How do our brains stay healthy as we age? | Tom Clandinin

Nicholas Weiler (0:10)

This is From Our Neurons to Yours, a podcast from the Wu Tsai Neurosciences Institute at Stanford University, bringing you to the frontiers of brain science.

I'm your host, Nicholas Weiler.

Today on the show, the complex biology behind a seemingly simple question, how do our brains stay healthy as we age? It seems like we're always hearing about a new secret to healthy aging, or a new root cause of dementia and neurodegeneration. Inflammation, estrogen levels, how brain cells clear out their own waste. And we've covered a lot of these stories ourselves on the show, and each one can feel like, yes, that's the answer, until the next one comes along. Honestly, this is sort of the nature of science. Scientists tend to try to reduce complicated systems into simpler questions that can be answered one at a time. But presumably, all of these different factors are at work at the same time, all contributing to the aging process. So this raises a harder question. When you've got a complex system involving dozens of different components, how do you figure out where to point the most effective intervention? This is a trickier question for scientists to wrap their heads around. But it's exactly the challenge we're going to talk about with today's guest, neurobiologist Tom Clandinin. Tom is probably best known for decades of fundamental science, unlocking the genetic systems that wire up the visual system of the fruit fly, which has helped us understand the visual system in all organisms, including humans. In recent years, Tom has turned his sights beyond the visual system. He's become fascinated by the question of the genetic systems that keep brain cells alive for years or decades, much longer than most of the cells in our bodies, and definitely longer than a fruit fly.

Understanding how these systems fail is crucial to understanding dementia and neurodegeneration, and it could also tell us how we could strengthen these systems to help us live healthier, longer.

Now, Tom is working with computational biologist Xiaojie Chu and neuroscientist Will Allen to achieve this vision. Together, they're launching a major initiative to address these questions, backed by Wu Tsai Neuros' Big Ideas in Neuroscience program, and the Knight Initiative for Brain Resilience. The team plans to use AI models and new genetic techniques to build a comprehensive portrait of the genetic networks responsible for maintaining our brain health. They'll ask how these complex interconnected systems break down as we age, and where we can find tractable leverage to enhance their resilience. Since thinking about all these genetic systems can be a bit of a brain twister, I started by asking Tom for an analogy that might help us picture the challenge he's setting out to solve.

Tom Clandinin (3:01)

Maybe a view or a way to look at it, just to think of it as an ecosystem, right? And you don't view a species in isolation, you view it as part of a larger structure that if you manipulate one species, or one species begins to suffer in some part of the ecosystem, it's going to have rippling effects across the ecosystem that are hard for us to predict, right? You can't a priori know how adding wolves into Yellowstone is going to affect everything, right? And so I think about kind of our absence of understanding of these really higher level organizations is really kind of a shocking gap. I think, as you highlight, many scientists, including myself, begin from kind of a reductionist perspective that tends to break problems down into small pieces and go, OK, this is a part I can understand. The Big Ideas project is really in this kind of non-reductionist perspective was really pioneered, I think, by Will Allen and Jiajie Chu, the two collaborators on this project, because Will really emphasized the point that, you know, while you're ultimately going to manipulate single genes in single cells, you need to understand both the consequences of those manipulations across many dimensions, as well as capture how cells with different perturbations interact and how those perturbations interact with physiological perturbations of the animal.

And you really need a computational framework to put that together, because I think as reductionists, we're ill-equipped to think about these very high-dimensional spaces, these large interactions. And that's where, you know, Xiaojie's perspective really makes it possible to use these models to make predictions and to understand interactions in a way that are difficult for a reductionist to hold in their heads. And so that's, I think, the exciting opportunity of this big idea.

Nicholas Weiler (5:00)

Well, frame the big idea for us, without necessarily getting into the hows of exactly what you're going to do, what is the big idea question that this project intends to tackle?