Study objectivesEmerging evidence suggests a possible link between anorexia nervosa (AN) and alterations in the gut microbiota. This study aimed to characterize the gut microbiota profile in a cohort of Chinese female patients with AN.MethodA comparative analysis of the gut microbiota was conducted between 30 female patients with AN and 30 sex- and age-matched healthy controls (HCs). Fecal samples were collected for 16S rRNA gene sequencing analysis. All participants were assessed using the Eating Disorder Inventory (EDI) and the Childhood Trauma Questionnaire (CTQ). Bioinformatics analysis was performed using QIIME2, and statistical analyses were carried out with SPSS 26.0 and R software. Correlations between microbiota differences and body mass index (BMI), EDI, and CTQ were further investigated.ResultsThe analysis revealed differences in beta diversity and the abundances of specific microbial taxa between the two groups; however, no significant differences were observed in alpha diversity nor in the associations between gut microbiota and BMI, disease severity, or childhood trauma.ConclusionsThis study identified limited differences in the gut microbiota composition between patients with AN and HCs. Critically, no robust associations between gut microbiota and clinical features were found after rigorous multiple comparison correction. While nominal (uncorrected) correlations were observed between the specific microbiota and psychological traits, these results are exploratory and should be considered hypothesis-generating. They highlight a potential avenue for future research but require validation in larger, longitudinal cohorts to determine their reproducibility and biological significance.
Inducible, split base editors for in vivo cancer functional genomics
Nature Biotechnology, Published online: 15 April 2026; doi:10.1038/s41587-026-03077-5
Inducible base editors enhance cancer genomic screens in vivo.
Rapid expansion of genotype D1.1 A(H5N1) influenza viruses in wild birds across North America during the 2024 migratory season
Nature Medicine, Published online: 15 April 2026; doi:10.1038/s41591-026-04300-1
Using active and passive genomic surveillance, researchers observed the rapid spread of high pathogenicity avian influenza H5N1 D1.1 viruses in wild birds during the 2024 migratory season, which coincided with detection in humans, but did not identify mammalian adaptive markers in viruses from wild birds.
Engineered Miniature CRISPR Boosts Gene‑Editing Efficiency in Human Cells
One of the biggest obstacles in targeting CRISPR therapy deliveries directly into the body isn’t the editing chemistry, it’s the size of the editors themselves. The field’s workhorse nucleases, including Cas9 and Cas12a, are considerably large (exceeding 1,300 amino acids) to fit inside adeno‑associated virus (AAV) vectors, the most widely used delivery vehicle for in vivo gene therapy. That size mismatch has forced most clinical applications to rely on ex vivo editing of blood or bone‑marrow‑derived cells, leaving many tissues out of reach. A smaller CRISPR system that can be packaged into AAV without sacrificing efficiency has long been a key missing piece.
A new study published in Nature Structural & Molecular Biology takes a major step toward that goal. Researchers at the University of Texas at Austin and collaborators report the discovery and engineering of a compact Cas12f nuclease that performs robustly in human cells, a notable advance for a class of miniature enzymes that have historically shown lower efficiencies in mammalian cells compared to larger systems. The paper is titled, “Comparative characterization of Cas12f orthologs reveals mechanistic features underlying enhanced genome editing efficiency.”
The team began by mining metagenomic datasets for naturally small CRISPR enzymes and identified a previously uncharacterized ortholog, Alistipes sp. Cas12f (Al3Cas12f). Despite its compact size—roughly one‑third that of Cas9—the nuclease showed unexpectedly strong activity in human cells. In initial screens, Al3Cas12f produced more than 50% editing at many genomic sites and exceeded 90% at several targets. The authors wrote, “Results from a gRNA screen targeting intron 1 of the ALB gene, exon 3 of the APOA1 gene and the AAVS1 site within PPP1R12C intron 1 showed that 27 target sites displayed >10% editing, 19 sites displayed >50% editing and 10 sites displayed >90% editing across AAVS1 and APOA1.”
Cryo‑EM structures revealed why this miniature enzyme punches above its weight. Compared with other Cas12f orthologs, Al3Cas12f forms a more extensive and interlocking dimer interface, creating a stable, preassembled complex that supports efficient R‑loop formation. The guide RNA scaffold also appears naturally streamlined: unlike other Cas12f gRNAs, it lacks an extraneous stem‑loop and adopts a compact conformation that docks cleanly into the protein. As the authors noted, Al3Cas12f achieves “efficient R‑loop formation through a stable dimer interface and a naturally optimized gRNA.”
