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GEN Protocols Expert Exchanges: Tools and Techniques to Develop Therapies for Neurological Disorders
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GEN Protocols Expert Exchanges: Tools and Techniques to Develop Therapies for Neurological Disorders
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ANJALI SARKAR: Hello, fellow scientists and science lovers. This is Anjali Sarkar, senior editor at GEN and GEN Protocols, welcoming you to GEN Protocols tech talk. Today I will be talking to Doctor John Ngai, director of the NIH's BRAIN Initiative, an acronym for Brain Research Through Advancing Innovative Technologies. Before we begin, I'd like to introduce you to GEN Protocols. The need for access to reliable and reproducible technical know-how in the biosciences inspired us at Genetic Engineering and Biotechnology News-- GEN to develop GEN Protocols, a freely accessible digital hub for scientific methods where researchers from academia and industry can share and showcase their technical expertise, nurture collaborations, and discuss technical challenges and solutions.
ANJALI SARKAR: GEN Protocols is open for submissions year round. Together with a rich resource of up-to-date protocols and applications, GEN Protocols brings you tech talks where experts in key areas of biosciences and biotechnology talk about methodological developments, challenges, and divisions. In today's tech talk, we will be talking to Dr. Ngai about tools and techniques that are poised to bolster the development of therapies for neurological disorders.
ANJALI SARKAR: Welcome Doctor Ngai.
JOHN NGAI: Thank you very much. I'm very pleased to be here.
ANJALI SARKAR: So before I ask you about the projects of the BRAIN Initiative, could you give us a bird's eye view about tools and techniques that, in your opinion, have been key in neuroscience research and will bolster the development of therapies for neurological disorders? In other words, how was brain activity studied traditionally and what are some of the new approaches to understanding the brain?
JOHN NGAI: Right. That's a great question. So when we think about the need or desire, I should say, to study brain activity, one can come at it from a number of different points of view. One is we'd like to understand how this amazing biological computer processes information. So for example, as we are speaking here on Zoom, we're seeing each other on the screen. We're hearing each other.
JOHN NGAI: We're processing information. We're responding. We're thinking about things, and we're putting out motor output in terms of speech. And so how are those processes carried out in the brain? So we would like to know that. Now, if you go back many, many decades, the way that we have measured neural activity, there's a neural basis for behaviors.
JOHN NGAI: In other words, the activity that's processed through various circuits in the brain give rise to these behaviors. Typically or historically, this has been done using various recording electrodes. And the challenge has been one could only record from a limited number of neurons or nerve cells at a time. So it's hard to get a global picture of how the entire network can process the information.
JOHN NGAI: So there are many developments along the way at different scales. If one is interested in understanding how the human brain might be processing information and generating outputs, one needs a way of looking at the entire brain globally, also at some resolution, both spatial and temporal. And many of these demands are kind of mutually in opposition with each other.
JOHN NGAI: So typically, there's a trade-off between how fine a resolution you could find with spatial resolution with the expanse of the brain one can look at any given time. Now one revolution came many decades ago with the advent of functional magnetic resonance imaging. So using changes in blood flow, oxygenated blood in the brain to act as a proxy for activity in different parts of the brain and other developments in non-invasive human brain imaging really did revolutionize the way we can think about how the human brain functions.
JOHN NGAI: But this is at a very limited resolution, both in terms of spatial resolution down to maybe the millimeter scale, as well as in temporal resolution on the order of seconds or so. And of course, we can think much faster than seconds. At least people other than me can think faster than on the order of seconds. So a big challenge there is to get a higher spatial and temporal resolution, but also be able to do this across scales.
JOHN NGAI: So we think about scales as being macro, meso, and macro, the mesoscale studies. We'd like to understand how different parts of the brain connect to each other. So that's an ongoing challenge, but we've certainly come a long way in being able actually to see how information might be processed as humans take part in various tasks and behaviors. So other advances have come in with other kinds of imaging and especially in non-human models and animal models for imaging.
JOHN NGAI: This has come about through a couple of revolutions. One is the revolution in terms of using either chemical dyes or genetically-encoded sensors of activity. So there was the invention of calcium-sensitive dyes that could be applied to other neurons and culture or actually in living preparations. This was pioneered by the late Roger Tsien. This has really given rise to this ability to look at neural activity here using calcium as a proxy for electrical activity.
JOHN NGAI: So it's not a direct measure of electrical activity, but typically, what a cell depolarizes or when it actually fire as an action potential, you see increases in intracellular calcium that can be read out as a proxy for activity. And depending on the dye you use, it's a very high temporal resolution. And what limits the resolution there in terms of time is the buffering of calcium as well.
JOHN NGAI: So that's been very, very powerful. And of course, now we have genetically encoded sensors for not only calcium, but now coming on board sensors for voltage, as well as sensors for neurotransmitters. That's very powerful. The complement of that is one needs better and better ways of imaging these probes, whether it's a chemical probe, or genetically encoded probe, or a combination of genetically encoded probe with an orthogonal chemical reagent attached to it.
JOHN NGAI: There have been many revolutions. Well, there's been a mini revolution in terms of optical imaging where now we can image living tissue, living neural tissue in awake behaving animals across a larger swath of the brain and using various tricks. Now we can record from thousands or even close to millions of cells at a time simultaneously using a combination of advanced optical imaging together with, married to these new types of probes, whether they be chemical or genetically encoded.
JOHN NGAI: And so that's been extremely useful in terms of getting a much more detailed picture of the kinds of activities that occur in different regions of the brain. And now the real challenge is to associate those in a causal way with actual behavioral outputs. So that's kind of on the imaging side. There have also been some great developments in terms of measuring electrical activity with electrodes.
JOHN NGAI: So before, back in the, quote unquote, "old days," if I may, you would have an electrode and you would stick it into the brain. There might be multiple sites, and you could either measure local field potentials, or you could actually detect spikes from individual cells. After the revolution of patch clamp from Neher and Sakmann that allowed an actual direct recording from cells to measure membrane potentials in a very sophisticated way.
JOHN NGAI: But those were generally lower throughput. And now with the advent of new developments in materials science and electronics, now we have high density arrays that can either be placed on the surface of the brain or actually penetrating in the brain, where we can now record from thousands of cells at a time in different regions and actually in a multiplex way, again, be able to correlate neural activity in different parts of the brain with specific behaviors.
JOHN NGAI: So we've come a long way. We have a long way to go, but we have many, many different tools at our disposal and many more that are being developed to be able to assay neuronal activity on a broad scale at increasing spatial and temporal resolution.
ANJALI SARKAR: Right. So how is the BRAIN Initiative-- coming now to the BRAIN Initiative, how's the BRAIN Initiative facilitating investigations into brain activity at the circuit level in both healthy individuals as well as under disease conditions at the level of individual circuits?
JOHN NGAI: So one of the main premises of the BRAIN Initiative is that in order to unlock the secrets of how the brain processes information, as we've been discussing, in order to really get at this sufficiently to help to truly understand this in a mechanistic level, we need better tools. We simply do not yet have the tools to be able to probe the brain and its activity and it's foundational principles in a way that we can truly fully understand it.
JOHN NGAI: So the BRAIN Initiative really started out as an initiative toward building better tools-- neurotechnologies. It's the N in BRAIN. Innovative Neurotechnologies is the IN of BRAIN. So we're approaching this from a number of different vantage points. One is to support the innervation of these new tools and the validation of the tools and then the eventual dissemination so they can be used by anyone who cares to use it, who has an interest in using it for the purpose of gaining more knowledge.
JOHN NGAI: Much of what the work we do and the work we support is on what would you call healthy brains, normal circuits. But one goal is to not only unlock the secrets of the brain, but really to also to understand what happens when things go wrong, whether It be in traumatic injury, stroke, neurodegenerative diseases, neuropsychiatric illnesses.
JOHN NGAI: If we look at, for example, movement disorders like Parkinson's disease., If we look at depression, obsessive compulsive disorders, disorders like that, ultimately, they boil-- and epilepsy-- ultimately, they boil down to circuit disorders, disorders of the circuits underlying these different functions and behaviors. So what we really want to do is to be able to generate on the one hand better tools so we can better understand these circuits.
JOHN NGAI: And then ultimately, if we understand how they work in a causal way and have tools to access these circuits in a very precise way, then we have a chance of intervening or modulating these circuits in conditions of disease. So this is kind of, in a very broad stroke, 50,000-foot level what the BRAIN Initiative is all about is to develop better tools for understanding how the brain works in health and disease and then to use that as a platform to develop new technologies, new therapies for treating what are currently intractable human brain disorders.
ANJALI SARKAR: You mentioned modulating neural activity as being one of the goals. So what are some of the new approaches that are being developed through the BRAIN Initiative to modulate neural activity and the role that the BRAIN Initiative is playing in these approaches?
JOHN NGAI: So I'll give you just-- I'll start with one example that's really quite nice. And so there's a technology or a therapy known as deep brain stimulation. I think many people are familiar with that. It's been around for probably decades by now for helping patients with movement disorders, including Parkinson's disease, control some of their symptoms, for example, of dyskinesia. So there are electrodes placed in different parts of the brain.
JOHN NGAI: They stimulate that part of the brain. And if all goes well and if the patient is amenable to that kind of therapy, many of these motor control symptoms can be alleviated. Now we're not treating the root cause of the disease. We're actually just addressing the symptoms. So that's actually been quite game changing in terms of quality of life of patients in these states.
JOHN NGAI: So as powerful and as effective as these therapies are, at the end of the day, there's somewhat-- I don't want people to take this the wrong way-- they're somewhat crude, right. You're basically just-- you're injecting electricity into these circuits, and you're seeing a positive effect, which is great, but it's not very fine tuned.
JOHN NGAI: So what we'd like to be able to do is to be able to read out the activity that's going wrong in the circuit and use that in what we call an adaptive or closed loop system to control it. So basically, if I'm steering a car and the car is drifting a bit to the left, I'm going to pull the wheel to the right based on that input. So similar idea.
JOHN NGAI: You can have a closed loop or adaptive system that can actually adapt to the patient's needs, the specific needs of that individual. Every human brain is different. And every patient, if you look into this every patient, while they might have common symptoms or common features of a particular disease like Parkinson's disease, every patient is going to be a little bit different. So can we really just fine tune these therapies not just to that patient, but also to the patient in that moment.
JOHN NGAI: So there've been studies now using adaptive deep brain stimulation. It's been deployed successfully in clinical trials for Parkinson's disease patients. So basically, the electrodes are used to read out activity in the brain that either correlate with or precede undesired movements that then tell the device when to stimulate.
JOHN NGAI: And the really fascinating thing is that now, there are devices that are being tested out in the field that allow this learning process. It's a learning process where the system has to receive that information. It's tied to sensors on the patient's limbs to record the movements, inappropriately undesired movements. And that's fed in and using artificial intelligence used to identify signatures that are going to correspond to these inappropriate movements that can then trigger the device to allay them.
JOHN NGAI: So this can all be done not just in the clinic or not just in the lab, but now that's being done in the patient's natural environment, at home, using wireless technologies. So this is showing great promise. There's several reasons why you would want the device not to be always on. You want it just to be correcting the movement when it's needed and not all the time. So there's great hope for that in that arena.
JOHN NGAI: Now we can extend this-- and again, these are still in an experimental phases, but showing great promise. We can extend that further out to ask, well, are there other circuit disorders that might benefit from these deep brain stimulation devices? So we've all heard about electroconvulsive therapy for treating treatment refractory depression. And based on pioneering work by Helen Mayberg several years ago, now people are starting to look at applying deep brain stimulation to help alleviate conditions for people suffering from such conditions as well as things like obsessive compulsive disorder or OCD.
JOHN NGAI: So there's been some success there. It's been a little bit rocky. Some success, some not so successful trials. And the hope, though, is that if one can apply an adaptive paradigm here, so in other words, if one can use these deep brain stimulation electrodes, which now the key here-- I forgot to mention-- is that they're not just delivering electricity into the brain.
JOHN NGAI: They're also recording. So the electrodes are what we call bidirectional. They both stimulate as well as record. If we can record so-called biomarkers, now here not associated with dyskinesia per se, but perhaps with mood or with certain behaviors, and then adapt that in a closed loop way, not to control movement, but to control mood, that could be quite powerful in terms of helping people that really have no other recourse for these devastating and debilitating conditions.
