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Nobel Prize in Medicine 2026 Explained: Optogenetics (EP 61) · From First Principles

• 0:00
  Hello, everybody.
• 0:01
  OK, here we go.
• 0:02
  I'm so glad to see you all here.
• 0:04
  Very welcome to Nobel Forum for the announcement of this year's Nobel Prize in Physiology.
• 0:11
  It's the same guy as last year.
• 0:13
  My name is Thomas Perlman.
• 0:15
  I'm the Secretary General of the Nobel Assembly and the Nobel Committee.
• 0:18
  What's it going to be?
• 0:20
  I will first read the announcement in Swedish and then immediately followed in English.
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  And we will then present the background
• 0:30
  To the prize and open up for questions.
• 0:51
  Peter Hegemann.
• 0:52
  Oh, wait, wait, wait, wait, bro.
• 0:54
  Carl Diceroth, Peter Hegemann, and... Bro, I called it.
• 0:58
  The first two... Bro, I called it.
• 1:00
  This is so good.
• 1:01
  I called it.
• 1:01
  This is so good.
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  Optogenetics.
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  What did I say?
• 1:05
  Optogenetics.
• 1:06
  What did I say?
• 1:07
  That's crazy.
• 1:09
  Maybe a year late.
• 1:10
  Yeah.
• 1:11
  That's good.
• 1:13
  Jointly to Carl Diceroth, Peter Hegemann, and Georg Nagel.
• 1:20
  Georg Nagel.
• 1:22
  So not Misenbach.
• 1:24
  Yes.
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  And not genetics.
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  The other MIT guy, Ed Boyden.
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  Yeah, yeah, yeah.
• 1:31
  Okay, interesting.
• 1:32
  Okay, we're going to dig into this, and we will be diving deep finally.
• 1:37
  Yeah.
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  On optogenetics.
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  pond algae two biophysicists in germany and a psychiatrist at stanford together they gave biology a light switch a protein that opens the moment light hits it chlamydomonas swims toward light nagel and haegemann found its sensor channel rhodopsin
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  Blue light opens the channel, ions rush in, and the cell gets an electrical signal.
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  In 2005, Diceroth put that gene into neurons.
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  A flash of blue light and the neuron fires, on command.
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  By 2007, it worked in living mice.
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  Today, it's how neuroscientists map the circuits behind memory, emotion, and behavior.
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  And it's being tested to restore sight.
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  This is From First Principles, and this is the 2026 Nobel Prize in Physiology or Medicine.
• 2:34
  Hello, Internet.
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  This is your captain speaking, Lester Nare, joined as always by my co-host and our resident PhD and predictor of the future, Krishna Chowdhury.
• 2:45
  We are here for the first of our three-part Nobel Prize special, and we're talking about medicine and physiology today.
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  First, we want to thank all of you who voted in our Instagram poll.
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  34% of you got it right.
• 3:02
  Well done.
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  Yeah, something like 500 plus people voted.
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  And I was actually very surprised at how few people voted something else.
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  Yeah.
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  People have a lot of faith in us.
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  That's crazy.
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  So again, thank you all so much for those votes.
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  We are going to talk about the science from the ground up today as always.
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  And this will be for the Nobel Prize in Medicine from First Principles.
• 3:28
  That's right.
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  The Nobel Assembly at Karolinska Institute has awarded the 2026 Nobel Prize in Physiology or Medicine jointly to Carl Dyseroth at the Howard Hughes Institute of Medicine and Stanford University, Peter Hegemann at the Humboldt University of Berlin, and Georg Nagel.
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  at the University of Würzburg in Germany for their discoveries concerning light-gated ion channels and optogenetics.
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  This is a prize that I have been calling since last year.
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  It didn't go last year.
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  I recycled the prediction this year, and voila, it's here.
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  So that's two years in a row that I've gotten something right.
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  What makes this prize particularly interesting is that it represents a remarkable convergence of fields.
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  We've got biophysics, molecular biology, microbial physiology, genetics, electrical engineering, neuroscience, and they're all coming together to solve one of the most fundamental problems in biology, which is namely...
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  How do you establish a causal relationship, a causality between single cells and their activities in the brain or in neural tissue, and as we'll see in other parts of the body, and the behavior of the entire organism?
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  That's a crazy link to go from a single cell all the way to behavior.
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  And that is the central question that this technology is trying to answer.
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  This is also a great example of how fundamental discoveries are seemingly coming from obscure biological systems, and they can totally transform medicine.
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  And this, as always, the Nobel Committee puts out artwork, and this is the particular artwork that is their flagship sort of thing,
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  thing that they're putting out to the press it depicts um a woman who's playing chess there's a light switch that goes into a particular neuron in her brain and it's got something to do with chess behavior by the end we should be able to understand everything about this image it's giving queens gambit yes um but i think also this dovetails with what we talked about with the golden goose awards where where you start and where you end are
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  there can be a large gap in between those two spaces.
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  And I think this is going to be an interesting additional proof point to that idea about basic research and then ultimately clinical-type outcomes.
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  Yeah, yeah, yeah.
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  This is a flagship example of exactly that.
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  It's pretty amazing.
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  So the whole point of neuroscience, right, is to understand the brain.
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  These are some famous author quotations about the brain.
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  The Nobel Institute in their press release quotes Virginia Woolf,
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  with her quote, my own brain to me is the most unaccountable of machinery, always buzzing, humming, soaring, roaring, driving, and then buried in mud.
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  And why?
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  What is the passion for?
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  They quote that one.
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  I brought up two more that I quite like.
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  The second one is from Arthur Conan Doyle, the author of Sherlock Holmes.
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  Sherlock Holmes says, I am a brain, Watson.
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  The rest of me is a mere appendix, which is quite interesting for him to say all the way back then, that really
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  the self, the illusion of reality, everything around us.
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  Finally, Maya Angelou, this one's quite funny.
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  The brain is wonderful.
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  It starts working the minute you're born and never stops until you get up to speak in public.
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  I quite like that last quote by Maya Angelou because Senator John Kennedy, who's very famously funny, he adopted it on the floor of the Senate and said, the brain is an incredible organ.
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  Yeah.
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  It starts working when you're born, and then it stops working when you get elected to Congress.
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  Well, it was one of those highlight videos of all the funny things he says.
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  But it's really quite amazing, right?
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  How can this organ that only weighs 1.3 kilograms, like if you put your hands like this, that is about how big your brain is, if you put your hands together like this.
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  And that 1.3 kilograms of fat and neural tissue is,
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  That holds childhood memories, daydreams, creativity.
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  It gives us joy, love, jealousy, all of the emotions.
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  It also controls the pace of heartbeats, the rhythm of breathing, the sleep cycle.
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  Literally everything that has to do with being alive is controlled by 1.3 kilograms that are in your skull.
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  And to give you a
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  sense of how complex this problem is.
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  This is a cubic millimeter of the mouse's visual cortex.
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  This is by the Allen Brain Institute.
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  They're part of the Microns project.
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  They took a cubic millimeter of the mouse visual cortex, sliced it up, and then stained it, and you can now...
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  trace the hundreds of thousands of neurons that are in that one cubic millimeter.
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  Each of those neurons can be traced using machine learning to like stitch everything together.
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  And you can see the amazing diversity of the cells there.
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  Yeah.
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  Yeah.
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  There's 500 million synapses in this one cubic millimeter.
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  That's so unbelievable.
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  And the length of axons is four kilometers worth of axons, like neural fiber, in this one cubic millimeter.
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  Now, that's a cubic millimeter of the mouse brain.
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  For the human brain, the whole human brain, that's 100 billion neurons, 500 trillion synapses.
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  So the question is, if we want to study this thing, how do we establish causality?
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  Yeah, right.
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  Right?
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  Right.
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  Because for most of the 20th century, neuroscience was fundamentally an observational science.
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  Okay?
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  You observe what the neuron is doing and you try to correlate it to behavior.
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  Mm-hmm.
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  And researchers could record electrical activity of neurons.
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  They could stimulate brain regions using electrodes.
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  They could lesion particular structures.
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  And this gives you a good sense of what is going on.
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  But these methods have significant limitations.
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  I can imagine because part of it's like you have to wait for the things to do what you're looking them to do and then hope and then track.
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  And it's not quite –
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  It's not quite like a two-way street of information.
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  It's a one-way street of information.
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  And it's done amazing things, right?
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  Like consider electrophysiological recordings.
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  So here you take an electrode.
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  You can either put it in the vicinity of the cell or sometimes you can use a patch clamp and go inside the cell.
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  and see what the voltage is across the membrane, from the inside to the outside.
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  Neurons fundamentally use the transport of ions to create a kind of electricity that they use to talk, and that's how you get these very, very fast responses.
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  Now, with electrophysiology, you can learn a lot about how action potentials
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  How neurons propagate from one end of the neuron to the other?
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  How neurons aggregate information?
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  Like if a bunch of neurons are talking to the single neuron, how do you actually get this particular neuron to fire?
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  Turns out there's ions that are coming in that change the membrane potential, right?
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  Because if you dump a bunch of positive ions on the inside, then that's going to change the electrical environment.
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  That electrical environment is then going to change the proteins that are nearby, which is going to let in more ions.
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  That's going to let in more ions.
