Mind’s Eye: Vision-restoring Brain Implants Spell Breakthrough

Electronic vision


John SAEKI

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Scientists are a step closer to restoring vision for the blind, after building an implant that bypasses the eyes and allows monkeys to perceive artificially induced patterns in their brains.

The technology, developed by a team at the Netherlands Institute for Neuroscience (NIN), was described in the journal Science on Thursday.

It builds on an idea first conceived decades ago: electrically stimulating the brain so it “sees” lit dots known as phosphenes, akin to pixels on a computer screen.

But the concept had never realized its full potential because of technical limitations.

A team led by NIN director Pieter Roelfsema developed implants consisting of 1,024 electrodes wired into the visual cortex of two sighted monkeys, resulting in a much higher resolution than has previously been achieved.

The visual cortex is located at the back of the brain and many of its features are common to humans and other primates.

“The number of electrodes that we have implanted in the visual cortex, and the number of artificial pixels that we can generate to produce high-resolution artificial images, is unprecedented,” said Roelfsema.

This allowed the pair of monkeys to make out shapes like letters of the alphabet, lines and moving dots, which they’d previously been trained to respond to by moving their eyes in a particular direction to win a reward.

The monochrome patterns are still crude compared to real vision, but represent a major leap over previous implants, which allowed human users to only determine vague areas of light and dark.

Roelfsema likened it to a highway matrix board, and said his team now had a “proof of principle” that laid the foundation for a neuro-prosthetic device for the world’s 40 million blind people.

This might consist of a camera that the user wears or a pair of glasses, which uses artificial intelligence to convert what it sees into a pattern it can send to the user’s brain.

Similar technology has appeared in works of science fiction, such as the visor device worn by Geordi La Forge on “Star Trek: The Next Generation.”

In a written commentary, Michael Beauchamp and Daniel Yoshor of the University of Pennsylvania hailed the breakthrough as a “technical tour de force.”

The NIN team benefited from advances in miniaturization, and also devised a system to make sure their input currents were big enough to create noticeable dots, but not so great that the pixels grew too large.

They achieved this by placing some electrodes at a more advanced stage of the visual cortex, to monitor how much signal was coming through and then adjust the input.

Wireless future

Roelfsema said his team hopes to make similar devices for humans in about three years.

But the electrodes the team used require silicon needles that work for about a year before tissue builds up around the needles and they no longer function.

“So we want to create new types of electrodes that are better accepted by the body,”

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Breakthrough A.I. Makes Huge Leap Toward Solving 50-Year-Old Problem in Biology | Smart News

Life on Earth relies on microscopic machines called proteins that are vital to everything from holding up the structure of each cell, to reading genetic code, to carrying oxygen through the bloodstream. With meticulous lab work, scientists have figured out the precise, 3-D shapes of about 170,000 proteins—but there are at least 200 million more to go, Robert F. Service reports for Science magazine.

Now, the artificial intelligence company DeepMind, which is owned by the same company that owns Google, has developed a tool that can predict the 3-D shapes of most proteins with similar results to experiments in the lab, Cade Metz reports for the New York Times. While lab experiments can take years to tease out a protein structure, DeepMind’s tool, called AlphaFold, can come up with a structure in just a few days, per Nature’s Ewen Callaway. The tool could help speed up studies in medicine development and bioengineering.

Molecular biologists want to know the structures of proteins because the shape of a molecule determines what it’s able to do. For instance, if a protein is causing damage in the body, then scientists could study its structure and then find another protein that fits it like a puzzle piece to neutralize it. AlphaFold could accelerate that process.

“This is going to empower a new generation of molecular biologists to ask more advanced questions,” says Max Planck Institute evolutionary biologist Andrei Lupas to Nature. “It’s going to require more thinking and less pipetting.”

DeepMind tested out AlphaFold by entering it in a biennial challenge called Critical Assessment of Structure Prediction, or CASP, for which Lupas was a judge. CASP provides a framework for developers to test their protein-prediction software. It’s been running since 1994, but the recent rise of machine learning in protein structure prediction has pushed participants to new levels. AlphaFold first participated last year and scored about 15% better than the other entries, per Science magazine. This year, a new computational strategy helped AlphaFold leave the competition in the dust.

Proteins are made of chains of chemicals called amino acids that are folded up into shapes, like wire sculptures. There are 20 kinds of amino acids, each with their own chemical characteristics that affect how they interact with others along the strand. Those interactions determine how the strand folds up into a 3-D shape. And because these chains can have dozens or hundreds of amino acids, predicting how a strand will fold based just on a list of amino acids is a challenge.

