Gene editing is like a molecular meet cute. When protein “scissors” dock onto the intended gene, even a tiny slip—no more than the width of a hydrogen atom—can ruin the connection, and the protein may latch onto similar DNA sequences nearby. In a rom-com, a missed connection means heartbreak; in gene therapy, it can trigger dangerous off-target effects.

Now, AI is playing matchmaker.

In one recent study, researchers used AI to engineer more faithful gene-editing scissors with higher fidelity than previous versions. In another, AI designed the scissors from scratch. Although the synthetic proteins are markedly different than their natural counterparts, they successfully edited genes in cells from multiple species.

The studies expand protein design. “The ability to customize the molecular geometry of genome editors will drive progress towards safer and more efficient therapies,” wrote Hoi Yee Chu and Alan Wong at the University of Hong Kong, who were not involved in either study.

Scientists still need to test the new molecular scissors inside the body. Meanwhile, they’ll continue searching for natural gene editors they can both employ and use to train AI.

Long Road to Precision

There’s no doubt CRISPR has transformed biology.

From blood disorders to inherited blindness and high cholesterol, the gene editor has gone from academic curiosity to a therapeutic powerhouse in just over a decade. Researchers and doctors are also using it to engineer immune cells that recognize and attack once untreatable cancers.

But it’s not all roses: CRISPR doesn’t always edit the right gene.

The gene editor’s protein scissors, called nucleases, are steered to a DNA sequence by a fragment of guide RNA. Once the arrive, the scissors cut the DNA and change the genome.

CRISPR was first used to inactivate target genes. A more sophisticated version, called base editing, can handle single DNA letter swaps. Yet precision is still a hurdle. Early CRISPR was even branded “genetic vandalism” for straying away from its intended target and making unpredictable genome-wide changes. Another problem is called bystander editing. This is when the tool alters neighboring DNA letters that weren’t supposed to be changed. Even a handful of unintended edits could undermine treatment.

Making CRISPR more precise is something of a holy grail. But nucleases are intricate molecular machines, and even small changes to a few critical building blocks can cripple them. To improve the proteins, studies have subtly altered existing nucleases and screened variants to surface versions that have better specificity without sacrificing activity, a tradeoff that has long plagued the field.

Both approaches are tedious and slow. And because they begin with natural enzymes, they explore only a tiny fraction of the protein designs that might actually work.

“What remains unclear is which amino-acid residues [protein building blocks] in Cas9 can be further engineered to maximize fidelity—that is, to ensure that the enzyme cleaves the genome at the correct site and makes the intended edit,” wrote Chu and Wong.

AI Intuition

A Chinese team turned to Google DeepMind’s AlphaFold 3 to open the black box. AlphaFold predicts not only protein shapes but also how proteins interact with DNA, drugs, and other biomolecules.

Most researchers use AlphaFold to CRISPR and its target DNA, revealing potential hotspots for engineering. This team took a different approach. Rather than focusing on a single protein-DNA structure, they used the AI to calculate the likelihood that specific parts of of CRISPRs protein scissors would interact with various DNA sequences.

They first mapped changes to the genome after base editing in human kidney cells and then compared thousands of off-target and on-target changes. To make sense of the data, they developed ContactSeek, an AI that pinpointed protein areas more often associated with mistaken targeting. These would be prime candidates for redesign.

They then used ContactSeek to improve a base editor that switches the DNA letter A to G. With only two changes, the new editor outperformed several existing high-fidelity editors. They also generated more selective CRISPR variants—those that used a different pair of protein scissors—without sacrificing editing efficiency.

Traditional methods often rely on individual trial-and-error experiments. But ContactSeek extracts patterns from thousands of predicted interactions, revealing contact regions that might be hard to detect from single tests. But like other AI models, ContactSeek’s predictions are only as good as the data used to train it. The tool could be further improved with more data and by adding complementary AI tools, such as RoseTTAFoldNA.

In a separate study, CRISPR pioneer Jennifer Doudna and colleagues asked AI to dream up entirely new nucleases. They focused on compact proteins that gave rise to Cas12, the proteins scissors often used in base editing. Instead of tweaking existing proteins, however, they fed an AI model the proteins’ 3D structure, and asked it to redesign them. The AI spooled out thousands of synthetic candidates.

But it didn’t give any hints about which might work, and testing each would be impractical.

Instead, the team trained a second AI on which parts of the proteins interact with each other and which with DNA. Eventually, the second model learned what sections could be changed and homed in on a handful of promising designs. They differed from their natural counterpart sequences by roughly 30 percent, far more than previous AI-designed CRISPR nucleases.

Despite being somewhat alien, several edited genes in bacterial, plant, and human cells. A few even outperformed their natural counterparts in terms of efficiency. Like ContactSeek’s designs, the synthetic nucleases must next prove themselves in the body. Researchers want to make sure they don’t trigger an immune attack and can edit enough cells to treat disease.

Neither study directly addressed bystander editing, another headache in the field. But the tools can work with each other. One fine-tunes nature’s gene editors; the other creates brand new designs. It’s early, but AI is beginning to help design the next generation of gene editing tools.

The post Scientists Are Designing CRISPR Gene Editors With AI appeared first on SingularityHub.

Scientists just transformed a living mouse’s kidney into an incubator for developing human sperm made from blood cells.

It sounds like sci-fi Mad Libs. But a team at the University of Pennsylvania, led by Kotaro Sasaki, pulled it off. For up to nine months, a tiny pouch of human cells nestled beneath a mouse’s kidney gradually developed into immature sperm. The study is the latest in a decade-long quest to grow sperm in the lab.

If successful, lab-grown sperm could open a new window into the earliest stages of sperm development, a process that’s notoriously difficult to study because it begins before birth. The research could also shed light on male infertility—which, in many cases, has no clear cause—and inspire treatments.

More controversially, lab-grown sperm could one day be used to make babies, offering hope to people struggling to conceive and same-sex couples who want to have children genetically related to both parents. That goal is still far off. Though gene activity was similar to their natural counterparts, none of the lab-grown cells were able to develop into functional sperm.

Those results starkly contrast similar attempts in mice. Researchers have already produced functional sperm and egg cells from rodent skin cells, and in two pioneering cases, used them to create healthy pups with two dads. But translating this capability to humans has been difficult, largely because reproductive development differs tons between species.