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  You have this kind of runaway effect that creates action potentials.
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  All of this was discovered using electrophysiology, meaning electrodes going into the physiology for us to measure precisely what the electrical environment is around a neuron or even inside of a neuron, right?
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  Right.
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  Amazing stuff.
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  Yes.
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  Several Nobel Prizes have happened because of this, okay?
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  But you reach a kind of ceiling on the kinds of science that you can do.
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  Right.
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  Because suppose, for example, you're recording from a neuron in the hippocampus while the animal is learning a spatial task.
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  This was very famously awarded the 2014 Nobel Prize in Medicine and Physiology for the discovery of place cells and grid cells in the hippocampus and in the entorhinal cortex.
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  And there on the lower right...
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  is my first paper that was in Nature.
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  I was a minor author in there.
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  But the point of that paper was that, you know, originally it had been thought that these place cells in the hippocampus only respond to place.
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  But the paper from my encumente lab that I was a part of, it showed that actually those same place cells can respond to visual cues.
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  Only visual cues.
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  No behavior.
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  Right?
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  So now...
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  You have a relationship between place and this activity, the neurons activity.
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  But now we've just shown that visual cues are enough to elicit the same cells to do the same kind of firing pattern that you see when the animal is moving around, right?
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  I remember we covered this briefly and it was very popular because the concept was very surprising for people.
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  Yeah, yeah, because they thought that place cells are place cells and that's it.
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  They respond to place.
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  creating the activity of this particular neuron.
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  And I just want to shout out one author of that particular paper, Shonali Dhingra.
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  She was one of the first authors.
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  She recently passed away.
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  She was a huge mentor for me.
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  She was a postdoc when I was a graduate student.
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  And a lot of the scientists that I became was because of her.
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  So, you know, in memory of Shonali, I'd like to dedicate this whole episode because she was a huge influence in my life as a scientist.
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  The point of that paper was...
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  Correlation doesn't establish causation, right?
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  The place does not establish why this particular place cell is firing.
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  And perhaps even the name place cell might not be that good of a name.
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  It might be a multimodal sensory cell in the hippocampus.
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  And so the idea being we've been doing this observational science, which has given us a lot of advances, but we can't necessarily jump all the way to the conclusion that we can define causation.
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  just using that one-way street that we've been using thus far.
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  Exactly, yeah.
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  And so you might say, okay, well, let's try to establish causation by stimulating the neuron electrically, right?
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  You've got an electrode in there.
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  Why don't I just pump electricity?
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  The problem there is electrical stimulation is spatially imprecise.
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  You've got an electrode, right?
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  Unless it's targeting a single cell, then maybe you can make that cell fire.
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  But then it's only going to be that cell, right?
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  Right.
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  And how many cells are you going to try to elicit some kind of behavior?
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  Oftentimes, behavior depends on networks of cells, not just a single cell.
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  Although you can get lucky sometimes.
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  There's the very famous Jennifer Aniston cell that responds only to when, like, the animal was shown a picture of Jennifer Aniston.
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  And it was literally called the Jennifer Aniston cell.
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  But they got extremely lucky somehow, right?
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  Where it's like they showed exactly the right actress and then they elicited a response.
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  So fine, there's causality.
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  But then most of behavior is because of networks of cells, right?
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  And sure, the electrical stimulation that you're putting an electrode in, that's going to cause some kind of activity.
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  But it's going to be spatially imprecise.
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  If it's outside the cell, those electrons are going to leak everywhere because of the fluids in the brain, the Navier-Stokes equation come back again.
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  And so...
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  You're not going to get a nice one-way street.
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  The resulting behavior could be caused by the neuron around there or it could be caused by single neurons somewhere else that are connected to some other part of the brain that is doing all of this nonsense.
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  This reminds me of the discussion around precision we talked about when we looked at histotripsy versus other ultrasound therapies in terms of using thermal ablation, which has more of a spread impact.
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  Yeah, because again, the brain is conductive in heat.
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  It's also conductive in electricity.
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  Versus having a more mechanistic approach.
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  So the precision really matters, especially when you talk about the brain.
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  Yes, exactly.
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  Because single neurons can elicit crazy stuff, right?
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  The other option you have is pharmacology.
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  Pharmacology meaning like you inject some kind of receptor agonist or antagonist, and then you try to like block some type of receptor that blocks the neuron's activity.
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  But again, same thing.
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  There's a problem, right?
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  Because you inject something that's going to spread around because of diffusion.
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  You're not sure whether you targeted that specific circuit or not.
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  The other problem with pharmacology is the response time is quite slow.
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  You're relying on diffusion.
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  So you're injecting something.
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  That thing has to spread, go to the neurons.
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  Neurons are fast.
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  So we've got to find a different way to do this, right?
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  Now, genetic approaches offer a way to get specific, right?
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  Because you can target specific neurons because you can be like, well, the pyramidal neurons in layer three have this specific type of genetic expression.
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  And if I can hone in on that, then I can like turn it on or off.
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  But that's not really reversible, one thing, right?
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  Once you turn it on, it's kind of hard to take out that kind of stuff.
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  And the brain also has time to compensate, reorganize, develop alternate pathways.
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  Again, not great.
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  We're trying to get to a place where we can create a two-way street where there's a very well-understood discrete control on our input into the system that does not have this, like, combinatorial effect in other ways that is unintended.
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  So we can isolate saying we only touched this thing and it only had this sort of next step outcome.
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  And that way we can start to really probe what we can do.
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  with an understanding that when we look for causation, we know that we didn't input more than we need.
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  We didn't put too many ingredients into the recipe.
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  Exactly, exactly.
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  We just changed one thing.
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  Okay, so there are three things that we need for this perfect, ideal technique.
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  Okay.
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  Okay?
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  Here are the three things.
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  First, we need genetic specificity, right?
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  We need to be able to target specific types of neurons in a very specific location, right?
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  Second, we need temporal precision, meaning timing needs to be extremely fast.
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  There you see the electrical signature of an action potential recorded with electrophysiology.
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  One of the great things with electrodes is that you can record at 40 kHz, so 40,000 data points per second.
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  So that's 40 data points in a single millisecond, right?
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  In a thousandth of a second, you'll get 40 data points.
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  So you can really look at the waveform and see what the waveform of an action potential looks like.
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  you can see that the width of an action potential is like a millisecond.
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  So we need whatever tool we're developing to be extremely fast.
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  And the second, and the third actually, I couldn't find a visual for it, so I just used the Uno Reverse card.
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  Uno Reverse.
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  But you want it to be experimentally fast.
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  reversible.
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  You want the ability to switch the neural activity on and off repeatedly.
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  That's how you really establish causation.
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  You turn it on, something happens.
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  Well, is that because you turn something else on?
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  Or if you turn this off, do I recover the old behavior?
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  Do you go back to baseline?
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  Exactly, yeah.
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  And...
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  One of the people who dreamed about this more precise tool to explore the brain was Francis Crick, the Nobel Prize winner in 1962 for the DNA double helix.
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  He, you know, he discovered the double helix in 1953.
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  And then decades later, he started investigating human consciousness.
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  He became obsessed with this idea about what is consciousness.
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  activate individual neurons in a living brain.
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  Okay.
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  And in...
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  Ambitious.
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  It is quite ambitious.
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  And he said it was quite ambitious.
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  And in 1999, he wrote this article where he forecasted that maybe we could use light.
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  He said light would be ideal, right?
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  If we could somehow manipulate nerve cells and get them to react to light...
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  That might give us the genetic specificity, because we can maybe only target certain neurons.
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  It could give us the temporal specificity, because light you can turn on and off at an arbitrarily fast time regime.
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  And also reversibility, because I can turn light on and off.
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  And he admits in that paper, it's like a perspective paper, and he admits that the idea sounds very far-fetched, but perhaps not impossible.
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  Okay.
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  Perhaps.
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  Perhaps not impossible.
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  And that vision of the future is what this Nobel Prize is about.
• 19:38
  So good.
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  The new field of optogenetics.
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  It was the nature method of the year in 2010.
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  It's been all over scientific journals.
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  Using light, researchers are now able to switch individual neural circuits on and off.
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  They can bring memories to life.
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  They can create feelings.
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  They can drive behaviors.
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  They can study the types of neurons that are involved in psychiatric and neurological disorders.
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  Optogenetics is, I mean, fundamentally transforming our understanding of the brain.
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  And every year, countless papers come out using this technique.
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  It's the hammer of the toolbox of neuroscience in the modern, in the context of being a tool with a lot of uses.
• 20:20
  Exactly, yeah.
• 20:21
  So how do we get there, right?
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  How do we get here?
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  The idea of using light to manipulate biological systems was already known.
• 20:29
  This might be a good time to talk about a particular individual who,
• 20:34
  Misenbach.
• 20:34
  Misenbach.
• 20:35
  Garo Misenbach.
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  In his Wikipedia article, it says...
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  Misenbach is known as the founder of optogenetics.
• 20:45
  Well, this is a bit awkward.
• 20:47
  This is why I originally thought he might be the third.
• 20:50
  Yeah.
• 20:50
  Right.
• 20:51
  They give it to Nagel instead.
• 20:52
  And we'll see why.
• 20:54
  Funny thing.