But that’s exactly what CASP asks participants to do. CASP assessors like Lupas have access to the answer key—the 3-D structure of a protein that was determined in a lab, but not yet published publicly. AlphaFold’s entries were anonymized as “group 427,” but after they solved structure after structure, Lupas was able to guess that it was theirs, he tells Nature.

“Most atoms are within an atom diameter of where they are in the experimental structure,” says CASP co-founder

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DeepMind AI breakthrough in protein folding will accelerate medical discoveries

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An illustration of the possible structure of a “membrane protein” associated with the coronavirus, according to a model created by DeepMind’s AlphaFold program. 


DeepMind.

DeepMind, a division of Alphabet, says it has solved one of the most difficult computing challenges in the world: predicting how protein molecules will fold. It is key to understanding important biological processes and treating diseases such as COVID-19.

The London-based organization said that its claims of a breakthrough had been verified by organizers of a competition held every two years to test computer models, the Critical Assessment of protein Structure Prediction (CASP). 

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DeepMind named its protein folding prediction system AlphaFold and said that the latest version has been four years in development. 

Writing on its blog, the AlphaFold team described the success of the system being due to methods that “draw inspiration from the fields of biology, physics, and machine learning, as well as of course the work of many scientists in the protein folding field over the past half-century.”

There are about 180 million known proteins but only about 170,000 protein structures have been mapped through X-ray crystallography and other techniques. X-ray crystallography is how DNA’s double-helix of amino acids structure was discovered and the structure revealed how it copied itself. But it can take months and sometimes years to determine a protein structure.

Complicated chains of amino acids can have vast numbers of permutations. Yet in nature proteins will only fold into a very specific shape and that shape determines its role in biological processes, including in viruses. 

Professor Andrei Lupas, Director of the Max Planck Institute for Developmental Biology, writing on the DeepMind blog: “AlphaFold’s astonishingly accurate models have allowed us to solve a protein structure we were stuck on for close to a decade, relaunching our effort to understand how signals are transmitted across cell membranes.”

DeepMind’s approach is ideal for membrane proteins which cannot be easily crystalized. 

The AlphaFold team said that in March it predicted two protein structures of SARS-CoV-virus, which had been separately identified months later by researchers. This shows its potential applications in predicting the shape of mutated viruses.

The CASP competition evaluates competing models of prediction by measuring the variation from actual structure in Angstroms — the width of an atom. Competitors analyze samples of proteins whose structure has never been published.  

Units called Global Distance Test (GDT) are used to evaluate each protein structure prediction. A score of 90 GDT or above is considered equal to experimental analysis. AlphaFold’s median score against all target proteins was 92.4 GDT.

What is additionally impressive about this achievement is the seemingly small amount of data AlphaFold was trained on. With only some 170,000 known protein structures in public databases AlphaFold had to determine the rules for a complex structure from very little information.

AlphaFold’s training was very fast compared

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DeepMind’s latest AI breakthrough can accurately predict the way proteins fold

Researchers and enthusiasts across the internet have met the news enthusiastically, with some proclaiming that AlphaFold has solved the “protein solving problem.” But what does that mean, exactly? And how do we stand to benefit from it?

To start answering these questions, we need to take a closer look at the proteins themselves. As your biology teacher might have said, proteins are the building blocks of life, responsible for countless functions inside and outside the human body. Each one starts as a series of amino acids strung together into a chain, but it doesn’t take long — sometimes just milliseconds  — before things start to get complicated. Some parts of the amino acid chain twist into helixes. Others fold back onto themselves as “sheets”. Before long, these helixes and sheets coalesce and contort into a protein’s final structure, and that’s what gives a protein the ability to perform specific tasks, like ferrying oxygen through your body or strengthening the structure of your bones. 

In other words, shape is everything, and researchers have spent decades trying to find a way to determine a protein’s final, folded structure based solely on the amino acids that make up its backbone. That’s where CASP comes in — since 1994, the program has served as a focal point of sorts for teams around the world working to crack the protein solving problem with computational ingenuity. The rules are fairly simple: Every other year, organizers select a series of target proteins from a bevy of submissions whose structures have been determined experimentally, but haven’t been published yet. Researchers then get a few months to tune their systems and make their predictions, which are then judged by experts in the field for about a month after submissions are closed. 