Still, the new system can help scientists probe the earliest stages of human sperm development. And because any future clinical applications would first need extensive testing in non-human primates, the team also generated immature sperm cells from monkeys, whose reproductive biology more closely mirrors our own.

Recapitulating sperm development in the lab has uses beyond fertility treatment too, such as testing whether drugs interfere with reproduction. The platform “establishes a robust framework for modeling primate germ cell [reproductive cell] development,” the team wrote.

Winning Recipe

For decades, scientist have been able to rewind adult cells into induced pluripotent stem cells (iPSCs). These cells can go on to  become nearly any other cell type. But steering them to become sperm has proven far trickier, largely because human sperm takes years to fully develop.

The journey begins before birth. Early stem cells give rise to spermatogonia, the founder cells that replenish sperm throughout life. These cells are largely dormant until puberty, when some begin meiosis, a special type of cell division that halves their chromosomes. That way, when sperm meets egg, the embryo gains a full genetic set.

But the cells don’t live in a vacuum. Proteins and other molecules instruct immature sperm when to grow, divide, or pause. Physical forces, such as the winding architecture of the testes and the flow of fluid, also play a role. Recreating this intricate environment in a dish has been one of the biggest challenges to the study of sperm development and our ability to grow them in the lab.

Roughly a decade ago, Sasaki and colleagues found a way to transform human iPSCs into early stem cells that could eventually give rise to sperm and egg. On paper, their gene expression profile closely matched that of natural counterparts. But in practice, the cells couldn’t mature further without the right environmental cues.

In an usual workaround, the team next mixed the immature cells with supportive, non-reproductive cells isolated from mice testes. While it was an usual environment, the mice cells provided nutrients and molecular signaling that nudged development forward.

Called xrTestis, the mixture spontaneously organized into tube-like structures resembling those inside testes. “Overall, our culture method accurately recapitulates in vivo human male GC [germ cell] development and allows us to understand the genetic pathways governing this process,” they wrote at the time.

Yet none of the immature sperm advanced beyond developmental stages normally seen in fetuses. And the miniature structure collapsed after 80 days, likely because it lacked a blood supply.

Unexpected Host

To prolong the mixture’s viability and push sperm development further, the team transplanted it into the kidneys of immunodeficient mice.

The graft organized itself into the hallmark tubular structures found in testes within a month and remained stable for at least half a year. The mice showed no signs of discomfort or immune rejection.

Six months later, some human cells developed into spermatogonia—the self-renewing stem cells that eventually generate sperm. Along the way, they underwent a major event: an epigenetic reset. During this process, chemical tags on DNA that influence whether genes are turned on or off are almost completely wiped clean. If that reset is incomplete, it could compromise any sperm eventually used for reproduction.

Here, the team found a “dramatic” genome-wide epigenetic reset. The cells’ gene activity mirrored their natural counterparts. Even though the graft survived for at least nine months, however, none of the cells were able to develop into mature sperm.

This is likely due to the environment. Human and mice testes don’t share the exact same signaling molecules or respond the same way to hormones and other developmental cues. Replacing the mouse support cells with human versions could help the spermatogonia develop further.

The Ultimate Test

The team also tested the technique in monkeys, with results similar to those found in human cells. “While our human iPSC system provided valuable insight into male gametogenesis [the formation of reproductive cells], future studies of fertility competency must be carried out in non-human primates,” they wrote.

Although the cells also halted at the immature stage, the results are still valuable. Previous studies have shown monkey spermatogonia can generate mature sperm after transplantation into recipient testes, opening the door to eventually testing if lab-grown cells can sire healthy offspring.

That idea is precisely what makes some bioethicists uneasy.

Mass-producing sperm and eggs in the lab could generate far more embryos for selection, making it easier for prospective parents to choose desirable traits such as eye color or height. Pairing the technology with gene editing makes “designer babies” less hypothetical. And if skin scrapings or a single hair can be turned into reproductive cells, someone could theoretically create sperm or eggs from another person without consent.

These scenarios are purely speculation, but regulators are already preparing for that future. In 2025, the United Kingdom’s Human Fertilization and Embryology Authority urged the government to explicitly tackle lab-grown reproductive cells in legislation. The International Society for Stem Cell Research has similarly called for careful oversight and public engagement before clinical use. Most countries, however, are only beginning to grapple with how these technologies should be dealt with.

Meanwhile, companies are pressing forward. Paterna Biosciences in Utah recently announced they had produced functional sperm from immature sperm collected during testicular biopsies. According to the company, early embryos created with the lab-grown sperm seemed comparable to those produced through standard in vitro fertilization (IVF). And California startup Conception recently reported generating early human egg cells from iPSCs. Neither company has released results in a preprint or journal article, making the claims hard to evaluate.

Like germline gene editing, conversations weighing the pros and cons of lab-grown reproductive cells will help decide not only what’s possible, but also what should be permitted. For now, the team stresses that their work is only a research tool—not a fertility treatment—and clinical use is a long way off.

The post Scientists Inch Closer to Creating Human Sperm in the Lab appeared first on SingularityHub.

Cancer researchers just found a new way to take on tumors.

CAR T cell therapy revolutionized blood cancer treatment by supercharging a patient’s own immune cells to hunt down cancers. But the approach has struggled in solid cancers. These are some of our top killers—breast, lung, prostate. Roughly two million Americans are expected to be diagnosed with cancer in 2026, and over 600,000 will likely succumb to the disease.

Unlike blood cancers, solid tumors rarely share a single, universal target for CAR T cells. Even cells within the same tumor are a mishmash. Some have little or none of a target protein, allowing them to evade the engineered immune cells, survive treatment, and fuel relapse.

“Target discovery remains a considerable challenge in the development and translation of

CAR T cell therapies for solid tumors,” wrote Christopher Mount and Marcela Maus at the Massachusetts General Brigham Cancer Institute.

Now, two independent teams have converged on the same promising target: A cell-surface protein called GPNMB. In one study, CAR T cells engineered to recognize GPNMB rapidly destroyed glioblastoma—a lethal brain cancer—in tissues taken from patients and shrank tumors in mice.