Using these structural insights, the team engineered an enhanced variant, Al3Cas12f RKK, that dramatically boosts editing efficiency across genomic loci. In human cells, the variant increased editing from below 10% to more than 80% at many targets, with some sites reaching 90%. The researchers tested the system in a leukemia‑derived human cell line, focusing on genes implicated in cancer, atherosclerosis, and ALS.
The mechanistic comparisons were equally revealing. By solving the structures of two additional Cas12f orthologs—Oscillibacter sp. Cas12f and Ruminiclostridium herbifermentans Cas12f—the team noted “divergent architectures and regulatory features governing protospacer-adjacent motif recognition, gRNA binding, dimerization, and DNA cleavage.” Al3Cas12f’s extended helices and mortise‑and‑tenon‑like interactions appear to be lineage‑specific adaptations that stabilize the nuclease and support high activity.
The next step is to test whether the enzyme maintains its performance when packaged into AAV vectors. If successful, the system could offer a blueprint for engineering future generations of compact CRISPR tools.
The post Engineered Miniature CRISPR Boosts Gene‑Editing Efficiency in Human Cells appeared first on GEN – Genetic Engineering and Biotechnology News.
The problem with thinking you’re part Neanderthal
You’ve probably heard some version of this idea before: that many of us have an “inner Neanderthal.” That is to say, around 45,000 years ago, when Homo sapiens first arrived in Europe, they met members of a cousin species—the broad-browed, heavier-set Neanderthals—and, well, one thing led to another, which is why some people now carry a small amount of Neanderthal DNA.
This DNA is arguably the 21st century’s most celebrated discovery in human evolution. It has been connected to all kinds of traits and health conditions, and it helped win the Swedish geneticist Svante Pääbo a Nobel Prize.
But in 2024, a pair of French population geneticists called into question the foundation of the popular and pervasive theory.
Lounès Chikhi and Rémi Tournebize, then colleagues at the Université de Toulouse, proposed an alternative explanation for the very same genomic patterns. The problem, they said, was that the original evidence for the inner Neanderthal was based on a statistical assumption: that humans, Neanderthals, and their ancestors all mated randomly in huge, continent-size populations. That meant a person in South Africa was just as likely to reproduce with a person in West Africa or East Africa as with someone from their own community.
Archaeological, genetic, and fossil evidence all shows, though, that Homo sapiens evolved in Africa in smaller groups, cut off from one another by deserts, mountains, and cultural divides. People sometimes crossed those barriers, but more often they partnered up within them.
In the terminology of the field, this dynamic is called population structure. Because of structure, genes do not spread evenly through a population but can concentrate in some places and be totally absent from others. The human gene pool is not so much an Olympic-size swimming pool as a complex network of tidal pools whose connectivity ebbs and flows over time.
This dynamic greatly complicates the math at the heart of evolutionary biology, which long relied on assumptions like randomly mating populations to extract general principles from limited data. If you take structure into account, Chikhi told me recently, then there are other ways to explain the DNA that some living people share with Neanderthals—ways that don’t require any interspecies sex at all.
“I believe most species are spatially organized and structured in different, complex ways,” says Chikhi, who has researched population structure for more than two decades and has also studied lemurs, orangutans, and island birds. “It’s a general failure of our field that we do not compare our results in a clear way with alternative scenarios.” (Pääbo did not respond to multiple requests for comment.)
The inner Neanderthal became a story we could tell ourselves about our flaws and genetic destiny: Don’t blame me; blame the prognathic caveman hiding in my cells.
Chikhi and Tournebize’s argument is about population structure, yes, but at heart, it is actually one about methods—how modern evolutionary science deploys computer models and statistical techniques to make sense of mountains upon mountains of genetic data.
They’re not the only scientists who are worried. “People think we really understand how genomes evolve and can write sophisticated algorithms for saying what happened,” says William Amos, a University of Cambridge population geneticist who has been critical of the “inner Neanderthal” theory. But, he adds, those models are “based on simple assumptions that are often wrong.”
And if they’re wrong, what’s at stake is far more than a single evolutionary mystery.
A captivating story of interspecies passion
Back in 2010, Pääbo’s lab pulled off something of a miracle. The researchers were able to extract DNA from nuclei in the cells of 40,000-year-old Neanderthal bones. DNA breaks down quickly after death, but the group got enough of it from three different individuals to produce a draft sequence of the entire Neanderthal genome, with 4 billion base pairs.
As part of their study, they performed a statistical test comparing their Neanderthal genome with the genomes of five present-day people from different parts of the world. That’s how they discovered that modern humans of non-African ancestry had a small amount of DNA in common with Neanderthals, a species that diverged from the Homo sapiens line more than 400,000 years ago, that they did not share with either modern humans of African ancestry or our closest living relative, the chimpanzee.