• 20:54
  I was watching the whole press conference right after they announced.
• 20:58
  The very first question was, why was Misenbach not awarded?
• 21:03
  I'm sure the guy knows him or something and was like, yo, what's going on?
• 21:07
  So...
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  The response was, well, we're writing a forward description about the scientific justification, and we do not comment on why someone got it or not.
• 21:19
  And then the follow-up was, if there were four recipients, would Misenbach have gotten it?
• 21:23
  And the guy was like, mate, come on.
• 21:25
  All right, let's go to the next question.
• 21:27
  Next question.
• 21:28
  Yeah.
• 21:29
  So here's what Misenbach was known for.
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  He developed this thing called the charge system.
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  It utilized this multi-gene strategy.
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  to co-express three distinct proteins from the Drosophila visual cascade.
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  Drosophila is the fruit fly.
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  He took proteins from that visual cascade and he expressed that in neurons.
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  And he, crucially, he expressed this in mammalian neurons.
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  The problem is the following, though.
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  If you look at figure A, and this is the paper that came out in 2002 where he did this, it's already kind of optogenetic-y in mammalian neurons already.
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  But the problem is, in part A, that's the response of this neuron.
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  So the light turns on when the gray bar ends.
• 22:17
  Look at the response time of this thing.
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  The scale bar down there, that's not milliseconds.
• 22:21
  That's seconds.
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  Okay?
• 22:23
  This is on the order of thousands of times the timescale of a neuron.
• 22:27
  We want things at the millisecond timescale.
• 22:29
  This is happening at seconds.
• 22:31
  So this is good, right?
• 22:32
  You can turn on neurons and turn off neurons, but you don't get the temporal precision.
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  Furthermore, you get a lot of variability.
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  On the right-hand side, you're seeing the gray is when the light was off, and then it's turned on.
• 22:44
  Sometimes the response is very quick, half a second to a second.
• 22:47
  Sometimes it's very, very long, all the way to 10 seconds later, right?
• 22:52
  It took a long time for all of this to accumulate and then that neuron to turn on in panel B. Yeah.
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  And so this is the – it's like a diet version of ultimately what happened in terms of it didn't have the precision from a time perspective.
• 23:07
  It didn't also have for the reversibility piece.
• 23:09
  It wasn't quite what it needed to be.
• 23:12
  Exactly, yeah.
• 23:12
  Because if you take this long to turn something on, then like when you turn it off, presumably it's going to take very long time to turn off as well, right?
• 23:20
  And the other thing, it required three proteins to go in.
• 23:24
  It also required cofactors, which are like things that bind to the protein to enable –
• 23:28
  Like there's all these working parts that are going in to create it.
• 23:32
  It's the first time though.
• 23:33
  It's 2002.
• 23:33
  So this is very early, right?
• 23:35
  It's quite early.
• 23:35
  And that's why he's been given many awards.
• 23:38
  It's not the Nobel this time.
• 23:40
  It's so tough.
• 23:40
  You know, that is tough.
• 23:41
  That's tough.
• 23:42
  That is tough.
• 23:43
  Um,
• 23:44
  And so the challenge now is to create a light control that is genetically targetable, and it's fast to operate at that timescale of neural computation.
• 23:54
  At this point, you're saying there's a chance.
• 23:57
  We now knew.
• 23:58
  We now knew there's a chance, but we got to find the right tool, right?
• 24:02
  We want a single protein to do the job.
• 24:03
  We don't want these three different proteins.
• 24:05
  Plus, I need to inject cofactors.
• 24:07
  So that means in order to keep this thing going, I need to keep injecting cofactors that are going to help this protein out.
• 24:14
  It's not great, right?
• 24:16
  And the solution comes from a very unexpected source, microbial photoreceptors.
• 24:22
  So to really understand optogenetics, we need to understand how certain microorganisms detect light.
• 24:28
  The relevant proteins belong to a broader family called microbial rhodopsins.
• 24:33
  These are proteins that have a nice architecture that lets them...
• 24:39
  Absorb light in some kind of retinal chromophore that's like a central chemical, you can imagine, on the inside of this protein that absorbs light.
• 24:50
  transforms that energy into a kind of kinetics that makes the outer protein change shape.
• 24:56
  And then that outer protein changing shape is going to create some kind of activity in the cell.
• 25:01
  The historical foundation of this is from halophilic archaeon, halophilic bacteria, by Dieter Osterhelt.
• 25:14
  In 1971, he publishes this paper that shows a pump.
• 25:18
  It's a photon-mediated pump, meaning there's a pump on the membrane that takes in light and then uses that energy to pump protons one way or the other.
• 25:29
  Across the membrane.
• 25:30
  Across the membrane.
• 25:31
  So we're getting close.
• 25:32
  We're getting to a protein that takes in light energy and then uses that to pump ions.
• 25:38
  In this case, it's just protons, right?
• 25:40
  H plus ions.
• 25:41
  Okay.
• 25:42
  Now...
• 25:43
  This is great, but this is a pump.
• 25:46
  A pump meaning like one single photon is coming in.
• 25:48
  That's going to cause one ion to move, right?
• 25:51
  Like that work is getting transformed.
• 25:53
  What we'd really want is one photon to come in.
• 25:57
  That opens a kind of gate.
• 25:59
  And then whatever ion concentration is already present, let's say there's a lot of,
• 26:05
  Some kind of ion, sodium, potassium, calcium on one side, less on the other side.
• 26:09
  There's already a gradient.
• 26:10
  All we want to do is open the dam.
• 26:12
  Instead of it being a toll booth with cars where each car has to stop, we want a drawbridge where multiple boats can go by because the drawbridge gets open.
• 26:20
  Yeah, yeah.
• 26:20
  And it's all because there's more boats on this side, so they're just like trying to leave.
• 26:23
  Yep.
• 26:24
  Right?
• 26:24
  That's what we'd like.
• 26:25
  Yeah, right.
• 26:27
  So let's consider the unicellular green algae system.
• 26:32
  Chlamydomonas reinhardti.
• 26:35
  I always have a hard time with these taxonomical names.
• 26:40
  They're obsessed with Latin, the biologists.
• 26:43
  But in any case, this is a green unicellular algae.
• 26:46
  And like other photosynthetic organisms, it needs to detect light.
• 26:50
  Right?
• 26:50
  Because it depends on light to actually live and eat.
• 26:55
  And so it's got a little eye.
• 26:57
  of some kind.
• 26:58
  It's not the eye that we have where we can like make images, but it's certainly got something that is receptive to light.
• 27:04
  Okay.
• 27:05
  It's got an eye spot and that eye spot senses light and it creates phototaxis.
• 27:10
  For example, you put it in a Petri dish.
• 27:12
  We've got light on one side, dark on the other side.
• 27:14
  You'll see all the algae migrate towards the light side.
• 27:17
  So there's got to be some kind of sensitivity in there that is making them sense.
• 27:20
  The light is in this direction.
• 27:21
  I'm going to swim in this direction.
• 27:23
  This is where my food is.
• 27:24
  We're going to go there.
• 27:25
  Exactly.
• 27:28
  And it possesses this kind of light-sensing architecture that can tell the flagella which way to swim.
• 27:32
  Quick aside, as I was doing research on this stuff, people use this algae for all different sorts of stuff.
• 27:38
  This is the Shoji Takeuchi Research Group at the University of Tokyo.
• 27:42
  They're making little tiny machines that are propelled by these algae.
• 27:47
  Like, the algae get trapped in these baskets.
• 27:50
  And then the flagellar motion, like, creates pinwheels and, like...
• 27:55
  movement in some sense.
• 27:57
  I just thought that was kind of cool that people are like doing this.
• 28:00
  I'm sure there are plenty of use cases, right, for this kind of stuff.
• 28:03
  I just thought it was kind of cool.
• 28:05
  So the point is researchers have been studying this algae for well over a century because it's really, really cool.
• 28:12
  The fact that a unicellular organism can respond to light
• 28:16
  Just from a fundamental biology perspective.
• 28:18
  There's so many different questions and experiments you'd want to do with that, knowing that that's there.
• 28:22
  Exactly.
• 28:23
  Now, the quest to uncover the molecular mechanism underlying this phototactic behavior, that takes a turn when Peter Hegeman, our first Nobel laureate that we're going to talk about, he recently graduated with his PhD from Dieter Osterholtz's lab at the Max Planck Institute.
• 28:40
  The guy who did the first proton pump.
• 28:42
  Okay.
• 28:42
  So graduated with a PhD from that lab.
• 28:45
  He joins the laboratory of Kenneth Foster in Syracuse University.
• 28:49
  The lineage thing is always so interesting when we talk about this story because it really does kind of matter.
• 28:54
  It really does matter, right?
• 28:56
  He goes to get his PhD from the guy who is the premier guy for bacterial phototaxis.
• 29:03
  And then he starts worrying about, okay, this eukaryote, this algae, how does it do it?
• 29:08
  He wants to understand the biophysical basis for these photoreceptor currents.
• 29:20
  the photoreceptor currents in wall deficient algae mutants.
• 29:24
  So there you've got like a pipette that kind of is stuck to the algae.
• 29:28
  And from that now you can record the activity of the algae and you can see like how fast is the light response.