A second team used a similar strategy against an aggressive soft tissue cancer to fight tumors in organoids and mice. In an early clinical trial involving a single participant, one infusion stabilized the disease for three months without serious side effects.

CAR T designers are often wary of broadly shared targets because they can trigger dangerous attacks on healthy tissue. But GPNMB is an odd duck. In addition to cancer cells, it also sits on immune cells that spur cancer growth or suppress the body’s innate ability to get rid of tumors.

“Our approach attacks both the tumor and the environment that allows it to thrive,” said Sheila Singh at McMaster, who led the glioblastoma study, in a press release. “We’re going beyond targeting the cancer alone and eliminating the immune cells that help shield it from treatment.”

Cancer Fortress

Solid cancers have plenty of tricks to outsmart CAR T cells.

Researchers make these supercharged immune cells  by extracting a patient’s own T cells and genetically engineering them to produce protein “claws” that latch onto a specific cancer target. After infusing the cells back into the body, they seek and destroy tumor cells. CAR T has transformed treatment for several blood cancers and is showing promise in autoimmune diseases and excessive heart and kidney scarring. To simplify the procedure, researchers are also exploring ways to directly transform T cells inside the body with gene therapy.

Solid cancers, however, are far tougher opponents. Unlike blood cancers, which are heavily coated with a shared target called an antigen, solid tumors are molecular patchworks. Cells within the same tumor can display different targets—or none at all—allowing some to evade a CAR T attack and trigger relapse. Many of these targets also appear on healthy tissues, raising the risk of dangerous side effects. And then there’s the tumor microenvironment: A toxic, glue-like “fortress” that hijacks immune cells and uses them to battle incoming CAR T cells.

These barriers aren’t impenetrable. Previous work enlisted  bacteria to help CAR T cells burrow into tumors. Other efforts engineered ultra-sensitive CAR T cells capable of detecting tiny amounts of a cancer target shared across multiple solid tumors.

“Recent reports of activity in several clinical trials reinforce optimism that these efforts may result in true clinical benefit,” wrote Mount and Maus, who were not involved in either study.

But these strategies require additional engineering steps, increasing complexity and cost. And most still leave one major roadblock intact: The tumor’s immune defenses.

One-Two Punch

In the glioblastoma study, the team at McMaster University scoured donated tumors for proteins that distinguished the most aggressive cancer cells. They found one standout: GPNMB. Another test of every protein dotting the cell surface confirmed it as a promising target. The protein is evident across a cancer cell’s membrane, making it readily accessible to CAR T cells.

In lab tests, CAR T cells engineered against GPNMB performed well, nearly eliminating tumors grown from patient samples and extending survival in mice.

The target turned out to be far more valuable than expected. The team soon realized that GPNMB also marked the immune cells that suppress anti-cancer drugs. CAR T cells attacked both fronts simultaneously, weakening the tumor’s immune shield and killing the cancer itself.

“Most approaches have focused on killing cancer cells alone,” said study author Shan Grewal. “Our work suggests we may also need to dismantle the immune support system that helps the tumor survive.”

The second team focused on alveolar soft-part sarcoma, a rare soft-tissue cancer that often spreads to the lungs, brain, and bones before it’s diagnosed. Treatment often comes too late.

The disease is driven by a type of “fusion” gene created when pieces of genetic material are accidentally stitched together. These genes are extremely tough to target directly. Instead, the team screened all surface proteins on the cancer cells and again landed on GPNMB as a top candidate for intervention. The protein’s levels closely tracked the activity of the fusion gene.

CAR T cells targeting GPNMB cleared tumors and prevented metastasis in mice. But because an earlier antibody drug against the protein caused severe skin toxicity in patients, the team also tested their CAR T cells in mice carrying small human skin grafts. Although inflammation initially flared, there were no signs of ongoing skin damage.

Encouraged, the team treated a patient with relapsed, metastasized alveolar soft-part sarcoma. After a single infusion, the engineered cells rapidly divided in the bloodstream and remained detectable for roughly a month. The treatment didn’t trigger skin rashes or more dangerous side effects, like cytokine release syndrome where the body mounts a hyperactive immune defense that harms healthy organs.

The treatment’s benefits outlasted the engineered cells themselves. For roughly three months, imaging tests found fewer of the small, round spots on the patient’s lungs that often signal metastatic cancer, suggesting the disease had stabilized.

A final analysis identified another roadblock: Clusters of cells that suppress the immune system and could blunt the benefits. Adding drugs to block these immune molecules boosted tumor killing in mice. Because the same kind of gene fusion drives other cancers, including kidney, the CAR T cells could have reach beyond this specific type of sarcoma.

Together, the studies underscore that the best CAR T targets might extend beyond cancer cells to expose and attack cancer’s immune cell supporters too. Finding a viable target is a delicate balancing act. Chosen well, and CAR T cells could tackle multiple drivers for cancer growth. Choose poorly, and healthy tissues could get hurt in the crossfire.

Even so, “these two studies indicate that GPNMB represents an actionable target for CAR T cell therapies in several solid tumors,” wrote Mount and Maus.

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Synthetic biologists have long dreamed of constructing artificial cells from the bottom up. Researchers have now taken a major step in this direction by demonstrating that non-living components can be assembled into a system that grows, copies its DNA, and divides.

The genomic revolution transformed our ability to understand and manipulate cellular machinery, allowing scientists to rewire cells’ genetic circuitry to fight disease, produce valuable chemicals, and make crops more resilient. The holy grail for the field, however, has been to use these tools to create entirely synthetic cells—a milestone that would signal humanity’s mastery of life’s key ingredients.

How best to do this has long been an open question. Genomics pioneer Craig Venter made significant progress by stripping living bacteria back to their bare essentials, culminating in the 2016 unveiling of a minimal cell with just 473 genes. The Synthetic Yeast Genome Project has taken the opposite approach, building artificial versions of all 16 yeast chromosomes from scratch, though they’ve yet to get them working together in a single cell.

Now, researchers from the University of Minnesota, have assembled a synthetic cell out of engineered, non-living components housed inside an artificial, cell-like membrane. Their creation was capable of the four hallmarks of a living entity—the ability to feed, grow, copy genetic material, and produce offspring.

