Singularity HUBModify

2024-12-24 23:00:28

We have only one example of biology forming in the universe—life on Earth. But what if life can form in other ways? How do you look for alien life when you don’t know what alien life might look like?

These questions are preoccupying astrobiologists—scientists who look for life beyond Earth. Astrobiologists have attempted to come up with universal rules that govern the emergence of complex physical and biological systems both on Earth and beyond.

I’m an astronomer who has written extensively about astrobiology. Through my research, I’ve learned that the most abundant form of extraterrestrial life is likely to be microbial, since single cells can form more readily than large organisms. But just in case there’s advanced alien life out there, I’m on the international advisory council for the group designing messages to send to those civilizations.

Detecting Life Beyond Earth

Since the first discovery of an exoplanet in 1995, over 5,000 exoplanets, or planets orbiting other stars, have been found.

Many of these exoplanets are small and rocky, like Earth, and in the habitable zones of their stars. The habitable zone is the range of distances between the surface of a planet and the star it orbits that would allow the planet to have liquid water and thus support life as we on Earth know it.

The sample of exoplanets detected so far projects 300 million potential biological experiments in our galaxy—or 300 million places, including exoplanets and other bodies such as moons, with suitable conditions for biology to arise.

The uncertainty for researchers starts with the definition of life. It feels like defining life should be easy, since we know life when we see it, whether it’s a flying bird or a microbe moving in a drop of water. But scientists don’t agree on a definition, and some think a comprehensive definition might not be possible.

NASA defines life as a “self-sustaining chemical reaction capable of Darwinian evolution.” That means organisms with a complex chemical system that evolve by adapting to their environment. Darwinian evolution says that the survival of an organism depends on its fitness in its environment.

The evolution of life on Earth has progressed over billions of years from single-celled organisms to large animals and other species, including humans.

Exoplanets are remote and hundreds of millions of times fainter than their parent stars, so studying them is challenging. Astronomers can inspect the atmospheres and surfaces of Earth-like exoplanets using a method called spectroscopy to look for chemical signatures of life.

Spectroscopy might detect signatures of oxygen in a planet’s atmosphere, which microbes called blue-green algae created by photosynthesis on Earth several billion years ago, or chlorophyll signatures, which indicate plant life.

NASA’s definition of life leads to some important but unanswered questions. Is Darwinian evolution universal? What chemical reactions can lead to biology off Earth?

Evolution and Complexity

All life on Earth, from a fungal spore to a blue whale, evolved from a microbial last common ancestor about four billion years ago.

The same chemical processes are seen in all living organisms on Earth, and those processes might be universal. They also may be radically different elsewhere.

In October 2024, a diverse group of scientists gathered to think outside the box on evolution. They wanted to step back and explore what sorts of processes created order in the universe—biological or not—to figure out how to study the emergence of life totally unlike life on Earth.

Two researchers present argued that complex systems of chemicals or minerals, when in environments that allow some configurations to persist better than others, evolve to store larger amounts of information. As time goes by, the system will grow more diverse and complex, gaining the functions needed for survival, through a kind of natural selection.

They speculated that there might be a law to describe the evolution of a wide variety of physical systems. Biological evolution through natural selection would be just one example of this broader law.

In biology, information refers to the instructions stored in the sequence of nucleotides on a DNA molecule, which collectively make up an organism’s genome and dictate what the organism looks like and how it functions.

If you define complexity in terms of information theory, natural selection will cause a genome to grow more complex as it stores more information about its environment.

Complexity might be useful in measuring the boundary between life and non-life.

However, it’s wrong to conclude that animals are more complex than microbes. Biological information increases with genome size, but evolutionary information density drops. Evolutionary information density is the fraction of functional genes within the genome, or the fraction of the total genetic material that expresses fitness for the environment.

Organisms that people think of as primitive, such as bacteria, have genomes with high information density and so appear better designed than the genomes of plants or animals.