JOHN NGAI: And so we're starting to see some progress here. There have been some successful applications of deep brain stimulation toward alleviating symptoms of depression, severe depression, as well as in a few scattered cases. I mean, these are still the case study level, but also in terms of alleviating behaviors associated with addiction. So that's actually quite tantalizing, and it really points toward the possibility of directly intervening in a circuit-- you can call it a circuit modulation-- to control these symptoms.
JOHN NGAI: Now in parallel, as I mentioned, what we'd like to do is to apply this in a closed loop or adaptive system. So what I've described to you so far has just been what you call an open loop system the stimulation is applied. And the promise here is that various groups have now been able to record from patients, in some case ask them-- and the key here is in these experimental systems, oftentimes, these electrodes are placed for a different reason.
JOHN NGAI: It might be placed in a Parkinson's disease patient or patient with movement disorders. We're not experimenting on humans just to experiment on them. These are what we call research opportunities in humans. One can ask the patient, the subject, subjectively what's their mood and simultaneously be recording from them. So these are experiments that are done with consent and with very careful consent, but because they're already in there for other reasons.
JOHN NGAI: And what can actually establish what we would call electrical biomarkers for various mood states. Now it's still very early days, but it's hopeful. Another study that was just published out of a group out of Houston actually recorded about 1,000 hours worth of activity from a patient who was implanted. So the hope then is we can use that information to drive these so-called adaptive closed loop systems to help alleviate symptoms or actual mood disorders in patients that are afflicted with them.
JOHN NGAI: So the future is bright for that, but again, using electrodes to stimulate large areas of the brain, it can be very useful for now. But we'd like to do better, because it's not very selective. You're actually stimulating all the neurons in the region. And there's one thing that we do know is that there are many, many different cell types. They're wired up in different ways.
JOHN NGAI: You're basically taking a big bundle of telephone lines and kind of interceding in all of them at once. And what you really like to do is only modulate the activity in the ones that matter. So this is kind of a big challenge for us going forward is how to develop higher precision access to these neural circuits. A, to understand how they contribute to behavior, but B, ultimately how we might modulate them in the human brain or human circuit disorders.
ANJALI SARKAR: Right. And what technologies would you say is helping us move towards this precision as to not just be either recording or stimulating a bunch of neurons, but recording and stimulating precise neurons as they fire or as they need to be simulated? What technologies would you, in your opinion, are helping the scientific community move towards this kind of precision approach?
JOHN NGAI: Right. So our view on this is if we're going to build a big and fancy house or a very tall house, we need to build a strong foundation. So the foundation we're building is going to be centered around what we're referring to is the BRAIN 2.0 transformative project. So The BRAIN Initiative is in its second five-year phase we're calling it, so hence BRAIN 2.0. The BRAIN Initiative has benefited from strategic plans that were put together by external advisory groups that were commissioned by the then NIH director Francis Collins and in the second report that was published in late 2019.
JOHN NGAI: It was both a reflection on where we had come, but also where we could go with an eye toward not just understanding how circuits work, but also toward cures. And their recommendation was to think about, given all the progress that's been made, not just in the BRAIN Initiative, but neuroscience more generally and especially in the physical sciences and engineering sciences and mathematical sciences, where are there large projects that we could promote and support that could truly transform the way we study the brain and how we might treat diseases.
JOHN NGAI: So it's going to come down to basically to two broad things. One is to find a better ground truth of what neural circuits actually look like. Then the second piece is to have ways of accessing what we're going to have as a better map of what neural circuits look like, not only to validate our ideas, but actually eventually to have a way in for this precision access that we were alluding to before.
JOHN NGAI: So the three projects are as follows-- one is to gain a complete understanding of all the cell types in the human brain. This parts list, if you will, we're calling the human brain cell atlas. So that's the parts list. The second piece will be a whole brain connectivity map or wiring diagram, if you will. So if we have the different components or different types of neurons in the brain, there might be based on what we're seeing from the mouse upwards of 1,000 or thousands of different cell types, each with different properties.
JOHN NGAI: And the very important property of a neuron or nerve cell is who it connects to, the connectivity patterns. So what we want to understand is what are the connectivity patterns of all the cells in the brain, of the entire brain across distances, both small, cell to cell at the synapse level, and that's at the nanometer level, all the way across the entire brain, which could be millimeters or longer. So that's the second project.
JOHN NGAI: And having that as a ground truth, the parts list and the wiring diagram, that gives us a map or an atlas of the brain. And now we would like to be able to use that information to start targeting the specific cell types we've identified using mainly molecular, but also physiological techniques and to be able to probe those cell types as defined that way to ascertain their function and to validate or invalidate models for how we think it works.
JOHN NGAI: So the third project is a precision cell access project, where we will be developing various tools for accessing cell types somatically. So in other words, not through the germline necessarily, but through either viral mediated transduction or non-viral ways of delivery, for example using nanoparticles. And these projects all dovetail with each other. So for example, from the cell atlas projects that we're conducting right now mainly involved in the mouse but also now extending to big brains, non-human primate brains, and human brains.
JOHN NGAI: We're learning a lot about what makes a cell a cell in terms of the genes they express, right. And of course, what a cell express, this has to do with which enhancers and various gene regulatory elements are being active in any given cell. Well, this gives us a way of then giving targeting for, say, a virally delivered payload by using that enhancer element that's cell specific, put that antivirus or nanoparticle, driving some payload.
JOHN NGAI: And then now we have a way of, say, inserting a genetically encoded sensor for calcium activity or voltage into a specific cell type based on this enhancer. And we learned that information from the Cell Atlas Project. So this, in turn, would allow us to look very precisely or exclusively at what we would define as a cell type or kind of a class of cells. We could study this activity. We could study its connectivity as well, right?
JOHN NGAI: Who is worrying up with and maybe when in development or over aging or in different conditions of learning? So, you see, all this is kind of mutually reinforcing because the [? Atlas ?] Projects give us information about how to access these cell types and circuits. And by accessing them, we can then analyze those maps and learn more about what those maps mean in terms of function, how they contribute to behaviors, combining that with other projects that we're standing up now that seek to synchronize or align studies of neural activity and actual behavioral outputs.
JOHN NGAI: Now we can have a more comprehensive view of the circuit bases of behavior because we start with a map. That's a ground truth. We have a way of interrogating that map. And then we have a way of modulating. So we can develop-- we can generate models of causality by modulating activity. What happens if I stimulate this cell type in this circuit at a certain point in time?
JOHN NGAI: How does that affect the behavior? What happens if I-- what happens if I inhibit activity in that particular type of cells at a given time? How does that affect their behavior? We can do that on a broad level. But as we get better tools to more precisely access cell types in the circuits, we'll be able to get much cleaner answers about exactly how they participate.
JOHN NGAI: So what does this have to do with circuit therapies in humans, for example? So right now, we're using mainly electrical stimulation, as well as pharmacology, to alter-- to modulate activity. Now you can imagine a case where, say there's a specific cell type that we've identified or a gene that's mutated in a specific disorder-- for example, there are some epilepsies, inherited epilepsies, that have mutation in a sodium channel that gives rise to aberrant circuit activity-- if one could target the cells that are affected in a given condition whether it be epilepsy or a mood disorder-- thinking way, way far out, right-- and if you had a way of modulating this activity by delivering some payload, some actuator, then we could have much more precise access than by placing an electrode in an anatomical region that's stimulating all the cells in the region.
JOHN NGAI: So we see these three transformative projects together, the cell atlas, the connectivity project, and the precision cell access project as eventually paving the way for developing precision gene therapies and molecular therapies for human circuit disorders. So that's the blue sky view of it. We have a long way to go. But as we're seeing, I mean, gene therapies are now showing some success in humans, gene editing among them.
JOHN NGAI: And we're hopeful that we can start applying these before I hang up the gloves into the humans to treat human circular disorders.
ANJALI SARKAR: Talking a bit more about the human brain cell atlas that you mentioned and talked about, so does this include, I believe, only human or also model animals, small and large model animal brain atlases as well?
JOHN NGAI: That's a great question. So the evolution of this project goes back-- let's see, so I would say from the BRAIN Initiative standpoint, this project started in 2014, so about seven and 1/2 years ago, late 2014. And I was actually-- before I started at NIH, I was actually an investigator on the first set of projects. And this was a set of pilot projects to determine which technologies would be viable to scale up to generate a complete or a comprehensive census and atlas of cell types in the mammalian brain.
JOHN NGAI: There were 10 projects funded. I was one of them, very, very fortunate to be among this group. And this came at a really great time. It was great timing, and the folks at NIH-- and I was not at NIH then, so I'm not boasting on behalf of myself-- they saw the future. And the future was in single cell sequencing, as we all know.
JOHN NGAI: That's a revolution in modern biology for sure, right, that follows on the heels of the revolution of high throughput sequencing. And the idea was that they sense that there were technologies that would allow us to characterize cell types in the brain. But it was going to go beyond just sequencing them, right, which at the time that we started in 2014, we were sequencing cells basically in micro wells.
JOHN NGAI: There were some microfluidics techniques that came out on fluid ion was dominant at the time. But we were really limited in throughput and by expense. But at the same time-- so we saw possibilities in terms of increasing throughput, which is, I guess, another way. The flip side of that is decreasing cost per cell. But we wanted more than just what James characterizes cell. So as cells, about more than just the genes that it expresses, although that's a huge part.
JOHN NGAI: Many, many other things that happen as a result of that, including activity and sensory-dependent changes. So the ideal was to be able to use the molecular information about a cell, the comprehensive molecular information, based on the whole transcriptome, and to use that as a basis, as it turns out, to gain other kinds of information about the cell as well. So the idea behind this is, if we have a good understanding of what makes a cell a cell, based on the genes it expresses, and layer on top of that its physiological properties, its electrical properties, its location in the brain, its morphological characteristics, and, very, very importantly, its connectivity.
JOHN NGAI: Who does it connect to? If I have information about all those parameters on a cell or cell type, I kind of have what I need to know to go further, to understand how it's contributing to circuit, and eventually into some behavior, or into some sensory pathway. So that was the goal of those pilot projects. We call that the Brain Initiative Cell Census, Consortium, BICCC.
JOHN NGAI: And that was started as a pilot phase that lasted three years. About a year and a half, the program officers came to us, and they said, if things work out, we're going to scale up. Actually, that's what they told us in the beginning. Things work out, we're going to scale up so we can apply this to the entire mammalian brain. Again, most of the emphasis was on mouse-- the rodent brain back then. And so that was that was the carrot that was stuck out in front of us.
JOHN NGAI: And we worked together. And about a year and a half in, they came in, and they said at a meeting-- I remember the day. And they asked us, are you guys ready to scale up? And some people were more enthusiastic than others. I was a little bit tentative. And then, the response was no. You guys are ready to scale up, right?
JOHN NGAI: [LAUGHTER] So in 2017, the projects found a whole other gear, and we formed what was called the Brain Initiative Cell Census Network, or BICCN. And so, we were told in the beginning, look, from the consortium, we're funding these 10 pilot phase projects. But don't expect all 10 to be scaled up. We're going to expect some realignments and some consolidation.
JOHN NGAI: So the BICCN, or the Cell Census Network, was formed around three large, large projects, so-called U19 projects. Multi-site. I was, again, fortunate to be among one of those projects. That particular project was-- we had five PIs on that project, across five institutions. And there were two others as well. The idea was, really, now, to make a comprehensive atlas of cells, based not just on molecular information but what we call a multimodal analysis.
JOHN NGAI: And here, the cool thing was-- and also based on work that was done in the pilot phase. It wasn't just measuring single-cell RNA, but also epigenomic information. DNA methylation combined with spatial information, a spatial transcriptome. So over the course of the BICCC, the little mini-revolutions that happened were droplet-based sequencing. And then, the commercialization of droplet-based sequencing by 10x Genomics, which made it fully democratized, and then spatial transcriptomics, which actually tells us where these cells are, based on molecular information.
JOHN NGAI: Combined with connectivity and physiology. So there's this really cool technique called [? petseek, ?] developed by one of the investigators, Andreas Tolias, at Baylor University. So what [? petseek ?] does is, you go in and you patch a cell in a slice, you measure its electrophysiological properties, you can inject a dye, and then image it in a microscope and understand its morphology and limited connectivity.
