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2026-02-25 05:50:18

In the early morning of April 2, 2024, the night sky over southern California lit up with flashes of blazing light. Residents were bewildered. Were they missiles? A crashing plane? The unusual activity confused even experts—until they realized it was a disposable part of China’s Shenzhou-15 spacecraft burning up in the atmosphere as it returned to Earth.

Scientists knew the event was on the horizon and had mapped out a potential entry point over the northern Atlantic Ocean, thousands of miles from metropolitan Los Angeles. Luckily, no one was hurt as the module broke apart over the city.

But the incident underlined an uncomfortable truth. We’re nowhere near being able to accurately predict the path of space debris as it rains down. As more spacecraft are launched and reenter the atmosphere, damage to infrastructure and Earthlings is only a matter of time.

Researchers are looking into a solution from an unexpected source: sensors that measure earthquakes. As space debris plummets to the ground at hypersonic speeds, it generates a sonic boom. This causes a slight tremor in the ground that the sensors readily register.

Using data from a network of these sensors, Benjamin Fernando at Johns Hopkins University and Constantinos Charalambous at Imperial College London developed a system that can reconstruct the path of space debris with unprecedented accuracy. They used the system to map Shenzhou-15’s speed, altitude, gradual disintegration, and final destination.

To be clear, this isn’t an early warning system. Because sonic booms lag behind the objects causing them, the method is like a forensic reconstruction of space debris’ final journey. Still, it can quickly identify potential fall-out zones for faster retrieval and cleanup, which is especially important if the junk is toxic or radioactive.

The work is “a crucial step toward near-real-time monitoring of natural and anthropogenic objects entering from space,” wrote Chris Carr at the Los Alamos National Laboratory, who was not involved in the work.

An Embarrassment of Riches

Launching satellites was once a colossal undertaking. But thanks to innovations by SpaceX and national space agencies across the world, it’s becoming far more routine.

These spacecraft have already changed life on Earth. Thousands of Starlink satellites beam the internet to previous dead zones and disaster areas. Miniature satellites are now an affordable research platform scientists use to profile weather, measure solar winds, and track the effects of microgravity and radiation on living cells. And a new space race will only grow the fleets of spacecraft already blanketing the Earth.

“The big change that we’ve seen since 2020 is the rise of satellite mega-constellations…companies not putting up a dozen spacecraft, but maybe a thousand or ten thousand over the course of a few years,” Fernando told Science.

Mega-constellations have already caused problems for scientists by polluting astronomical images with  bright streaks. They may also increase the rate at which space debris rains down. In a paper describing their system, Fernando and Charalambous write that in 2025 there were roughly four to five re-entries a day, and the numbers are likely to rapidly grow.

We already monitor spacecraft in orbit. Telescopes bring real-time visuals. Radar tracks location and speed. But these tools struggle as a spacecraft drifts into the Earth’s upper atmosphere.

The interaction between fragments and air becomes “really chaotic,” said Fernando. “We can no longer predict with particularly good accuracy exactly where [and when] a piece of re-entering space debris is going to enter the atmosphere.”

Radar can track spacecraft parts as they return to Earth, but the technology is limited to small regions of the world and barely covers the oceans. Even when we know the final fate of a piece of debris, it’s often difficult to reconstruct its full trajectory.

Supersonic Waves

The new work was inspired by the way scientists track meteoroids using a dense network of earthquake sensors to detect tiny vibrations in the ground.

The Shenzhou-15 capsule entered the atmosphere going roughly 25 to 30 times the speed of sound. Like a fighter jet, it triggered a powerful sonic boom roughly 80 kilometers (50 miles) above the ground. The boom traveled to Earth’s surface where seismic sensors detected it.

It’s like picking up an earthquake, only “in this case the waves are coming from up versus with earthquakes they tend to come from down,” said Fernando.

Southern California is heavily dotted with seismic sensors, each measuring activity in a small area. To model the spacecraft’s path and speed, the team compiled the largest sonic boom each sensor registered and its arrival time and compiled the data into a map.