A universal theory of life is still elusive. Such a theory would include the concepts of complexity and information storage, but it would not be tied to DNA or the particular kinds of cells we find in terrestrial biology.

Implications for the Search for Extraterrestial Life

Researchers have explored alternatives to terrestrial biochemistry. All known living organisms, from bacteria to humans, contain water, and it is a solvent that is essential for life on Earth. A solvent is a liquid medium that facilitates chemical reactions from which life could emerge. But life could potentially emerge from other solvents, too.

Astrobiologists Willam Bains and Sara Seager have explored thousands of molecules that might be associated with life. Plausible solvents include sulfuric acid, ammonia, liquid carbon dioxide, and even liquid sulfur.

Alien life might not be based on carbon, which forms the backbone of all life’s essential molecules—at least here on Earth. It might not even need a planet to survive.

Advanced forms of life on alien planets could be so strange that they’re unrecognizable. As astrobiologists try to detect life off Earth, they’ll need to be creative.

One strategy is to measure mineral signatures on the rocky surfaces of exoplanets, since mineral diversity tracks terrestrial biological evolution. As life evolved on Earth, it used and created minerals for exoskeletons and habitats. The hundred minerals present when life first formed have grown to about 5,000 today.

For example, zircons are simple silicate crystals that date back to the time before life started. A zircon found in Australia is the oldest known piece of Earth’s crust. But other minerals, such as apatite, a complex calcium phosphate mineral, are created by biology. Apatite is a primary ingredient in bones, teeth, and fish scales.

Another strategy for finding life unlike that on Earth is to detect evidence of a civilization, such as artificial lights, or the industrial pollutant nitrogen dioxide in the atmosphere. These are examples of tracers of intelligent life called technosignatures.

It’s unclear how and when a first detection of life beyond Earth will happen. It might be within the solar system, or by sniffing exoplanet atmospheres, or by detecting artificial radio signals from a distant civilization.

The search is a twisting road, not a straightforward path. And that’s for life as we know it—for life as we don’t know it, all bets are off.

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Image Credit: NASA’s Goddard Space Flight Center/Francis Reddy

2024-12-23 23:00:48

Proteins are biology’s molecular machines. They’re our bodies’ construction workers—making muscle, bone, and brain; regulators—keeping systems in check; and local internet—responsible for the transmission of information between cells and regions. In a word, proteins are crucial to our survival. When they work, we’re healthy. When they don’t, we aren’t.

Which is why recent leaps in our understanding of protein structure and the emerging ability to design entirely new proteins from scratch, mediated by AI, is such a huge development. It’s why three computer scientists won Nobel prizes in chemistry this year for their work in the field.

Things are by no means standing still. 2024 was another winning year for AI protein design.

Earlier this year, scientists expanded AI’s ability to model how proteins bind to other biomolecules, such as DNA, RNA, and the small molecules that regulate their shape and function. The study broadened the scope of RoseTTAFold, a popular AI tool for protein design, so that it could map out complex protein-based molecular machines at the atomic level—in turn, paving the way for more sophisticated therapies.

DeepMind soon followed with the release of AlphaFold3, an AI model that also predicts protein interactions with other molecules. Now available to researchers, the sophisticated AI tool will likely lead to a flood of innovations, therapeutics, and insights into biological processes.

Meanwhile, protein design went flexible this year. AI models generated “effector” proteins that could shape-shift in the presence of a molecular switch. This flip-flop structure altered their biological impact on cells. A subset of these morphed into a variety of arrangements, including cage-like structures that could encapsulate and deliver medicines like tiny spaceships.

They’re novel, but do any AI-designed proteins actually work? Yes, according to several studies.