WELLCOME COLLECTION
Pääbo’s team interpreted this as evidence of sexual reproduction between ancient Homo sapiens and the Neanderthals they encountered after they expanded out of Africa. “Neanderthals are not totally extinct,” Pääbo said to the BBC in 2010. “In some of us, they live on a little bit.”
The discovery was monumental on its own—but even more so because it reversed a previous consensus. More than a decade earlier, in 1997, Pääbo had sequenced a much smaller amount of Neanderthal DNA, in that case from a cell structure called a mitochondrion. It was different enough from Homo sapiens mitochondrial DNA for his team to cautiously conclude there had been “little or no interbreeding” between the two species.
After 2010, though, the idea of hybridization, also called admixture, effectively became canon. Top journals like Science and Nature published study after study on the inner Neanderthal. Some scientists have argued that Homo sapiens would never have adapted to colder habitats in Europe and Asia without an infusion of Neanderthal DNA. Other research teams used Pääbo’s techniques to find genetic traces of interbreeding with an extinct group of hominins in Asia, called the Denisovans, and a mysterious “ghost lineage” in Africa. Biologists used similar tests to find evidence of interbreeding between chimpanzees and bonobos, polar and brown bears, and all kinds of other animals.
The inner-Neanderthal hypothesis also took a turn for the personal. Various studies linked Neanderthal DNA to a head-spinning range of conditions: alcoholism, asthma, autism, ADHD, depression, diabetes, heart disease, skin cancer, and severe covid-19. Some researchers suggested that Neanderthal DNA had an impact on hair and skin color, while others assigned individuals a “NeanderScore” that was correlated with skull shape and prevalence of schizophrenia markers. Commercial genetic testing companies like 23andMe started offering customers Neanderthal ancestry reports.
The inner Neanderthal became a story we could tell ourselves about our flaws and genetic destiny: Don’t blame me; blame the prognathic caveman hiding in my cells. Or as Latif Nasser, a host of the popular-science program Radiolab, put it when he was hospitalized with Crohn’s disease, another Neanderthal-associated condition: “I just keep imagining these tiny Neanderthals … just, like, stabbing me and drawing these little droplets of blood out of me.”
“These things become meaningful to people,” Chikhi says. “What we say will be important to how people view themselves.”
The pitfalls of simplistic solutions
When population geneticists built the theoretical framework for evolutionary biology in the early 20th century, genes were only abstract units of heredity inferred from experiments with peas and fruit flies. Population genetics developed theory far more quickly than it accumulated data. As a result, many data-driven scientists dismissed the study of evolution as a form of storytelling based on unexamined assumptions and preconceived ideas.
By the ’90s, though, genes were no longer abstractions but sequenced segments of DNA. Genomic sequencing grounded evolutionary studies in the kind of hard data that a chemist or physicist could respect.
Yet biologists could not simply read evolutionary history from genomes as though they were books. They were trying to determine which of a nearly infinite number of plausible histories was the most likely to have created the patterns they observed in a small sample of genomes. For that, they needed simplified, algorithmic models of evolution. The study of evolution shifted from storytelling to statistics, and from biology to computer science.
That suited Chikhi, who as a child was drawn to the predictable laws and numerical precision of math and science. He entered the field in the mid-’90s just as the first big studies of human DNA were settling old debates about human origins. DNA showed that Africa harbored far more genetic diversity than the entire rest of the planet. The new evidence supported the idea that modern humans evolved for hundreds of thousands of years in Africa and expanded to the other continents only in the last 100,000 years. For Chikhi, whose parents were Algerian immigrants, this discovery was a powerful challenge to the way some archaeologists and biologists talked about race. DNA could be used to deconstruct rather than encourage the pernicious idea that human races had deep-seated evolutionary differences based on their places of origin.
At the same time, though, he was wary of the tendency to treat DNA as the final verdict on open questions in evolution. Chikhi had been surprised when, back in 1997, Pääbo and his team used that small amount of mitochondrial DNA to rule out hybridization between Homo sapiens and Neanderthals. He didn’t think that the absence of Neanderthal DNA there necessarily meant it wouldn’t be found elsewhere in the Homo sapiens genome.
Chikhi’s own research in the aughts opened his eyes to the gaps between historical reality and models of evolution. For one, despite the assumption of random mating, none of the animals Chikhi studied actually mated randomly. Orangutans lived in highly fragmented habitats, which restricted their pool of potential mates, and female birds were often extremely picky about their male partners.
These factors could confound an evolutionary biologist’s traditional statistical tool kit. Scientists were starting to apply a mathematical technique to estimate historical population sizes for a species from the genome of just a single individual. This method showed sharp population declines in the histories of many different species. Chikhi realized, though, that the apparent declines could be an artifact of treating a structured population as one that evolved with random mating; in that case, the technique could indicate a bottleneck even if all the subgroups were actually growing in size. “This is completely counterintuitive,” he says.