“We’ve replicated in chemistry what only used to be possible in biology: the complete set of behaviors of a cell,” Kate Adamala, who led the project, said in a press release. “It proves that the most fundamental functions of life, like growth and replication, do not need a mysterious magical spark.”

The researchers outline the design for their synthetic organism—nicknamed SpudCell for its potato-like shape under the microscope—in a non-peer reviewed paper uploaded to bioRxiv. SpudCell features a genome 90,000 base pairs long, which is considerably smaller than the 113,000 base pairs researchers had previously predicted would be the bare minimum needed to support a viable cell.

Rather than housing all the genes in a single chromosome, the team split them across several small, circular DNA molecules called plasmids, each specialized to fulfill specific functions. The researchers say this makes it possible to modify different aspects of the organism more easily.

To read the genome and build proteins, SpudCell uses a pre-defined kit of 36 purified enzymes drawn largely from E. coli. The whole assembly sits inside a liposome, a hollow bubble of the same fatty molecules that form natural cell membranes.

The artificial cell feeds in two distinct ways. Small molecules pass directly into the cell through protein pores implanted across the membrane. Molecules too large to squeeze through—like ribosomes and enzymes—are packaged inside tiny lipid bubbles that fuse with the membrane and empty their contents inside.

While the cell can feed, it’s entirely reliant on the researchers providing it with specially prepared meals. This means it’s a long way from surviving in the wild, which is both a major limitation and a key safety mechanism. “It’s a bed-ridden Frankenstein’s monster that has to be spoon-fed,” Adamala told New Scientist. “There’s no danger of it running amok.”

After ingesting “food,” SpudCell’s genes use the material to churn out proteins, while folding the incoming lipids into its membrane. This causes the whole cell structure to swell. Within a few hours, it’s bulked up enough to reproduce by dividing into two smaller cells.

Replicating cell division has been a longstanding challenge in the field. Natural cells split using an intricate protein scaffold called a cytoskeleton that’s fiendishly difficult to recreate. Adamala’s team sidestepped this problem by using a completely different mechanism, in which proteins bunch up on the membrane’s surface, putting it under mechanical strain. Eventually this squeezes two parts of the membrane together to pinch off a new cell.

The cells even manage a crude form of evolution. When the researchers introduced a genetic tweak boosting the cells’ ability to feed, those with the variant outcompeted the original lineage within five generations, and their edge widened when the researchers exposed the population to nutrient scarcity.

However, no one is claiming SpudCell is alive. Crucially, the cells cannot make their own ribosomes—the machines that build proteins from genetic instructions—and the ribosomes provided by the researchers degrade over time, limiting the cells to five to ten divisions.

The University of Chicago’s Jack Szostak told Quanta the work is an “impressive step” but the inability to produce ribosomes seriously limits potential for sustained growth. “If their system was able to generate its own ribosomes and other proteins and RNAs, it would be much closer to existing biological cells such as bacteria,” he said.

Nonetheless, the researchers think these artificial cells are a promising way to manufacture drugs, fuels, and materials without the toxic, energy-hungry industrial chemistry we rely on today. And they’ve created a new nonprofit called Biotic to share the tools they’ve developed with researchers.

The post This Synthetic Cell Grows, Copies Its DNA, and Produces Offspring—But It Isn’t Alive appeared first on SingularityHub.

Researchers at the University of Cambridge have developed what they describe as a fundamentally new type of vaccine using artificial intelligence. The vaccine’s key component was designed entirely by AI and has now been tested in people for the first time.

The goal is ambitious: a single vaccine that works not just against all known human coronavirus variants, but against related bat viruses that could jump from animals to humans and cause future pandemics.

Traditional vaccines train our immune system to recognize one specific virus. The problem is that viruses mutate. When they change enough, the vaccine stops working, which is why we need a new flu shot every year and why Covid vaccines have been updated repeatedly since 2021.

AI offers a way around this. By analyzing genetic data from thousands of related viruses, it can identify the parts that stay the same across different strains and that are unlikely to change over time. Target those stable features, and you have a vaccine that should work against the whole family, not just the strain you started with.

This is exactly what the Cambridge team did. They used AI to scan viruses from the sarbecovirus family, which includes the viruses that cause both SARS and Covid, as well as a range of animal coronaviruses—looking for shared features that evolution has left largely untouched. Those features became the basis of the vaccine.

DNA Vaccines

While many people are familiar with the mRNA shots used during the pandemic, this new vaccine uses DNA. DNA vaccines are generally more stable than mRNA vaccines, making them easier to store and transport. This is a significant advantage in lower-income countries where “cold-chain” infrastructure is limited.

They can also be administered without needles. A high-pressure stream of liquid delivers the vaccine through the skin, making administration less painful and easier to scale up during an outbreak.

Could It Protect Against Future Pandemics?

These practical advantages matter most if the vaccine itself can do something no existing jab can: protect against viruses we haven’t encountered yet.

Broad-spectrum vaccines could change the way the world responds to emerging infectious diseases. By offering much wider protection than traditional vaccines, they could provide rapid immunity against new and emerging viral threats. This would equip public health officials with tools to stop future outbreaks in their tracks before they have a chance to turn into global pandemics.

They could also transform our approach to more familiar diseases. Influenza is a prime target because it exists in many different strains and evolves so rapidly. Scientists have to predict which strains will dominate each flu season, and if they guess wrong, vaccine effectiveness can suffer. A universal flu vaccine that targets features shared across multiple strains could eventually end the annual race to keep up with the virus.

The Ebola virus shows why this matters right now. The recent outbreak in the Democratic Republic of the Congo and Uganda is driven by the Bundibugyo strain, which bypasses existing vaccines. While researchers rush to create a new vaccine specifically for this strain, local communities remain at high risk. A broad-spectrum vaccine designed to cover an entire virus family could transform that picture.

What the Trial Found

This is the first human trial of an AI-designed vaccine. The results showed that this DNA vaccine was able to stimulate the immune system to produce antibodies that can recognize different types of sarbecoviruses. The technology was found to be safe and well tolerated.

This is an exciting advance because it demonstrates how AI has the potential to design variant-proof vaccines against future pandemic threats. The needle-free delivery system could also make the vaccine easier to administer and distribute worldwide.