One used AI to dream up a universe of potential CRISPR gene editors. Inspired by large language models—like those that gave birth to ChatGPT—the AI model in the study eventually designed a gene editing system as accurate as existing CRISPR-based tools when tested on cells. Another AI designed circle-shaped proteins that reliably turned stem cells into different blood vessel cell types. Other AI-generated proteins directed protein “junk” into the lysosome, a waste treatment blob filled with acid inside cells that keeps them neat and tidy.

Outside of medicine, AI designed mineral-forming proteins that, if integrated into aquatic microbes, could potentially soak up excess carbon and transform it into limestone. While still early, the technology could tackle climate change with a carbon sink that lasts millions of years.

It seems imagination is the only limit to AI-based protein design. But there are still a few cases that AI can’t yet fully handle. Nature has a comprehensive list, but these stand out.

Back to Basics: Binders

When proteins interact with each other, binder molecules can increase or break apart those interactions. These molecules initially caught the eyes of protein designers because they can serve as drugs that block damaging cellular responses or boost useful ones.

There have been successes. Generative AI models, such as RFdiffusion, can readily model binders, especially for free-floating proteins inside cells. These proteins coordinate much of the cell’s internal signaling, including signals that trigger senescence or cancer. Binders that break the chain of communication could potentially halt the processes. They can also be developed into diagnostic tools. In one example, scientists engineered a glow-in-the-dark tag to monitor a cell’s status, detecting the presence of a hormone when the binder grabbed onto it.

But binders remain hard to develop. They need to interact with key regions on proteins. But because proteins are dynamic 3D structures that twist and turn, it’s often tough to nail down which regions are crucial for binders to latch onto.

Then there’s the data problem. Thanks to hundreds of thousands of protein structures available in public databases, generative AI models can learn to predict protein-protein interactions. Binders, by contrast, are often kept secret by pharmaceutical companies—each organization has an in-house database cataloging how small molecules interact with proteins.

Several teams are now using AI to design simple binders for research. But experts stress these need to be tested in living organisms. AI can’t yet predict the biological consequences of a binder—it could either boost a process or shut it down. Then there’s the problem of hallucination, where an AI model dreams up binders that are completely unrealistic.

From here, the goal is to gather more and better data on how proteins grab onto molecules, and perhaps add a dose of their underlying biophysics.

Designing New Enzymes

Enzymes are proteins that catalyze life. They break down or construct new molecules, allowing us to digest food, build up our bodies, and maintain healthy brains. Synthetic enzymes can do even more, like sucking carbon dioxide from the atmosphere or breaking down plastic waste.

But designer enzymes are still tough to build. Most models are trained on natural enzymes, but biological function doesn’t always rely on the same structure to do the same thing. Enzymes that look vastly different can perform similar chemical reactions. AI evaluates structure, not function—meaning we’ll need to better understand how one leads to the other.

Like binders, enzymes also have “hotspots.” Scientists are racing to hunt these down with machine learning. There are early signs AI can design hotspots on new enzymes, but they still need to be heavily vetted. An active hotspot usually requires a good bit of scaffolding to work properly—without which it may not be able to grab its target or, if it does, let it go.

Enzymes are a tough nut to crack especially because they’re in motion. For now, AI struggles to model their transformations. This is, as it turns out, a challenge for the field at large.

Shape-Shifting Headaches

AI models are trained on static protein structures. These snapshots have been hard won with decades of work, in which scientists freeze a protein in time to image its structure. But these images only capture a protein’s most stable shape, rather than its shape in motion—like when a protein grabs onto a binder or when an enzyme twists to fit into a protein nook.

For AI to truly “understand” proteins, researchers will have to train models on the changing structures as proteins shapeshift. Biophysics can help model a protein’s twists and turns, but it’s extremely difficult. Scientists are now generating libraries of synthetic and natural proteins and gradually mutating each to see how simple changes alter their structures and flexibility.

Adding a bit of “randomness” to how an AI model generates new structures could also help. AF-Cluster, built on AlphaFold2, injected bits of uncertainty into its neural network processes when predicting a known shape-shifting protein and did well on multiple structures.

