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New Microscope Technique Provides Real-Time 3D Views

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Most of the “cool” videos shared on my blog are borne of countless hours behind a microscope. Researchers must move a biological sample through a microscope’s focus, slowly acquiring hundreds of high-res 2D snapshots, one painstaking snap at a time. Afterwards, sophisticated computer software takes this ordered “stack” of images, calculates how the object would look from different perspectives, and later displays them as 3D views of life that can be streamed as short videos.

But this video is different. It was created by what’s called a multi-angle projection imaging system. This new optical device requires just a few camera snapshots and two mirrors to image a biological sample from multiple angles at once. Because the device eliminates the time-consuming process of acquiring individual image slices, it’s up to 100 times faster than current technologies and doesn’t require computer software to construct the movie. The kicker is that the video can be displayed in real time, which isn’t possible with existing image-stacking methods.

The video here shows two human melanoma cells, rotating several times between overhead and side views. You can see large amounts of the protein PI3K (brighter orange hues indicate higher concentrations), which helps some cancer cells divide and move around. Near the cell’s perimeter are small, dynamic surface protrusions. PI3K in these “blebs” is thought to help tumor cells navigate and survive in foreign tissues as the tumor spreads to other organs, a process known as metastasis.

The new multi-angle projection imaging system optical device was described in a paper published recently in the journal Nature Methods [1]. It was created by Reto Fiolka and Kevin Dean at the University of Texas Southwestern Medical Center, Dallas.

Like most technology, this device is complicated. Rather than the microscope and camera doing all the work, as is customary, two mirrors within the microscope play a starring role. During a camera exposure, these mirrors rotate ever so slightly and warp the acquired image in such a way that successive, unique perspectives of the sample magically come into view. By changing the amount of warp, the sample appears to rotate in real-time. As such, each view shown in the video requires only one camera snapshot, instead of acquiring hundreds of slices in a conventional scheme.

The concept traces to computer science and an algorithm called the shear warp transform method. It’s used to observe 3D objects from different perspectives on a 2D computer monitor. Fiolka, Dean, and team found they could implement a similar algorithm optically for use with a microscope. What’s more, their multi-angle projection imaging system is easy-to-use, inexpensive, and can be converted for use on any camera-based microscope.

The researchers have used the device to view samples spanning a range of sizes: from mitochondria and other tiny organelles inside cells to the beating heart of a young zebrafish. And, as the video shows, it has been applied to study cancer and other human diseases.

In a neat, but also scientifically valuable twist, the new optical method can generate a virtual reality view of a sample. Any microscope user wearing the appropriately colored 3D glasses immediately sees the objects.

While virtual reality viewing of cellular life might sound like a gimmick, Fiolka and Dean believe that it will help researchers use their current microscopes to see any sample in 3D—offering the chance to find rare and potentially important biological events much faster than is possible with even the most advanced microscopes today.

Fiolka, Dean, and team are still just getting started. Because the method analyzes tissue very quickly within a single image frame, they say it will enable scientists to observe the fastest events in biology, such as the movement of calcium throughout a neuron—or even a whole bundle of neurons at once. For neuroscientists trying to understand the brain, that’s a movie they will really want to see.

Reference:

[1] Real-time multi-angle projection imaging of biological dynamics. Chang BJ, Manton JD, Sapoznik E, Pohlkamp T, Terrones TS, Welf ES, Murali VS, Roudot P, Hake K, Whitehead L, York AG, Dean KM, Fiolka R. Nat Methods. 2021 Jul;18(7):829-834.

Links:

Metastatic Cancer: When Cancer Spreads (National Cancer Institute)

Fiolka Lab (University of Texas Southwestern Medical Center, Dallas)

Dean Lab (University of Texas Southwestern)

Microscopy Innovation Lab (University of Texas Southwestern)

NIH Support: National Cancer Institute; National Institute of General Medical Sciences


Artificial Intelligence Accurately Predicts RNA Structures, Too

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A mechanical claw grabs molecular models
Credit: Camille L.L. Townshend

Researchers recently showed that a computer could “learn” from many examples of protein folding to predict the 3D structure of proteins with great speed and precision. Now a recent study in the journal Science shows that a computer also can predict the 3D shapes of RNA molecules [1]. This includes the mRNA that codes for proteins and the non-coding RNA that performs a range of cellular functions.

This work marks an important basic science advance. RNA therapeutics—from COVID-19 vaccines to cancer drugs—have already benefited millions of people and will help many more in the future. Now, the ability to predict RNA shapes quickly and accurately on a computer will help to accelerate understanding these critical molecules and expand their healthcare uses.