• 29:35
  And look at the timescale on that light response.
• 29:38
  That's what we're talking about.
• 29:39
  That's one millisecond.
• 29:39
  That's what we're talking about.
• 29:40
  That's what we're talking about.
• 29:42
  Okay?
• 29:42
  Immediately when you see that, you go, nice.
• 29:45
  We got him.
• 29:46
  We got him, right?
• 29:47
  This is in 1996.
• 29:49
  Hegeman comes out with this paper.
• 29:50
  Okay.
• 29:51
  Now, this one millisecond timescale, it's hard to reconcile this with what we normally know about rhodopsins.
• 29:59
  For example, the vertebrate rhodopsin, the visual rhodopsin that's in our rods and cones in our eye, here's how it works.
• 30:06
  There's the opsin, which is the optical element, the protein that has the optical ability to...
• 30:15
  taken light, that thing is coupled to a G-protein receptor.
• 30:21
  So it's called a G-protein coupled receptor.
• 30:24
  And the signaling happens in a cascade.
• 30:29
  Okay.
• 30:29
  Okay?
• 30:30
  So the light absorption initiates some kind of biochemical signaling cascade that ultimately then regulates a cyclic GMP-gated ion channel that then causes depolarization of...
• 30:41
  To then give us the sensation of light.
• 30:44
  And so this is a multi-stage process where light is an instigator in now three other downstream steps.
• 30:51
  Yeah, and then there's an ion channel.
• 30:53
  Then there's an ion channel that the drawbridge opens.
• 30:55
  Yeah.
• 30:55
  And there's like these two intermediate steps.
• 30:57
  Exactly.
• 30:57
  Now, presumably, this is not going to be that fast, though.
• 31:01
  Right, right.
• 31:01
  Because there's a lot going on.
• 31:03
  There's a lot going on there in between.
• 31:04
  And so it's going to take time.
• 31:06
  Not that much time, but not a millisecond, right?
• 31:10
  A millisecond is telling you that the left part, the opson part, which is the light detector, and the ion channel part are somehow a single entity.
• 31:22
  So I think I get what you're saying.
• 31:24
  By the fact that we could see that this was happening at a response time that was at millisecond timescales, there is some other relationship between the opsin and the ion channel that we're not seeing that is not necessarily related to this G-protein cascade that was the starting point.
• 31:40
  Exactly.
• 31:40
  Okay.
• 31:41
  That's the point, right?
• 31:42
  So in the early 2000s,
• 31:46
  Hegeman and his colleagues, they start investigating what is the molecular basis for that phototaxis, okay?
• 31:51
  In the 90s, he's established that this thing is fast.
• 31:54
  The 2000s, he gets his own lab, and he's like, I'm going to go after it, okay?
• 31:57
  I'm going to see what exactly is happening.
• 32:00
  Now, traditional biochemical approaches, they're difficult because these proteins are present in very low abundance.
• 32:05
  They're only in the eye spot, and even then, it's not a lot of them.
• 32:09
  And it's difficult to purify in the traditional sense.
• 32:13
  So the research team pursues a different molecular approach using genes.
• 32:17
  They want to identify candidate genes.
• 32:19
  And one thing that they piggybacked on is the fact that the algae's expressed sequence tag database, this is by Asamizu and other authors in 2000, they had already published the sequence tag database for this gene.
• 32:40
  algae.
• 32:41
  So the genetic data is now available for people to try and access.
• 32:47
  And they didn't need to create that from scratch.
• 32:49
  So they would be able to piggyback again.
• 32:52
  Science is interdisciplinary.
• 32:54
  Only three people got the Nobel Prize, but
• 32:57
  There are multiple people that are involved in this research, right?
• 33:00
  And it wouldn't have happened without the entire community going after this problem.
• 33:05
  I wanted to make that clear.
• 33:06
  So in the early 2000s, three research groups, one led by Peter Hegeman, but another one by John Spudich and then Toshio Takahashi, they independently identify the DNA sequences that encode for these proteins.
• 33:19
  What they look for is a protein that looks kind of like
• 33:23
  the rod protein in our eyes.
• 33:26
  And that's exactly what they find.
• 33:27
  On the left is our vertebrate rhodopsin, right?
• 33:30
  This is the stuff that's in our rods.
• 33:32
  And on the right-hand side is the protein structure of what they found.
• 33:35
  You can see that there's immediate similarities, right?
• 33:38
  All these alpha helices that are clustered around a central chromophore, right?
• 33:46
  And so what they can look for is analogs.
• 33:49
  You already know the sequence over here from the Human Genome Project and all that other stuff.
• 33:53
  And then you can look for homologues in the algae.
• 33:56
  And so we already had this database that was done previously.
• 34:00
  We know how it works in the humans.
• 34:01
  And we're basically saying, okay, in this database, what looks like the thing that we have on the human side?
• 34:05
  Exactly.
• 34:06
  And as a way to identify where to do the work, where to start.
• 34:09
  Like instead of doing a blind, like starting and where do you begin?
• 34:12
  What do you even look for, right?
• 34:14
  The genetic sequence is massive.
• 34:15
  Right.
• 34:16
  So from this, they identify two segments of DNA.
• 34:20
  They call it channel Opsin 1 and channel Opsin 2.
• 34:25
  These are then put into the genetic databank.
• 34:30
  And now these are like maybe the thing that is creating the photo ability of these algae, right?
• 34:36
  The idea that these guys can maybe...
• 34:39
  Let the algae respond to light.
• 34:41
  Okay.
• 34:42
  The decisive functional breakthrough emerges when Peter Hegeman contacts George Nagel.
• 34:48
  Georg Nagel, I should say.
• 34:51
  Georg Nagel, strong electrophysiological expertise in studying ion channels and pumps.
• 34:56
  He has already used his expertise with pumps to look at how the original, you know, the proton pumps that I was talking about from Dieter?
• 35:08
  Yeah.
• 35:09
  He put these inside the Xenopus laevis oocytes, which are frog embryos, effectively.
• 35:16
  And he had figured out that these are proton pumps.
• 35:18
  That's how we know that they're proton pumps.
• 35:19
  This is the guy who did it.
• 35:20
  He genetically engineered frog embryos to express that particular protein.
• 35:27
  And then you can actually characterize that this is a light-driven proton pump.
• 35:31
  So he's already got expertise in doing this.
• 35:33
  So Peter Hegeman goes to him and he's like, look, I got these two pieces of DNA, chop one and chop two.
• 35:39
  And I want you to express them and study their function.
• 35:42
  In 2002, Georg Nagel, Ernest Bamberg, and Peter Hegeman and their colleagues, they published this seminal paper.
• 35:51
  This is the first of several papers that we're going to go through that earns them the Nobel Prize.
• 35:56
  Channel rhodopsin won a light-gated proton channel in green algae.
• 36:00
  This is in science.
• 36:01
  Shout out to AAAS.
• 36:02
  Yes.
• 36:03
  So he showed that green light illumination can induce inward photocurrents
• 36:09
  that are primarily carried by protons.
• 36:11
  This is, again, still protons.
• 36:12
  This thing is opening up, and if there's a gradient of protons, this thing is going to let protons in.
• 36:18
  Somehow the algae maintains a gradient of protons.
• 36:21
  That's for another story.
• 36:22
  But if there's already a gradient of protons, there's more protons on one side than the other, you open up this dam, the protons are going to go through.
• 36:29
  And not just a single one this time.
• 36:30
  It's going to have the flow of multiple.
• 36:32
  Exactly.
• 36:32
  It doesn't need...
• 36:33
  light to do work.
• 36:34
  It's just opening up a dam.
• 36:36
  Yep.
• 36:36
  Right?
• 36:36
  And this biophysical fingerprint provides the first direct evidence of light-gated ion channels.
• 36:43
  Okay.
• 36:44
  And this is the first time we get a formal designation channel rhodopsin 1.
• 36:48
  Okay?
• 36:49
  Because now we've established that this is the protein that's doing it.
• 36:52
  This is amazing already, right?
• 36:53
  This thing can function as a light-activated ion channel.
• 36:55
  Yeah, yeah, yeah.
• 36:56
  It's already amazing.
• 36:57
  Yeah, yeah, yeah.
• 36:57
  But it's not the complete story because the electrical response...
• 37:03
  isn't particularly spectacular, and it's doing protons.
• 37:08
  Right.
• 37:09
  We would like something that messes around with sodium, potassium ions, things like that.
• 37:15
  The things that are in the brain.
• 37:17
  In the brain.
• 37:18
  Right.
• 37:18
  That stuff that the neurons use.
• 37:19
  Neurons don't use protons.
• 37:20
  Mitochondria use protons, but I want to turn on a whole cell.
• 37:24
  This is cool.
• 37:25
  We want to go to the big leagues.
• 37:27
  Yeah.
• 37:27
  Yeah.
• 37:27
  And so the next year, subsequent landmark paper by Nagel and his colleagues, channel rhodopsin 2.
• 37:33
  This is the second landmark.
• 37:34
  piece of cDNA that they had identified.
• 37:37
  This is a directly light-gated cat-ion selective membrane channel.
• 37:41
  That word, cat-ion selective, is massive here.
• 37:45
  Cat-ions are positively charged ions.