JOHN NGAI: And then, you can suck out the contents and sequence it. So you have three modalities on the same cell. And then over time, now, other things that have been developed is, in the early days, we would take measurements from the epigenomic data, whether it be [? atekseek ?] technique or methyl C sequencing, and we have independent assays for each of those, and independent assays for the RNA sequence of the transcriptome.
JOHN NGAI: And one of the cool things that came out of the BICCN was a collaborative effort to analyze the data together. So a joint molecular analysis. Which is great, and we actually were able to, amazingly enough, come up with a cohesive and coherent picture of cell types, based on molecular properties from these different modalities. And going in, it wasn't clear how to do that. Well now, of course, to complement and augment those studies, one can now measure both chromatin accessibility and RNA expression on the same cell.
JOHN NGAI: And actually, we can also layer on-- there's some really cool tricks that have been developed, in part from the folks within this group, to also look at say DNA methylation at the same time. So get multiple modalities. So that was kind of the evolution there, and in the BICCN, again the scale of phase, the three large production-- three large projects were focused mainly on mouse.
JOHN NGAI: But there were also smaller, more focused studies on different aspects of generating cell census and mouse, including a number of projects looking at doing connectivity at scale, which is a huge lift. But also, there were several projects looking at non-human primate brains, as well as human brains. So this project was launched in 2017, and it was decided early on that we needed a type of way to work together, to have everybody pulling in the same direction, and hopefully to have the total add up to more than a sum of the parts.
JOHN NGAI: So we would have semiannual meetings in person-- back when you meet in person, right? And literally, I was on two or three Zoom calls every week. This is even pre-pandemic, we were on Zoom a lot, because the labs were scattered across three continents. And we decided early on to have a demonstration project to ask, well, if we all looked at the same part of the brain, would we come up with a-- could we come up with a unified answer?
JOHN NGAI: The nightmare scenario was that we'd-- if you put 20 groups on the same part of the brain, you'd come up with 20 different answers. And we definitely didn't want to find out we were going to be in that boat. So the idea was, let's pick a region of the brain. The region we picked was a primary motor cortex. This is the part of the cortex that's responsible for generating movement.
JOHN NGAI: So all the groups kind of dove in on this. This was what we refer to was the Minneapolis Project, which became a running joke because what the Minneapolis Project wound up being by the end of 2021 was 17 papers in one issue of Nature. It was a monumental effort. 250-plus scientists across three continents, and it was just an amazing experience. So what we did is, we combined all these different means of analysis.
JOHN NGAI: Single-cell RNA sequencing, [? atekseek, ?] methyl C sequencing to look at DNA methylation patterns. All that integrated together, combined with spatial transcriptomics, combined with connectivity and physiology. Really, really quite amazing stuff. But in the course of putting these studies together, we thought, OK, the idea is we would hopefully publish a few papers together.
JOHN NGAI: This was back in, say, 2019. But we-- 2018, 2019. But we also thought, we want to have this be interesting. Not just 20 studies, or 17 studies on the same part of the brain. We wanted a cohesive picture. So then we realized what's been done in similar projects like this-- for example, the N-CODE Projects, is that there's a flagship paper that kind of pulls everything together.
JOHN NGAI: And so the boring way to do that would have been just to have the flagship simply recount what the other study-- what the individual studies show. That wasn't so satisfying. We're looking for a hook, and the hook was, we thought, well, let's do a comparative analysis between mice, non-human primates-- in this case, marmosets-- and humans. I've always said, evolution is a wonderful teacher.
JOHN NGAI: You just need to listen to what she's saying to you. That was just tremendous because what we found, by comparing the analysis of the different cell types that we identified, using these different modalities between mouse, marmoset, and humans, the one amazing thing-- maybe not too surprising-- is that, by large, you're seeing the same major cell types. So when we look at the taxonomy of cells that we've identified, and again, using mainly molecular techniques, maybe not too surprisingly, there's a hierarchical arrangement.
JOHN NGAI: So it goes from class to subclass to cell type. And if you look at the intermediate phase of what we might call subclass, there's a grouping of about a couple of dozen-- 25, 27-- subclasses, that no matter what asset you use, whether it's [? atekseek ?] or RNA sequencing or other methods, you always come to more or less the same conclusion at that level. As you dive down deeper within each of those subclasses, there are now types.
JOHN NGAI: So if you think about the broad-- the thicker branches, and then as you get down to the leaves, then, depending on how you assay it, you can come up with, at the limit, maybe 130 cell types based on RNA sequencing, and maybe fewer using other, different techniques. But a lot of that is depending on how you look. So we were asked, through multiple rounds of reviews, you guys need to come up with a number.
JOHN NGAI: How many cell types are there? And I remember saying very flippantly in one of our author meetings, I said, well, it depends how you look. And then we thought about it, and we realize, it really does depend on how you look. And that's not to say how many there are. The ground truth is harder to get at. But one of the things we did in the joint analysis is we did a replicability analysis, which is to say, if I look at this landscape through different lenses, can I replicate these results using a different approach, and at what level will it replicate?
JOHN NGAI: So it replicates poorly at the level of 100 times, at the leaves, but it replicates quite well at the level of subclass, which is maybe a couple of dozen. It's about 25, 27. So that was really cool. So we could actually get a coherent picture. But not only using different approaches, but actually, also, looking between different species. So at that level, a mouse and a marmoset and a human, they're very similar, but there are differences.
JOHN NGAI: And you start seeing specific differences as you get to the finer leaves. So the basic theme is the same, but the specific-- how specifically executed in terms of the more granular cell types, that's what's going to define one species from another. It could even define one individual from another. And so that was really cool. And so most-- so that's when you start seeing differences.
JOHN NGAI: Now, not too surprisingly, if you look at the cell types identified, the ones you see in the humans tend to be more closely aligned to marmoset than to mouse, and mouse to marmoset than human. Maybe not too surprising. Again, we do see some differences. One of the cool things was one of the human-specific cell types that was found in the cortex also happens to be a cell type that's particularly vulnerable in Alzheimer's disease.
JOHN NGAI: So this tells us a couple of things. One is that, the comparative analysis tells us what's the same-- what's similar, I should say. And therefore, if one wants to model a human disease in an animal model-- and if you think of cell type is involved, it's probably a good model if that cell type is also in the humans, and probably not a good model if it's not found in humans.
JOHN NGAI: That's one thing. So there's a lot of talk about, this model system is poor-- this is a poor model system because of the inability to translate to humans. That's a pretty broad statement. What it misses is that, well, let's understand why. It's not going to be the case for every disease. And it's not going to be identical. Nothing will be identical from a mouse to human.
JOHN NGAI: But the idea is to get a toehold. So I think this so-called loss of translation-- or, lost in translation-- can be looked at with greater precision if you can ask, OK-- if I'm interested in this process, and I'm studying a certain circuit in the mouse, and if I think I want to model a disease on it, now I have the tools to ask, is that circuit, or the cell types in that circuit, are they also found in the eventual organism I want to study, which is humans?
JOHN NGAI: And if the answer is yes, that's great. You go ahead. And if the answer is no, you need to rethink it. So that's one-- so one thing that was revealed was that, not too surprisingly, not all cell types are present across all species. And if you want to translate from one to the other, whether it's translate up from mouse to human, or to do what we call reverse translation, of a human to mouse, you have to make sure you're working with the same set of players.
JOHN NGAI: So that's one thing. The other surprising thing to me, is if you look at what we would define as a similar cell type between two species-- so you can look at these maps. You can identify these cells are common between mouse and human. If you look to see what you would call a marker gene for those cells, they would be-- oftentimes they're different.
JOHN NGAI: So even though, on the global level, it's cell-- there's a cell type A that's found in both human and mouse, and they'll have similar properties. If you're going to look at what defines them genetically, from an experimental point of view, you could be fooled. So it also says that, OK, if something isn't adding up between-- or comporting between these two species, there could be multiple explanations.
JOHN NGAI: One is that cell just isn't in both, or the other is actually they are, functionally, but because of the way that the transcriptional programs played out, there could be a divergence in terms of what other genes are expressed that you would otherwise be using as markers. And not just as markers, but as a way of targeting those cell types. So now, we're also moving from the point of thinking about gene-specific targeting.
JOHN NGAI: So as it turns out, historically, many cell types in the brain are defined by whatever marker somebody assigned to them. But now, we're moving away from thinking about gene-specific targeting to cell-specific targeting. So if I might have identified a cell type based on makere gene X, but in going in to see what genes are expressed by that cell, or what might be the most specific enhancers, that might be associated with gene Y.
JOHN NGAI: So it's, again, a way in to get better precision access, both for experimental reasons, but also, hopefully, eventually, for therapeutics. So there's this been this great evolution, maybe close to revolution, in the way we're thinking about how we can access cell types and circuits in the brain. It's really been a whole world that's been uncovered to us because of this ability to get in at the single-cell level and to do it at scale, and also marry those techniques with other techniques like spatial information, morphology, anatomy, physiology, and, most importantly, connectivity.
JOHN NGAI: So these are what gets us really excited. So to go back to your question, these are providing the resources to build tools to access circuits. So you can go back to deep brain stimulation if we better understood which circuits are being involved, even if we can't access them precisely based on molecular information. Anatomically, we might be able to do a better job. Or we might have different patterns of stimulation that might be more effective.
JOHN NGAI: So I think, again, these three transformative projects, the Cell Atlas, the Connectivity Project, Precision Cell Access Project, we think that, together, they will form a strong foundation that will give us the information we can use to really build the models for how neural circuits work, how they drive behavior, and ultimately how we can access them.
ANJALI SARKAR: Regarding the point you make about cells being present in one species that cannot be-- correlates of which cannot be found in humans or vise versa, do you think it makes the case for the need for multiple lab models to study the human brain, and not just the mouse or the marmoset?
JOHN NGAI: Well, I think having different perspectives, different model systems always help. We've learned so much about how all nervous systems work based on model systems. Look at the pioneering work and the amazing work that's been done in [INAUDIBLE] 302 neurons. But still, many of the principles hold. The fruit fly Drosophila. Larval zebrafish has been a great model for studying the circuit basis of behavior.
JOHN NGAI: And through the various mammalian models as well. Not to mention other ones. Amphibians and so forth. I think what's important to bear in mind is, we often focus on what's similar as being useful. And oftentimes, you don't think about what's different. Again, it comes back to evolution. What has evolution done? These organisms have found some common solutions to common problems.
JOHN NGAI: But often, there's been different strategies that have been adopted in different species to approach the solution. And we can learn from that as well. So the contrast is as important for us to pay attention to as the similarities. So in the brain portfolio, we have what I lovingly refer to as our zoo, the brain zoo, where we're open to the study of different model organisms, not so much just for the fun of doing it, but for the potential that they can really teach us about how neural circuits work in general.
JOHN NGAI: There are going to be some foundational principles that are going to apply to all systems in different ways-- perhaps in different embodiments, but certainly as a foundation. And they're not going to be so easily revealed by just studying one system, one organism, one system. So that the approach here is to say, OK, what does this model have to offer? What can it teach us?
JOHN NGAI: And if we learn something from this, if we have a hypothesis that we can test and validate or invalidate, how will that apply to other systems we're also trying to understand?
ANJALI SARKAR: Right. Talking about the Micro Connectivity Project, one question that comes to mind is, the connectivity in the brain is dynamic, based on the experience of the person and the environment. How is the Micro Connectivity Project tackling this dynamicity of micro connectivity, and mapping that and offering that as a resource?
JOHN NGAI: That's a great question, and it's an issue that has come up in hot debate. One context in which it came up was-- we didn't get into this, but the idea of creating a whole mammalian brain connecting with synapse resolution, right now, is out of reach. So right now, there are two studies that are out on bioarchive at the moment that each look at about a millimeter to millimeter and a half-- sorry, a millimeter, a cubic millimeter to about a cubic 1 and 1/2 millimeters of mammalian cortex, in one case mouse.
JOHN NGAI: This is the microns project that was supported by ARPA. In another case, a human cortex. Study from Jeff Lichtman and his group of collaborators. So a millimeter and a 1/2 cubed. The issue is, in a reliable and accurate way, assigning all the connections to it. And each of those data sets occupies about a petabyte.
JOHN NGAI: So let's call it a millimeter cubed. A mouse brain is 500 millimeters cubed. So we have to scale up by 500-fold. It's going to occupy about an exabyte of data. And the issues are going to come in in terms of segmenting, basically being able to draw around a cell and its axion processes. But not only within this very, very small region of the brain, volume of the brain, but over an entire brain.