Like proteins, the shapes of single-stranded RNA molecules are important for their ability to function properly inside cells. Yet far less is known about these RNA structures and the rules that determine their precise shapes. The RNA elements (bases) can form internal hydrogen-bonded pairs, but the number of possible combinations of pairings is almost astronomical for any RNA molecule with more than a few dozen bases.

In hopes of moving the field forward, a team led by Stephan Eismann and Raphael Townshend in the lab of Ron Dror, Stanford University, Palo Alto, CA, looked to a machine learning approach known as deep learning. It is inspired by how our own brain’s neural networks process information, learning to focus on some details but not others.

In deep learning, computers look for patterns in data. As they begin to “see” complex relationships, some connections in the network are strengthened while others are weakened.

One of the things that makes deep learning so powerful is it doesn’t rely on any preconceived notions. It also can pick up on important features and patterns that humans can’t possibly detect. But, as successful as this approach has been in solving many different kinds of problems, it has primarily been applied to areas of biology, such as protein folding, in which lots of data were available for researchers to train the computers.

That’s not the case with RNA molecules. To work around this problem, Dror’s team designed a neural network they call ARES. (No, it’s not the Greek god of war. It’s short for Atomic Rotationally Equivariant Scorer.)

To start, the researchers trained ARES on just 18 small RNA molecules for which structures had been experimentally determined. They gave ARES these structural models specified only by their atomic structure and chemical elements.

The next test was to see if ARES could determine from this small training set the best structural model for RNA sequences it had never seen before. The researchers put it to the test with RNA molecules whose structures had been determined more recently.

ARES, however, doesn’t come up with the structures itself. Instead, the researchers give ARES a sequence and at least 1,500 possible 3D structures it might take, all generated using another computer program. Based on patterns in the training set, ARES scores each of the possible structures to find the one it predicts is closest to the actual structure. Remarkably, it does this without being provided any prior information about features important for determining RNA shapes, such as nucleotides, steric constraints, and hydrogen bonds.

It turns out that ARES consistently outperforms humans and all other previous methods to produce the best results. In fact, it outperformed at least nine other methods to come out on top in a community-wide RNA-puzzles contest. It also can make predictions about RNA molecules that are significantly larger and more complex than those upon which it was trained.

The success of ARES and this deep learning approach will help to elucidate RNA molecules with potentially important implications for health and disease. It’s another compelling example of how deep learning promises to solve many other problems in structural biology, chemistry, and the material sciences when—at the outset—very little is known.

Reference:

[1] Geometric deep learning of RNA structure. Townshend RJL, Eismann S, Watkins AM, Rangan R, Karelina M, Das R, Dror RO. Science. 2021 Aug 27;373(6558):1047-1051.

Links:

Structural Biology (National Institute of General Medical Sciences/NIH)

The Structures of Life (National Institute of General Medical Sciences/NIH)

RNA Biology (NIH)

RNA Puzzles

Dror Lab (Stanford University, Palo Alto, CA)

NIH Support: National Cancer Institute; National Institute of General Medical Sciences


Artificial Intelligence Accurately Predicts Protein Folding

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Caption: Researchers used artificial intelligence to map hundreds of new protein structures, including this 3D view of human interleukin-12 (blue) bound to its receptor (purple). Credit: Ian Haydon, University of Washington Institute for Protein Design, Seattle

Proteins are the workhorses of the cell. Mapping the precise shapes of the most important of these workhorses helps to unlock their life-supporting functions or, in the case of disease, potential for dysfunction. While the amino acid sequence of a protein provides the basis for its 3D structure, deducing the atom-by-atom map from principles of quantum mechanics has been beyond the ability of computer programs—until now. 

In a recent study in the journal Science, researchers reported they have developed artificial intelligence approaches for predicting the three-dimensional structure of proteins in record time, based solely on their one-dimensional amino acid sequences [1]. This groundbreaking approach will not only aid researchers in the lab, but guide drug developers in coming up with safer and more effective ways to treat and prevent disease.

This new NIH-supported advance is now freely available to scientists around the world. In fact, it has already helped to solve especially challenging protein structures in cases where experimental data were lacking and other modeling methods hadn’t been enough to get a final answer. It also can now provide key structural information about proteins for which more time-consuming and costly imaging data are not yet available.

The new work comes from a group led by David Baker and Minkyung Baek, University of Washington, Seattle, Institute for Protein Design. Over the course of the pandemic, Baker’s team has been working hard to design promising COVID-19 therapeutics. They’ve also been working to design proteins that might offer promising new ways to treat cancer and other conditions. As part of this effort, they’ve developed new computational approaches for determining precisely how a chain of amino acids, which are the building blocks of proteins, will fold up in space to form a finished protein.