The map captured where, when, and how the capsule broke down as it hurtled through the atmosphere. Earlier on, the sensors recorded large, discrete signals. These later became more scattered and complex, suggesting the capsule gradually disintegrated rather than blowing up all at once.

The results are “consistent with on-ground observations, including videos and witness reports of multiple fireballs flying across the sky,” wrote Carr. After more deeply combing through the data, the team showed it could also be used to measure the size of each piece of decaying debris.

The spacecraft’s sonic signature differed from those generated by meteorites, making it possible to tease apart human-made objects and those of natural origins.

Differentiating the two categories is key. Meteorites pose “kinetic risk” as chunks slam into the ground, damaging cars, houses, and other infrastructure. Human space debris, however, could also contain metals, toxic or flammable material, or in rare cases, radioactive components. The model also reconstructed how different parts of the spacecraft disintegrated, potentially making it easier to predict whether chunks have burned up completely in the atmosphere or have reached the ground, making it useful for recovery or clean-up missions.

Crash-and-burn isn’t a spacecraft’s only destiny. Engineers are also working to move defunct satellites into higher orbits that would be stable for “thousands of years” according to Fernando, though this doesn’t solve the space junk problem. Other researchers are exploring ways to design spacecraft such that they completely burn up both safely and predictably.

For now, the technology works best in places with lots of seismic sensors, which are rare. But there’s a push to add sensors in places that are vulnerable due to sensitive ecology or geology at prices far lower than building radar systems to track re-entry, said Fernando.

The post More Space Junk Is Plummeting to Earth. Seismic Sensors Can Track It by the Sonic Booms. appeared first on SingularityHub.

2026-02-24 06:35:51

The inner workings of large AI systems remain largely opaque, raising significant safety and trust issues. Researchers have now developed a technique to extract and manipulate the internal concepts governing model behavior, providing a new way to understand and steer their activity.

Modern AI models are marvels of engineering, but even their creators remain in the dark about how they represent knowledge internally. This is why subtle shifts in prompting can produce surprisingly different outputs. Simply asking a model to show its work before answering often improves accuracy, while certain deliberately malicious prompts can override built-in safety features.

This has motivated significant research aimed at teasing out the patterns of activity in these models’ neural networks that correspond to specific concepts. Investigators hope to use these methods to better understand why models behave certain ways and potentially modify their behavior on the fly.

Now researchers have unveiled an efficient new way of extracting concepts from models that works across language, reasoning, and vision algorithms. In a paper in Science, the researchers used these concepts to both monitor and effectively steer model behavior.

“Our results illustrate the power of internal representations for advancing AI safety and model capabilities,” the authors write. “We showed how these representations enabled model steering, through which we exposed vulnerabilities and improved model capabilities.”

Key to the team’s approach is a new algorithm called the Recursive Feature Machine (RFM). They trained the algorithm on pairs of prompts—some containing a concept of interest, others not—and then identified patterns of activity in the model’s neural network tracking each concept.

This allows the algorithm to learn “concept vectors”—essentially patterns of activity that nudge the model in the direction of a specific concept. The vectors can be used to modify the model’s internal processes when it’s generating an output to steer it toward or away from specific concepts or behaviors.

To test the approach, the researchers asked GPT-4o to produce 512 concepts across five concept classes and generate training data on each. They extracted concept vectors from the data and used the vectors to steer the behavior of several large AI models.

The approach worked well across a broad range of model types, including large language models, vision-language models, and reasoning models. Surprisingly, they found newer, larger, and better-performing models were actually more steerable than some smaller ones.

Crucially, the team showed they could use the technique to expose and address serious vulnerabilities in the models. In one test, they created a vector for the concept of “anti-refusal,” which allowed them to bypass built-in safety features in vision-language models to prevent them from giving advice on how take drugs. But they also learned a vector for “anti-deception,” which they successfully used to steer a model away from giving misleading answers.

One of the study’s more interesting findings was that the extracted features were transferable across languages. A concept vector learned with English training data could be used to alter outputs in other languages. The researchers also found they could combine multiple concept vectors to manipulate model behavior in more sophisticated ways.