Protein prediction is a competitive race. But teams will likely need to work together too. Building a collaborative infrastructure for the rapid sharing of data could speed efforts. Adding so-called “negative data,” such as when AI-designed proteins or binders are toxic in cells, could also guide other protein designers. A harder problem is that verifying AI-designed proteins could take years—when the underlying algorithm has already been updated.

Regardless, there’s no doubt AI is speeding protein design. Let’s see what next year has to offer.

Image Credit: Baker Lab

2024-12-22 02:30:39

ARTIFICIAL INTELLIGENCE

OpenAI Upgrades Its Smartest AI Model With Improved Reasoning Skills
Will Knight | Wired
“The o3 model scores much higher on several measures than its predecessor, OpenAI says, including ones that measure complex coding-related skills and advanced math and science competency. It is three times better than o1 at answering questions posed by ARC-AGI, a benchmark designed to test an AI models’ ability to reason over extremely difficult mathematical and logic problems they’re encountering for the first time.”

ROBOTICS

New Physics Sim Trains Robots 430,000 Times Faster Than Reality
Benj Edwards | Ars Technica
“On Thursday, a large group of university and private industry researchers unveiled Genesis, a new open source computer simulation system that lets robots practice tasks in simulated reality 430,000 times faster than in the real world. …’One hour of compute time gives a robot 10 years of training experience. That’s how Neo was able to learn martial arts in a blink of an eye in the Matrix Dojo,’ wrote Genesis paper co-author Jim Fan on X, who says he played a ‘minor part’ in the research.”

AUTOMATION

Waymo Still Doing Better Than Humans at Preventing Injuries and Property Damage
Andrew J. Hawkins | The Verge
“They found that the performance of Waymo’s vehicles was safer than that of humans, with an 88 percent reduction in property damage claims and a 92 percent reduction in bodily injury claims. Across 25.3 million miles, Waymo was involved in nine property damage claims and two bodily injury claims. The average human driving a similar distance would be expected to have 78 property damage and 26 bodily injury claims, the company says.”

BIOTECH

A Third Person Has Received a Transplant of a Genetically Engineered Pig Kidney
Emily Mullin | Wired
“Towana Looney, 53, is off of kidney dialysis after undergoing the procedure at NYU Langone Health on November 25. She was discharged from the hospital on December 6, and her doctors say she is in good health. Her surgery is the latest in a series of similar procedures known as xenotransplantation, the practice of transplanting organs from one species to another.”

SPACE

We’re About to Fly a Spacecraft Into the Sun for the First Time
Eric Berger | Ars Technica
“On Christmas Eve, the Parker Solar Probe will make its closest approach yet to the Sun. It will come within just 3.8 million miles (6.1 million km) of the solar surface, flying into the solar atmosphere for the first time. Yeah, it’s going to get pretty hot. Scientists estimate that the probe’s heat shield will endure temperatures in excess of 2,500° Fahrenheit (1,371° C) on Christmas Eve, which is pretty much the polar opposite of the North Pole.”

TECH

Smart Glasses Won Me Over, and This Is the Pair That Did It
Joanna Stern | The Wall Street Journal
“Meta’s Ray-Bans and its prototype Orion hint at the future of smart glasses—sleek, stylish and truly wearable. This was the year smart glasses won me over. These lighter-weight face computers are the next step in how we interact with each other and our surroundings. This isn’t virtual-reality or a detour to the metaverse—you see the real world, just with digital stuff in it. And you look at your phone a lot less.”

ROBOTICS

‘Deep Research’ Shows How Google Can Win the AI Race
Mark Sullivan | Fast Company
“After I agreed to the plan, the agent got busy. …I watched as it raced over the internet and began compiling a list of sources. About three minutes later it had compiled a 60-item list of  source articles and publications, including research papers, journal articles, Medium posts, and Reddit discussions. From all these sources, the agent synthesized a 2,100-word, citation-filled essay that answered my question. Impressive.”