That’s at least partly why, when Pääbo’s 2010 Neanderthal genome came out, Chikhi was impressed with the sheer technical accomplishment but also leery of the findings about hybridization. “It was the type of thing we conclude too quickly based on genetic data,” he says. Pääbo’s work mentioned population structure as a possible alternative explanation—but didn’t follow up.
Just a couple of years later, a pair of independent scientists named Anders Eriksson and Andrea Manica picked up the idea, building a model with simple population structure that explicitly excluded admixture. They simulated human evolution starting from 500,000 years ago and found that their model produced the same genomic patterns Pääbo’s group had interpreted as evidence of hybridization.
“Working with structured models is really out of the comfort zone of a lot of population geneticists,” says Eriksson, now a professor at the University of Tartu in Estonia.
Their research impressed Chikhi. “At the time, I thought people would focus on population structure in the evolution of humans,” he says. Instead, he watched as the inner-Neanderthal hypothesis took on a life of its own. Scientists produced new methods to quantify hybridization but rarely examined whether population structure would yield the same results. To Chikhi, this wasn’t science; it was storytelling, like some of the old narratives about the evolution of racial differences.
Chikhi and Tournebize decided to take a crack at the problem themselves. “I’ve always been very skeptical about science, and population genetics in particular,” says Tournebize, now a researcher at the French National Research Institute for Sustainable Development. “We make a lot of assumptions, and the models we use are very simplistic.” As detailed in a 2024 paper published in Nature Ecology & Evolution, they built a model of human evolution that replaced randomly mating continent-wide populations with many smaller populations linked by occasional migration. Then they let it run—a million times.
At the end of the simulation, they kept the 20 scenarios that produced genomes most similar to the ones in a sample of actual Homo sapiens and Neanderthals. Many of these scenarios produced long segments of DNA like the ones their peers argued could only have been inherited from Neanderthals. They showed that several statistics, which other scientists had proposed as measurements of Neanderthal DNA, couldn’t actually distinguish between hybridization and population structure. What’s more, they showed that many of the models that supported hybridization failed to accurately predict other known features of human evolution.
“A model will say there was admixture but then predict diversity that is totally incompatible with what we actually know of human diversity,” Chikhi says. “Nobody seems to care.”
So how did Neanderthal DNA wind up in living people if not via interspecies passion? Chikhi and Tournebize think it’s more likely that it was inherited by both Neanderthals and some sapiens groups in Africa from a common ancestor living at least half a million years ago. If the sapiens groups carrying those genetic variants included the people who migrated out of Africa, then the two human species would have already had the DNA in common when they came into contact in Europe and Asia—no sex required.
“The interpretation of genetic data is not straightforward,” Chikhi says. “We always have to make assumptions. Nobody takes data and magically comes up with a solution.”
Embracing the uncertainty
Most of the half-dozen population geneticists I spoke with praised Chikhi and Tournebize’s ingenuity and appreciated the spirit of their critique. “Their paper forces us to think more critically about the model we use for inference and consider alternatives,” says Aaron Ragsdale, a population geneticist at the University of Wisconsin–Madison. His own work likewise suggests that the earliest Homo sapiens populations in Africa were probably structured—and that this is the likely reason for genomic patterns that other research groups had attributed to hybridization with a mysterious “ghost lineage” of hominins in Africa.
Yet most researchers still believe that modern humans and Neanderthals did probably have children with each other tens of thousands of years ago. Several pointed to the fact that fossil DNA of Homo sapiens who died thousands of years ago had longer chunks of apparent Neanderthal DNA than living people, which is exactly what you would expect if they had a more recent Neanderthal ancestor. (To address this possibility, Chikhi and Tournebize included DNA from 10 ancient humans in their study and found that most of them fit the structured model.) And while the Harvard population geneticist David Reich, who helped design the statistical test from Pääbo’s 2010 study, declined an interview, he did say he thought Chikhi and Tournebize’s model was “weak” and “very contrived,” adding that “there are multiple lines of evidence for Neanderthal admixture into modern humans that make the evidence for this overwhelming.” (Two other authors of that study, Richard Green and Nick Patterson, did not respond to requests for comment.)
Nevertheless, most scientists these days welcome the development of structured, or “spatially explicit,” models that account for the fact that any given member of a population is usually more closely related to individuals living nearby than to those living far away.
Loosening our attachment to certain narratives of evolution can create space for wonder at the sheer complexity of life’s history.