However, there is more work to do. Although the results in this study are encouraging, the immune responses following vaccination were modest. It was also uncertain how long the protection lasts and whether further boosters will be required. Larger trials are also needed to determine whether the vaccine can prevent or reduce viral infections in the real world.

A universal vaccine remains a few years away. And any new vaccine must still pass larger trials to prove it is safe, effective, and provides lasting protection. But this study shows the goal is getting closer—and AI may help us get there faster.

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

The post The First AI‑Designed Vaccine Has Been Tested in People. Here’s What Happened. appeared first on SingularityHub.

A handful of healthy senior citizens are about to trip on psilocybin—to see if the psychedelic protects aging brains.

Psilocybin, the active ingredient in magic mushrooms, is best known for its ties to 1960s counterculture. But now it may also herald a new genre of mental health treatment. From severe depression to post-traumatic stress disorder, studies have highlighted psychedelic drugs’ ability to reshape brain networks and relieve debilitating symptoms.

Most of these studies have focused on younger people with mental health conditions that don’t respond to standard treatment. The field’s success is prompting scientists to ask if psychedelics could also help healthy brains age better.

A team from the UC Berkeley Center for the Science of Psychedelics is about to find out. In a first-of-its-kind study focused on adults between the ages of 60 and 85, they’ll investigate how psilocybin affects perception, emotion, and memory using a battery of psychological tests.

Multiple scans before and after dosing will track changes in the brain. And detailed surveys will gauge broader shifts in well-being: Do participants feel more “in tune” with their emotions, feel less isolated, or experience a renewed sense of wonder about the world?

“What really excites me is that we’re focused on healthy older adults,”  said Tyler Toueg, who co-led the study’s design, in a press release. “Most clinical trials with older adults are focused on people who already have a diagnosis. We’re asking whether we can actually promote positive outcomes in older adults who are healthy.”

Called PLASTICITY, the trial could also open a rare window into how a psychedelic experience reshapes healthy brain networks. And because the drug alters our sense of self, psilocybin could help researchers probe the ways in which the brain constructs reality.

“I’m very interested in psilocybin as a potential mental health treatment, but I’m also interested in it as a way to shed light on these central mysteries in neuroscience and psychology,” said study designer Michael Silver.

Chasing the White Rabbit

Psychedelic research was highly restricted for decades. But advocates, including the non-profit Multidisciplinary Association for Psychedelic Studies, have steadily pushed to reopen the field, arguing that these drugs might keep mental health symptoms at bay.

Early results helped usher psychedelics into the mainstream. In 2023, a randomized, placebo-controlled trial found that a single dose of psilocybin, paired with therapy, eased depression. Oregon later approved supervised psilocybin therapy—though the drug remains federally illegal in the US—and Australia became the first country to greenlight it for depression and post-traumatic stress disorder. More recently, two late-stage studies reported strong effects in severe depression, potentially paving the way for FDA approval.

Scientists still don’t fully understand how psilocybin works in the brain. But there are hints. The drug appears to rapidly reorganize the connections between brain cells, particularly in the hippocampus, a region of the brain central to learning, memory, and navigation.

Neurons constantly change their connections in a process called plasticity that encodes experiences into neural networks, allowing the brain to process information, learn, and lock in memories. In youth, these connections are flexible and expansive. But with age and in conditions like depression, the brain’s flexibility wanes.

The birth of new neurons, or neurogenesis, also contributes to plasticity. Neurogenesis only occurs in two brain regions, one of which is the hippocampus. Although whether it actually takes place in humans is controversial, it is strongly linked to learning, memory, and emotion, and it declines in both psychiatric disorders and aging.

Psilocybin may reset brain plasticity to a more youthful state.

In rats modeling depression, for example, a study showed the drug shifted dark moods into behavior that was more exploratory and engaging. Where traditional antidepressants, like Prozac, tend to blunt symptoms, psilocybin seems to overwrite entrenched negative patterns. This suggests deeper circuit-level changes.

In another study, the drug reopened a critical window for learning in mice. During adolescence, the brain is highly plastic, but it begins to stiffen in adulthood. Psilocybin temporarily restored malleability and changed the mice’s social behavior. In some brain regions, the drug increased sensitivity to oxytocin, the so-called “love hormone.” The authors suggested the drug induced a state called metaplasticity, in which neurons are more likely to respond to oxytocin and other regulators to rewire, form new connections, and grow their branches.

Psilocybin’s effects may extend beyond the brain. Depression, chronic stress, and the immune system are tightly linked. In a third study, researchers identified a brain-spleen connection that drives fear and anxiety. Psilocybin suppressed inflammatory immune cells associated with brain inflammation and dampened anxiety-like behavior in stressed mice, even under threat.

Both effects—dialing up plasticity and lowering inflammation—make psilocybin an intriguing way to potentially counter changes in the aging brain.

“We know that with age, we lose synaptic connections, especially in certain brain regions like the hippocampus and prefrontal cortex,” said Toueg. “There’s a lot of overlap between the mental states that psychedelics influence and those associated with successful aging.”

Tomorrow Never Knows

The PLASTICITY trial is designed to test whether psilocybin can produce lasting changes in neuroplasticity in healthy adults aged 60 to 85. Participants will first undergo assessments of cognition, visual perception, and brain structure using advanced MRI techniques.

Diffusion MRI scans will focus on the hippocampus to capture microscopic changes in its structure. Functional MRI will focus on brain activity as participants perform learning and memory tasks, offering a dynamic view of how activity shifts after dosing.

The study will also see if psilocybin increases vagus nerve activity, which has been linked to better stress recovery. Participants will complete detailed surveys about the experience ranging from emotional responses, like wonder, to potential shifts in outlook and social cognition.

“Things like depression, anxiety, stress and rumination are all associated with worse aging outcomes,” said Toueg. “Things like having purpose in life, emotional regulation, and awe are all associated with more successful aging.”

The trial began enrolling participants in November last year. Two volunteers have already completed the tests, and the team aims to dose 20 people by the end of 2026.

Although psilocybin trials are now widespread, older adults remain underrepresented. One estimate suggests just 1.4 percent of participants are 65 or older, despite potentially being among those most likely to benefit from interventions that enhance plasticity.