FUTURE

How to Disappear Completely
s.e. smith | The Verge
“We are watching the internet slip away as websites and apps rise and fall, swallowed by private equity, shuttered by burnout, or simply frozen in time—taking with it our memories, our cultural phenomena, our memes. …How comfortable are we with the disappearance of entire swaths of careers and artistic pursuits? And who is making these decisions—private equity or journalists, AI or archivists, billionaires or workers? The answers to these questions, and the way we define ourselves today, will shape our culture of the future.”

Image Credit: Resource Database on Unsplash

2024-12-20 23:00:54

In the late 1800s, Spanish neuroscientist Santiago Ramón y Cajal drew hundreds of images of neurons. His exquisite work influenced our understanding of what they look like: Cells with a bulbous center, a forest of tree-like branches on one end, and a long, smooth tail on the other.

Centuries later, these images remain textbook. But a controversial study now suggests Ramón y Cajal, and neuroscientists since, might have missed a crucial detail.

A team from Johns Hopkins University found tiny “bubbles” dotted along the long tail—called the axon. Normally depicted as a mostly smooth, cylindrical cable, axons may instead look like “pearls on a string.”

Why care? Axons transmit electrical signals connecting the neural networks that give rise to our thoughts, memories, and emotions. Small changes in their shape could alter these signals and potentially the brain’s output—that is, our behavior.

“Understanding the structure of axons is important for understanding brain cell signaling,” Shigeki Watanabe at the Johns Hopkins University School of Medicine, who led the study, said in a press release.

The work took advantage of a type of microscopy that better preserves neuron structure. In three types of mouse neurons—some grown in petri dishes, others from adult mice and mouse embryos—the team consistently saw the nanopearls, suggesting they’re part of an axon’s normal shape.

“These findings challenge a century of understanding about axon structure,” said Watanabe.

The nanopearls weren’t static. Adding sugar to the neurons’ liquid environment or stripping neurons of cholesterol in their membranes—the fatty protective outer layer—altered the nanopearls’ size and distribution and the speed signals traveled down axons.

Reactions to the study were split. Some scientist welcomed the findings. Over the last 70 years, scientists have extensively studied axon shape and recognized its complex structure. With improving microscope technologies, discovering new structures isn’t surprising, but it is rather exciting.

Others are more skeptical. Speaking to Science, Christophe Leterrier of Aix-Marseille University, who was not involved in the study, said: “I think it’s true that [the axon is] not a perfect tube, but it’s not also just this kind of accordion that they show.”

Cable With a Chance of Stress Balls

Axons stretch inches in the brain with diameters 100 times thinner than a human hair. Although mostly tubular in shape, they’re dotted with occasional bubbles, called synaptic varicosities, that contain chemicals for the transmission of information with neighboring neurons. These long branches mainly come in two types: Some are wrapped in fatty sheaths and others are “bare,” without the cushioning.

Although often compared to tree branches, axons are shapeshifters. A brief burst of electrical signaling, for example, causes synaptic varicosities to temporarily expand by 20 percent. The axons also grow slightly wider for a longer period, before settling back to their normal size.

These tiny changes have large impacts on brain computation. Like an electrical cable that can change its properties, they fine-tune signal strength between networks, and in turn, the overall function of neurons.

Axons have another trick up their sleeves: They shrink up into “stress balls” with injury, such as an unsuspected blow to the head during sports, or in Alzheimer’s or Parkinson’s disease. Stress balls are relatively large compared to synaptic varicosities. But they’re transient. The structures eventually loosen and regain a tubular shape. Rather than harmful, they likely protect the brain by limiting damage to smaller regions and nurture axons during recovery.

But axons’ shape-shifting prowess is temporary and often only under duress. What do axons look like in a healthy brain?