Other scientists also say that random mating isn’t the only assumption in population genetics that merits scrutiny. Models rarely factor in natural selection, which can also create genetic patterns that look like hybridization. Another common assumption is that everyone’s DNA mutates at the same, constant rate. “All the theory says the mutation rate is fixed,” says Amos, the Cambridge population geneticist. But he thinks that rate would have slowed drastically in the group of Homo sapiens that expanded to Europe around 45,000 years ago. This, too, could have created genomic patterns that other scientists interpret as evidence of interbreeding with Neanderthals.
COURTESY OF 23ANDME
The point here isn’t that a complex model of evolution with many moving pieces is necessarily better than a simple one. Scientists need to reduce complexity in order to see the underlying processes more clearly. But simple models require assumptions, and scientists need to reevaluate those assumptions in light of what they learn. “As you get more data, you can justify more complex models of the world,” says Mark Thomas, a population geneticist at University College London, who wrote a history of random mating in population genetics that highlighted how the field was starting to see it as “a limiting assumption as opposed to a simplifying one.”
It can feel discouraging to couch conversations about the past in confusing terms like “population structure” and “mutation rates.” It seems almost antithetical to the spirit of science to talk more about uncertainty at the same time we are developing powerful technologies and enormous data sets for analyzing evolution. These tools often yield novel answers, but they can also limit the questions we ask. The French archaeologist Ludovic Slimak, for example, has complained that the idea of the inner Neanderthal has domesticated our image of Neanderthals and made it difficult to imagine their humanity as distinct from our own. Investigating Neanderthal DNA is sexier to many young researchers than searching for archaeological and fossil evidence of how Neanderthals actually lived.
Loosening our attachment to certain narratives of evolution can create space for wonder at the sheer complexity of life’s history. Ultimately, that’s what Chikhi and Tournebize hope to do. After all, they don’t believe the question of population structure versus hybridization is either-or. It’s possible, and even likely, that both played a role in human evolution. “Our structured model does not necessarily mean that no admixture ever took place,” Chikhi and Tournebize wrote in their study. “What our results suggest is that, if admixture ever occurred, it is currently hard to identify using existing methods.”
Future methods might disentangle the different factors, but it’s just as important, Chikhi says, for scientists to be up-front about their assumptions and test alternatives. “There’s still so much uncertainty on so many aspects of the demographic history of Neanderthals and Homo sapiens,” he notes.
Keep that in mind the next time you read about your inner Neanderthal. The association between this DNA and some diseases may be real, of course—but would journals publish these studies without the additional claim that the DNA is from Neanderthals? Any good storyteller knows that sex sells, even in science.
Ben Crair is a science and travel writer based in Berlin.
Advancing Fully Walkaway Automation in Genomics Workflows
SPT Labtech and the European Molecular Biology Laboratory’s Genomics Core Facility (EMBL GeneCore) in Heidelberg, Germany, agreed to collaborate to advance fully walkaway automated genomics workflows. As part of the collaboration, SPT Labtech’s firefly®+ all-in-one liquid handling platform has been installed at EMBL GeneCore.
Officials at EMBL GeneCore say they will expand the facility’s capacity to develop new protocols and further validate and optimize existing workflows for challenging applications such as low-input and metagenomics samples to support the broader genomics community. The SPT platform is designed to simplify complex genomics workflows, combining pipetting, dispensing, incubating, and shaking technologies into a single instrument.
The automated protocols use New England Biolabs (NEB) library preparation kits, NEBNext®, to generate libraries from a wide input range. According to a SPT spokesperson, the installation of the company’s firefly+ platform at EMBL GeneCore, combined with NEB kits, creates strong foundation for fully walkaway automation, enabling more streamlined, end-to-end workflows and supporting labs to scale automation more easily.
“The installation of SPT Labtech’s firefly+ platform as part of our collaboration underscores our commitment to remain at the forefront of scientific innovation. Fully walkaway automation will address key bottlenecks in genomics workflows, helping us develop high-quality, scalable NGS protocols,” says Vladimir Benes, head of EMBL GeneCore.
“Our latest collaboration with EMBL GeneCore marks a significant step towards advancing fully walkaway automation, providing end-to-end genomics workflows for a much wider range of applications, including environmental and rare species research,” adds Morten Frost, CCO, SPT Labtech.
“Integration of our library prep kits with SPT Labtech’s firefly+ platform at EMBL GeneCore creates a compelling opportunity for faster, scalable DNA and RNA-Seq workflows,” notes Bjoern Textor, PhD, sales and senior applications manager, New England Biolabs.
The post Advancing Fully Walkaway Automation in Genomics Workflows appeared first on GEN – Genetic Engineering and Biotechnology News.