JOHN NGAI: And that's a huge challenge because there's a challenge in terms of sectioning it and not losing information between them. So you have to put Humpty Dumpty back together again, and you have to very reliably be able to assign an axon that might traverse a very, very long region. So we don't have yet have the tools for that. So one argument has been made, well, let's get let's do a connectome for one mouse brain, with one genome.
JOHN NGAI: And then the next one is going to come easier. And the challenge is, we could probably do it eventually, but it's going to be error prone. There's going to be a lot of uncertainties to that. And then the question-- then the counterargument is, well, sure but one is what is one connectome going to tell you? Especially when you get down to the synapse level, where we would expect that, depending on the time in which you sample, the experience-- I mean, there's developmental time, there's experience dependent changes.
JOHN NGAI: Does that really represent-- does that represent all animals, or does that represent that animal in that state when you took the brain? It's going to be the latter. So then the question is, OK, at what point will these maps truly be useful? I mean, let's be greedy. We don't want to have a map of just one brain. We want to have maps of many brains.
JOHN NGAI: So first of all, what's the inter-individual variability at, say, a given-- let's say an 8-week-old, a P56 mouse. What's going to be the individual variability? Where is it going to manifest? At what level? Unlike the way that I describe the cell census, the Cell Atlas, where you have, at the subclass level, maybe a couple of dozen cell types, but as you get into the leaves, the more finer sections, it's harder to tell.
JOHN NGAI: Where are you going to see that breakdown? Where are you going to see absolute, conserved patterns in the connectivity map, and where is it going to start breaking down? You're not going to know that by studying one animal. You're going to have to study many animals. And then, you're going to have to say, OK, well what state was that animal in? Was it happy or sad?
JOHN NGAI: Was it asleep or awake? And so on and so forth. Then you get into different genetic strains. So what we have to do in order to really get at this is to develop technologies that can be scaled to entire brains. And it's not just experimental technology. It's like tissue sectioning and preservation and imaging, which is huge. But also, the whole computational side.
JOHN NGAI: How do you retrieve, store, handle, and analyze an exabyte of data? How do you do that over and over and over again? This is a gigantic problem. We don't have solutions for that yet. And so, ideally, what we'd like to do is to be able to answer the kinds of questions you just asked. What's going to be the variability?
JOHN NGAI: What will it look like for an animal in different states, or for from one animal from one animal to another, or from one animal of a particular genetic strain to another animal of a different genetic strain? Things like that. So these are the questions we really want to know the answers to. One of the main goals of this connectivity, so-called transformative project, is, if we can come up with a way to do this at scale across multiple animals, then we can start getting closer to answering the kinds of questions that you just raised, which is what we're interested in.
JOHN NGAI: What makes each person different from the other? What's going on when I'm asleep, before and after I have my coffee? Things like that. What's changing? And the kind of things we really want to know-- and it's going to take this ground truth information that I referred to, and in order to get at that ground truth information for the connectivity project, we're in earlier days in terms of having the tools for that, in comparison, say, to the Cell Atlas, the Cell Census Project, where we've had the benefit of multiple revolutions in DNA sequencing technologies.
JOHN NGAI: Which isn't where it ends, but it really gives us a big leg up. Again, these tools can help reinforce each other. These projects really will reinforce each other as they go along.
ANJALI SARKAR: Coming to the neuroimaging techniques that have come out of the Brain Initiative like Prism and DNA Paint and the passive clarity technique, could you talk a bit about these, and how they work, and what they do?
JOHN NGAI: Yeah. So that's a pretty broad swath. So there are many challenges that we have when we look at imaging. One is to be able to-- let me see if I can tick them off in a rational way. One is being able to identify cells in a complex tissue, and part of that's going to depend on the genetic definition. We want to look at activity.
JOHN NGAI: Not just a one cell at a time, but many, many cells at a time, and how the different members of the orchestra are playing at any given moment, and how does that symphony play out? We need ways of monitoring activity, identifying the cells, and, very importantly, probing deep into the tissue. So brain's kind of a-- once you get beyond a millimeter from the surface, it gets very tricky to look inside, either in a living brain or in a fixed brain.
JOHN NGAI: So the techniques you mentioned are aimed at addressing some of these challenges. So one big challenge that we have not yet solved, like we have with transcriptomics or epigenomics, is proteomics. Single-cell proteomics is still a ways off. But we have tools that allow us to look at, not just a handful of proteins at a time, but more. So conventionally, when I was in grad school centuries ago, doing double label immunofluorescence was kind of-- you were pretty good if you could do that reliably.
JOHN NGAI: But you want to do more. Now, you can do-- you can multiplex. You have more colors. The Paint and Prism technique is basically a way of gaining more labels for antibodies. So you can have in your possession a whole panel of antibodies against different antigens that will help you identify or define what a cell is. But you need to be able to keep track of which antibodies finding were we given time.
JOHN NGAI: So here, we realize that DNA sequences can help us here in terms of labeling these antibodies. So you can have literally an infinite combination of DNA sequences, just depending on how long you want to make that sequence to tag the antibody. So that's the basic idea behind this Paint and Prism technique. There are ways of looking at neural activity that have changed. Various high-resolution, both high spatial and temporal resolution techniques for monitoring neural activity, again, using various chemical or genetically encoded sensors.
JOHN NGAI: So there's just advances every day. I can't even keep track of new improvements. Not just sensors, but also what we call actuators, optogenetic tools. Derivatives of, say, [INAUDIBLE] which can either activate or inhibit cell types, respectively. So that's really ongoing, very, very exciting. There's the application of things like adaptive optics, which have been used in astronomy to allow astronomers with their telescopes to get high-resolution images of galaxies far away, where the light coming in is being distorted by everything in the way.
JOHN NGAI: So if you look-- if you think about looking deep into the brain there's all kinds of stuff in your way, and adaptive optics are now being applied there as well. And so on and so on and so forth. Very fast scanning, multiphoton fluorescence microscopy, now to get much higher spatial resolution images, things like that. So there's just so many things going on, some of which are brain supported, some of which aren't brain supported.
JOHN NGAI: Different ways of illumination. Light sheath microscopy allows us to get into larger tissues, also to do it in live tissue. You mentioned there's clearing techniques. The brain is kind of a big blob. If you want to see, for example, how-- if you had a way of labeling a cell and all of its connections, going through the entire brain, can you do that without suctioning the brain?
JOHN NGAI: Can you look at it an intact tissue? What the clearing techniques do-- this was developed initially from [? Karl Dizero's ?] lab 5, 10 years ago-- basically, in that technique, I think the original [INAUDIBLE] lipids and replaced it with biogels. And now the newer techniques can do that passively and very efficiently. So there's been multiple iterations of this initial idea.
JOHN NGAI: If we can get this stuff out and clarify the brain, then we can actually look deeper in using, for example, light sheath microscopy. So that was a breakthrough, but then that breakthrough was iterated. And sometimes, if you iterate it enough times, or the iteration is big enough, it becomes another mini-breakthrough, where it can be democratized, where anybody can do it.
JOHN NGAI: You don't need to have a degree in engineering to do some of these techniques. Whereas before, the folks who developed it had to raise an engineering-- [INTERPOSING VOICES]
JOHN NGAI: That gets to another point, that we're trying to support the development of techniques that will work not just in the hands of the experts, but in the hands of experimenters who have specific questions they want to ask, who aren't necessarily going to be the tool developers. Oftentimes, the tool developers are the end users, but what we really want to do is we want to democratize these techniques. So, again, single-cell sequencing is a great example.
JOHN NGAI: We were doing it back in the old day when it was an artisanal process. And now, you still need to be careful. You still need to be skilled. But you can now literally buy a kit and isolate cells and put the stuff on a machine that you bought, on a microfluidic well that you bought, and actually come out with some really clean data. And this is the kind of principle we'd like to apply to as much of the technology that we're developing as possible.
JOHN NGAI: But to get these tools into people's hands-- because there's so many researchers out there asking great questions. With a great question, you can develop a better tool. With a better tool, you can ask better questions. And you have this virtuous cycle between generating hypotheses and generating better tools, and it just keeps on making the whole ecosystem much, much more powerful, in terms of being able to understand how these biological processes work.
ANJALI SARKAR: And in addition to this basic, fundamental approach at understanding basic biology, what I'd finally like to ask you is from the perspective of molecular medicine, personalized medicine, if you will. Efforts to develop such maps, universal maps, if you will, cellular or functional, or proteomic transcriptome maps that will provide resources for the entire scientific community can, at times, appear too gross a level to be applicable in the discovery of therapies, particularly in the context of personalized medicine.
ANJALI SARKAR: So could you share with us how you envision the Atlas resources at these various levels of the cell map, the blueprint of the wiring, and the precision targeting. How can these resources, generated by the Brain Initiative, find application in neurological drug discovery and personalized medicine?
JOHN NGAI: Right. So, again, it's a matter of having a good reference Atlas. You need something to compare it to. Every human on the planet is different from each other. Even identical twins, because they might be genetically identical, but in terms of how that genome is written out is going to change. It's going to be influenced by environment. So we're all different, but we need a reference, and we can use that reference in a very powerful way.
JOHN NGAI: Now, setting aside personalized medicine for the moment, certainly we can use that reference as a way of understanding disease states. So one of the goals of the Brain Initiative, at least in terms of human brain disorders, will have this so-called normative set of resources, and then investigators can come in and study human diseases-- not just human models of disease, but human diseases, actual humans-- using this as a reference.
JOHN NGAI: And not just using the Atlas as a reference, but also using the know-how that we've gained. So it's not so simple as, well, I'm just going to sequence this region of a brain of this disease individual. You want to be able to do so in a way that you can compare it, not just to the normal type, but to other related diseases. I don't want to so much use the word standardization, but at some point, you need to be able to integrate information from across modalities, across laboratory sites, across investigators, and across diseases, and that will be very powerful.
JOHN NGAI: The other thing to remember is that the diseases can actually inform how we interpret the so-called normal state. If you think of any human disorder, especially genetic disorders, there's something that went on that's causing some disease, which you could think of as a phenotype. But you want to be able to compare between phenotypes with different genetic bases. So that's going to be very powerful.
JOHN NGAI: Now, as we get down to what you might call precision or personalized medicine, again, there are certain neurological disorders that you can trace down to a single-- monogenic disorder you can trace down to a mutation in a single gene. Or mutations in a single gene, or in a cluster of genes. And so, again, having an Atlas that tells you what the ground truth is, the cell types the connectivity pattern-- and we talked about variability before.
JOHN NGAI: We don't want to look at, say, from the molecular level, all the cell types in one human brain. We want to do this for multiple human brains. And that gives us the power to do a couple of things. If we have, in our eventual human cell Atlas, representation from multiple individuals, but clearly from both sexes, both biological sexes, ideally at different stages of the lifetime, and crucially from people with different ancestral or genetic backgrounds-- and what this will do is it'll tell us, on the one hand, what's common between individuals, from these different ancestral or other backgrounds.
JOHN NGAI: What's in common is going to be very important, because that's where you're going to be able to find therapies that can target not just one type of person, but across all of humankind. But also, if you identify the differences-- again, it comes down to what are the differences. Some populations are more susceptible or resilient to diseases than others. If you understand the cellular, molecular, genetic basis of that, you won't learn not-- you will learn not only how to treat the people that are vulnerable, but if you look at the people that are resilient, you'll find maybe a biological clue that can treat those that are susceptible.
JOHN NGAI: So it's all going to come down to having a broad representation across what we call the human species, the human condition, in order to be able to target, in a precise way, individuals that might have a certain genetic makeup that predisposes them to a disease or actually causes that disease. So at the end of the day, these reference Atlases are crucial, but they need to be made in a useful way, and a useful way is not just to do it from one individual.
JOHN NGAI: It's not just to do it from, say, white Europeans, which many studies until recently were really focused on-- and it's been shown genetically that the lessons with the genes-- the gene associations you identify in one subpopulation do not often translate to other subpopulations. So I think the key to precision medicine is to have a broad base with which to compare.