Pearls on a String

Roughly a decade ago, Watanabe noticed tiny bubbles in the axons of roundworms while developing a new microscopy technique. Although the structures were much smaller and more tightly packed than stress balls, he banked the results as a curiosity but didn’t investigate further. Years later, the University of Bergen’s Pawel Burkhardt also noticed pearly axons in comb jellies, a tiny marine invertebrate.

In the new study, Watanabe and colleagues revisited the head-scratching findings, armed with a newer microscopy technique: High-pressure freezing. To image fine details in the brain, scientists usually dose it with multiple chemicals to set neurons in place. The treated brains are then sliced extremely thin, and the pieces are individually scanned with a microscope.

The procedure takes days. Without care, it can distort a neuron’s membrane and damage or even shred delicate axons. In contrast, high-pressure freezing better locks in the cell’s shape.

Using an electron microscope—which outlines a cell’s structure by shooting beams of electrons at it—the team studied “bare” axons from three sources: mouse neurons grown in a lab dish and those from thin slices of adult and embryonic mouse brains.

All axons had the peculiar pearl-like blobs along their entire length. Roughly 200 nanometers across, the nanopearls are far smaller than stress balls, and they’re spaced closer together. The beads likely form due to biophysics. Recent studies show that under tension, sections of a long tube crumple into beads—a phenomenon dubbed “membrane-driven instability.” Why this happens and its impact on brain function remains mostly mysterious, but the team has ideas.

Seeing Is Believing?

Using mathematical simulations, they modeled how changes in the surrounding environment impacts an axon’s pearling and its electrical transmission.

Axons are surrounded by a goopy, protective protein gel, like a bubble suit. But they still experience physical forces—like when we rapidly snap our heads. Simulations found that physical tension surrounding neurons is a key player in managing axon pearling.

In another test, the team stripped cholesterol from the neurons—a component in their membranes—to make them more flexible and fluid-like. The tweak lessened pearling in simulations and slowed electrical signals as they passed through the simulated axon.

Recording electrical signals from living mouse neurons led to similar results. Smaller and more compactly packed nanopearls slowed signals down, whereas axons with larger and widely spaced ones led to faster transmission.

The results suggest an “intriguing idea” that changing biophysical forces could directly alter the speed of the brain’s electrical signaling, wrote the authors.

Not everyone is convinced.

Some scientists think the nanopearls are an artifact stemming from the preparation process. “While quick freezing is an extremely rapid process, something may happen during the manipulation of the sample” to cause beading, Pietro De Camilli at the Yale School of Medicine, who was not involved in the study, told Science. Others question if—like a stress ball—the nanopearls form during stress and will eventually unfold. We don’t yet know: Microscopy is a snapshot in time, rather than a movie.

Despite pushback, the team is turning to human axons. Healthy human brain tissue is hard to come by. They plan to look for signs of nanopearls in brain tissue removed during epilepsy surgery and from those who passed away due to neurodegenerative diseases. Brain organoids, or “mini-brains” developed from healthy people could also help decipher axon shape.

Regardless, the study spurs the question: When it comes to brain anatomy, what else have we missed?

Image Credit: Bioscience Image Library by Fayette Reynolds on Unsplash

2024-12-19 23:00:22

Brain implants have improved dramatically in recent years, but they’re still invasive and unreliable. A new kind of brain-machine interface using living neurons to form connections could be the future.

While companies like Neuralink have recently provided some flashy demos of what could be achieved by hooking brains up to computers, the technology still has serious limitations preventing wider use.

Non-invasive approaches like electroencephalograms (EEGs) provide only coarse readings of neural signals, limiting their functionality. Directly implanting electrodes in the brain can provide a much clearer connection, but such risky medical procedures are hard to justify for all but the most serious conditions.

California-based startup Science Corporation thinks that an implant using living neurons to connect to the brain could better balance safety and precision. In recent non-peer-reviewed research posted on bioarXiv, the group showed a prototype device could connect with the brains of mice and even let them detect simple light signals.