Combined Small-Cell Lung Cancer Evolution Insights May Improve Diagnosis, Treatment
A spatial multi-omics study of the rare lung cancer subtype known as combined small-cell lung cancer (cSCLC) has shown that these mixed tumors, which contain features of both small-cell and non-small-cell lung cancer (NSCLC), arise from a single ancestral cell that evolves and transitions between the two cancer types over time.
The results “provide a foundation for understanding cSCLC evolution and advancing innovative diagnostics and therapeutics,” write the authors in Cell Reports Medicine.
cSCLC represents approximately 2–5% of all SCLC cases, with diagnosis primarily based on pathologic evaluation of surgically resected tumor specimens. This can lead to underdiagnosis because small biopsies may not capture the full histological diversity of the tumor. Although cSCLC is more diverse and has a worse prognosis than typical SCLC, it is usually treated the same way because its underlying biology and tumor environment are not well understood.
Traditionally, the mixed histologies within cSCLC were thought to arise from independent tumor populations, but recent studies have revealed that the different histological components share common genetic mutations.
To investigate further, Zhuo Wang, from Fudan University in Shanghai, China, and colleagues applied spatially resolved genomic and transcriptomic sequencing, alongside single-cell RNA sequencing, to 19 treatment-naive cSCLC tumors.
“We found that these tumors are not simply mixtures of different cancer types,” said Wei Wei, PhD, associate professor at the Institute for Systems Biology in Seattle and co-corresponding author of the study. “They are dynamic systems, with cancer cells actively changing their identity. That flexibility may help explain why they are so difficult to treat.”
The team reports that the different tumor components originate from a single clone but later diverge as they acquire different mutations and copy-number changes.
The study also revealed that tumor cells can exist in intermediate or hybrid states, carrying features of multiple cancer types at once. About one-third of the SCLC-like tumor cells analyzed showed these mixed identities, suggesting that cancer progression is not a simple on-off switch, but a continuum.
In addition, the researchers found that different regions within the same tumor create distinct microenvironments. Some areas were rich in immune cells, while others were largely immune-excluded. Dense bands of fibroblasts often separated these regions. Those fibroblast-rich boundaries may help wall off parts of the tumor from immune attack.
“By combining spatial genomics, single-cell analysis, and multi-region sequencing, we were able to trace how these tumors evolve across both space and time,” said Fudan University’s Qihui Shi, PhD, co-corresponding author of the study. “This approach allowed us to capture transitional cell states that are not visible using conventional methods.”
Finally, the researchers developed “cSCLC Detector,” a four-gene diagnostic tool that may help identify these mixed tumors more accurately. The tool was built on a key insight from the study: although the small-cell and non-small-cell parts of cSCLC can look very different under the microscope, they come from the same ancestral tumor and share early trunk mutations.
In independent biopsy and blood samples, the assay, which detects mutations in the NSCLC-specific driver genes EGFR, KRAS, BRAF, and PIK3CA, identified cSCLC-like cases in 14% of samples, compared with a prevalence of 2%–5% estimated from surgically resected specimens.
The findings highlight the importance of understanding not just the genetic mutations in cancer, but also how cancer cells change state and interact with their environment.
“Cancer is not static,” Wei said. “To treat it effectively, we need to understand how it evolves—not just what it is at a single point in time.”
The post Combined Small-Cell Lung Cancer Evolution Insights May Improve Diagnosis, Treatment appeared first on Inside Precision Medicine.
Transcriptomic profiling and targeted validation reveal molecular mechanisms of oxygen therapy in high-altitude cerebral injury
BackgroundExposure to high-altitude hypoxia is associated with an increased risk of impaired brain structure and function, with oxidative stress and neuroinflammation widely recognized as key mechanisms involved. In this context, hyperbaric oxygen therapy is considered a potential intervention; however, the mechanism by which it affects cerebral function changes caused by high-altitude exposure remains to be further elucidated.ObjectiveThis study aims to explore and compare the therapeutic effects of normobaric oxygen (NBO) and hyperbaric oxygen (HBO) on high-altitude cerebral injury (HACI), and to elucidate the molecular mechanisms underlying their neuroprotective effects using transcriptomic profiling and targeted validation.MethodsA mouse model of high-altitude cerebral injury was established using a hypobaric hypoxia chamber. Mice were exposed to a simulated altitude of 7,000 m (approximately 9.8% O₂ at 0.47 ATA) for 3 consecutive days to induce severe hypoxia. Animals were divided into four groups: Control (Con), High-Altitude exposure (HH), post-HH treated with normobaric oxygen (NBO; 100% O₂ at 1.0 ATA for 1 h daily for 3 days), and post-HH treated with hyperbaric