ANJALI SARKAR: That brings us to the end of today's GEN Protocols Tech Talk. Thank you, Dr. Ngai, for a very illuminating discussion. A reminder to all scientists among our viewers, GEN Protocols is open for submissions, and we welcome your protocols in all aspects of biotechnology. So until next time, good luck in your research, and goodbye from all of us at GEN Protocols. [MUSIC PLAYING]
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ANJALI SARKAR: Hello, fellow scientists and science lovers. This is Anjali Sarkar, senior editor at GEN and GEN Protocols, welcoming you to GEN Protocols tech talk. Today I will be talking to Doctor John Ngai, director of the NIH's BRAIN Initiative, an acronym for Brain Research Through Advancing Innovative Technologies. Before we begin, I'd like to introduce you to GEN Protocols. The need for access to reliable and reproducible technical know-how in the biosciences inspired us at Genetic Engineering and Biotechnology News-- GEN to develop GEN Protocols, a freely accessible digital hub for scientific methods where researchers from academia and industry can share and showcase their technical expertise, nurture collaborations, and discuss technical challenges and solutions.
ANJALI SARKAR: GEN Protocols is open for submissions year round. Together with a rich resource of up-to-date protocols and applications, GEN Protocols brings you tech talks where experts in key areas of biosciences and biotechnology talk about methodological developments, challenges, and divisions. In today's tech talk, we will be talking to Dr. Ngai about tools and techniques that are poised to bolster the development of therapies for neurological disorders.
ANJALI SARKAR: Welcome Doctor Ngai.
JOHN NGAI: Thank you very much. I'm very pleased to be here.
ANJALI SARKAR: So before I ask you about the projects of the BRAIN Initiative, could you give us a bird's eye view about tools and techniques that, in your opinion, have been key in neuroscience research and will bolster the development of therapies for neurological disorders? In other words, how was brain activity studied traditionally and what are some of the new approaches to understanding the brain?
JOHN NGAI: Right. That's a great question. So when we think about the need or desire, I should say, to study brain activity, one can come at it from a number of different points of view. One is we'd like to understand how this amazing biological computer processes information. So for example, as we are speaking here on Zoom, we're seeing each other on the screen. We're hearing each other.
JOHN NGAI: We're processing information. We're responding. We're thinking about things, and we're putting out motor output in terms of speech. And so how are those processes carried out in the brain? So we would like to know that. Now, if you go back many, many decades, the way that we have measured neural activity, there's a neural basis for behaviors.
JOHN NGAI: In other words, the activity that's processed through various circuits in the brain give rise to these behaviors. Typically or historically, this has been done using various recording electrodes. And the challenge has been one could only record from a limited number of neurons or nerve cells at a time. So it's hard to get a global picture of how the entire network can process the information.
JOHN NGAI: So there are many developments along the way at different scales. If one is interested in understanding how the human brain might be processing information and generating outputs, one needs a way of looking at the entire brain globally, also at some resolution, both spatial and temporal. And many of these demands are kind of mutually in opposition with each other.
JOHN NGAI: So typically, there's a trade-off between how fine a resolution you could find with spatial resolution with the expanse of the brain one can look at any given time. Now one revolution came many decades ago with the advent of functional magnetic resonance imaging. So using changes in blood flow, oxygenated blood in the brain to act as a proxy for activity in different parts of the brain and other developments in non-invasive human brain imaging really did revolutionize the way we can think about how the human brain functions.
JOHN NGAI: But this is at a very limited resolution, both in terms of spatial resolution down to maybe the millimeter scale, as well as in temporal resolution on the order of seconds or so. And of course, we can think much faster than seconds. At least people other than me can think faster than on the order of seconds. So a big challenge there is to get a higher spatial and temporal resolution, but also be able to do this across scales.
JOHN NGAI: So we think about scales as being macro, meso, and macro, the mesoscale studies. We'd like to understand how different parts of the brain connect to each other. So that's an ongoing challenge, but we've certainly come a long way in being able actually to see how information might be processed as humans take part in various tasks and behaviors. So other advances have come in with other kinds of imaging and especially in non-human models and animal models for imaging.
JOHN NGAI: This has come about through a couple of revolutions. One is the revolution in terms of using either chemical dyes or genetically-encoded sensors of activity. So there was the invention of calcium-sensitive dyes that could be applied to other neurons and culture or actually in living preparations. This was pioneered by the late Roger Tsien. This has really given rise to this ability to look at neural activity here using calcium as a proxy for electrical activity.
JOHN NGAI: So it's not a direct measure of electrical activity, but typically, what a cell depolarizes or when it actually fire as an action potential, you see increases in intracellular calcium that can be read out as a proxy for activity. And depending on the dye you use, it's a very high temporal resolution. And what limits the resolution there in terms of time is the buffering of calcium as well.
JOHN NGAI: So that's been very, very powerful. And of course, now we have genetically encoded sensors for not only calcium, but now coming on board sensors for voltage, as well as sensors for neurotransmitters. That's very powerful. The complement of that is one needs better and better ways of imaging these probes, whether it's a chemical probe, or genetically encoded probe, or a combination of genetically encoded probe with an orthogonal chemical reagent attached to it.
JOHN NGAI: There have been many revolutions. Well, there's been a mini revolution in terms of optical imaging where now we can image living tissue, living neural tissue in awake behaving animals across a larger swath of the brain and using various tricks. Now we can record from thousands or even close to millions of cells at a time simultaneously using a combination of advanced optical imaging together with, married to these new types of probes, whether they be chemical or genetically encoded.
JOHN NGAI: And so that's been extremely useful in terms of getting a much more detailed picture of the kinds of activities that occur in different regions of the brain. And now the real challenge is to associate those in a causal way with actual behavioral outputs. So that's kind of on the imaging side. There have also been some great developments in terms of measuring electrical activity with electrodes.
JOHN NGAI: So before, back in the, quote unquote, "old days," if I may, you would have an electrode and you would stick it into the brain. There might be multiple sites, and you could either measure local field potentials, or you could actually detect spikes from individual cells. After the revolution of patch clamp from Neher and Sakmann that allowed an actual direct recording from cells to measure membrane potentials in a very sophisticated way.
JOHN NGAI: But those were generally lower throughput. And now with the advent of new developments in materials science and electronics, now we have high density arrays that can either be placed on the surface of the brain or actually penetrating in the brain, where we can now record from thousands of cells at a time in different regions and actually in a multiplex way, again, be able to correlate neural activity in different parts of the brain with specific behaviors.
JOHN NGAI: So we've come a long way. We have a long way to go, but we have many, many different tools at our disposal and many more that are being developed to be able to assay neuronal activity on a broad scale at increasing spatial and temporal resolution.
ANJALI SARKAR: Right. So how is the BRAIN Initiative-- coming now to the BRAIN Initiative, how's the BRAIN Initiative facilitating investigations into brain activity at the circuit level in both healthy individuals as well as under disease conditions at the level of individual circuits?
JOHN NGAI: So one of the main premises of the BRAIN Initiative is that in order to unlock the secrets of how the brain processes information, as we've been discussing, in order to really get at this sufficiently to help to truly understand this in a mechanistic level, we need better tools. We simply do not yet have the tools to be able to probe the brain and its activity and it's foundational principles in a way that we can truly fully understand it.
JOHN NGAI: So the BRAIN Initiative really started out as an initiative toward building better tools-- neurotechnologies. It's the N in BRAIN. Innovative Neurotechnologies is the IN of BRAIN. So we're approaching this from a number of different vantage points. One is to support the innervation of these new tools and the validation of the tools and then the eventual dissemination so they can be used by anyone who cares to use it, who has an interest in using it for the purpose of gaining more knowledge.
JOHN NGAI: Much of what the work we do and the work we support is on what would you call healthy brains, normal circuits. But one goal is to not only unlock the secrets of the brain, but really to also to understand what happens when things go wrong, whether It be in traumatic injury, stroke, neurodegenerative diseases, neuropsychiatric illnesses.
JOHN NGAI: If we look at, for example, movement disorders like Parkinson's disease., If we look at depression, obsessive compulsive disorders, disorders like that, ultimately, they boil-- and epilepsy-- ultimately, they boil down to circuit disorders, disorders of the circuits underlying these different functions and behaviors. So what we really want to do is to be able to generate on the one hand better tools so we can better understand these circuits.
JOHN NGAI: And then ultimately, if we understand how they work in a causal way and have tools to access these circuits in a very precise way, then we have a chance of intervening or modulating these circuits in conditions of disease. So this is kind of, in a very broad stroke, 50,000-foot level what the BRAIN Initiative is all about is to develop better tools for understanding how the brain works in health and disease and then to use that as a platform to develop new technologies, new therapies for treating what are currently intractable human brain disorders.
ANJALI SARKAR: You mentioned modulating neural activity as being one of the goals. So what are some of the new approaches that are being developed through the BRAIN Initiative to modulate neural activity and the role that the BRAIN Initiative is playing in these approaches?
JOHN NGAI: So I'll give you just-- I'll start with one example that's really quite nice. And so there's a technology or a therapy known as deep brain stimulation. I think many people are familiar with that. It's been around for probably decades by now for helping patients with movement disorders, including Parkinson's disease, control some of their symptoms, for example, of dyskinesia. So there are electrodes placed in different parts of the brain.
JOHN NGAI: They stimulate that part of the brain. And if all goes well and if the patient is amenable to that kind of therapy, many of these motor control symptoms can be alleviated. Now we're not treating the root cause of the disease. We're actually just addressing the symptoms. So that's actually been quite game changing in terms of quality of life of patients in these states.
JOHN NGAI: So as powerful and as effective as these therapies are, at the end of the day, there's somewhat-- I don't want people to take this the wrong way-- they're somewhat crude, right. You're basically just-- you're injecting electricity into these circuits, and you're seeing a positive effect, which is great, but it's not very fine tuned.
JOHN NGAI: So what we'd like to be able to do is to be able to read out the activity that's going wrong in the circuit and use that in what we call an adaptive or closed loop system to control it. So basically, if I'm steering a car and the car is drifting a bit to the left, I'm going to pull the wheel to the right based on that input. So similar idea.
JOHN NGAI: You can have a closed loop or adaptive system that can actually adapt to the patient's needs, the specific needs of that individual. Every human brain is different. And every patient, if you look into this every patient, while they might have common symptoms or common features of a particular disease like Parkinson's disease, every patient is going to be a little bit different. So can we really just fine tune these therapies not just to that patient, but also to the patient in that moment.
JOHN NGAI: So there've been studies now using adaptive deep brain stimulation. It's been deployed successfully in clinical trials for Parkinson's disease patients. So basically, the electrodes are used to read out activity in the brain that either correlate with or precede undesired movements that then tell the device when to stimulate.
JOHN NGAI: And the really fascinating thing is that now, there are devices that are being tested out in the field that allow this learning process. It's a learning process where the system has to receive that information. It's tied to sensors on the patient's limbs to record the movements, inappropriately undesired movements. And that's fed in and using artificial intelligence used to identify signatures that are going to correspond to these inappropriate movements that can then trigger the device to allay them.
JOHN NGAI: So this can all be done not just in the clinic or not just in the lab, but now that's being done in the patient's natural environment, at home, using wireless technologies. So this is showing great promise. There's several reasons why you would want the device not to be always on. You want it just to be correcting the movement when it's needed and not all the time. So there's great hope for that in that arena.
JOHN NGAI: Now we can extend this-- and again, these are still in an experimental phases, but showing great promise. We can extend that further out to ask, well, are there other circuit disorders that might benefit from these deep brain stimulation devices? So we've all heard about electroconvulsive therapy for treating treatment refractory depression. And based on pioneering work by Helen Mayberg several years ago, now people are starting to look at applying deep brain stimulation to help alleviate conditions for people suffering from such conditions as well as things like obsessive compulsive disorder or OCD.
JOHN NGAI: So there's been some success there. It's been a little bit rocky. Some success, some not so successful trials. And the hope, though, is that if one can apply an adaptive paradigm here, so in other words, if one can use these deep brain stimulation electrodes, which now the key here-- I forgot to mention-- is that they're not just delivering electricity into the brain.
JOHN NGAI: They're also recording. So the electrodes are what we call bidirectional. They both stimulate as well as record. If we can record so-called biomarkers, now here not associated with dyskinesia per se, but perhaps with mood or with certain behaviors, and then adapt that in a closed loop way, not to control movement, but to control mood, that could be quite powerful in terms of helping people that really have no other recourse for these devastating and debilitating conditions.