But the new technique’s real power is in its efficiency. It took fewer than 500 training samples and less than a minute of processing time on a single Nvidia A100 GPU to identify activity patterns associated with a concept and steer towards it.

The researchers say this could not only make it possible to systematically map concepts within large AI models, but it could also lead to more efficient ways of tweaking model behavior after training compared to existing methods.

The approach is still a long way from delivering complete model transparency. But it’s a useful addition in the growing arsenal of model analysis tools that will become increasingly important as AI pushes deeper into all of our lives.

The post Researchers Break Open AI’s Black Box—and Use What They Find Inside to Control It appeared first on SingularityHub.

2026-02-21 23:00:00

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2026-02-20 23:00:00

Ahead of an artificial intelligence conference held last April, peer reviewers considered papers written by “Carl” alongside other submissions. What the reviewers did not know was that, unlike other authors, Carl wasn’t a scientific researcher, but rather an AI system built by the tech company Autoscience Institute, which says that the model can accelerate artificial intelligence research. And at least according to the humans involved in the review process, the papers were good enough for the conference: In the double-blind peer review process, three of the four papers, which were authored by Carl (with varying levels of human input) were accepted.

Carl joins a growing group of so-called “AI scientists,” which include Robin and Kosmos, research agents developed by the San Francisco-based nonprofit research lab FutureHouse, and The AI Scientist, introduced by the Japanese company Sakana AI, among others. AI scientists are made up from multiple large language models. For example, Carl differs from chatbots in that it’s devised to generate and test ideas and produce findings, said Eliot Cowan, co-founder of Autoscience Institute. Companies say these AI-driven systems can review literature, devise hypotheses, conduct experiments, analyze data, and produce novel scientific findings with varying degrees of autonomy. 

The goal, said Cowan, is to develop AI systems that can increase efficiency and scale up the production of science. And other companies like Sakana AI have indicated a belief that AI scientists are unlikely to replace human ones.

Still, the automation of science has stirred a mix of concern and optimism among the AI and scientific communities. “You start feeling a little bit uneasy, because, hey, this is what I do,” said Julian Togelius, a professor of computer science at New York University who works on artificial intelligence. “I generate hypotheses, read the literature.”

AI scientists are made up from multiple large language models. Carl differs from chatbots in that it’s devised to generate and test ideas and produce findings.

Critics of these systems, including scientists who themselves study artificial intelligence, worry that AI scientists could displace researchers of the next generation, flood the system with low quality or untrustworthy data, and erode trust in scientific findings. The advancements also pose a question about where AI fits into the inherently social and human scientific enterprise, said David Leslie, director of ethics and responsible innovation research at The Alan Turing Institute in London. “There’s a difference between the full-blown shared practice of science and what’s happening with a computational system.”

In the last five years, automated systems have already led to important scientific advances. For example, AlphaFold, an AI system developed by Google DeepMind, was able to predict the three-dimensional structures of proteins with high resolution more quickly than scientists in the lab. The developers of AlphaFold, Demis Hassabis and John Jumper, won a 2024 Nobel Prize in Chemistry for their protein prediction work.

Now companies have expanded to integrate AI into other aspects of the scientific discovery, creating what Leslie calls computational Frankensteins. The term, he says, refers to the convergence of various generative AI infrastructure, algorithms, and other components used “to produce applications that attempt to simulate or approximate complex and embodied social practices (like practices of scientific discovery).” In 2025 alone, at least three companies and research labs—Sakana AI, Autoscience Institute, and FutureHouse (which launched a commercial spinoff called Edison Scientific in November)—have touted their first “AI-generated” scientific results. Some US government scientists have also embraced artificial intelligence: Researchers at three federal labs, Argonne National Laboratory, the Oak Ridge National Laboratory, and Lawrence Berkeley National Laboratory, have developed AI-driven, fully automated materials laboratories.

“You start feeling a little bit uneasy, because, hey, this is what I do.”