But the ability to predict a protein’s precise structure or shape from its sequence alone had proven to be a difficult problem to solve despite decades of effort. In search of a solution, research teams from around the world have come together every two years since 1994 at the Critical Assessment of Structure Prediction (CASP) meetings. At these gatherings, teams compete against each other with the goal of developing computational methods and software capable of predicting any of nature’s 200 million or more protein structures from sequences alone with the greatest accuracy.

Last year, a London-based company called DeepMind shook up the structural biology world with their entry into CASP called AlphaFold. (AlphaFold was one of Science’s 2020 Breakthroughs of the Year.) They showed that their artificial intelligence approach—which took advantage of the 170,000 proteins with known structures in a reiterative process called deep learning—could predict protein structure with amazing accuracy. In fact, it could predict most protein structures almost as accurately as other high-resolution protein mapping techniques, including today’s go-to strategies of X-ray crystallography and cryo-EM.

The DeepMind performance showed what was possible, but because the advances were made by a world-leading deep learning company, the details on how it worked weren’t made publicly available at the time. The findings left Baker, Baek, and others eager to learn more and to see if they could replicate the impressive predictive ability of AlphaFold outside of such a well-resourced company.

In the new work, Baker and Baek’s team has made stunning progress—using only a fraction of the computational processing power and time required by AlphaFold. The new software, called RoseTTAFold, also relies on a deep learning approach. In deep learning, computers look for patterns in large collections of data. As they begin to recognize complex relationships, some connections in the network are strengthened while others are weakened. The finished network is typically composed of multiple information-processing layers, which operate on the data to return a result—in this case, a protein structure.

Given the complexity of the problem, instead of using a single neural network, RoseTTAFold relies on three. The three-track neural network integrates and simultaneously processes one-dimensional protein sequence information, two-dimensional information about the distance between amino acids, and three-dimensional atomic structure all at once. Information from these separate tracks flows back and forth to generate accurate models of proteins rapidly from sequence information alone, including structures in complex with other proteins.

As soon as the researchers had what they thought was a reasonable working approach to solve protein structures, they began sharing it with their structural biologist colleagues. In many cases, it became immediately clear that RoseTTAFold worked remarkably well. What’s more, it has been put to work to solve challenging structural biology problems that had vexed scientists for many years with earlier methods.

RoseTTAFold already has solved hundreds of new protein structures, many of which represent poorly understood human proteins. The 3D rendering of a complex showing a human protein called interleukin-12 in complex with its receptor (above image) is just one example. The researchers have generated other structures directly relevant to human health, including some that are related to lipid metabolism, inflammatory conditions, and cancer. The program is now available on the web and has been downloaded by dozens of research teams around the world.

Cryo-EM and other experimental mapping methods will remain essential to solve protein structures in the lab. But with the artificial intelligence advances demonstrated by RoseTTAFold and AlphaFold, which has now also been released in an open-source version and reported in the journal Nature [2], researchers now can make the critical protein structure predictions at their desktops. This newfound ability will be a boon to basic science studies and has great potential to speed life-saving therapeutic advances.

References:

[1] Accurate prediction of protein structures and interactions using a three-track neural network. Baek M, DiMaio F, Anishchenko I, Dauparas J, Grishin NV, Adams PD, Read RJ, Baker D., et al. Science. 2021 Jul 15:eabj8754.

[2] Highly accurate protein structure prediction with AlphaFold. Jumper J, Evans R, Pritzel A, Green T, Senior AW, Kavukcuoglu K, Kohli P, Hassabis D. et al. Nature. 2021 Jul 15.

Links:

Structural Biology (National Institute of General Medical Sciences/NIH)

The Structures of Life (NIGMS)

Baker Lab (University of Washington, Seattle)

CASP 14 (University of California, Davis)

NIH Support: National Institute of Allergy and Infectious Diseases; National Institute of General Medical Sciences


An Evolutionary Guide to New Immunotherapies

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Credit: Dave Titensor, University of Utah, Salt Lake City

One of the best ways to learn how something works is to understand how it’s built. How it came to be. That’s true not only if you play a guitar or repair motorcycle engines, but also if you study the biological systems that make life possible. Evolutionary studies, comparing the development of these systems across animals and organisms, are now leading to many unexpected biological discoveries and promising possibilities for preventing and treating human disease.

While there are many evolutionary questions to ask, Brenda Bass, a distinguished biochemist at University of Utah, Salt Lake City, has set her sights on a particularly profound one: How has innate immunity evolved through the millennia in all living things, including humans? Innate immunity is the immune system’s frontline defense, the first responders that take control of an emerging infectious situation and, if needed, signal for backup.

Exploring the millennia for clues about innate immunity takes a special team, and Bass has assembled a talented one. It includes her Utah colleague Nels Elde, a geneticist; immunologist Dan Stetson, University of Washington, Seattle; and biochemist Jane Jackman, Ohio State University, Columbus.