“The principal advantages of a biohybrid implant are that it can dramatically change the scaling laws of how many neurons you can interface with versus how much damage you do to the brain,” Alan Mardinly, director of biology at Science Corporation, told New Scientist.

The company’s CEO Max Hodak is a former president of Neuralink, and his company also produces a retinal implant using more conventional electronics that can restore vision in some patients. But the company has been experimenting with so-called “biohybrid” approaches, which Hodak thinks could provide a more viable long-term solution for brain-machine interfaces.

“Placing anything into the brain inevitably destroys some amount of brain tissue,” he wrote in a recent blog post. “Destroying 10,000 cells to record from 1,000 might be perfectly justified if you have a serious injury and those thousand neurons create a lot of value—but it really hurts as a scaling characteristic.”

Instead, the company has developed a honeycomb-like structure made of silicon featuring more than 100,000 “microwells”—cylindrical holes roughly 15 micrometers deep. Individual neurons are inserted into each of these microwells, and the array can then be surgically implanted onto the surface of the brain.

The idea is that while the neurons remain housed in the implant, their axons—long strands that carry nerve signals away from the cell body—and their dendrites—the branched structures that form synapses with other cells—will be free to integrate with the host’s brain cells.

To see if the idea works in practice they installed the device in mice, using neurons genetically modified to react to light. Three weeks after implantation, they carried out a series of experiments where they trained the mice to respond whenever a light was shone on the device. The mice were able to detect when this happened, suggesting the light-sensitive neurons had merged with their native brain cells.

While it’s early days, the approach has significant benefits. You can squeeze a lot more neurons into a millimeter-scale chip than electrodes and each of those neurons can form many connections. That means the potential bandwidth of a biohybrid device could be much more than a conventional neural implant. The approach is also much less damaging to the patient’s brain.

However, the lifetime of these kinds of devices could be a concern—after 21 days, only 50 percent of the neurons had survived. And the company needs to find a way to ensure the neurons don’t illicit a negative immune response in the patient.

If the approach works though, it could be an elegant and potentially safer way to merge man and machine.

Image Credit: Science Corporation

2024-12-18 23:00:19

When Jeanne Calment died at the age of 122, her longevity had researchers scratching their heads. Although physically active for most of her life, she was also a regular smoker and enjoyed wine—lifestyle choices that are generally thought to decrease healthy lifespan.

Teasing apart the intricacies of human longevity is complicated. Diet, exercise, and other habits can change the trajectory of a person’s health as they grow older. Genetics also plays a role—especially during the twilight years. But experiments to test these ideas are difficult, in part because of our relatively long lifespan. Following a large population of people as they age is prohibitively expensive, and results could take decades. So, most studies have turned to animal aging models—including flies, rodents, and dogs—with far shorter lives.

But what if we could model human “aging in a dish” using cells derived from people with exceptionally long lives?

A new study, published in Aging Cell, did just that. Leveraging blood draws from the New England Centenarian Study—the largest and most comprehensive database of centenarians—they transformed blood cells into induced-pluripotent stem cells (iPSCs).

These cells contain their donor’s genetic blueprint. In essence, the team created a biobank of cells that could aid researchers in their search for longevity-related genes.

“Models of human aging, longevity, and resistance to and/or resilience against disease that allow for the functional testing of potential interventions are virtually non-existent,” wrote the team.

They’ve already shared these “super-aging” stem cells with the rest of the longevity community to advance understanding of the genes and other factors contributing to a healthier, longer life.

“This bank is really exciting,” Chiara Herzog, a longevity researcher at Kings College London, who was not involved in the study, told Nature.

Precious Resource

Centenarians are rare. According to the Pew Research Center, based on data from the US Census Bureau, they make up only 0.03 percent of the country’s population. Across the globe, roughly 722,000 people have celebrated their 100th birthday—a tiny fraction of the over eight billion people currently on Earth.