Indeed, these AI systems, like large language models, could be potentially used to synthesize literature and mine vast amounts of data to identify patterns. Particularly, they may be useful in material sciences, in which AI systems can design or discover new materials, and in understanding the physics of subatomic particles.

Systems can “basically make connections between millions, billions, trillions of variables” in ways that humans can’t, said Leslie. “We don’t function that way, and so just in virtue of that capacity, there are many, many opportunities.” For example, FutureHouse’s Robin mined literature and identified a potential therapeutic candidate for a condition that causes vision loss, proposed experiments to test the drug, and then analyzed the data.

But researchers have also raised red flags. While Nihar Shah, a computer scientist at Carnegie Mellon University, is “more on the optimistic side” about how AI systems can enable new discoveries, he also worries about AI slop, or the overflow of the scientific literature with AI-generated studies of poor quality and little innovation. Researchers have also pointed out other important caveats regarding the peer review process.

In a recent study that is yet to be peer reviewed, Shah and colleagues tested two AI models that aid in the scientific process: Sakana’s AI Scientist-v2 (an updated version of the original) and Agent Laboratory, a system developed by AMD, a semiconductor company, in collaboration with Johns Hopkins University, to perform research assistant tasks. Shah’s goal with the study was to examine where these systems might be failing.

One AI system, the AI Scientist-v2, reported 95 and sometimes even 100 percent accuracy on a specified task, which was impossible given that the researchers had intentionally introduced noise into the dataset. Seemingly, both systems were sometimes making up synthetic datasets to run the analysis on while stating in the final report that it was done on the original dataset. To address this, Shah and his team developed an algorithm to flag methodological pitfalls they identified, such as cherry-picking favorable datasets to run their analysis and selective reporting of positive results.

Some research suggests generative AI systems have also failed to produce innovative ideas. One study concluded that one generative AI chatbot, ChatGPT4, can only produce incremental discoveries, while a recent study published last year in Science Immunology found that, despite being able to synthesize the literature accurately, AI chatbots failed to generate insightful hypotheses or experimental proposals in the field of vaccinology. (Sakana AI and FutureHouse did not respond to requests for comments.)

Even if these systems continue being used, a human place in the lab will likely not disappear, Shah said. “Even if AI scientists become super-duper duper capable, still there’ll be a role for people, but that itself is not entirely clear,” said Shah, “as to how capable will AI scientists be and how much would still be there for humans?”

Historically, science has been a deeply human enterprise, which Leslie described as an ongoing process of interpretation, world-making, negotiation, and discovery. Importantly, he added, that process is dependent on the researchers themselves and the values and biases they hold.

A computational system trained to predict the best answer, in contrast, is categorically distinct, Leslie said. “The predictive model itself is just getting a small slice of a very complex and deep, ongoing practice, which has got layers of institutional complexity, layers of methodological complexity, historical complexity, layers of discrimination that have arisen from other injustices that define who gets to do science, who doesn’t get to do science, and what science has done for whom, and what science has not done because people aren’t sending to have their questions answered.”

Researchers at three federal labs have developed AI-driven, fully automated materials laboratories.

Rather than as a substitute for scientists, some experts see AI scientists as an additional, augmentative tool for researchers to help draw out insights, much like a microscope or a telescope. Companies also say they do not intend to replace scientists. “We do not believe that the role of a human scientist will be diminished. If anything, the role of a scientist will change and adapt to new technology, and move up the food chain,” Sakana AI wrote when the company announced its AI Scientist.

Now researchers are beginning to ponder what the future of science might look like alongside AI systems, including how to vet and validate their output. “We need to be very reflective about how we classify what’s actually happening in these tools, and if they’re harming the rigor of science as opposed to enriching our interpretive capacity by functioning as a tool for us to use in rigorous scientific practice,” said Leslie.