“This study allows us to directly test whether the promising findings from animal models translate to older humans and to generate data that will inform future research on aging, cognition, and mental health,” said Silver.

Toueg agrees. “I think that no matter what we find, this study will have implications for how we think about intervening in the aging brain,” he said.

The post Can Psychedelics Reboot Aging Brains? We’re About to Find Out appeared first on SingularityHub.

Scientists at Columbia University have used a precise gene-editing tool, base editing, to make changes in three disease-linked genes in early-stage human embryos. The goal wasn’t to create pregnancies, but to test the safety and limits of rewriting DNA at the very early stages of life.

The paper, not yet peer reviewed, sparked immediate controversy. Some researchers hailed it as a technical milestone that could one day prevent devastating inherited diseases before birth. Others warned it edges society closer to the prospect of “designer babies”—an idea bioethicists have argued is akin to modern eugenics.

The debate is hardly hypothetical. The work has already attracted commercial interest. New York-based Nucleus Genomics, which screens in vitro fertilization (IVF) embryos for serious genetic disorders, has also developed predictive models for complex traits such as intelligence. The company plans to sponsor future research by study leader Dieter Egli and team.

Critics worry that even experimental advances could fuel demand from wealthy patients while encouraging companies to develop and market embryo-editing technologies, despite unresolved ethical and safety concerns.

Egli argues the findings should be public precisely because these debates are no longer academic curiosity. He has repeatedly called for scientists, regulators, and the public to weigh the pros and cons of editing human embryos. As for clinical use today, his position is unequivocal: “You can’t use it. It’s as clear as day and night,” he told Nature.

Conceptual Shift

Why edit embryos at all?

Cells in an early embryo eventually give rise to every tissue in the body. Correct a harmful mutation at the start of development, and the fix could, in theory, propagate throughout a child’s entire body—and even be passed on to future generations.

The strategy could help in genetic disorders that hamper fetal development or trigger diseases in newborns. For some developmental and metabolic conditions, intervention after birth may already be too late. Even when treatment is possible, gene editors must be able to target various organs, which is an ongoing challenge.

In various efforts, scientists have already repaired disease-causing mutations in mouse embryos and fetuses, including those linked to blood disorders. But mice aren’t humans. Early embryos from the two species repair DNA damage in fundamentally different ways, making it tough to gauge whether a strategy that works in mice will succeed, or prove safe, in people. That uncertainty has fueled interest in testing gene-editing tools directly in human embryos.

Not everyone is on board. International scientific groups have repeatedly called for a temporary ban on editing human embryos, and the practice is illegal in several countries.

That didn’t stop Chinese scientist He Jiankui. In 2018, he announced the birth of gene-edited babies after using a tool called CRISPR-Cas9, claiming the changes would protect them against HIV infection. Global outrage ensued.

By then, years of research had already highlighted CRISPR’s risk. The tool cuts both strands of DNA and relies on the body’s repair machinery to stitch them back together. But the process can go awry, introducing unintended mutations, deleting large chunks of DNA, or altering the wrong locations on the DNA strands altogether. He’s reckless experiment resulted in three years of imprisonment, although he still defends the work.

Subsequent studies only deepened concerns. In some cases, CRISPR editing in human embryos caused extensive genetic damage. In one study,  it completely destroyed the chromosome that housed the target gene.

An Imperfect Upgrade

The new study tested a next-generation gene editor designed to overcome some of CRISPR’s biggest shortcomings.

Egli and team used an approach called base editing, which rewrites individual DNA letters. Unlike CRISPR, base editing only nicks the DNA strands and is generally thought to be more precise. The technology hit a major milestone last year when it helped cure a baby with a potentially fatal genetic disorder, and earlier lab studies hinted it could also succeed in human embryos.

Working with early-stage embryos, the team edited three genes with the potential to cause illness. In each case, they converted the genetic letter A to G at precise locations. One of the genes, PCSK9, regulates “bad” cholesterol levels. Mutations are associated with a high risk of heart problems. The team’s edit was designed to switch off the gene, mirroring strategies already being explored in adults.

The other two targets, HBG1 and HBG2, control production of fetal hemoglobin, an oxygen-carrying protein. The edits made here reflected a natural protective variant that could lessen symptoms in blood disorders, such as sickle cell disease and beta thalassemia.

The team found no signs of widespread DNA damage, suggesting the tool is more precise than CRISPR. But it wasn’t perfect. Many embryos emerged as so-called genetic mosaics, with some cells carrying the intended edit and others retaining their original genetic blueprint.

That’s a huge problem. As an embryo develops, unedited cells could outcompete edited ones, leaving the disease-causing mutation largely intact. In some embryos, edited cells stopped dividing altogether.

And a lack of obvious chromosome damage doesn’t guarantee safety. The edits could still trigger harmful effects that aren’t noticeable until after birth—when it’s already too late to reverse them.

Calls for Scrutiny

Egli stresses that embryo editing is still far from being ready for the clinic. “These base editors—they can have damaging effects on the embryo. So why would you use it if you don’t fully understand that?” he told Nature.

His team is now working to reduce mosaicism and plans to test the technology in embryos that have developed to roughly 100 cells. This is when fertility clinics typically evaluate and freeze embryos.

Speaking to The New York Times, fertility expert Paula Amato at Oregon Health & Science University, who was not involved in the work, called the strategy “promising.” Genomics researcher Greg Neely at the University of Sydney in Australia also praised the work: “This will go down in history in a positive way—less reckless, more careful and ethical than previous attempts.”

Others remain deeply skeptical. Critics argue that embryo editing permanently alters the genetic inheritance of future generations, who have no say in the decision. The study’s ties to Nucleus Genomics also raised eyebrows. The company previously drew controversy for developing genetic predictions for traits such as intelligence and height and for its slogan “have your best baby.”

To Kian Sadeghi, CEO and cofounder of Nucleus, embryo editing extends that vision. The technology could help couples carrying mutations who struggle to produce enough unaffected embryos for selection during IVF.

Fyodor Urnov at the University of California, Berkeley, who was not involved in the study, isn’t convinced. IVF clinics already screen embryos for many inherited disorders without altering their DNA. Given the risks, selecting an unaffected embryo is often a safer option than rewriting its genome.

