classroom. Just kidding. , my screen sharing is being |
|
| 63:20 | Oops, that's not |
|