Going forward, Shah proposed, journals and conferences should vet AI research output by auditing log traces of the research process and generated code to both validate the findings and identify any methodological flaws. And companies, such as Autoscience Institute, say they are building systems to make sure that experiments hold to the same ethical standards as “an experiment run by a human at an academic institution would have to meet,” said Cowan. Some of the standards baked into Carl, Cowan noted, include preventing false attribution and plagiarism, facilitating reproducibility, and not using human subjects or sensitive data, among others.

While some researchers and companies are focused on improving the AI models, others are stepping back to ask how the automation of science will affect the people currently doing the research. Now is a good time to begin to grapple with such questions, said Togelius. “We got the message that AI tools that make that make us better at doing science, that’s great. Automating ourselves out of the process is terrible,” he added “How do we do one and not the other?” 

This article was originally published on Undark. Read the original article.

The post What the Rise of AI Scientists May Mean for Human Research appeared first on SingularityHub.

2026-02-19 23:00:00

You’re driving in a winter storm at midnight. Icy rain smashes your windshield, immediately turning it into a sheet of frost. Your eyes dart across the highway, seeking any movement that could be wildlife, struggling vehicles, or highway responders trying to pass. Whether you find safe passage or meet catastrophe hinges on how fast you see and react.

Even experienced drivers struggle with bad weather. For self-driving cars, drones, and other robots, a snowstorm could cause mayhem. The best computer-vision algorithms can handle some scenarios, but even running on advanced computer chips, their reaction times are roughly four times greater than a human’s.

“Such delays are unacceptable for time-sensitive applications…where a one-second delay at highway speeds can reduce the safety margin by up to 27m [88.6 feet], significantly increasing safety risks,” Shuo Gao at Beihang University and colleagues wrote in a recent paper describing a new superfast computer vision system.

Instead of working on the software, the team turned to hardware. Inspired by the way human eyes process movement, they developed an electronic replica that rapidly detects and isolates motion.

The machine eye’s artificial synapses connect transistors into networks that detect changes in the brightness of an image. Like biological neural circuits, these connections store a brief memory of the past before processing new inputs. Comparing the two allows them to track motion.

Combined with a popular vision algorithm, the system quickly separates moving objects, like walking pedestrians, from static objects, like buildings. By limiting its attention to motion, the machine eye needs far less time and energy to assess and respond to complex environments.

When tested on autonomous vehicles, drones, and robotic arms, the system sped up processing times by roughly 400 percent and, in most cases, surpassed the speed of human perception without sacrificing accuracy.

“These advancements empower robots with ultrafast and accurate perceptual capabilities, enabling them to handle complex and dynamic tasks more efficiently than ever before,” wrote the team.

Two Motion Pictures

A mere flicker in the corner of an eye captures our attention. We’ve evolved to be especially sensitive to movement. This perceptual superpower begins in the retina. The thin layer of light-sensitive tissue at the back of the eye is packed with cells fine-tuned to detect motion.

Retinal cells are a curious bunch. They store memories of previous scenes and spark with activity when something in our visual field shifts. The process is a bit like an old-school film reel: Rapid transitions between still frames lead to the perception of movement.

Every cell is tuned to detect visual changes in a particular direction—for example, left to right or up to down—but is otherwise dormant. These activity patterns form a two-dimensional neural map that the brain interprets as speed and direction within a fraction of a second.

“Biological vision excels at processing large volumes of visual information” by focusing only on motion, wrote the team. When driving across an intersection, our eyes intuitively zero in on pedestrians, cyclists, and other moving objects.

Computer vision takes a more mathematical approach.

A popular type called optical flow analyzes differences between pixels across visual frames. The algorithm segments pixels into objects and infers movement based on changes in brightness. This approach assumes that objects maintain brightness as they move. A white dot, for example, remains a white dot as it drifts to the right, at least in simulations. Pixels near each other should also move in tandem as a marker for motion.

Although inspired by biological vision, optical flow struggles in real-world scenarios. It’s an energy hog and can be laggy. Add in unexpected noise—like a snowstorm—and robots running optical flow algorithms will have trouble adapting to our messy world.

Two-Step Solution

To get around these problems, Gao and colleagues built a neuron-inspired chip that dynamically detects regions of motion and then focuses an optical flow algorithm on only those areas.