JOHN NGAI: And so we're starting to see some progress here. There have been some successful applications of deep brain stimulation toward alleviating symptoms of depression, severe depression, as well as in a few scattered cases. I mean, these are still the case study level, but also in terms of alleviating behaviors associated with addiction. So that's actually quite tantalizing, and it really points toward the possibility of directly intervening in a circuit-- you can call it a circuit modulation-- to control these symptoms.
JOHN NGAI: Now in parallel, as I mentioned, what we'd like to do is to apply this in a closed loop or adaptive system. So what I've described to you so far has just been what you call an open loop system the stimulation is applied. And the promise here is that various groups have now been able to record from patients, in some case ask them-- and the key here is in these experimental systems, oftentimes, these electrodes are placed for a different reason.
JOHN NGAI: It might be placed in a Parkinson's disease patient or patient with movement disorders. We're not experimenting on humans just to experiment on them. These are what we call research opportunities in humans. One can ask the patient, the subject, subjectively what's their mood and simultaneously be recording from them. So these are experiments that are done with consent and with very careful consent, but because they're already in there for other reasons.
JOHN NGAI: And what can actually establish what we would call electrical biomarkers for various mood states. Now it's still very early days, but it's hopeful. Another study that was just published out of a group out of Houston actually recorded about 1,000 hours worth of activity from a patient who was implanted. So the hope then is we can use that information to drive these so-called adaptive closed loop systems to help alleviate symptoms or actual mood disorders in patients that are afflicted with them.
JOHN NGAI: So the future is bright for that, but again, using electrodes to stimulate large areas of the brain, it can be very useful for now. But we'd like to do better, because it's not very selective. You're actually stimulating all the neurons in the region. And there's one thing that we do know is that there are many, many different cell types. They're wired up in different ways.
JOHN NGAI: You're basically taking a big bundle of telephone lines and kind of interceding in all of them at once. And what you really like to do is only modulate the activity in the ones that matter. So this is kind of a big challenge for us going forward is how to develop higher precision access to these neural circuits. A, to understand how they contribute to behavior, but B, ultimately how we might modulate them in the human brain or human circuit disorders.
ANJALI SARKAR: Right. And what technologies would you say is helping us move towards this precision as to not just be either recording or stimulating a bunch of neurons, but recording and stimulating precise neurons as they fire or as they need to be simulated? What technologies would you, in your opinion, are helping the scientific community move towards this kind of precision approach?
JOHN NGAI: Right. So our view on this is if we're going to build a big and fancy house or a very tall house, we need to build a strong foundation. So the foundation we're building is going to be centered around what we're referring to is the BRAIN 2.0 transformative project. So The BRAIN Initiative is in its second five-year phase we're calling it, so hence BRAIN 2.0. The BRAIN Initiative has benefited from strategic plans that were put together by external advisory groups that were commissioned by the then NIH director Francis Collins and in the second report that was published in late 2019.
JOHN NGAI: It was both a reflection on where we had come, but also where we could go with an eye toward not just understanding how circuits work, but also toward cures. And their recommendation was to think about, given all the progress that's been made, not just in the BRAIN Initiative, but neuroscience more generally and especially in the physical sciences and engineering sciences and mathematical sciences, where are there large projects that we could promote and support that could truly transform the way we study the brain and how we might treat diseases.
JOHN NGAI: So it's going to come down to basically to two broad things. One is to find a better ground truth of what neural circuits actually look like. Then the second piece is to have ways of accessing what we're going to have as a better map of what neural circuits look like, not only to validate our ideas, but actually eventually to have a way in for this precision access that we were alluding to before.
JOHN NGAI: So the three projects are as follows-- one is to gain a complete understanding of all the cell types in the human brain. This parts list, if you will, we're calling the human brain cell atlas. So that's the parts list. The second piece will be a whole brain connectivity map or wiring diagram, if you will. So if we have the different components or different types of neurons in the brain, there might be based on what we're seeing from the mouse upwards of 1,000 or thousands of different cell types, each with different properties.
JOHN NGAI: And the very important property of a neuron or nerve cell is who it connects to, the connectivity patterns. So what we want to understand is what are the connectivity patterns of all the cells in the brain, of the entire brain across distances, both small, cell to cell at the synapse level, and that's at the nanometer level, all the way across the entire brain, which could be millimeters or longer. So that's the second project.
JOHN NGAI: And having that as a ground truth, the parts list and the wiring diagram, that gives us a map or an atlas of the brain. And now we would like to be able to use that information to start targeting the specific cell types we've identified using mainly molecular, but also physiological techniques and to be able to probe those cell types as defined that way to ascertain their function and to validate or invalidate models for how we think it works.
JOHN NGAI: So the third project is a precision cell access project, where we will be developing various tools for accessing cell types somatically. So in other words, not through the germline necessarily, but through either viral mediated transduction or non-viral ways of delivery, for example using nanoparticles. And these projects all dovetail with each other. So for example, from the cell atlas projects that we're conducting right now mainly involved in the mouse but also now extending to big brains, non-human primate brains, and human brains.
JOHN NGAI: We're learning a lot about what makes a cell a cell in terms of the genes they express, right. And of course, what a cell express, this has to do with which enhancers and various gene regulatory elements are being active in any given cell. Well, this gives us a way of then giving targeting for, say, a virally delivered payload by using that enhancer element that's cell specific, put that antivirus or nanoparticle, driving some payload.
JOHN NGAI: And then now we have a way of, say, inserting a genetically encoded sensor for calcium activity or voltage into a specific cell type based on this enhancer. And we learned that information from the Cell Atlas Project. So this, in turn, would allow us to look very precisely or exclusively at what we would define as a cell type or kind of a class of cells. We could study this activity. We could study its connectivity as well, right?
JOHN NGAI: Who is worrying up with and maybe when in development or over aging or in different conditions of learning? So, you see, all this is kind of mutually reinforcing because the [? Atlas ?] Projects give us information about how to access these cell types and circuits. And by accessing them, we can then analyze those maps and learn more about what those maps mean in terms of function, how they contribute to behaviors, combining that with other projects that we're standing up now that seek to synchronize or align studies of neural activity and actual behavioral outputs.
JOHN NGAI: Now we can have a more comprehensive view of the circuit bases of behavior because we start with a map. That's a ground truth. We have a way of interrogating that map. And then we have a way of modulating. So we can develop-- we can generate models of causality by modulating activity. What happens if I stimulate this cell type in this circuit at a certain point in time?
JOHN NGAI: How does that affect the behavior? What happens if I-- what happens if I inhibit activity in that particular type of cells at a given time? How does that affect their behavior? We can do that on a broad level. But as we get better tools to more precisely access cell types in the circuits, we'll be able to get much cleaner answers about exactly how they participate.
JOHN NGAI: So what does this have to do with circuit therapies in humans, for example? So right now, we're using mainly electrical stimulation, as well as pharmacology, to alter-- to modulate activity. Now you can imagine a case where, say there's a specific cell type that we've identified or a gene that's mutated in a specific disorder-- for example, there are some epilepsies, inherited epilepsies, that have mutation in a sodium channel that gives rise to aberrant circuit activity-- if one could target the cells that are affected in a given condition whether it be epilepsy or a mood disorder-- thinking way, way far out, right-- and if you had a way of modulating this activity by delivering some payload, some actuator, then we could have much more precise access than by placing an electrode in an anatomical region that's stimulating all the cells in the region.
JOHN NGAI: So we see these three transformative projects together, the cell atlas, the connectivity project, and the precision cell access project as eventually paving the way for developing precision gene therapies and molecular therapies for human circuit disorders. So that's the blue sky view of it. We have a long way to go. But as we're seeing, I mean, gene therapies are now showing some success in humans, gene editing among them.
JOHN NGAI: And we're hopeful that we can start applying these before I hang up the gloves into the humans to treat human circular disorders.
ANJALI SARKAR: Talking a bit more about the human brain cell atlas that you mentioned and talked about, so does this include, I believe, only human or also model animals, small and large model animal brain atlases as well?
JOHN NGAI: That's a great question. So the evolution of this project goes back-- let's see, so I would say from the BRAIN Initiative standpoint, this project started in 2014, so about seven and 1/2 years ago, late 2014. And I was actually-- before I started at NIH, I was actually an investigator on the first set of projects. And this was a set of pilot projects to determine which technologies would be viable to scale up to generate a complete or a comprehensive census and atlas of cell types in the mammalian brain.
JOHN NGAI: There were 10 projects funded. I was one of them, very, very fortunate to be among this group. And this came at a really great time. It was great timing, and the folks at NIH-- and I was not at NIH then, so I'm not boasting on behalf of myself-- they saw the future. And the future was in single cell sequencing, as we all know.
JOHN NGAI: That's a revolution in modern biology for sure, right, that follows on the heels of the revolution of high throughput sequencing. And the idea was that they sense that there were technologies that would allow us to characterize cell types in the brain. But it was going to go beyond just sequencing them, right, which at the time that we started in 2014, we were sequencing cells basically in micro wells.
JOHN NGAI: There were some microfluidics techniques that came out on fluid ion was dominant at the time. But we were really limited in throughput and by expense. But at the same time-- so we saw possibilities in terms of increasing throughput, which is, I guess, another way. The flip side of that is decreasing cost per cell. But we wanted more than just what James characterizes cell. So as cells, about more than just the genes that it expresses, although that's a huge part.
JOHN NGAI: Many, many other things that happen as a result of that, including activity and sensory-dependent changes. So the ideal was to be able to use the molecular information about a cell, the comprehensive molecular information, based on the whole transcriptome, and to use that as a basis, as it turns out, to gain other kinds of information about the cell as well. So the idea behind this is, if we have a good understanding of what makes a cell a cell, based on the genes it expresses, and layer on top of that its physiological properties, its electrical properties, its location in the brain, its morphological characteristics, and, very, very importantly, its connectivity.
JOHN NGAI: Who does it connect to? If I have information about all those parameters on a cell or cell type, I kind of have what I need to know to go further, to understand how it's contributing to circuit, and eventually into some behavior, or into some sensory pathway. So that was the goal of those pilot projects. We call that the Brain Initiative Cell Census, Consortium, BICCC.
JOHN NGAI: And that was started as a pilot phase that lasted three years. About a year and a half, the program officers came to us, and they said, if things work out, we're going to scale up. Actually, that's what they told us in the beginning. Things work out, we're going to scale up so we can apply this to the entire mammalian brain. Again, most of the emphasis was on mouse-- the rodent brain back then. And so that was that was the carrot that was stuck out in front of us.
JOHN NGAI: And we worked together. And about a year and a half in, they came in, and they said at a meeting-- I remember the day. And they asked us, are you guys ready to scale up? And some people were more enthusiastic than others. I was a little bit tentative. And then, the response was no. You guys are ready to scale up, right?
JOHN NGAI: [LAUGHTER] So in 2017, the projects found a whole other gear, and we formed what was called the Brain Initiative Cell Census Network, or BICCN. And so, we were told in the beginning, look, from the consortium, we're funding these 10 pilot phase projects. But don't expect all 10 to be scaled up. We're going to expect some realignments and some consolidation.
JOHN NGAI: So the BICCN, or the Cell Census Network, was formed around three large, large projects, so-called U19 projects. Multi-site. I was, again, fortunate to be among one of those projects. That particular project was-- we had five PIs on that project, across five institutions. And there were two others as well. The idea was, really, now, to make a comprehensive atlas of cells, based not just on molecular information but what we call a multimodal analysis.
JOHN NGAI: And here, the cool thing was-- and also based on work that was done in the pilot phase. It wasn't just measuring single-cell RNA, but also epigenomic information. DNA methylation combined with spatial information, a spatial transcriptome. So over the course of the BICCC, the little mini-revolutions that happened were droplet-based sequencing. And then, the commercialization of droplet-based sequencing by 10x Genomics, which made it fully democratized, and then spatial transcriptomics, which actually tells us where these cells are, based on molecular information.
JOHN NGAI: Combined with connectivity and physiology. So there's this really cool technique called [? petseek, ?] developed by one of the investigators, Andreas Tolias, at Baylor University. So what [? petseek ?] does is, you go in and you patch a cell in a slice, you measure its electrophysiological properties, you can inject a dye, and then image it in a microscope and understand its morphology and limited connectivity.
JOHN NGAI: And then, you can suck out the contents and sequence it. So you have three modalities on the same cell. And then over time, now, other things that have been developed is, in the early days, we would take measurements from the epigenomic data, whether it be [? atekseek ?] technique or methyl C sequencing, and we have independent assays for each of those, and independent assays for the RNA sequence of the transcriptome.