oxygen (HBO; 100% O₂ at 2.0 ATA for 1 h daily for 3 days). Brain tissues were analyzed using H&E staining, RNA sequencing (RNA-seq), Western blotting for key pathway proteins, immunofluorescence for glial cell activation, and ELISA for inflammatory cytokines. Oxidative stress markers (SOD, MDA, GSH, NO) were also assessed.ResultsHistopathological analysis confirmed cerebral damage in the HH group, which was significantly ameliorated by both HBO and NBO treatments. RNA-seq revealed widespread disruption of the cerebral transcriptome following high-altitude exposure. Oxygen therapy was associated with partial restoration of global gene expression patterns. KEGG pathway analysis highlighted significant enrichment in pathways related to NF-κB signaling, cytokine–cytokine receptor interaction, IL-17 signaling, and PI3K–AKT signaling. Subsequent targeted validation demonstrated that oxygen treatment reduced oxidative stress (increased SOD and GSH; decreased MDA and NO) and modulated the PI3K–AKT signaling pathway (increased p-AKT/AKT). Concurrently, oxygen therapy attenuated neuroinflammatory responses, inhibiting microglial and astrocytic activation, reducing pro-inflammatory cytokine levels (IL-1β, IL-6, TNF-α), and modulating the TLR4–NF-κB signaling axis (decreased TLR4 and p-p65/p65). HBO treatment was associated with broader modulation of several molecular pathways involved in oxidative stress and inflammation.ConclusionExisting evidence suggests that HBO may exert protective effects against altitude-related brain injury. This mechanism likely involves activating the PI3K–AKT/Nrf2 axis to alleviate oxidative stress and inhibiting the TLR4–NF-κB pathway to reduce neuroinflammation, thereby partially restoring transcriptional homeostasis. However, the causal relationships between these pathways and their interactions require further validation and refinement.
Chasing the Zero That Matters
Mary Royal almost skipped her mammogram.
At 51, the mother of four from Wichita Falls, Texas, was busy,
tired, and juggling the overlapping demands of work, family, and everyday life. The appointment felt routine—easy to reschedule and easy to dismiss. In a decision that would change everything, she went.
In 2023, Royal was diagnosed with stage 2B multicentric invasive lobular and ductal carcinoma. What followed was a cascade familiar to many cancer patients but deeply personal in its toll: a double bilateral mastectomy, months of chemotherapy and radiation, and the discovery of a nodule in her chest cavity. Another scan later revealed a mass on her ovary, prompting a preventative radical hysterectomy. By the end of the year, Royal had endured positron emission tomography (PET) scans, injections, fasting, and what she called “all that nuclear medicine.”
For many patients, completing treatment is supposed to signal relief. In reality, it often marks the beginning of a new phase—one defined by uncertainty. Surveillance imaging, blood tests, and follow-up visits can feel like checkpoints in an endless waiting game. Every scan carries both hope and fear.
Royal knows this phase well. Like many survivors, she lives with what patients and clinicians call scan anxiety. “I’ve never met a person diagnosed with cancer who did not live with scan anxiety,” she said.
That anxiety eventually led her to consider a different way of monitoring her disease—one that looks not for tumors large enough to be seen on a scan, but for microscopic traces of cancer that may remain in the body after treatment. These traces are known as measurable, or minimal, residual disease (MRD).
MRD basics
MRD refers to the small number of cancer cells that can persist after treatment, even when imaging and conventional tests show no evidence of disease. These cells are often invisible to computed tomography (CT), magnetic resonance imaging (MRI), or PET scans, yet they can drive relapse months or years later.
Historically, MRD testing has been best established in hematologic malignancies such as leukemia, lymphoma, and multiple myeloma. In these diseases, molecular and flow-based techniques can detect one malignant cell among tens of thousands, or even millions, of normal cells. In solid tumors, however, detecting MRD has been far more challenging. That is now changing.
Advances in liquid biopsy technologies allow researchers to analyze circulating tumor DNA (ctDNA): tiny fragments of DNA shed by cancer cells into the bloodstream. With increasingly sensitive assays, it is now possible to detect residual disease at levels far below what imaging can reveal.
MRD matters because cancer recurrence is often a race against time. The earlier residual disease is detected, the greater the opportunity to intervene—whether by intensifying therapy, switching treatments, or, in some cases, sparing patients from unnecessary additional therapy if no disease is detected.
Regulators are taking note. In January 2026, the U.S. Food and Drug Administration (FDA) issued draft guidance supporting the use of MRD negativity as an endpoint in clinical trials for multiple myeloma. The move signaled growing confidence in MRD as a meaningful surrogate for long-term outcomes, potentially accelerating clinical trials and access to new therapies.
Deciding to look closer
When Royal’s oncologist suggested the Personalis NeXT Personal® test, a blood-based MRD assay, her initial reaction was hesitation.