Their initial design immediately hit a roadblock. Traditional computer chips can’t adjust their wiring. So the team fabricated a neuromorphic chip that, true to its name, computes and stores information at the same spot, much like a neuron processes data and retains memory.

Because neuromorphic chips don’t shuttle data from memory to processors, they’re far faster and more energy-efficient than classical chips. They outshine standard chips in a variety of tasks, such as sensing touch, detecting auditory patterns, and processing vision.

“The on-device adaptation capability of synaptic devices makes human-like ultrafast visual processing possible,” wrote the team.

The new chip is built from materials and designs commonly used in other neuromorphic chips. Similar to the retina, the array’s artificial synapses encode differences in brightness and remember these changes by adjusting their responses to subsequent electrical signals.

When processing an image, the chip converts the data into voltage changes, which only activate a handful of synaptic transistors; the others stay quiet. This means the chip can filter out irrelevant visual data and focus optical flow algorithms on regions with motion only.

In tests, the two-step setup boosted processing speed. When analyzing a movie of a pedestrian about to dash across a road, the chip detected their subtle body position and predicted what direction they’d run in roughly 100 microseconds—faster than a human. Compared to conventional computer vision, the machine eye roughly doubled the ability of self-driving cars to detect hazards in a simulation. It also improved the accuracy of robotic arms by over 740 percent thanks to better and faster tracking.

The system is compatible with computer vision algorithms beyond optical flow, such as the YOLO neural network that detects objects in a scene, making it adjustable for different uses.

“We do not completely overthrow the existing camera system; instead, by using hardware plug-ins, we enable existing computer vision algorithms to run four times faster than before, which holds greater practical value for engineering applications,” Gao told the South China Morning Post.

The post This ‘Machine Eye’ Could Give Robots Superhuman Reflexes appeared first on SingularityHub.

2026-02-18 06:56:48

Gene editing is a numbers game. For any genetic tweaks to have notable impact, a sufficient number of targeted cells need to have the disease-causing gene deleted or replaced.

Despite a growing gene-editing arsenal, the tools share a common shortcoming: They only work once in whatever cells they reach. Viruses, in contrast, readily self-replicate by hijacking their host’s cellular machinery and then, their numbers swelling, drift to infect more cells.

This strategy inspired a team at the University of California, Berkeley and collaborators to modify the gene editor, CRISPR-Cas9, to similarly replicate and spread to surrounding cells.

Led by gene-editing pioneer and Nobel Prize winner, Jennifer Doudna, the scientists added genetic instructions for cells to make a virus-like transporter that can encapsulate the CRISPR machinery. Once manufactured in treated cells, the CRISPR cargo ships to neighboring cells.

The upgraded editor was roughly three times more effective at gene editing lab-grown cells compared to standard CRISPR. It also lowered the amount of a harmful protein in mice with a genetic metabolic disorder, while the original version had little effect at the same dose.

The technology is “a conceptual shift in the delivery of therapeutic cargo,” wrote the team in a bioRxiv preprint.

Recoding Genetics

CRISPR has completely transformed gene therapy. In just a few years, the technology exploded from a research curiosity into a biotechnology toolbox that can tackle previously untreatable inherited diseases. Some CRISPR versions delete or inactivate pathogenic genes. Others swap out single mutated DNA letters to restore health.

The first CRISPR therapies focus on blood disorders and require doctors to remove cells from the body for treatment. The therapies are tailored to each patient but are slow and costly. To bring gene therapy to the masses, scientists are developing gene editors that edit DNA directly inside the body with a single injection.

From reprogramming faulty blood cells and treating multiple blood disorders to lowering dangerous levels of cholesterol and tackling mitochondrial diseases, CRISPR has already proven it has the potential to unleash a new universe of gene therapies at breakneck speed.

Gene editors “promise to revolutionize medicine by overriding or correcting the underlying genetic basis of disease,” wrote the team. But all these tools are throttled by one basic requirement: Enough cells have to be edited that they override their diseased counterparts.