JOHN NGAI: And one of the cool things that came out of the BICCN was a collaborative effort to analyze the data together. So a joint molecular analysis. Which is great, and we actually were able to, amazingly enough, come up with a cohesive and coherent picture of cell types, based on molecular properties from these different modalities. And going in, it wasn't clear how to do that. Well now, of course, to complement and augment those studies, one can now measure both chromatin accessibility and RNA expression on the same cell.
JOHN NGAI: And actually, we can also layer on-- there's some really cool tricks that have been developed, in part from the folks within this group, to also look at say DNA methylation at the same time. So get multiple modalities. So that was kind of the evolution there, and in the BICCN, again the scale of phase, the three large production-- three large projects were focused mainly on mouse.
JOHN NGAI: But there were also smaller, more focused studies on different aspects of generating cell census and mouse, including a number of projects looking at doing connectivity at scale, which is a huge lift. But also, there were several projects looking at non-human primate brains, as well as human brains. So this project was launched in 2017, and it was decided early on that we needed a type of way to work together, to have everybody pulling in the same direction, and hopefully to have the total add up to more than a sum of the parts.
JOHN NGAI: So we would have semiannual meetings in person-- back when you meet in person, right? And literally, I was on two or three Zoom calls every week. This is even pre-pandemic, we were on Zoom a lot, because the labs were scattered across three continents. And we decided early on to have a demonstration project to ask, well, if we all looked at the same part of the brain, would we come up with a-- could we come up with a unified answer?
JOHN NGAI: The nightmare scenario was that we'd-- if you put 20 groups on the same part of the brain, you'd come up with 20 different answers. And we definitely didn't want to find out we were going to be in that boat. So the idea was, let's pick a region of the brain. The region we picked was a primary motor cortex. This is the part of the cortex that's responsible for generating movement.
JOHN NGAI: So all the groups kind of dove in on this. This was what we refer to was the Minneapolis Project, which became a running joke because what the Minneapolis Project wound up being by the end of 2021 was 17 papers in one issue of Nature. It was a monumental effort. 250-plus scientists across three continents, and it was just an amazing experience. So what we did is, we combined all these different means of analysis.
JOHN NGAI: Single-cell RNA sequencing, [? atekseek, ?] methyl C sequencing to look at DNA methylation patterns. All that integrated together, combined with spatial transcriptomics, combined with connectivity and physiology. Really, really quite amazing stuff. But in the course of putting these studies together, we thought, OK, the idea is we would hopefully publish a few papers together.
JOHN NGAI: This was back in, say, 2019. But we-- 2018, 2019. But we also thought, we want to have this be interesting. Not just 20 studies, or 17 studies on the same part of the brain. We wanted a cohesive picture. So then we realized what's been done in similar projects like this-- for example, the N-CODE Projects, is that there's a flagship paper that kind of pulls everything together.
JOHN NGAI: And so the boring way to do that would have been just to have the flagship simply recount what the other study-- what the individual studies show. That wasn't so satisfying. We're looking for a hook, and the hook was, we thought, well, let's do a comparative analysis between mice, non-human primates-- in this case, marmosets-- and humans. I've always said, evolution is a wonderful teacher.
JOHN NGAI: You just need to listen to what she's saying to you. That was just tremendous because what we found, by comparing the analysis of the different cell types that we identified, using these different modalities between mouse, marmoset, and humans, the one amazing thing-- maybe not too surprising-- is that, by large, you're seeing the same major cell types. So when we look at the taxonomy of cells that we've identified, and again, using mainly molecular techniques, maybe not too surprisingly, there's a hierarchical arrangement.
JOHN NGAI: So it goes from class to subclass to cell type. And if you look at the intermediate phase of what we might call subclass, there's a grouping of about a couple of dozen-- 25, 27-- subclasses, that no matter what asset you use, whether it's [? atekseek ?] or RNA sequencing or other methods, you always come to more or less the same conclusion at that level. As you dive down deeper within each of those subclasses, there are now types.
JOHN NGAI: So if you think about the broad-- the thicker branches, and then as you get down to the leaves, then, depending on how you assay it, you can come up with, at the limit, maybe 130 cell types based on RNA sequencing, and maybe fewer using other, different techniques. But a lot of that is depending on how you look. So we were asked, through multiple rounds of reviews, you guys need to come up with a number.
JOHN NGAI: How many cell types are there? And I remember saying very flippantly in one of our author meetings, I said, well, it depends how you look. And then we thought about it, and we realize, it really does depend on how you look. And that's not to say how many there are. The ground truth is harder to get at. But one of the things we did in the joint analysis is we did a replicability analysis, which is to say, if I look at this landscape through different lenses, can I replicate these results using a different approach, and at what level will it replicate?
JOHN NGAI: So it replicates poorly at the level of 100 times, at the leaves, but it replicates quite well at the level of subclass, which is maybe a couple of dozen. It's about 25, 27. So that was really cool. So we could actually get a coherent picture. But not only using different approaches, but actually, also, looking between different species. So at that level, a mouse and a marmoset and a human, they're very similar, but there are differences.
JOHN NGAI: And you start seeing specific differences as you get to the finer leaves. So the basic theme is the same, but the specific-- how specifically executed in terms of the more granular cell types, that's what's going to define one species from another. It could even define one individual from another. And so that was really cool. And so most-- so that's when you start seeing differences.
JOHN NGAI: Now, not too surprisingly, if you look at the cell types identified, the ones you see in the humans tend to be more closely aligned to marmoset than to mouse, and mouse to marmoset than human. Maybe not too surprising. Again, we do see some differences. One of the cool things was one of the human-specific cell types that was found in the cortex also happens to be a cell type that's particularly vulnerable in Alzheimer's disease.
JOHN NGAI: So this tells us a couple of things. One is that, the comparative analysis tells us what's the same-- what's similar, I should say. And therefore, if one wants to model a human disease in an animal model-- and if you think of cell type is involved, it's probably a good model if that cell type is also in the humans, and probably not a good model if it's not found in humans.
JOHN NGAI: That's one thing. So there's a lot of talk about, this model system is poor-- this is a poor model system because of the inability to translate to humans. That's a pretty broad statement. What it misses is that, well, let's understand why. It's not going to be the case for every disease. And it's not going to be identical. Nothing will be identical from a mouse to human.
JOHN NGAI: But the idea is to get a toehold. So I think this so-called loss of translation-- or, lost in translation-- can be looked at with greater precision if you can ask, OK-- if I'm interested in this process, and I'm studying a certain circuit in the mouse, and if I think I want to model a disease on it, now I have the tools to ask, is that circuit, or the cell types in that circuit, are they also found in the eventual organism I want to study, which is humans?
JOHN NGAI: And if the answer is yes, that's great. You go ahead. And if the answer is no, you need to rethink it. So that's one-- so one thing that was revealed was that, not too surprisingly, not all cell types are present across all species. And if you want to translate from one to the other, whether it's translate up from mouse to human, or to do what we call reverse translation, of a human to mouse, you have to make sure you're working with the same set of players.
JOHN NGAI: So that's one thing. The other surprising thing to me, is if you look at what we would define as a similar cell type between two species-- so you can look at these maps. You can identify these cells are common between mouse and human. If you look to see what you would call a marker gene for those cells, they would be-- oftentimes they're different.
JOHN NGAI: So even though, on the global level, it's cell-- there's a cell type A that's found in both human and mouse, and they'll have similar properties. If you're going to look at what defines them genetically, from an experimental point of view, you could be fooled. So it also says that, OK, if something isn't adding up between-- or comporting between these two species, there could be multiple explanations.
JOHN NGAI: One is that cell just isn't in both, or the other is actually they are, functionally, but because of the way that the transcriptional programs played out, there could be a divergence in terms of what other genes are expressed that you would otherwise be using as markers. And not just as markers, but as a way of targeting those cell types. So now, we're also moving from the point of thinking about gene-specific targeting.
JOHN NGAI: So as it turns out, historically, many cell types in the brain are defined by whatever marker somebody assigned to them. But now, we're moving away from thinking about gene-specific targeting to cell-specific targeting. So if I might have identified a cell type based on makere gene X, but in going in to see what genes are expressed by that cell, or what might be the most specific enhancers, that might be associated with gene Y.
JOHN NGAI: So it's, again, a way in to get better precision access, both for experimental reasons, but also, hopefully, eventually, for therapeutics. So there's this been this great evolution, maybe close to revolution, in the way we're thinking about how we can access cell types and circuits in the brain. It's really been a whole world that's been uncovered to us because of this ability to get in at the single-cell level and to do it at scale, and also marry those techniques with other techniques like spatial information, morphology, anatomy, physiology, and, most importantly, connectivity.
JOHN NGAI: So these are what gets us really excited. So to go back to your question, these are providing the resources to build tools to access circuits. So you can go back to deep brain stimulation if we better understood which circuits are being involved, even if we can't access them precisely based on molecular information. Anatomically, we might be able to do a better job. Or we might have different patterns of stimulation that might be more effective.
JOHN NGAI: So I think, again, these three transformative projects, the Cell Atlas, the Connectivity Project, Precision Cell Access Project, we think that, together, they will form a strong foundation that will give us the information we can use to really build the models for how neural circuits work, how they drive behavior, and ultimately how we can access them.
ANJALI SARKAR: Regarding the point you make about cells being present in one species that cannot be-- correlates of which cannot be found in humans or vise versa, do you think it makes the case for the need for multiple lab models to study the human brain, and not just the mouse or the marmoset?
JOHN NGAI: Well, I think having different perspectives, different model systems always help. We've learned so much about how all nervous systems work based on model systems. Look at the pioneering work and the amazing work that's been done in [INAUDIBLE] 302 neurons. But still, many of the principles hold. The fruit fly Drosophila. Larval zebrafish has been a great model for studying the circuit basis of behavior.
JOHN NGAI: And through the various mammalian models as well. Not to mention other ones. Amphibians and so forth. I think what's important to bear in mind is, we often focus on what's similar as being useful. And oftentimes, you don't think about what's different. Again, it comes back to evolution. What has evolution done? These organisms have found some common solutions to common problems.
JOHN NGAI: But often, there's been different strategies that have been adopted in different species to approach the solution. And we can learn from that as well. So the contrast is as important for us to pay attention to as the similarities. So in the brain portfolio, we have what I lovingly refer to as our zoo, the brain zoo, where we're open to the study of different model organisms, not so much just for the fun of doing it, but for the potential that they can really teach us about how neural circuits work in general.
JOHN NGAI: There are going to be some foundational principles that are going to apply to all systems in different ways-- perhaps in different embodiments, but certainly as a foundation. And they're not going to be so easily revealed by just studying one system, one organism, one system. So that the approach here is to say, OK, what does this model have to offer? What can it teach us?
JOHN NGAI: And if we learn something from this, if we have a hypothesis that we can test and validate or invalidate, how will that apply to other systems we're also trying to understand?
ANJALI SARKAR: Right. Talking about the Micro Connectivity Project, one question that comes to mind is, the connectivity in the brain is dynamic, based on the experience of the person and the environment. How is the Micro Connectivity Project tackling this dynamicity of micro connectivity, and mapping that and offering that as a resource?
JOHN NGAI: That's a great question, and it's an issue that has come up in hot debate. One context in which it came up was-- we didn't get into this, but the idea of creating a whole mammalian brain connecting with synapse resolution, right now, is out of reach. So right now, there are two studies that are out on bioarchive at the moment that each look at about a millimeter to millimeter and a half-- sorry, a millimeter, a cubic millimeter to about a cubic 1 and 1/2 millimeters of mammalian cortex, in one case mouse.
JOHN NGAI: This is the microns project that was supported by ARPA. In another case, a human cortex. Study from Jeff Lichtman and his group of collaborators. So a millimeter and a 1/2 cubed. The issue is, in a reliable and accurate way, assigning all the connections to it. And each of those data sets occupies about a petabyte.
JOHN NGAI: So let's call it a millimeter cubed. A mouse brain is 500 millimeters cubed. So we have to scale up by 500-fold. It's going to occupy about an exabyte of data. And the issues are going to come in in terms of segmenting, basically being able to draw around a cell and its axion processes. But not only within this very, very small region of the brain, volume of the brain, but over an entire brain.