• 37:47
  This is a general-purpose ion channel.
• 37:51
  You give it sodium, it'll let in sodium.
• 37:54
  You give it potassium, it will let in potassium.
• 37:56
  Okay?
• 37:56
  This is a big deal.
• 37:57
  This is a big deal.
• 37:58
  This is a big deal.
• 37:59
  Right?
• 37:59
  This is the second protein, channelrhodopsin 2.
• 38:01
  And the other thing that I want to show you, this is, again, he used the oocyte embryos to do this.
• 38:07
  In panel B, what you're seeing is the response time of this guy, right?
• 38:13
  Immediately it turns on and it actually plateaus.
• 38:15
  It's not like it turns on and then goes to baseline.
• 38:18
  The plateau, it still keeps producing a current, even when you keep it on for a whole second, right?
• 38:26
  Mm-hmm.
• 38:27
  So the illumination is directly opened by this ion-conducting pathway.
• 38:31
  This is good.
• 38:32
  And it's fundamentally different from the conventional G-protein-coupled stuff that we have in our eye, because now this is directly light-gated.
• 38:39
  Yes.
• 38:39
  So this is a protein that absorbs light and then immediately just turns on.
• 38:42
  There's no...
• 38:44
  There's none of those two steps that we talked about earlier.
• 38:49
  This is more that direct relationship from the initial image.
• 38:52
  Exactly.
• 38:53
  And that's why we're getting that really fast timescale of work.
• 38:56
  Okay.
• 38:57
  Right?
• 38:57
  Okay.
• 38:59
  This was an amazing paper that was out in Proceedings of National Academy of Sciences.
• 39:03
  And the researchers immediately recognized a potentially much broader implication.
• 39:09
  If this protein is expressed in an unrelated cell, and if that cell could then be made to be electrically responsive to light, then you can use it to control cells in general.
• 39:21
  Yeah, right.
• 39:22
  Right?
• 39:22
  Right.
• 39:22
  It's a platform.
• 39:23
  And in that work, the last line is actually quite amazing.
• 39:27
  This is the last line of their paper in 2003.
• 39:29
  Additionally, we have shown that expression of channelrhodopsin 2 in oocytes or mammalian cells is...
• 39:35
  may be used as a powerful tool to increase cytoplasmic calcium 2 plus concentration or to depolarize the cell membrane simply by illumination.
• 39:45
  That's so crazy.
• 39:46
  Right?
• 39:46
  So you are literally mimicking synapses in some sense.
• 39:49
  Like when a bunch of neurons synapse onto a particular neuron, they release calcium 2 plus ions.
• 39:55
  And then those calcium 2 plus ions go inside or they release a bunch of neurotransmitters
• 40:00
  That induces the calcium 2 plus ions to go inside the cell, depolarize the cell, and create an action potential.
• 40:06
  Here, you're opening up the calcium 2 plus ions, the channel.
• 40:10
  And the calcium 2 plus ion goes in, depolarizes the cell membrane simply by illumination.
• 40:15
  We now have this understanding of this cellular channel of communication that we can then now use to go back to our two-way street.
• 40:25
  We could always read.
• 40:26
  Yeah.
• 40:27
  We were trying to figure out what language, how can we write with those three elements we talked about at the beginning.
• 40:32
  Yep.
• 40:33
  The time responsiveness, the experimental reproducibility, and there was a third one that's escaping me.
• 40:38
  Reversibility, time, and genetic specificity.
• 40:41
  Specificity.
• 40:42
  And we kind of have now hit.
• 40:44
  Yeah, we've hit them all.
• 40:44
  All three of those in that 2003 paper.
• 40:48
  Yes, exactly.
• 40:49
  Yeah.
• 40:49
  Yeah.
• 40:49
  And so now the next step is how do we go from a microbial ion channel to now optical control of neurons, right?
• 40:56
  You said that that could work.
• 40:57
  Right.
• 40:57
  But now we need to make this kind of a general purpose thing, right?
• 41:00
  How do we do it for like anything?
• 41:03
  It theoretically could work.
• 41:04
  Yes.
• 41:04
  That's what they said, 2003.
• 41:06
  Theoretically.
• 41:07
  So let's talk about neurons just in general real quick.
• 41:09
  Neurons are electrically excitable cells.
• 41:12
  Right.
• 41:14
  They work with ion channels that open and close and lead in sodium and potassium.
• 41:20
  Now, crucially, these things are voltage-gated ion channels.
• 41:23
  Here's what I mean by that.
• 41:25
  The protein in its native state is closed.
• 41:28
  And now let's say I get a bunch of calcium 2 plus coming in because of either channel rhodopsin or because synapses put in a bunch of neurotransmitters and then now that leads to a bunch of calcium 2 plus coming in.
• 41:40
  That's going to change the voltage across the cell, right?
• 41:42
  If you change the voltage across the cell, you've got an electrical force, an electrical field, and that literally changes the shape.
• 41:49
  So it's a voltage-gated ion channel because it's an ion channel that turns on or off depending on the voltage.
• 41:56
  And if I can control the voltage by...
• 41:58
  letting in calcium 2 plus ions, then I can control these voltage-gated ion channels, and that is going to create my action potential that goes from one place to the other.
• 42:07
  The key question now is whether the relatively small photocurrent that is produced by the channel rhodopsin is enough to trigger all of the rest.
• 42:17
  Because usually the synapses, they dump a bunch of neurotransmitters, a bunch of calcium 2 plus goes in, and then that causes the membrane potential to change that causes this cascade effect.
• 42:26
  Can channel rhodopsin do this?
• 42:27
  Right, across this.
• 42:29
  Yeah, yeah, yeah.
• 42:30
  Can it trigger the runaway cascade, right?
• 42:33
  You can imagine neurons are like in this unstable equilibria of like an inverted pendulum.
• 42:38
  Okay.
• 42:38
  And it needs a kind of kick to like go down and fire.
• 42:42
  Yep.
• 42:42
  Right?
• 42:42
  But is the channel rhodopsin kick enough?
• 42:45
  To have it.
• 42:45
  Right?
• 42:46
  Yep.
• 42:46
  That's the question.
• 42:47
  That's a great visual.
• 42:47
  So in 2005, Ed Boyden, Bang Zhang, George Nagel, and Ernst Bangberg, and Carl Dyseroth
• 42:56
  They publish one of the field's defining papers.
• 42:59
  This thing has like 5,000 citations by now.
• 43:02
  It's in Nature Neuroscience.
• 43:03
  Millisecond timescale, genetically targeted optical control of neural activity.
• 43:08
  And this is where we get all three.
• 43:10
  This is the translation now from the algae to now neurons.
• 43:15
  Yeah.
• 43:15
  This paper demonstrates that you can take channelrhodopsin.
• 43:18
  And you can express it in mammalian neurons and use that to trigger action potentials with millisecond timescale precision.
• 43:27
  So I want to talk about those two second authors.
• 43:30
  Okay.
• 43:30
  Carl Diceroth is on the left.
• 43:32
  He's the one who won the prize.
• 43:34
  Ed Boyden was a PhD student at the time.
• 43:37
  He kind of was at the start of this whole thing.
• 43:41
  He did a lot of the work, and he hasn't gotten the prize.
• 43:43
  He's at MIT right now.
• 43:44
  There's going to be a lot of drama out there about why he didn't get it.
• 43:48
  The third guy, Feng Zhang, this is the second time that he's... Because the first time we talked about was CRISPR.
• 43:56
  Oh, that's right.
• 43:56
  That's the same Feng Zhang.
• 43:59
  He's the same guy who used CRISPR to finally...
• 44:04
  to finally create a type of programmable genetic scissor in mammalian cells.
• 44:10
  Remember?
• 44:10
  We had the whole drama between Berkeley and MIT.
• 44:13
  Berkeley was Jennifer Doudna.
• 44:15
  This is the MIT guy who has the patent.
• 44:17
  I don't know.
• 44:19
  And this is the second time.
• 44:20
  That's tough.
• 44:21
  It's tough, bro.
• 44:22
  It's crazy that he's been at the forefront of both CRISPR and optogenetics.
• 44:27
  That's actually pretty crazy.
• 44:28
  Yeah, I mean, the guy's a beast.
• 44:29
  Yeah, that's, yeah, okay.
• 44:31
  So, but, you know, MIT Broad Institute just getting shafted.
• 44:36
  I plead the fifth.
• 44:37
  Yeah.
• 44:37
  You know who didn't get shafted last year?
• 44:39
  Princeton.
• 44:40
  Yeah, that's right.
• 44:42
  And perhaps coming this year, tomorrow or the day after.
• 44:45
  In any case, so here's what the experiment was.
• 44:47
  It was conceptually quite simple, but it's technically very, very important.
• 44:51
  This is a Nobel Prize committee project.
• 44:54
  reproduction of some of their work.
• 44:56
  They took hippocampal neurons in a Petri dish, so from the hippocampus, mammalian hippocampus, they expressed channelrhodopsin in those hippocampal neurons and showed that the hippocampus can actually affect the firing of the neurons.
• 45:13
  The light can actually affect the firing of the neurons.
• 45:16
  Also,
• 45:17
  With suitable illumination, the depolarization can be controlled like the amount of depolarization can be controlled, right?