While CASP has been running for 26 years, it’s been in the past few that the scientific community has been able to bring quantum leaps in compute power and machine learning to bear on the challenge. In DeepMind’s case, that involved training AlphaFold 2’s prediction model on about 170,000 known protein structures, along with a vast number of protein sequences whose 3D structures haven’t yet been determined. This testing data, the team admits, is fairly similar to what it used in 2018, when the original AlphaFold system achieved top marks during CASP 13. (At the time, organizers hailed DeepMind’s “unprecedented progress in the ability of computational methods to predict protein structure.”) 

That said, the team made some notable changes to its machine learning approach — they haven’t published a full paper yet, but the CASP 14 abstract book highlights some of their modifications. And beyond that, DeepMind also relied on about 128 of Google’s cloud-based TPUv3 cores, which ultimately gave AlphaFold 2 the ability to accurately determine a protein’s structure within just days, if not sooner — the New York Times notes that, in some cases, predictions can be generated in a matter of hours. 

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DeepMind Breakthrough Helps to Solve How Diseases Invade Cells

(Bloomberg) — Google’s artificial intelligence unit took a giant step to predict the structure of proteins, potentially decoding a problem that has been described as akin to mapping the genome.



a hand holding a cellphone: A Deepmind Health logo sits displayed on the screen of an Apple Inc. iPhone in this arranged photograph in London, U.K. on Monday, Nov. 26, 2018. Three years ago, artificial intelligence company DeepMind Technologies Ltd. embarked on a landmark effort to transform health care in the U.K. Now plans by owner Alphabet Inc. to wrap the partnership into its Google search engine business are tripping alarm bells about privacy.


© Bloomberg
A Deepmind Health logo sits displayed on the screen of an Apple Inc. iPhone in this arranged photograph in London, U.K. on Monday, Nov. 26, 2018. Three years ago, artificial intelligence company DeepMind Technologies Ltd. embarked on a landmark effort to transform health care in the U.K. Now plans by owner Alphabet Inc. to wrap the partnership into its Google search engine business are tripping alarm bells about privacy.

DeepMind Technologies Ltd.’s AlphaFold reached the threshold for “solving” the problem at the latest Critical Assessment of Structure Prediction competition. The event started in 1994 and is held every two years to accelerate research on the topic.

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Different folds in a protein determine how it will interact with other molecules, and understanding them has implications for discovering how new diseases like Covid-19 invade our cells, designing enzymes to break down pollutants and improving crop yields.

DeepMind became a subsidiary of Google after a 2014 acquisition and is best known for its gamer AI, teaching itself to beat Atari video games and defeating world-renowned Go players like Lee Sedol. The company’s ambition has been to develop AI that can be applied to broader problems, and it’s so far created systems to make Google’s data centers more energy efficient, identify eye disease from scans and generate human-sounding speech.

DeepMind also won the competition in 2018 at the first time of entering, when it accurately predicted the structure of 25 out of 43 proteins.

Read More: AI Drug Hunters Could Give Big Pharma a Run for Its Money

“These algorithms are now becoming strong enough and powerful enough to be applicable to scientific problems,” DeepMind Chief Executive Officer Demis Hassabis said in a call with reporters. After four years of development “we have a system that’s accurate enough to actually have biological significance and relevance for biological researchers.”

DeepMind is now looking into ways of offering scientists access to the AlphaFold system in a “scalable way,” Hassabis said.

“Citizen Science”

CASP scientists analyzed the shape of amino acid sequences for a set of about 100 proteins. Competitors were given the sequences, and charged with predicting their shape. AlphaFold’s assessment lined up almost perfectly with the CASP analysis for two-thirds of the proteins, compared to about 10% from the other teams, and better than what DeepMind’s tool achieved two years ago

Hassabis said his inspiration for AlphaFold came from “citizen science” attempts to find unknown protein structures, like Foldit, which presented amateur volunteers with the problem in the form of a puzzle. In its first two years, the human gamers proved to be surprisingly good at solving the riddles, and ended up discovering a structure that had baffled scientists and designing a new enzyme that was later confirmed in the lab.

“Determining a single protein structure often required years of experimental

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Oxford University breakthrough on global COVID-19 vaccine

OXFORD, United Kingdom, Nov. 23, 2020 (GLOBE NEWSWIRE) — Vaccitech’s scientific founders at University of Oxford announce positive high-level results from an interim analysis of clinical trials of AZD1222 in the UK and Brazil.

