For some reason, what actually stands out to me in this paper is the method in which they verified the rotational motion. They used single-molecule fluorescence and optically tracked the circular trajectories that the single molecule traced out while spinning. That's the most impressive part, in my opinion... I didn't know we could even resolve fluorescing particles on that scale, much less track their trajectories over time.
Single molecule fluroescence has been around for a while; when I was in grad school, students in another lab were doing this. They'd label motor proteins (which use energy to move in a specific direction) to visualize them on a surface and calculate their velocity.
The particle can be much smaller than the resolution, as long as it's really bright, it will just sort of "smear out" over multiple adjacent pixels and it's possible, with some arcane trickery, to then localize to a sub-pixel.
See the section titled "Fluorescence microscopy data analysis". Basically, when you have a single molecule fluorescing you "just" need to do some math to figure out the center of the samples over time. See https://www.microscope.healthcare.nikon.com/products/super-r... for an overview
I am not exactly an expert, I just happened to do a deep dive into microscopy techniques a couple years ago :-)
The general term for these types of techniques (e.g., ones that let you image things below the "diffraction limit", which is roughly half the wavelength of light being used to image, see [1]), is super resolution microscopy[2]. There are a few other types you might find interesting.
based on your understanding, do you think it's possible to do live imaging of RNA molecules < 200 nucleotides -- without altering biological processes?
super resolution microscopy references DNA imaging but doesn't delve into contamination risk, which is the critical bit.
the first link didn't mention DNA/RNA applications at all.
do you mind sharing the other types you recommend investigating?
this paper came out a few years ago using super resolution fluorescence and dna origami to track unwinding of dna by single helicase enzymes! its not an easy technique but it is doable with the right equipment (the 2014 Nobel Prize in chemistry was for super resolution microscopy)
That's absolutely fascinating! The use of single-molecule fluorescence to verify rotational motion is indeed impressive. I'm curious, how do you think this groundbreaking technique could potentially impact future research in the field?
Here's something I've been wondering that maybe some smart hackernews-ian can explain to me. I watched a spider build a web last night. A ton of extremely complicated engineering went into it, all driven by pure instinct, presumably coded in DNA somewhere.
If you gave me enough time I could lay out how a semi-conducting material turns into a T-gate transistor, which can be chained with other transistors to create AND, OR, NOT and NOR gates, which are executed in the CPU by machine language code, which is created by compiling down from higher-level C code, which is executed by Javascript code running in the browser, which uses React to power the Facebook front end. Maybe I missed a step in there, but you get the idea.
Is that how DNA tells a spider how to build a web? Or tells me I should drink water when I feel thirsty? Does it all boil down to ones and zeros stored in protein sequences? And if so, are there layers of abstraction like in computer code? Or is there some other fundamental mechanism?
Also interested in the answer, and I thought a bit about this and have a hypothesis that DNA does not encode everything, instead it depends on implicit assumptions about the environment.
To give an example, gravity is likely not encoded in the DNA, but instead, there are many encoded behaviors that would make sense only on an environment where gravity is present. The same for the presence of predators, wind, solar radiation, etc., i.e. many of the things that we take for granted.
That's how you would get more than 750MB of behavior on 750MB of DNA data [1]
Interesting! I like that thought. I enjoy thinking about how surprisingly good we are at predicting trajectories, good enough that we can throw, catch, and dodge small objects with remarkable accuracy. I wonder if our internal calculations of acceleration due to gravity is purely learned, or future space-faring infants will be surprised by how their block castles react when they knock them over.
biology and physics operate at more dimensions where physical forces conspire to be leveraged by some neat tricks. Chemical gradients, various pressures, mechanical forces, electrostatic gradients, all sorts of differences out there to ride--some are passive, some are actively produced and take energy.
Here is not a very good answer to your question, but think about how you know how to walk. It's something that is not entirely conscious. You legs just move between places that you desire to go without any thought to the angle of your knee or tension in your gluts. You feel unbalanced when you center of gravity is too far forward despite having no conscious understanding of where exactly your center of gravity is in the first place.
Spiders likely have a similar feeling but even further refined. They don't know how to build a truss, but they do end up building analogous structures because in their tiny bug brains it just feels right. This is the same way a human knows they are more stable with their legs spread apart and knees slightly bent. Part of that certainly comes from learning but a lot of it is built into our biology too.
Also by falling a lot until your brain figures out how to handle the various sensor input to get it right - can't remember but our kid took a month to get it right waling short distances after figuring out how to stand upright with support of the furniture.
A newly born antelope calf has 3 minutes to figure this all out before it becomes prey.
You are looking to emergent properties of biological systems from a reductionist philosophical perspective. It's pretty common to have that view. I believe the nature-nurture debate of the 80s (iirc) will be of your interest.
Honestly, I don’t think we have metaphors or figurative language that really captures how biological systems work.
That said, I think the most faithful description is something like a dynamical system with incredibly structured and robust emergent behavior. This means that the underlying rules of biology are simple, just physics, but life/biology maintains very complex states. So there’s this bug difference between how much information it takes to describe a biological system’s state and how much information it takes to describe the underlying rules. Like to describe myself, I would have to know the state of quadrillions of proteins and molecules across about 30 trillion cells, down to atomic precision. To describe how that state changes, I only need to know a few physics equations.
For most software the situation is more or less reversed. To describe a program, there’s a few state variables, and a ton of rules describing how the program manipulates those variables.
The consequence of this for biology is that there’s no true abstraction between the spatial or time scales. Like, a few atoms in the wrong place can often lead to effects across the whole organism. Think genetic mutations leading to cancer: a single DNA base change drastically changes the whole organism. But, the arrow can also point the other way. Large scale changes like deciding to smoke or take chemotherapy causes molecular changes that then filter up to whole organism changes.
Like I said, it’s hard to describe because some of these properties are also true for human engineered systems so it’s tempting to use our existing engineering abstractions to describe life. But, they’re woefully insufficient for capturing some of the most important characteristics of life. My opinion on the matter is that we just don’t have good ways of deciding how life works because we don’t really understand it; there are fundamental laws at work that we haven’t captured mathematically or even conceptually.
I'm not a programmer but can any one tell me, like how does a computer know stuff? Is it like how I know stuff, I have to read it, do computer read like that?
(warning: ignorant curiosity from someone with no chemical engineering background)
This is a nice design: the chiral DNA turbine follows a leading leash into the nanopore, and the trailing cap keeps it from flowing through. The ion/electrical differential drives the turbine blades to turn (presumably the cap/leash assymmetry requires unidirectional flow).
What's unclear is how this can be converted to work in the presence of ionic flow.
It's unclear if the cap or leash could be repurposed; they're presumably spinning along with the turbine. That leaves the chiral arms, passing by the nanopore walls. I could see some charge-dependent reaction resulting from passing a charged arm-tip proximal to the nanopore-wall (enzyme), but by hypothesis we're in a charged flow so that seems like a no-go.
The converse question is whether a charged DNA arms could be induced to spin by the nanopore; that could actually drive flow (albeit likely not against any ionic or osmotic current, so it would be hard to see how it could be useful).
On the other hand, something like this might be useful not as a motor but as a discrete gating factor, e.g., to serialize flow of other molecules through a nanopore. E.g., to improve quality in the current nanopore DNA sequencing, a slow rotation time (5/s here) could enable an upstream nanopore to select one segment of DNA to be read by a downstream nanopore without the interaction of the to-be-processed segment tail with other DNA molecules near the nanopore entry. (but that might already be a solved problem for all I know.)