• 45:25
  The intensity of the light will tell you sort of how much firing happens.
• 45:31
  You turn the volume dial to 11, the depolarization is at a discrete point that's similar to that volume 11.
• 45:38
  You turn it to 5, 5.
• 45:39
  It looks like...
• 45:40
  So you now have a granularity of control where the intensity of light actually drives the level... Like how the depolarization happens.
• 45:51
  Which then gives you more degrees of movement when you're trying to do this.
• 45:56
  Yeah, and you can get reliable spiking frequency of tens of hertz, which is on the order of what normal neurons would do in a brain.
• 46:05
  Now, this is extraordinary because, one, it shows the molecular machinery...
• 46:08
  that was discovered in the green algae.
• 46:10
  You can now express it inside mammalian neurons.
• 46:12
  Yeah.
• 46:14
  The protein is genetically encoded.
• 46:15
  So once you put the gene into the neuron, the cell can produce...
• 46:19
  the light-sensitive channel by itself.
• 46:21
  You don't need to keep pumping stuff in.
• 46:23
  Exactly.
• 46:23
  It'll just make the channel rhodopsin on its own.
• 46:26
  And the channel rhodopsin will go and be expressed on the membrane because the cell kind of knows what to do with it.
• 46:31
  This is quite nice.
• 46:32
  In some sense, right?
• 46:33
  And the other thing is the chromophore that's in the middle of this whole thing.
• 46:37
  The protein surrounds, it's a bunch of alpha helices that surround a chromophore that is the actual thing that absorbs the light.
• 46:44
  That chromophore, retinol, is naturally found.
• 46:49
  in mammalian tissue at exactly the right concentration.
• 46:53
  So you don't need to keep pumping this cofactor into it, right?
• 46:56
  Like we talked about earlier.
• 46:57
  Uh-huh.
• 46:59
  There's an abundance of the source material.
• 47:01
  Exactly.
• 47:01
  So all you have to do is somehow figure out how to encode the gene and express that gene.
• 47:06
  That's all you got to do.
• 47:07
  Okay.
• 47:07
  Right?
• 47:08
  That's a big deal.
• 47:09
  The other thing, that third thing,
• 47:10
  Is it reversible?
• 47:11
  Yes.
• 47:12
  The stimulation is reversible.
• 47:14
  So on the left-hand side, we're seeing the thing turn on for a particular amount of time in the blue.
• 47:19
  Boom, boom, boom, boom, boom.
• 47:20
  It fires.
• 47:20
  You turn it off.
• 47:21
  It goes back.
• 47:22
  It doesn't fire.
• 47:23
  On the right-hand side, we actually see that other neurons...
• 47:27
  that are not related to the specific one, other neurons can actually stop firing because this particular neuron that started firing is maybe inhibiting that one.
• 47:36
  Yep, yep.
• 47:37
  Right?
• 47:38
  So you can have second order effects and actually start looking at how do networks behave.
• 47:42
  This is, yeah, now we can, yeah, exactly.
• 47:44
  Right?
• 47:44
  You can be like, well, this neuron was definitely affected by this one, but I never expressed channel rhodopsin in this one.
• 47:50
  So it's got to be tied together as part of the same network.
• 47:53
  Right.
• 47:54
  And that same year.
• 47:55
  So this was in 2005.
• 47:58
  That very same year.
• 48:00
  Paper after paper about channel rhodopsin.
• 48:03
  Dicerats was the first one.
• 48:05
  But that same year, we've got independent studies published within a year that confirms the efficacy of channelrhodopsin in controlling the activity of hippocampal neurons.
• 48:15
  That's Lee et al.
• 48:16
  in 2005.
• 48:17
  Lee is in Stefan Herlitz's lab at Case Western.
• 48:21
  We also have motor activity in the embryonic chick spinal cord, also by Lee.
• 48:27
  Behavior in C. elegans, which is huge.
• 48:30
  see elegans as the model organism for so much developmental studies and reinforcement learning in drosophila by shroll so we're seeing this being applied to different types of organisms living organisms right it's happening this is and this is what we talk about when you say you know
• 48:50
  usually get a Nobel.
• 48:52
  You want to see all the labs have all the universities have a lab that is doing work in this area.
• 49:00
  You're seeing multiple unlocks from some fundamental discovery point.
• 49:07
  And then usually you'll then want to see how it then has positive impact at some endpoint.
• 49:13
  But at first it has to be something that starts to spread and becomes a fundamental basis for some area of study.
• 49:19
  Yeah, yeah.
• 49:20
  And Dysorot's lab really took that to the max because he's the first guy who had – he had Ed Boyden.
• 49:26
  He had Feng Zhang.
• 49:28
  He kind of got lucky in being at Stanford and having these amazing scientists who were postdocs and grad students at the time.
• 49:35
  Mm-hmm.
• 49:36
  So now the term optogenetics captures the central idea of this technique, right?
• 49:40
  You're combining genetic targeting with optimal control, with optical control, right?
• 49:45
  Genetics provides that specificity.
• 49:48
  Which is such a not like as for me, it just seems so unintuitive or non-intuitive that you would be able to have genetic control.
• 49:57
  Yeah.
• 49:57
  Yeah.
• 49:57
  Yeah.
• 49:58
  Yeah.
• 50:13
  what's happening at the micro level of biology to the end behavior human behavior that we well we haven't gotten to behavior but like that's like the idea like that's where we're going we're going it's so i'm like it's already so crazy yeah it's already so crazy and we haven't even gotten to the second piece yeah yeah
• 50:31
  The second piece is now we want to make this thing better, both in terms of genetic specificity and the general use case, and in terms of we want to make the channelrhodopsin itself better.
• 50:40
  Okay?
• 50:41
  Okay.
• 50:41
  So the first piece is how do we express this in living organisms at a general level?
• 50:47
  Like suppose I want to target this particular neuron, right?
• 50:50
  Right.
• 50:51
  How do I do that?
• 50:51
  Well, I can set up a viral vector or I can create transgenic animals.
• 50:56
  And that's what these papers did.
• 50:58
  This was in 2006 by B. et al., Ishizuka, and Zhang.
• 51:02
  These are the three papers that I highlighted.
• 51:04
  Specifically what you do is you create a viral vector, a kind of genetic element that has a part that recognizes a specific type of neuron and another part that has the genetics for channel rhodopsin.
• 51:18
  This thing goes into the specific neuron that it recognizes.
• 51:22
  And then that specific neuron, because it has the other part, the channel rhodopsin part, it's going to express channel rhodopsin.
• 51:28
  And now I can very specifically target those neurons.
• 51:32
  The beauty of this approach is that the light itself is not specific, right?
• 51:36
  I'm lighting up the entire tissue.
• 51:39
  But only the neurons that I targeted genetically are the ones that have the ability to actually respond to the light.
• 51:46
  It's the delivery mechanism that allows a singular outside light source, but then targeted internal activation.
• 51:53
  Yeah.
• 51:54
  And that becomes super important in some of the later studies that we're going to see.
• 51:57
  Now, the next thing we want to do is also make channelrhodopsin a bit more general.
• 52:03
  Because right now, it's cation-specific, right?
• 52:05
  It's positive ions.
• 52:06
  Positive ions mean that the neuron is going to fire because it's going to...
• 52:12
  create the correct type of voltage to make that runaway effect.
• 52:15
  What if I want to silence the neurons?
• 52:17
  Then I need cholinergic activation, meaning like chlorine ions, negative ions, need to go through so that the voltage becomes opposite and it shuts off the neuron.
• 52:26
  So Feng Zhang comes out with this paper, Multimodal Fast Optical Interrogation of Neural Circuitry, again with Carl Diceroth in 2007.
• 52:37
  And this is where he introduces the chlorine version
• 52:41
  They were going off in the mid-2000s.
• 52:42
  They were going off, dude.
• 52:43
  Dice Rod's lab.
• 52:44
  They were going off.
• 52:45
  Yeah, this is two years later.
• 52:46
  Yeah, that's the reason I feel like 2007.
• 52:49
  Yeah, which means that they had already, like by the time that they had published 2005, they were already in on this.
• 52:55
  Right, right.
• 52:55
  And they're like, this is clearly what's next.
• 52:57
  And over there you can see the cation version.
• 53:01
  You turn it on, boom, lots of spiking.
• 53:04
  The chlorine version, there's a lot of spiking.
• 53:06
  You turn it on, spiking stops.
• 53:08
  So good.
• 53:09
  Right?
• 53:10
  Amazing.
• 53:11
  And so that was halorhodopsin, which is a microbial light-driven chlorine pump that they've now used for this exact purpose.
• 53:22
  Right?
• 53:22
  And now the last thing that Dicerat's lab did that – I mean it's not the last thing, but the last thing in terms of increasing the efficacy of this stuff is they developed cheetah.
• 53:32
  That's what we're seeing over here.
• 53:34
  Ultra fast cheetah.
• 53:37
  optogenetic control in 2010.
• 53:39
  On the left-hand side, what you're seeing is normal channel rhodopsin.
• 53:43
  Okay?
• 53:44
  You see the little blue ticks?
• 53:46
  Yep.
• 53:46
  That shows when channel rhodopsin went on.