“In practical terms, therefore, this preprint will solely impact the rapidly growing movement of embryo editors for purposes of ‘baby improvement’,” he said.

That movement, once taboo, is gaining steam. Yet the traits most often cited by proponents—height, intelligence, emotional regulation—are shaped by hundreds or even thousands of genes, which scientists still don’t fully understand. Such enhancements are far beyond the reach of today’s technology. Every additional edit also increases the chance of unintended consequences.

For Egli, that’s precisely why the research should be discussed openly. “Research is necessary to provide information to discourage the wrong use of a technology,” he said.

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For decades, a handful of molecular switches has haunted the nightmares of cancer researchers. The switches trigger runaway tumor growth and cause the disease to spread across the body in multiple cancers. In theory, this makes them perfect treatment targets. Blocking even one could lead to drugs that are effective against a variety of cancers.

But despite considerable efforts, these switches—all of which are proteins—have escaped our most advanced cancer treatments, earning them the term “undruggable.” This is largely due to a shared trait: They all have smooth surfaces, making it difficult for drugs to interact with them.

But maybe not for much longer.

Researchers recently reported promising results for a new medication targeting a family of undruggable proteins in a clinical trial for advanced pancreatic cancer. The drug, daraxonrasib, nearly doubled survival time compared to chemotherapy, with fewer side effects. It’s not a total cure. But the treatment gives patients precious time, adding roughly 13 months after diagnosis. Patients also reported less pain and better quality of life.

Daraxonrasib is the latest in a new generation of drugs aimed at undruggable proteins. And AI-based tools are now poised to further accelerate progress in the field.

RAS Attack

The RAS family was the first group of oncogenes—or genes that drive cancer—ever discovered. The genes became a major focus in 1982 when several teams independently showed the mutation of a single DNA letter could transform RAS genes into a potent cancer trigger.

The proteins RAS genes encode are like spring-loaded molecular switches that relay signals from a cell’s surroundings. When proteins called growth factors latch onto a cell, RAS switches flip on to promote cell growth and survival, while built-in safeguards quickly turn them off again.

Cancerous mutations break this cycle. The switches get stuck in the “on” position, continuously instructing cells to grow and divide. This is, of course, a hallmark of cancer.

An ideal drug would simply switch RAS off. But most drugs are like rock climbers. They need grooves, pockets, or bumps on a protein to grab onto. Similar to a smooth rock face, RAS offers few such features. Making matters worse, different mutations subtly reshape the protein, so it’s tough to build a one-size-fits-all inhibitor.

The first RAS drug wasn’t approved in the US until 2021, nearly four decades after discovering the genes’ role in cancer. Even then, the drug targeted just one family member of three, limiting its reach to a relatively small group of patients. Many eventually developed resistance.

That’s why daraxonrasib turned heads. Developed by Revolution Medicines in Redwood City, California, the drugs switches off all three RAS family members. Rather than trying to grip the slippery proteins directly, it binds to a partner molecule that helps RAS proteins fold into their final 3D shapes. In this way, the drug hitches a ride on active RAS and shuts the proteins down.

The workaround paid off. The new study enrolled 500 people worldwide with advanced pancreatic cancer. All participants had already tried cancer therapies with limited success. On average, patients receiving daraxonrasib lived 13.2 months and spent most of that time with limited pain. The most common discomfort was a rash. Those receiving chemotherapy fared worse, living roughly 6.6 months and experienced more severe side effects.

The results don’t rival the dramatic success of CAR T cell therapies in blood cancer. In CAR T, caregivers engineer a patient’s own immune cells to recognize and attack tumors, sometimes producing long-lasting remission after a single infusion.

But the findings have energized the field. If approved, a daily daraxonrasib pill would likely be far more affordable and easier to administer than a personalized cell therapy. And because RAS mutations fuel many solid cancers—which CAR T still struggles to control—the drug could offer a new defense against deadly cancers that are largely beyond cell therapy’s reach. Combining daraxonrasib with earlier-generation RAS inhibitors may further boost its effects.

The Genome Guardian

Daraxonrasib didn’t appear overnight. Scientists used a crystallized snapshot of its target protein as a molecular blueprint. Years of medicinal chemistry followed, with scientists repeatedly tweaking candidate compounds to boost potency, improve selectivity, and minimize toxicity.

AI could dramatically accelerate similar efforts against other undruggable cancer targets. Among the most coveted is p53, often called the “guardian of the genome” for its dizzying array of roles. The protein orchestrates the activity of over 300 genes involved in DNA repair, metabolism, cell death, and inflammation, making it one of the cell’s most important defense systems.

Since its discovery in 1979, p53 has been both a holy grail and a headache for cancer researchers. Mutations in the gene are common in multiple cancers. But like RAS, the protein is flat and smooth. Some mutations destabilize its structure; others turn it into misfolded clumps. A universal p53 drug has remained elusive.

Some researchers are trying to restore the protein. In a small trial earlier this year, they tested a drug that restabilizes a common mutant form of p53. Within 21 days, tumors shrank roughly 20 percent in patients with ovarian, breast, and several other solid cancers.

Other researchers aim to selectively kill cells carrying the mutation. Using AI, a team at Baylor College of Medicine screened nearly 10 million compounds that cause mutated p53 cells to self-destruct, while sparing healthy cells. The search uncovered 83 chemically distinct candidates. One called H3 dramatically suppressed tumor growth in mice.

“These results highlight the potential use of AI-powered drug screening to investigate individual p53 mutants in the future,” they wrote. Although the approach is early-stage and only focused on one mutation, the team is hopeful it can be extended to other cancerous mutations.

Most Wanted

MYC is another formerly undruggable protein that could now be vulnerable. Roughly 70 percent of cancers have abnormal MYC activity. Normally, the protein is a master regulator of growth, directing cells to manufacture proteins, replicate DNA, absorb nutrients, and divide when needed.

Cancer finds many ways to hijack the system and keep cells in a state of runaway growth. MYC gene mutations aren’t just single-letter swaps. Sometimes the gene duplicates or is rearranged across the genome, churning out excessive amounts of the protein it encodes. This genetic diversity makes approaches using gene therapy difficult. And again, like RAS, the MYC protein’s smooth, featureless surface lacks stable anchors for drugs.