How many depends on the genetic disorder. Treatments need to correct around 20 percent of blood stem cells to keep sickle cell disease at bay. For Duchenne muscular dystrophy, an inherited disease that weakens muscles, over 15 percent of targeted cells need to be edited.

These numbers may seem low, but they’re still challenging for current CRISPR technologies.

“Once delivered to cells, editing machinery is confined to the cells it initially enters,” wrote the team. To compensate, scientists often increase the dosage, but this risks triggering immune attacks and off-target genetic edits.

Work Smarter, Not Harder

Although membrane-bound and seemingly isolated, cells are actually quite chatty.

Some cells package mRNA molecules into bubbles and eject them towards their neighbors, essentially sharing instructions for how to make proteins. Other cells, including neurons, form extensive nanotube networks that shuttle components between cells, such as energy-producing mitochondria.

Inspired by these mechanisms, scientists have transferred small proteins and RNA across cells. So, the team thought, why couldn’t a similar mechanism spread CRISPR too?

The team adapted a carrier developed a few years back from virus proteins. The proteins automatically form a hollow shell that buds off from cells, drifts across to neighboring cells, and fuses with them to release encapsulated cargo.

The system, called NANoparticle-Induced Transfer of Enzyme, or NANITE, combines genetic instructions for the carrier molecules and CRISPR machinery into a single circular piece of DNA. This ensures the Cas9 enzyme is physically linked to the delivery proteins as both are being made inside a cell. It also means the final delivery vehicle encapsulates guide RNA as well, the “bloodhound” that tethers Cas9 to its DNA target.

Like a benevolent virus, NANITE initially “infects” a small number of cells. Once inside, it instructs each cell to make the full CRISPR tool, package it up, and send it along to other cells. Uninfected cells absorb the cargo and are dosed with the gene editor, allowing it to spread beyond treated cells.

Compared to classic CRISPR-Cas9, NANITE was roughly three times more efficient at editing multiple types of cells grown in culture. Adding protein “hooks” helped NANITE locate and latch on to specific populations of cells with a matching “eye” protein, increasing editing specificity. NANITE punched far beyond its weight: Edited cells averaged nearly 300 percent of the initially treated number, suggesting the therapy had spread to untreated neighbors.

In another test, the team tailored NANITE to slash a disease-causing protein called transthyretin in the livers of mice. Mutations to the protein eventually lead to heart and nerve failure and can be deadly. The researchers injected NANITE directly into the rodents’ veins using a high-pressure system. This technique reliably sends circular DNA to the liver, the target organ for the disease, and shows promise in people.

Within a week, NANITE had reduced transthyretin nearly 50 percent while editing only around 11 percent of liver cells. Such results would likely improve and stabilize the disease according to previous clinical trials, although the team did not report symptoms. In contrast, classic CRISPR-Cas9 only edited four percent of cells and had minimal effect on transthyretin production.

The failure could be because the gene editor was confined to a small group of cells, whereas NANITE spread to others, “enabling more efficient tissue-level editing,” wrote the team. Extensive liver and blood tests in mice treated with NANITE detected no toxic side effects.

A three-fold boost in editing is just the beginning. The team is working to increase NANITE efficacy and to potentially convert the system into mRNA, similar to the technology underlying Covid-19 vaccines. Compared to shuttling circular DNA into the body—a long-standing headache—there is a far wider range of established delivery systems for mRNA.

Still, these early results suggest it’s possible to “amplify therapeutic effects by spreading cargo” beyond the initially edited cells. Avoiding the need for relatively large doses, NANITE could increase the safety profile of gene-editing treatments and potentially expand the technology to tissues and organs that are more challenging to genetically alter than the liver.

The technology changes the numbers game. Even if only a fraction of the NANITE therapy reaches its target tissue, its ability to spread could still deliver enough impact to cure currently untouchable genetic diseases. “By lowering effective dose requirements, NANITE could make genome editing more practical and accessible for treating human disease,” wrote the team.

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