• 53:51
  And you can see that, you know, there's a bunch of spikes, but there's extra spikes.
• 53:55
  You don't just get one spike.
• 53:56
  You get extra spikes, and then there's kind of a trailing off of the membrane potential because, like, the thing depolarizes, but then it kind of just stays because maybe the channel rhodopsin itself...
• 54:07
  has a kind of relaxation time, right?
• 54:09
  That it opens really quickly, so that's good.
• 54:12
  But I want to be very, very precise.
• 54:14
  And so this is where we're seeing that kind of like that little hill here on the left in the top charts.
• 54:21
  But we basically effectively only want to have a spike with as minimal amount of drop-off as possible.
• 54:25
  I don't want a prolonged depolarization.
• 54:27
  I want a boom, and then I want you to turn off.
• 54:30
  And that's cheetah on the right-hand side.
• 54:32
  You see?
• 54:32
  Same timescale, but now I'm affecting single spikes.
• 54:36
  Isn't that insane?
• 54:38
  Yeah, with no prolonged depolarization.
• 54:40
  Yeah, and it's also, this is all just...
• 54:43
  Right?
• 54:44
  It's amazing.
• 54:45
  And then on the lower, you can see that, like, sometimes the cell gets, the channel rhodopsin gets tired.
• 54:51
  Yeah.
• 54:51
  And you get mist spikes.
• 54:52
  Because it's been firing for so much, sometimes it gets tired, the cell gets tired.
• 54:56
  With cheetah, because it's so precise...
• 54:59
  You can just go.
• 55:01
  It's like a metronome.
• 55:02
  You don't miss any spikes, right?
• 55:05
  This is the kind of stuff that is required to really now dig in to how neurons are working.
• 55:11
  These faster variants allow more precise control of high-frequency neurons if we want to do that.
• 55:17
  It's made possible the production of sustained changes in neural activity.
• 55:22
  It's huge, right?
• 55:23
  So the evolution of channelrhodopsin, there's a larger point about the nature of technological breakthroughs in biology.
• 55:30
  You start with something and you just keep getting better and better because you start seeing the efficacy of the tool.
• 55:37
  And you start honing in on exactly what you want for each use case.
• 55:41
  And it is a tool.
• 55:43
  And then different people are going to use that tool for different purposes.
• 55:46
  And then ultimately, we get this better understanding because like everything, like humanity in general, we've just continued to make better tools which have allowed us to then have better outcomes because we better understand the world around us at an increasingly large scale and increasingly small scale.
• 56:07
  Yeah.
• 56:08
  Absolutely incredible.
• 56:10
  This is good.
• 56:11
  Yeah.
• 56:12
  We've gotten to single neurons.
• 56:13
  Yes.
• 56:13
  Now let's do circuits.
• 56:14
  Yes.
• 56:15
  Okay.
• 56:15
  So this is what makes optogenetics particularly powerful for dissecting human circuits, for dissecting neural circuits, I should say.
• 56:24
  So here in this paper, this is in 2007, what they're showing is...
• 56:32
  an optical neural interface.
• 56:35
  Here they're expressing it in a living mouse.
• 56:39
  They've used that viral vector type thing to target the part of the cortex that controls the whiskers, the mouse.
• 56:50
  We're trying to go from now neural circuits to behavior.
• 56:55
  This is the link.
• 56:56
  This is the paper that links the two.
• 56:58
  The idea is if I target the rodent...
• 57:03
  motor system that is involved with the whisker deflection, can I make the whiskers deflect?
• 57:07
  The whiskers are how a lot of rodents sense their environment.
• 57:10
  If I turn these neurons on, can the whiskers deflect?
• 57:13
  That's a behavior response.
• 57:14
  This is the first time that this happens, okay?
• 57:17
  In 2007.
• 57:18
  And now at last, we finally have Francis Crick's theoretical vision.
• 57:23
  Yeah.
• 57:24
  Scientists can now control specific genetically ordained neurons in an alert animal.
• 57:33
  using only light right alert awake behaving animal using light i i plead the fifth but and we've talked about this so many times and every time we talk about it i have the same reaction because i we're just so we're so brilliant and i think we're able to do things that part of the benefit of talking through this amount of detail is again going to the specificity of well how is it that you know what's going on in the brain like how like you know or like
• 58:03
  science is not a black box.
• 58:04
  Yeah.
• 58:05
  Right?
• 58:06
  And you can literally trace the lineage of this.
• 58:08
  Yeah.
• 58:09
  And it kind of starts with Crick's kind of far-fetched.
• 58:13
  Yeah.
• 58:14
  He said it's far-fetched.
• 58:15
  At the time.
• 58:15
  Yeah.
• 58:17
  But I know we still have a little bit more to go, but I just, this is so, the behavior piece is so interesting because it's a little bit,
• 58:24
  It's like different than the biological piece in terms of there is a huge leap here to go to behavior and ascribing the behavior to a very particular underlying change.
• 58:38
  Yeah, it's causality.
• 58:39
  Right.
• 58:39
  It's that causality point.
• 58:41
  And you want to be very sure when you're saying that's what's happening.
• 58:46
  And this tool lets you say that.
• 58:48
  Be very sure.
• 58:49
  It offers a way to selectively manipulate specific populations.
• 58:52
  So I'm going to just real quick go through some of the work that has come about in optogenetics.
• 58:58
  There's obviously a huge repertoire of work that we cannot get to.
• 59:02
  Perhaps another episode that is based on what optogenetics has given us.
• 59:07
  But a few that I'll go into.
• 59:09
  First one, Ilana Witten.
• 59:12
  Carl Diceroth and colleagues.
• 59:14
  Ilana Witten is Ed Witten's daughter, who's now at Princeton University.
• 59:19
  She's a professor there.
• 59:21
  In this particular paper, she used optogenetic approaches to investigate reward-related circuitry and dissected the role of cholinergic neurons that make up only 1% of the local neurons.
• 59:34
  Turns out they play a very specific and significant role in modulating driving behavior and
• 59:41
  like addictive behavior, like in this particular case, cocaine conditioning.
• 59:46
  Okay.
• 59:46
  Right?
• 59:47
  So we can now start targeting specific neurons and be like, yeah, this is actually involved in the kind of addiction and things like that, right?
• 59:56
  You can also do optical manipulation of hippocampal circuits for learning and memory.
• 1:00:03
  This one's pretty crazy.
• 1:00:04
  One profound application is the search for the memory engram, which is the physical cellular substrate for encoding a specific memory.
• 1:00:12
  Like, I've got a specific memory.
• 1:00:14
  That lives somewhere in the brain, right?
• 1:00:16
  Very famously, Tsusumu Tonegawa...
• 1:00:18
  who is a Nobel Prize winner in his own right in 1987 for the discovery of genetic principles underlying antibody diversity.
• 1:00:27
  He's seeking his second Nobel by searching for the memory engram.
• 1:00:31
  Good on you.
• 1:00:32
  Good on you.
• 1:00:32
  Yep.
• 1:00:33
  He's trying.
• 1:00:34
  What he wants to find is the cellular basis for like stored memories.
• 1:00:38
  Yeah.
• 1:00:38
  Okay.
• 1:00:39
  In 2012, he published a paper with Liu and colleagues.
• 1:00:43
  It's a landmark paper in nature that,
• 1:00:46
  demonstrates optogenetic activation of a neural ensemble.
• 1:00:51
  It's associated with a particular experience that triggers the expression of fear.
• 1:00:56
  Okay?
• 1:00:57
  This is pretty crazy.
• 1:00:58
  This is pretty crazy.
• 1:00:59
  It's a striking demonstration of the possibility of, like, manipulating memory-related neural representations.
• 1:01:06
  Obviously, a lot of weird implications.
• 1:01:09
  Also, a lot of clinical implications.
• 1:01:11
  For example, with PTSD, things like that, right?
• 1:01:15
  So...
• 1:01:15
  It is worth being precise about what these experiments establish, though.
• 1:01:18
  Like, you're activating a neural ensemble.
• 1:01:21
  You've targeted a bunch of neurons, and you've activated them using this channelrhodopsin trick.
• 1:01:26
  Now, that does not mean that a complete, fully formed memory is literally stored in that population, okay?
• 1:01:32
  Because memory is a weird thing.
• 1:01:33
  It depends on distributed networks, specific synaptic modifications, other brain networks that you might not have targeted.
• 1:01:40
  What this optogenetics tool is making possible is a causal test that, yeah, this particular set of neurons elicits a behavioral response.
• 1:01:49
  Okay.
• 1:01:50
  So I do want to make that distinction.
• 1:01:51
  That's actually an important one.
• 1:01:52
  Yeah.
• 1:01:53
  Yeah.
• 1:01:53
  And then finally, the last one I'll talk about is a causal link.
• 1:01:57
  between manipulating dopaminergic activity and prediction and reward and the error from that prediction.
• 1:02:07
  Like I predict something.
• 1:02:08
  If I get it, that's good.
• 1:02:09
  If I don't get it, that's bad.
• 1:02:10
  The brain fundamentally is a kind of predicting machine, right?
• 1:02:14
  Oh, are you saying we're just the next token prediction machine?
• 1:02:17
  Yeah, exactly.
• 1:02:18
  Well, this particular paper kind of says that.