JOHN NGAI: And that's a huge challenge because there's a challenge in terms of sectioning it and not losing information between them. So you have to put Humpty Dumpty back together again, and you have to very reliably be able to assign an axon that might traverse a very, very long region. So we don't have yet have the tools for that. So one argument has been made, well, let's get let's do a connectome for one mouse brain, with one genome.
JOHN NGAI: And then the next one is going to come easier. And the challenge is, we could probably do it eventually, but it's going to be error prone. There's going to be a lot of uncertainties to that. And then the question-- then the counterargument is, well, sure but one is what is one connectome going to tell you? Especially when you get down to the synapse level, where we would expect that, depending on the time in which you sample, the experience-- I mean, there's developmental time, there's experience dependent changes.
JOHN NGAI: Does that really represent-- does that represent all animals, or does that represent that animal in that state when you took the brain? It's going to be the latter. So then the question is, OK, at what point will these maps truly be useful? I mean, let's be greedy. We don't want to have a map of just one brain. We want to have maps of many brains.
JOHN NGAI: So first of all, what's the inter-individual variability at, say, a given-- let's say an 8-week-old, a P56 mouse. What's going to be the individual variability? Where is it going to manifest? At what level? Unlike the way that I describe the cell census, the Cell Atlas, where you have, at the subclass level, maybe a couple of dozen cell types, but as you get into the leaves, the more finer sections, it's harder to tell.
JOHN NGAI: Where are you going to see that breakdown? Where are you going to see absolute, conserved patterns in the connectivity map, and where is it going to start breaking down? You're not going to know that by studying one animal. You're going to have to study many animals. And then, you're going to have to say, OK, well what state was that animal in? Was it happy or sad?
JOHN NGAI: Was it asleep or awake? And so on and so forth. Then you get into different genetic strains. So what we have to do in order to really get at this is to develop technologies that can be scaled to entire brains. And it's not just experimental technology. It's like tissue sectioning and preservation and imaging, which is huge. But also, the whole computational side.
JOHN NGAI: How do you retrieve, store, handle, and analyze an exabyte of data? How do you do that over and over and over again? This is a gigantic problem. We don't have solutions for that yet. And so, ideally, what we'd like to do is to be able to answer the kinds of questions you just asked. What's going to be the variability?
JOHN NGAI: What will it look like for an animal in different states, or for from one animal from one animal to another, or from one animal of a particular genetic strain to another animal of a different genetic strain? Things like that. So these are the questions we really want to know the answers to. One of the main goals of this connectivity, so-called transformative project, is, if we can come up with a way to do this at scale across multiple animals, then we can start getting closer to answering the kinds of questions that you just raised, which is what we're interested in.
JOHN NGAI: What makes each person different from the other? What's going on when I'm asleep, before and after I have my coffee? Things like that. What's changing? And the kind of things we really want to know-- and it's going to take this ground truth information that I referred to, and in order to get at that ground truth information for the connectivity project, we're in earlier days in terms of having the tools for that, in comparison, say, to the Cell Atlas, the Cell Census Project, where we've had the benefit of multiple revolutions in DNA sequencing technologies.
JOHN NGAI: Which isn't where it ends, but it really gives us a big leg up. Again, these tools can help reinforce each other. These projects really will reinforce each other as they go along.
ANJALI SARKAR: Coming to the neuroimaging techniques that have come out of the Brain Initiative like Prism and DNA Paint and the passive clarity technique, could you talk a bit about these, and how they work, and what they do?
JOHN NGAI: Yeah. So that's a pretty broad swath. So there are many challenges that we have when we look at imaging. One is to be able to-- let me see if I can tick them off in a rational way. One is being able to identify cells in a complex tissue, and part of that's going to depend on the genetic definition. We want to look at activity.
JOHN NGAI: Not just a one cell at a time, but many, many cells at a time, and how the different members of the orchestra are playing at any given moment, and how does that symphony play out? We need ways of monitoring activity, identifying the cells, and, very importantly, probing deep into the tissue. So brain's kind of a-- once you get beyond a millimeter from the surface, it gets very tricky to look inside, either in a living brain or in a fixed brain.
JOHN NGAI: So the techniques you mentioned are aimed at addressing some of these challenges. So one big challenge that we have not yet solved, like we have with transcriptomics or epigenomics, is proteomics. Single-cell proteomics is still a ways off. But we have tools that allow us to look at, not just a handful of proteins at a time, but more. So conventionally, when I was in grad school centuries ago, doing double label immunofluorescence was kind of-- you were pretty good if you could do that reliably.
JOHN NGAI: But you want to do more. Now, you can do-- you can multiplex. You have more colors. The Paint and Prism technique is basically a way of gaining more labels for antibodies. So you can have in your possession a whole panel of antibodies against different antigens that will help you identify or define what a cell is. But you need to be able to keep track of which antibodies finding were we given time.
JOHN NGAI: So here, we realize that DNA sequences can help us here in terms of labeling these antibodies. So you can have literally an infinite combination of DNA sequences, just depending on how long you want to make that sequence to tag the antibody. So that's the basic idea behind this Paint and Prism technique. There are ways of looking at neural activity that have changed. Various high-resolution, both high spatial and temporal resolution techniques for monitoring neural activity, again, using various chemical or genetically encoded sensors.
JOHN NGAI: So there's just advances every day. I can't even keep track of new improvements. Not just sensors, but also what we call actuators, optogenetic tools. Derivatives of, say, [INAUDIBLE] which can either activate or inhibit cell types, respectively. So that's really ongoing, very, very exciting. There's the application of things like adaptive optics, which have been used in astronomy to allow astronomers with their telescopes to get high-resolution images of galaxies far away, where the light coming in is being distorted by everything in the way.
JOHN NGAI: So if you look-- if you think about looking deep into the brain there's all kinds of stuff in your way, and adaptive optics are now being applied there as well. And so on and so on and so forth. Very fast scanning, multiphoton fluorescence microscopy, now to get much higher spatial resolution images, things like that. So there's just so many things going on, some of which are brain supported, some of which aren't brain supported.
JOHN NGAI: Different ways of illumination. Light sheath microscopy allows us to get into larger tissues, also to do it in live tissue. You mentioned there's clearing techniques. The brain is kind of a big blob. If you want to see, for example, how-- if you had a way of labeling a cell and all of its connections, going through the entire brain, can you do that without suctioning the brain?
JOHN NGAI: Can you look at it an intact tissue? What the clearing techniques do-- this was developed initially from [? Karl Dizero's ?] lab 5, 10 years ago-- basically, in that technique, I think the original [INAUDIBLE] lipids and replaced it with biogels. And now the newer techniques can do that passively and very efficiently. So there's been multiple iterations of this initial idea.
JOHN NGAI: If we can get this stuff out and clarify the brain, then we can actually look deeper in using, for example, light sheath microscopy. So that was a breakthrough, but then that breakthrough was iterated. And sometimes, if you iterate it enough times, or the iteration is big enough, it becomes another mini-breakthrough, where it can be democratized, where anybody can do it.
JOHN NGAI: You don't need to have a degree in engineering to do some of these techniques. Whereas before, the folks who developed it had to raise an engineering-- [INTERPOSING VOICES]
JOHN NGAI: That gets to another point, that we're trying to support the development of techniques that will work not just in the hands of the experts, but in the hands of experimenters who have specific questions they want to ask, who aren't necessarily going to be the tool developers. Oftentimes, the tool developers are the end users, but what we really want to do is we want to democratize these techniques. So, again, single-cell sequencing is a great example.
JOHN NGAI: We were doing it back in the old day when it was an artisanal process. And now, you still need to be careful. You still need to be skilled. But you can now literally buy a kit and isolate cells and put the stuff on a machine that you bought, on a microfluidic well that you bought, and actually come out with some really clean data. And this is the kind of principle we'd like to apply to as much of the technology that we're developing as possible.
JOHN NGAI: But to get these tools into people's hands-- because there's so many researchers out there asking great questions. With a great question, you can develop a better tool. With a better tool, you can ask better questions. And you have this virtuous cycle between generating hypotheses and generating better tools, and it just keeps on making the whole ecosystem much, much more powerful, in terms of being able to understand how these biological processes work.
ANJALI SARKAR: And in addition to this basic, fundamental approach at understanding basic biology, what I'd finally like to ask you is from the perspective of molecular medicine, personalized medicine, if you will. Efforts to develop such maps, universal maps, if you will, cellular or functional, or proteomic transcriptome maps that will provide resources for the entire scientific community can, at times, appear too gross a level to be applicable in the discovery of therapies, particularly in the context of personalized medicine.
ANJALI SARKAR: So could you share with us how you envision the Atlas resources at these various levels of the cell map, the blueprint of the wiring, and the precision targeting. How can these resources, generated by the Brain Initiative, find application in neurological drug discovery and personalized medicine?
JOHN NGAI: Right. So, again, it's a matter of having a good reference Atlas. You need something to compare it to. Every human on the planet is different from each other. Even identical twins, because they might be genetically identical, but in terms of how that genome is written out is going to change. It's going to be influenced by environment. So we're all different, but we need a reference, and we can use that reference in a very powerful way.
JOHN NGAI: Now, setting aside personalized medicine for the moment, certainly we can use that reference as a way of understanding disease states. So one of the goals of the Brain Initiative, at least in terms of human brain disorders, will have this so-called normative set of resources, and then investigators can come in and study human diseases-- not just human models of disease, but human diseases, actual humans-- using this as a reference.
JOHN NGAI: And not just using the Atlas as a reference, but also using the know-how that we've gained. So it's not so simple as, well, I'm just going to sequence this region of a brain of this disease individual. You want to be able to do so in a way that you can compare it, not just to the normal type, but to other related diseases. I don't want to so much use the word standardization, but at some point, you need to be able to integrate information from across modalities, across laboratory sites, across investigators, and across diseases, and that will be very powerful.
JOHN NGAI: The other thing to remember is that the diseases can actually inform how we interpret the so-called normal state. If you think of any human disorder, especially genetic disorders, there's something that went on that's causing some disease, which you could think of as a phenotype. But you want to be able to compare between phenotypes with different genetic bases. So that's going to be very powerful.
JOHN NGAI: Now, as we get down to what you might call precision or personalized medicine, again, there are certain neurological disorders that you can trace down to a single-- monogenic disorder you can trace down to a mutation in a single gene. Or mutations in a single gene, or in a cluster of genes. And so, again, having an Atlas that tells you what the ground truth is, the cell types the connectivity pattern-- and we talked about variability before.
JOHN NGAI: We don't want to look at, say, from the molecular level, all the cell types in one human brain. We want to do this for multiple human brains. And that gives us the power to do a couple of things. If we have, in our eventual human cell Atlas, representation from multiple individuals, but clearly from both sexes, both biological sexes, ideally at different stages of the lifetime, and crucially from people with different ancestral or genetic backgrounds-- and what this will do is it'll tell us, on the one hand, what's common between individuals, from these different ancestral or other backgrounds.
JOHN NGAI: What's in common is going to be very important, because that's where you're going to be able to find therapies that can target not just one type of person, but across all of humankind. But also, if you identify the differences-- again, it comes down to what are the differences. Some populations are more susceptible or resilient to diseases than others. If you understand the cellular, molecular, genetic basis of that, you won't learn not-- you will learn not only how to treat the people that are vulnerable, but if you look at the people that are resilient, you'll find maybe a biological clue that can treat those that are susceptible.
JOHN NGAI: So it's all going to come down to having a broad representation across what we call the human species, the human condition, in order to be able to target, in a precise way, individuals that might have a certain genetic makeup that predisposes them to a disease or actually causes that disease. So at the end of the day, these reference Atlases are crucial, but they need to be made in a useful way, and a useful way is not just to do it from one individual.
JOHN NGAI: It's not just to do it from, say, white Europeans, which many studies until recently were really focused on-- and it's been shown genetically that the lessons with the genes-- the gene associations you identify in one subpopulation do not often translate to other subpopulations. So I think the key to precision medicine is to have a broad base with which to compare.
ANJALI SARKAR: That brings us to the end of today's GEN Protocols Tech Talk. Thank you, Dr. Ngai, for a very illuminating discussion. A reminder to all scientists among our viewers, GEN Protocols is open for submissions, and we welcome your protocols in all aspects of biotechnology. So until next time, good luck in your research, and goodbye from all of us at GEN Protocols. [MUSIC PLAYING]
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