An emerging strategy is to disrupt MYC’s interaction with other proteins that it needs to function. A designer protein blocking MYC activity, for example, recently showed promise in a small trial against solid cancers. Other teams are using AI to identify drugs that limit MYC’s ability to fix damaged DNA in tumors, kneecapping their ability to divide. Meanwhile, biotechnology companies are deploying AI to map out MYC’s structure and molecular interactions in search of new ways to shut the protein down.

Daraxonrasib’s success shows that undruggable proteins aren’t untouchable. There’s a lot more work ahead to prove other similar drugs can work too. But scientists are increasingly leaning into AI during all stages of drug development to speed up the process. Maybe, one day, “undruggable” will disappear from our vocabulary altogether.

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There’s truth to the old adage, “Age is just a number.” People of the same age differ vastly in health and mental capabilities. One 80-year-old may be vibe coding with Claude, while another is gradually forgetting familiar faces and memories.

To better gauge this difference, scientists have been developing “clocks” that measure biological age. Rather than the number of candles on a birthday cake, these tools capture health at the cellular level and are remarkably accurate at estimating disease risk and even life expectancy. But how they work is hard to explain.

Now Harvard scientists and collaborators have released a powerful and more interpretable clock. Using the gene activity of thousands of individuals and animals, the clock predicts biological age in rodents, monkeys, and humans, including how many years they have left.

The analysis involved over 11,000 gene activity profiles across four species, highlighted shared mechanisms during aging, and responded to known anti-aging interventions—such as parabiosis, during which aging animals receive blood from a young donor.

Although the clock isn’t ready for clinical use, it is a boon to scientists working to slow or even reverse the unstoppable progression of time. It “could help researchers to pinpoint which processes are modulated by interventions or diseases,” wrote João Pedro de Magalhães at the University of Birmingham, who was not involved in the work.

Tick, Tock

Biological clocks come in a variety of flavors.

Most rely on AI to make sense of information held in large databases of people. One of these, for example, uses blood proteins related to brain aging to reflect cognition and its decline better than chronological age. Another type, metabolomic age clocks, sorts through protein and fatty acid building blocks to estimate biological age. These clocks correlate well with risk of inflammation, chronic disease, and frailty (where the body struggles to recover from a mild infection or minor fall). More recent multi-omics clocks combine blood measures, metabolism, gene activity, and clinical data for a comprehensive bird’s-eye view of biological age.

But epigenetic clocks remain the field’s defining breakthrough.

As we age, chemical tags accumulate on DNA, switching genes on or off. The pattern of these tags shifts over time and is shaped by everyday life—diet, exercise, stress, sleep quality. Studies have found that the age gaps between biological and lived years measured by the well-known Horvath epigenetic clock, which relies on DNA methylation, were associated with the risk of various types of diseases. Later versions of the Horvath clock could predict maximum lifespan. And other groups have developed “pan-mammalian” epigenetic clocks that work across species.

“One drawback of epigenetic clocks, however, is their limited interpretability,” wrote Magalhães. “The mechanisms that underpin age-related methylation changes are still debated.”

Clocking In

In the new study, the team measured aging by looking at gene activity, or transcriptomics. Transcriptome profiles capture which genes are switched on at any given moment.

Previous studies have linked the aging transcriptome to chronic inflammation, faltering mitochondria, and the gradual breakdown of the extracellular matrix, the molecular scaffolding that supports tissues and organs. With age, these systems go awry.

“Because the signatures reflect changes in the activity of specific genes, transcriptomic biomarkers are more interpretable than are epigenetic ones,” wrote Magalhães. The tradeoff is that gene activity is far more dynamic than DNA methylation, the epigenetic signature used in the Horvath clock. A transcriptome can shift in response to stress, illness, exercise, or even the time of day, making it a less reliable measure of aging.

To make the new clock, the team assembled over 11,000 transcriptomes, heavily relying on data from the Interventions Testing Program, a giant effort to study longevity treatments in mice. The dataset included mice exposed to genetic tweaks, drugs, and dietary therapies known to affect aging and lifespan. The team also added more than 2,600 samples from monkeys, several hundred from rats, and over 4,000 from humans to deliver a cross-species view of aging.

They then built multiple transcriptome clocks that estimated age and mortality risk. To validate the clocks, they turned to an independent dataset that included rodent models of accelerated aging, Alzheimer’s diseases, chronic kidney disease, and other age-related conditions. When applied to individual cells, the clocks yielded older transcriptomic ages in more than 90 percent of the samples, suggesting that aging is deeply rooted at the cellular level.

In humans, the clocks accurately predicted the lifespans of participants enrolled in a large heart health study. They were also sensitive to environmental factors that affect aging, ticking forward after exposure to radiation or chronic diseases and rewinding after treatments such as young-blood transfusion, a strategy shown to rejuvenate elderly rodents.

An analysis of the genes driving the clocks highlighted many of the usual molecular suspects. Aging turned on genes involved in inflammation, cellular energy disfunction, and senescence—where failing cells leak toxic molecules. Many of these signatures appeared across organs and species, suggesting that core aspects of aging have been conserved in mammals.

These findings are especially valuable for longevity researchers, who often work with rodent models. Despite living a fraction of a human lifespan, aging rodents undergo transcriptomic shifts similar to those found in us. The new clock could easily test their biological age after potential anti-aging treatments, capture the immediate effects, and predict lifespan, long before they die. It could, in theory, speed up aging research and the quest for treatments.

But to be clear, like other aging clocks, it isn’t a crystal ball. Scientists don’t know if the transcriptome changes drive aging or merely reflect its aftermath. The signatures could be capturing overall health and resilience, rather than molecular changes associated with aging per se.

That distinction matters. As we grow older, cells activate a variety of protective genes to counter rising stress, inflammation, and damage. Not every age-related transcriptomic change is harmful. Some changes reflect the body’s attempt to fight back. Because transcriptomes capture only a snapshot in time, scientists still need to differentiate genes that contribute to aging from those that help defend against it and learn how those patterns shift over time.

There’s a broader challenge too. Researchers are building more and more biological clocks using different criteria, and they don’t always agree. One may say you’re far older than another. This highlights “the need for any aging biomarker to be validated carefully,” wrote Magalhães.

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