  • Phase 3 interim analysis including 131 Covid-19 cases indicates that the vaccine is 70.4% effective when combining data from two dosing regimens
  • In the two different dose regimens vaccine efficacy was 90% in one and 62% in the other
  • Higher efficacy regime used a halved first dose and standard second dose
  • Early indication that vaccine could reduce virus transmission from an observed reduction in asymptomatic infections
  • There were no hospitalised or severe cases in anyone who received the vaccine
  • Large safety database from over 24,000 volunteers from clinical trials in the UK, Brazil and South Africa, with follow up since April
  • Crucially, vaccine can be easily administered in existing healthcare systems, stored at ‘fridge temperature’ (2-8 °C) and distributed using existing logistics
  • Large scale manufacturing ongoing in over 10 countries to support equitable global access

The vaccine, ChAdOx1 nCoV-19, also known as AZD1222, was co-invented by Vaccitech and Oxford University’s Jenner Institute.

Professor Andrew Pollard, Director of the Oxford Vaccine Group and Chief Investigator of the Oxford Vaccine Trial, said:

“These findings show that we have an effective vaccine that will save many lives. Excitingly, we’ve found that one of our dosing regimens may be around 90% effective and if this dosing regime is used, more people could be vaccinated with planned vaccine supply. Today’s announcement is only possible thanks to the many volunteers in our trial, and the hard working and talented team of researchers based around the world.”

Professor Sarah Gilbert, Professor of Vaccinology at the University of Oxford, said:

“The announcement today takes us another step closer to the time when we can use vaccines to bring an end to the devastation caused by SARS-CoV-2. We will continue to work to provide the detailed information to regulators. It has been a privilege to be part of this multi-national effort which will reap benefits for the whole world.”

The University of Oxford, in collaboration with AstraZeneca plc, today announces interim trial data from its Phase III trials that shows its candidate vaccine, ChAdOx1 nCoV-2019, is effective at preventing COVID-19 (SARS-CoV-2) and offers a high level of protection.

Bill Enright, Vaccitech Chief Executive Officer, remarked:

“The world needs a cost effective, easy to distribute, COVID-19 vaccine that demonstrates safety and works to control the continued spread of this devastating pandemic. Vaccitech is proud to have been a small part of the team, together with Oxford University and AstraZeneca, that moved this vaccine from concept to reality in record time. These latest data give us further confidence in the potential of our ChAdOx technology platform to address other major unmet needs in infectious diseases and cancer.”

Following the trial reaching the target for interim analysis, the independent Data and Safety Monitoring Board (DSMB) recommended that the team at Oxford conduct its first analysis on all the

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Gemini North observations enable breakthrough in centuries-old effort to unravel astronomical mystery

Blast from the past
The enigmatic CK Vulpeculae nebula. The team of astronomers measured the speeds and changes in positions of the two small reddish arcs about 1/4 of the way up from the bottom and 1/4 of the way down from the top to help determine that the nebula is expanding five times faster than previously thought. Credit: International Gemini Observatory/NOIRLab/NSF/AURAImage processing: Travis Rector (University of Alaska Anchorage), Mahdi Zamani & Davide de Martin

An international team of astronomers using Gemini North’s GNIRS instrument have discovered that CK Vulpeculae, first seen as a bright new star in 1670, is approximately five times farther away than previously thought. This makes the 1670 explosion of CK Vulpeculae much more energetic than previously estimated and puts it into a mysterious class of objects that are too bright to be members of the well-understood type of explosions known as novae, but too faint to be supernovae.


350 years ago, the French monk Anthelme Voituret saw a bright new star flare into life in the constellation of Vulpecula. Over the following months, the star became almost as bright as Polaris (the North Star) and was monitored by some of the leading astronomers of the day before it faded from view after a year. The new star eventually gained the name CK Vulpeculae and was long considered to be the first documented example of a nova—a fleeting astronomical event arising from an explosion in a close binary star system in which one member is a white dwarf, the remnant of a Sun-like star. However, a string of recent results have thrown the longstanding classification of CK Vulpeculae as a nova into doubt.

In 2015, a team of astronomers suggested that CK Vulpeculae’s appearance in 1670 was the result of two normal stars undergoing a cataclysmic collision. Just over three years later, the same astronomers further proposed that one of the stars was in fact a bloated red giant star, following their discovery of a radioactive isotope of aluminum in the immediate surroundings of the site of the 1670 explosion. Complicating the picture even further, a separate group of astronomers proposed a different interpretation. In their paper, also published in 2018, they suggested that the sudden brightening in 1670 was the result of the merger between a brown dwarf—a failed star too small to shine via thermonuclear fusion that powers the Sun—and a white dwarf.