• 1:02:27
  corroborated by some of these optogenetic experiments, where what you can do is specifically target these dopaminergic neurons, make them fire when errors happen or when the prediction comes true, and see how the circuit adapts, right?
• 1:02:44
  So you can literally now causally test this model of prediction and error.
• 1:02:49
  in this neural circuit.
• 1:02:50
  That's very nice.
• 1:02:51
  You can study much more complicated behavior.
• 1:02:53
  Diceroth, for example, has gone even beyond neuroscience.
• 1:02:57
  Okay.
• 1:02:58
  Okay?
• 1:02:58
  Diceroth has demonstrated that the heart rhythm can affect our emotions because he's developed another type of extra-sensitive opsin called kermine.
• 1:03:09
  I think it's channelrhodopsin-mine.
• 1:03:11
  And that thing is so sensitive that you can put it deep within the body and it'll still respond to the light.
• 1:03:18
  So you can stick it in the heart, in the neural circuitry in the heart that controls heart rhythm, and now you can shine light on it through the muscles and everything.
• 1:03:27
  Oh, that's really, yeah.
• 1:03:28
  And then the heart is going to now start beating faster, and that has an effect on the brain in terms of anxiety.
• 1:03:33
  Mm-hmm.
• 1:03:34
  It used to be thought that, no, the brain is doing the anxiety part down.
• 1:03:39
  But actually, there's something going up as well.
• 1:03:41
  It's a two-way street.
• 1:03:43
  It's starting to sound like that street in Hollywood where it's like six.
• 1:03:48
  It's a circular six entryway.
• 1:03:50
  Oh, yeah, yeah.
• 1:03:52
  In Beverly Hills.
• 1:03:52
  In Beverly Hills.
• 1:03:53
  Yeah, yeah.
• 1:03:53
  In Beverly Hills.
• 1:03:54
  That was the worst stop sign in LA.
• 1:03:56
  Yeah.
• 1:03:56
  Yeah.
• 1:03:58
  Exactly.
• 1:03:58
  I mean, the body is a complicated thing, right?
• 1:04:00
  100%.
• 1:04:02
  Optogenetics is most closely associated with neuroscience, but it's got a much broader significance.
• 1:04:07
  As we just showed, it can be used to talk about the heart.
• 1:04:11
  Light-sensitive proteins can be engineered to regulate intracellular signaling pathways inside the cell.
• 1:04:18
  They can be used to control light-sensitive systems like Roe family GTP aces, which are another set of
• 1:04:27
  cytoskeletal proteins that like can make the cell move and contort in development biology light sensitive tools can be used to investigate how you know spatially localized tissue becomes what they are as the animal grows up right you've got a ball of tissue how does this know to become the head and this become the tail we talk about all the time we talk about that all the time that's a that's a problem that can be used
• 1:04:50
  that optogenetics can be used for.
• 1:04:52
  So it's, it's huge, right?
• 1:04:55
  And finally, um, there is hope for new medical treatments.
• 1:04:58
  I mean, beyond just the fundamentals of understanding how the brain works, right?
• 1:05:03
  That is going to clearly give us a better understanding about depression, depression,
• 1:05:07
  anxiety, PTSD, Alzheimer's, dementia, Parkinson's, you name it, right?
• 1:05:12
  At the end of the day, understanding the brain at a fundamental level is going to give us insights into all of these ailments, right?
• 1:05:21
  Because fundamental science, I think, happens first.
• 1:05:23
  But in a very direct sense, it's also...
• 1:05:27
  used as the first step towards an optogenetics treatment for blindness.
• 1:05:34
  So there's ongoing clinical trials that are happening to restore vision in people who have become blind due to retinitis pigmentosa.
• 1:05:43
  It's a disease that destroys the rods and cones.
• 1:05:46
  Here, they've inserted channelrhodopsin-like proteins into the eye of a blind person,
• 1:05:53
  Those neurons then express channel rhodopsin.
• 1:05:57
  And now the person has regained some kind of vision.
• 1:06:00
  This is a paper that shows like special glasses that emit light into the person's eye.
• 1:06:05
  And then that person is able to discern and grasp objects.
• 1:06:09
  Someone who was, you know, previously blind.
• 1:06:13
  I mean, again, both things are now concurrently happening, which is the expansion of the basic and fundamental research across all of the areas of the human body, not just the brain.
• 1:06:25
  But we are in the clinical trial phase for applied clinical or medical outcomes that are impacting people's real lived experience.
• 1:06:38
  Yeah.
• 1:06:39
  uh and like obviously we're human beings our eyesight is so such a core part of what how we experience the world exactly if you have a very like effective way to restore that for people um what's not to love what's not to love right and when we predicted when we predicted optogenetics there were a lot of comments that were saying that this is going to clearly lead to zombies and mind control now i'd like to
• 1:07:06
  Brace that type of speculation.
• 1:07:09
  I don't think optogenetics is going to lead to zombies because I don't think, you know, we're going to get viral vectors in our brain anytime soon.
• 1:07:16
  On the other hand, the more we understand about the brain...
• 1:07:20
  the more nefarious applications can become.
• 1:07:23
  So, you know, there is some concern here that, like the memory engrams, right?
• 1:07:28
  Yeah.
• 1:07:28
  Clearly, of course, it's going to be used for PTSD and clinical approaches to understanding trauma and maybe eliminating trauma, past trauma of an individual.
• 1:07:39
  That would be the dream.
• 1:07:40
  But at the same time, you know, with the advent of tools like Neuralink and AI,
• 1:07:48
  compounding on tools like Neuralink, you can imagine a world where things get very scary.
• 1:07:54
  Right?
• 1:07:55
  We've discussed on this podcast several brain-machine interfaces, like the reading inner thoughts.
• 1:08:01
  Yep.
• 1:08:02
  That was a good one.
• 1:08:04
  Yeah.
• 1:08:04
  So, you know, putting this in the context of all of the neuroscience that we've looked at, it's both...
• 1:08:11
  something that gives us hope because optogenetics is an incredible tool that is going to let us understand very nitty-gritty causal links between brain neural circuits and behavior and perhaps even maybe consciousness.
• 1:08:25
  That was another question that was asked during the Nobel Press Conference.
• 1:08:28
  And the person said, well, not yet, but maybe in the future, maybe in the future, right?
• 1:08:32
  We'll get some idea.
• 1:08:33
  But at the same time, you know, we have to be vigilant of
• 1:08:38
  all of the other weird things that could happen because we learn more and more about the brain because the brain is really the organ that makes us us.
• 1:08:45
  Yeah.
• 1:08:45
  Right.
• 1:08:45
  And if we lose control of that, if we lose sovereignty over the brain, who are we in some sense?
• 1:08:52
  Right.
• 1:08:52
  Right.
• 1:08:53
  So, um, I, I mean, I've been calling optogenetics for a while, even before this podcast.
• 1:09:00
  So the first year that we did this podcast, I said optogenetics, I recycled this year and I got it right.
• 1:09:05
  Right.
• 1:09:05
  And so anyone who said, oh, but you recycled, it's because it was correct.
• 1:09:10
  Sometimes you got to recycle.
• 1:09:14
  We have a lot of great previous episodes as well that touch on a variety of different areas in neuroscience.
• 1:09:21
  And again, it's interesting how these things all connect.
• 1:09:23
  The myelin sheath stuff we talked about, about...
• 1:09:26
  how it then create the degradation of that, and then the repair in order to have these action potentials and these ion channels continue to be able to operate.
• 1:09:33
  We did whole deep episodes on that.
• 1:09:35
  I think the Nobel Prize medicine deep dive last year and this year are two of my favorites because I think it's –
• 1:09:44
  Sometimes for other subjects, it's hard for people to get how it relates to themselves.
• 1:09:49
  And this one can feel very personal.
• 1:09:52
  So we are going to keep this tight today.
• 1:09:54
  We will be back again tomorrow early morning for one of the favorites.
• 1:10:01
  which is going to be the physics Nobel.
• 1:10:04
  For those of you who are joining us for the first time, welcome to the best science show on the planet.
• 1:10:10
  You can follow us on all of our socials at FFPpod, as well as check out our episodes and all of the research papers.
• 1:10:17
  that we covered in this episode.
• 1:10:18
  If you want to actually take a look at them, you can go to ffppod.com.
• 1:10:22
  Each one of our episodes always catalogs all of the research papers we cover.
• 1:10:27
  And if you would like to watch, we're available on YouTube, all the podcast networks to listen as well.
• 1:10:33
  We appreciate you as always.
• 1:10:35
  My favorite week of the year.
• 1:10:37
  How are you feeling about already being on the board after day one?
• 1:10:41
  I'm so happy because now there's no pressure for like physics and chem, you know?
• 1:10:46
  Yeah.
• 1:10:47
  Already done it.
• 1:10:48
  Already did it, yeah.
• 1:10:48
  Already done it.
• 1:10:50
  Maybe we'll get Princeton on the board tomorrow.
• 1:10:52
  We'll see.
• 1:10:53
  My name is Asunari, joined as always by my co-host and our resident PhD and the Oracle, Krishna, the Oracle, Chowdhury.
• 1:11:02
  We will see you all tomorrow for the Physics Nobel Prize.