With a 2020 NIH Director’s Transformative Research Award, this hard-working team will embark on studies looking back at 450 million years of evolution: the point in time when animals diverged to develop very distinct methods of innate immune defense [1]. The team members hope to uncover new possibilities encoded in the innate immune system, especially those that might be latent but still workable. The researchers will then explore whether their finds can be repurposed not only to boost our body’s natural response to external threats but also to internal threats like cancer.

Bass brings a unique perspective to the project. As a postdoc in the 1980s, she stumbled upon a whole new class of enzymes, called ADARs, that edit RNA [2]. Their function was mysterious at the time. It turns out that ADARs specifically edit a molecule called double-stranded RNA (dsRNA). When viruses infect cells in animals, including humans, they make dsRNA, which the innate immune system detects as a sign that a cell has been invaded.

It also turns out that animal cells make their own dsRNA. Over the years, Bass and her lab have identified thousands of dsRNAs made in animal cells—in fact, a significant number of human genes produce dsRNA [3]. Also interesting, ADARs are crucial to marking our own dsRNA as “self” to avoid triggering an immune response when we don’t need it [4].

Bass and others have found that evolution has produced dramatic differences in the biochemical pathways powering the innate immune system. In vertebrate animals, dsRNA leads to release of the immune chemical interferon, a signaling pathway that invertebrate species don’t have. Instead, in response to detecting dsRNA from an invader, and repelling it, worms and other invertebrates trigger a gene-silencing pathway known as RNA interference, or RNAi.

With the new funding, Bass and team plan to mix and match immune strategies from simple and advanced species, across evolutionary time, to craft an entirely new set of immune tools to fight disease. The team will also build new types of targeted immunotherapies based on the principles of innate immunity. Current immunotherapies, which harness a person’s own immune system to fight disease, target infections, autoimmune disorders, and cancer. But they work through our second-line adaptive immune response, which is a biological system unique to vertebrates.

Bass and her team will first hunt for more molecules like ADARs: innate immune checkpoints, as they refer to them. The name comes from a functional resemblance to the better-known adaptive immune checkpoints PD-1 and CTLA-4, which sparked a revolution in cancer immunotherapy. The team will run several screens that sort molecules successful at activating innate immune responses—both in invertebrates and in mammals—hoping to identify a range of durable new immune switches that evolution skipped over but that might be repurposed today.

Another intriguing direction for this research stems from the observation that decreasing normal levels of ADARs in tumors kickstarts innate immune responses that kill cancer cells [5]. Along these lines, the scientists plan to test newly identified immune switches to look for novel ways to fight cancer where existing approaches have not worked.

Evolution is the founding principle for all of biology—organisms learn from what works to improve their ability to survive. In this case, research to re-examine such lessons and apply them for new uses may help transform bygone evolution into a therapeutic revolution!

References:

[1] Evolution of adaptive immunity from transposable elements combined with innate immune systems. Koonin EV, Krupovic M. Nat Rev Genet. 2015 Mar;16(3):184-192.

[2] A developmentally regulated activity that unwinds RNA duplexes. Bass BL, Weintraub H. Cell. 1987 Feb 27;48(4):607-613.

[3] Mapping the dsRNA World. Reich DP, Bass BL. Cold Spring Harb Perspect Biol. 2019 Mar 1;11(3):a035352.

[4] To protect and modify double-stranded RNA – the critical roles of ADARs in development, immunity and oncogenesis. Erdmann EA, Mahapatra A, Mukherjee P, Yang B, Hundley HA. Crit Rev Biochem Mol Biol. 2021 Feb;56(1):54-87.

[5] Loss of ADAR1 in tumours overcomes resistance to immune checkpoint blockade. Ishizuka JJ, Manguso RT, Cheruiyot CK, Bi K, Panda A, et al. Nature. 2019 Jan;565(7737):43-48.

Links:

Bass Lab (University of Utah, Salt Lake City)

Elde Lab (University of Utah)

Jackman Lab (Ohio State University, Columbus)

Stetson Lab (University of Washington, Seattle)

Bass/Elde/Jackman/Stetson Project Information (NIH RePORTER)

NIH Director’s Transformative Research Award Program (Common Fund)

NIH Support: Common Fund; National Cancer Institute


Welcoming First Lady Jill Biden to NIH!

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Video Event

It was wonderful to have First Lady Jill Biden pay a virtual visit to NIH on February 3, 2021, on the eve of World Cancer Day. Dr. Biden joined me, National Cancer Institute (NCI) Director Ned Sharpless, and several NCI scientists to discuss recent advances in fighting cancer. On behalf of the entire NIH community, I thanked the First Lady for her decades of advocacy on behalf of cancer education, prevention, and research. To view the event, go to 53:20 in this video. Credit: Adapted from White House video.


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