Centenarians don’t just live longer. They’re also healthier, even in extreme old age, and less likely to suffer age-related diseases, such as dementia, Type 2 diabetes, cancer, or stroke. Some evade these dangerous health problems altogether until the very end.

What makes them special? In the last decade, several studies have begun digging into their genes to see which are active (or not) and how this relates to healthy aging. Others have developed aging clocks, which use myriad biomarkers to determine a person’s biological age—that is, how well their bodies are working. Centenarians frequently stood out, with a genetic landscape and bodily functions resembling people far younger than expected for their chronological age.

Realizing the potential for studying human aging, the New England Centenarian Study launched in 1995. Now based at Boston University and led by Tom Perls and Stacy Andersen, both authors of the new study, the project has recruited centenarians through a variety of methods—voter registries, news articles, or mail to elderly care facilities.

Because longevity may have a genetic basis, their children were also invited to join, with spouses serving as controls. All participants reported on their socioeconomic status and medical history. Researchers assessed their cognition on video calls and screened for potential mental health problems. Finally, some participants had blood samples taken. Despite their age, many centenarians remained sharp and could take care of themselves.

Super-Ager Stem Cells

The team first tested participants with a variety of aging clocks. These measured methylation, which shuts genes down without changing their DNA sequences. Matching previous results, centenarians were, on average, six and a half years younger than their chronological age.

The anti-aging boost wasn’t as prominent in their children. Some had higher biological ages and others lower. This could be because of variation in who inherited a genetic “signature” associated with longevity, wrote the team.

They then transformed blood cells from 45 centenarians into iPSCs. The people they chose were “at the extremes of health and functionality,” the team wrote. Because of their age, they initially expected that turning back the clock might not work on old blood cells.

Luckily, they were wrong. Several proteins showed the iPSCs were healthy and capable of making other cells. They also mostly maintained their genomic integrity—although surprisingly, cells from three male centenarians showed a slight loss of the Y chromosome.

Previous studies have found a similar deletion pattern in blood cells from males over 70 years of age. It could be a marker for aging and a potential risk factor for age-related conditions such as cancer and heart disease. Women, on average, live longer than men. The findings “allow for interesting research opportunities” to better understand why Y chromosome loss happens.

Unraveling Aging

Turning blood cells into stem cells erases signs of aging, especially those related to the cells’ epigenetic state. This controls whether genes are turned on or off, and it changes with age. But the underlying genetic code remains the same.

If the secrets to longevity are, even only partially, hidden in the genes, these super-aging stem cells could help researchers figure out what’s protective or damaging, in turn prompting new ideas that slow the ticking of the clock.

In one example, the team nudged the stem cells to become cortical neurons. These neurons form the outermost part of the brain responsible for sensing and reasoning. They’re also the first to decay in dementia or Alzheimer’s disease. Those derived from centenarians better fought off damage, such as rapidly limiting the spread of toxic proteins that accumulate with age.

Researchers are also using the cells to test for resilience against Alzheimer’s. Another experiment observed cell cultures made of healthy neurons, immune cells, and astrocytes. The latter, supporting cells that help keep brains healthy, were created using centenarian stem cells. Astrocytes have increasingly been implicated in Alzheimer’s, but their role has been hard to study in humans. Those derived from centenarian stem cells offer a way forward.

Each line of centenarian stem cells is linked to its donor—their demographics, cognitive, and physical state. This additional information could guide researchers in choosing the best centenarian cell line for their investigations into different aspects of aging. And because the cells can be transformed into a wide variety of tissues that decline with age—muscles, heart, or immune cells—they offer a new way to explore how aging affects different organs, and at what pace.

“The result of this work is a one-of-a-kind resource for studies of human longevity and resilience that can fuel the discovery and validation of novel therapeutics for aging-related disease,” wrote the authors.

Image Credit: Danie Franco on Unsplash