Now, adding to the ongoing mystery surrounding CK Vulpeculae, new observations from the international Gemini Observatory, a Program of NSF’s NOIRLab, reveal that this enigmatic astronomical object is much farther away and has ejected gas at much higher speeds than previously reported.

Blast from the past
This wide-field view shows the sky around the location of the historical exploding star CK Vulpeculae. The remains of the nova are only very faintly visible at the center of this picture. Credit: ESO/Digitized Sky Survey 2.Acknowledgment: Davide De Martin

This team, led by Dipankar Banerjee of Physical Research Laboratory Ahmedabad, India, Tom Geballe of Gemini Observatory, and Nye Evans of Keele University

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Scientists hail Oxford University vaccine breakthrough

Watch: Oxford COVID vaccine ‘up to 90% effective’



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Scientists have hailed the news of the latest successful trial of a coronavirus vaccine, saying they “can see the end of the tunnel”.

The results of a large-scale trial of the COVID-19 vaccine developed by the University of Oxford and manufacturer AstraZeneca has been welcomed by health experts and the government.

England’s chief medical officer, Professor Chris Whitty, tweeted that it was a “very encouraging step forward”.

Overall, the vaccine was revealed to be 70% effective against coronavirus. However, it can be up to 90% effective when one half dose is given followed by a full dose at least one month apart.

The UK has placed orders for 100m doses of the vaccine, enough to vaccinate most of the population if it is approved. Health secretary Matt Hancock said life in the UK could return to normal after Easter.

The government has also ordered 40m doses of a jab from Pfizer and BioNTech that has been shown to be 95% effective.

Another jab from Moderna is also 95% effective, trials have shown – the UK has ordered 5m doses.



a close up of a bottle: An illustration picture shows vials with Covid-19 Vaccine stickers attached and syringes, with the logo of the University of Oxford and its partner British pharmaceutical company AstraZeneca, on November 17, 2020. (Photo by JUSTIN TALLIS / AFP) (Photo by JUSTIN TALLIS/AFP via Getty Images)


© Provided by Yahoo! News UK
An illustration picture shows vials with Covid-19 Vaccine stickers attached and syringes, with the logo of the University of Oxford and its partner British pharmaceutical company AstraZeneca, on November 17, 2020. (Photo by JUSTIN TALLIS / AFP) (Photo by JUSTIN TALLIS/AFP via Getty Images)

Commenting on Monday’s, AstraZeneca vaccine announcement, Peter Horby, professor of emerging infectious diseases and global health at the Nuffield Department of Medicine, University of Oxford, said: “This is very welcome news, we can clearly see the end of the tunnel now.

“There were no COVID hospitalisations or deaths in people who got the Oxford vaccine.

“Importantly, from what we have heard the vaccine seems to prevent infection not just disease. This is important as the vaccine could reduce the spread of the virus as well as protect the vulnerable from severe disease.”

He pointed out that the Oxford/AstraZeneca vaccine can be stored in a fridge, unlike the Pfizer and Moderna jabs, making it a “more practical solution for use worldwide”.

One member of the government’s Scientific Advisory Group for Emergencies (Sage) said the latest vaccine news could mean social restrictions being eased by spring.

Dr Michael Tildesley, associate professor in infectious disease modelling at the University of Warwick, told Times Radio: “The vaccine is on the horizon.

“We’ve had some great news about three different vaccines over the last three weeks.

“I would say I’m more hopeful that by the spring we might be starting to ease out of these restrictions.”

Peter Openshaw, professor of experimental

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How a digital breakthrough could revolutionize drug industry

REB Images | Image Soure | Getty Images

In June, the U.S. government purchased the vast majority of world’s supply of remdesivir—a FDA-approved antiviral treatment for Covid-19—for July through September. Gilead, the company that makes the compound, recently announced that it would meet international demand by the end of October. Yet all along, digital instructions for whipping up a batch of the nearly 400-atom molecule at the push of a button have been sitting on Github, an online software repository, freely available to anyone with the hardware needed to execute the chemical “program.”

 A dozen such chemical computers or “chemputers” sit in the University of Glasgow lab of Lee Cronin, the chemist who designed the bird’s nest of tubing, pumps, and flasks, and wrote the remdesivir code that runs on it. He’s spent years dreaming of a future where researchers can distribute and produce molecules as easily as they email and print PDFs, making not being able to order a drug as archaic as not being able to locate a modern text.

 “If we have standard way of discovering molecules, making molecules, and then manufacturing them, suddenly nothing goes out of print,” he says. “It’s like an ebook reader for chemistry.”

 Cronin and his colleagues described their machine’s capability to produce multiple molecules last year, and now they’ve taken a second major step toward digitizing chemistry with an accessible way to program with the machine. Their software turns academic papers into chemputer-executable programs that researchers can edit without learning to code, they announced earlier this month in Science. And they’re not alone. The team represents one of dozens of groups spread across academia and industry all racing to bring chemistry into the digital age, a development that could lead to safer drugs, more efficient solar panels, and a disruptive new industry.

A chemical computer or “chemputer” sits in the University of Glasgow lab of Leroy Cronin, the chemist who designed the bird’s nest of tubing, pumps, and flasks, and wrote the remdesivir code that runs on it. He’s spent years dreaming of a future where researchers can distribute and produce molecules as easily as they email and print PDFs.

Leroy Cronin,

The Cronin team hopes their work will enable what they describe as “Spotify for chemistry”— an online repository of downloadable recipes for important molecules that they say could help developing countries more easily access medications, enable more efficient international scientific collaboration, and even support the human exploration of space.

 “The majority of chemistry hasn’t changed from the way we’ve been doing it for the last 200 years. It’s very manual, artisan driven process,” says Nathan Collins, the chief strategy officer of SRI Biosciences, a division of SRI International, a research company developing another automated chemistry system that’s not involved in the Glasgow research. “There’s billions of dollars of opportunity there.”

 At the heart of Cronin’s new work lies what he calls a chemical description language or XDL (the “X” is pronounced “kai” after the first letter in the

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Microscopy breakthrough reveals how proteins behave in 3-D

Microscopy breakthrough reveals how proteins behave in 3D
A new microscopy system that can can image individual molecules in 3D and capture the way they “wobble” uses a specially engineered glass plate developed by University of Rochester optical scientists. Credit: University of Rochester photo / J. Adam Fenster

Six years ago, the Nobel Prize in chemistry was awarded to three scientists for finding ways to visualize the pathways of individual molecules inside living cells.


Now, researchers at the University of Rochester and the Fresnel Institute in France have found a way to visualize those molecules in even greater detail, showing their position and orientation in 3-D, and even how they wobble and oscillate. The work could shed invaluable insights into the biological processes involved, for example, when a cell and the proteins that regulate its functions react to the virus that causes COVID-19.

“When a protein changes shape, it exposes other atoms that enhance the biological process, so the change of shape of a protein has a huge effect on other processes inside the cell,” says Sophie Brasselet, director of the Fresnel Institute, who collaborated with Miguel Alonso and Thomas Brown, both professors of optics at Rochester.

Nicknamed CHIDO—for “Coordinate and Height super-resolution Imaging with Dithering and Orientation”—the technology is described in a new paper published in Nature Communications. Designed and built by lead authors Valentina Curcio, a Ph.D. student in Brasselet’s group, and Luis Aleman-Castaneda, a Ph.D. student in Alonso’s group, CHIDO is precise within “tens of nanometers in position and a few degrees of orientation” in determining the parameters of single molecules,” the team reports.

Using a glass plate subjected to uniform stress all around its periphery, the device can create and extrapolate wavelength oscillations and changes in polarization that occur when molecules are observed in a fluorescence microscope. The new technology transforms the image of a single molecule into a distorted focal spot, the shape of which directly encodes more precise 3-D information than previous measurement tools. In effect, CHIDO can produce beams that have every possible polarization state.

“This is one of the beauties of optics,” Brown says. “If you have a device that can create just about any polarization state, then you also have a device that can analyze just about any possible polarization state.”

The glass plate originated in Brown’s lab as part of his long interest in developing beams with unusual polarizations. Alonso, an expert on the theory of polarization, worked with Brown on ways to refine this “very simple but very elegant device” and expand its applications. During a visit to Marseille, Alonso described the plate to Brasselet, an expert in novel instrumentation for fluorescence and nonlinear imaging. Brasselet immediately suggested its possible use in the microscopy techniques she was working on to image individual molecules.

“It’s been a very complementary team,” Brasselet says.

20 years in the making

In 1873, Ernst Abbe stipulated that microscopes would never obtain better resolution than half the wavelength of light. That barrier stood until Nobel laureates Eric Betzig and William Moerner—with their

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