“I said, ‘Let me think about it,’” she recalled. As she researched the test online, her anxiety rose. “I thought, ‘No, thank you. I have had so much anxiety already.’”
Her husband disagreed. “You are insane,” he told her, “Why would you not want to do that?” Her oncologist offered a different perspective: “What is the point of science if we don’t use it?”
“That really resonated with me,” Royal said.
She agreed to the test and had her first ctDNA draw in early 2024. Since then, she has taken it 13 times.
“Seeing that zero in the results is a huge relief,” she said. “I really appreciate how much easier the test is on me, both mentally and physically. Now, I cannot believe anyone would say ‘no’ to this. It brings me so much comfort. And I want to know what to do next. I don’t want to just sit around waiting for something when I have the ability to see things early on.”
Her experience reflects a growing shift in survivorship—from episodic imaging to continuous molecular monitoring.
An ultrasensitive approach
For Richard Chen, MD, CMO at Personalis, the goal of ultrasensitive MRD testing has always been to address the uncertainty patients live with after treatment.
Chief Medical Officer
Personalis
“Our NeXT Personal test pioneered ‘ultrasensitive MRD’ down to about one part per million of ctDNA, designed to be a leap forward in detecting very small traces of cancer from a blood sample earlier,” Chen said.
The test is tumor-informed, meaning that it begins with whole-genome sequencing of a patient’s tumor. From that data, up to approximately 1,800 tumor-specific mutations are identified to create a personalized molecular signature. Blood samples are then analyzed for that signature.
“The groundbreaking clinical data that we have published in lung and breast cancer shows that the ultrasensitive capabilities of NeXT Personal enable it to detect cancer many months to years ahead of imaging,” Chen said, “potentially allowing for earlier intervention and treatment of the patient.” Equally important, he added, is the reassurance that a highly sensitive negative result can provide.
Personalis is expanding MRD testing beyond simple detection. A new opt-in feature, the Real-Time Variant Tracker®, allows clinicians and patients to view potentially actionable mutations detected in ctDNA, including those associated with treatment resistance.
MRD testing is increasingly viewed not just as a prognostic tool, but as a way to actively guide care. Chen outlines three major applications: earlier detection of residual or recurrent disease; earlier de-escalation of therapy for patients who have cleared their cancer at a molecular level; and real-time monitoring of treatment response.
“Cancer is often a race against time,” he said. “If you can detect cancer that’s coming back much earlier than before, then you have the opportunity to intervene earlier with additional treatment for the patient.”
Adding biological precision
Sensitivity alone, however, is not the only challenge in MRD detection. Biological precision—understanding which cells persist and why—is equally important.
Chief Medical Officer
Mission Bio
Zivjena Vucetic, MD, PhD, CMO at Mission Bio, points to the limitations of bulk sequencing approaches, which average signals across mixed-cell populations.
Mission Bio’s single-cell MRD assay simultaneously detects genetic mutations and surface protein expression across thousands of individual cells in acute myeloid leukemia. This approach reveals whether mutations coexist in the same cell and how they relate to cellular phenotypes.
“Our integrated single-cell approach provides a more biologically precise definition of measurable residual disease,” Vucetic said, which might improve risk stratification beyond conventional molecular or flow-based methods.
By identifying rare, therapy-resistant clones, single-cell MRD technologies offer insight into clonal evolution and emerging resistance. This information can guide treatment selection and drug development.
Decentralizing monitoring
Accessibility and turnaround time are also shaping the MRD landscape. For example, QIAGEN is advancing MRD monitoring by pairing tumor-informed assay design with decentralized digital polymerase chain reaction (dPCR), aiming to make longitudinal molecular monitoring faster, more accessible, and more informative for research and drug development.
In June 2025, QIAGEN announced a partnership with Tracer Biotechnologies to integrate Tracer’s tumor-informed assay design with QIAGEN’s QIAcuity dPCR platform. The approach begins with sequencing a patient’s tumor, often leveraging existing next-generation sequencing (NGS) data, to identify somatic mutations. Tracer then designs personalized multiplex dPCR assays to detect ctDNA carrying those mutations in blood samples.
Vice President
QIAGEN
Running these assays on QIAcuity enables absolute quantification of rare tumor-derived molecules by partitioning samples into thousands of reactions. According to Richard Watts, vice president of partnering for precision diagnostics at QIAGEN, “The result is a decentralized, high-frequency monitoring solution,” with turnaround times measured in hours to days rather than weeks. He noted that this model significantly reduces cost and logistical complexity compared with centralized NGS-based MRD testing while enabling earlier detection of molecular recurrence, often before radiographic changes are visible.
While currently intended for exploratory research use, the platform has clear implications for oncology drug development. By allowing assays to be run on standard dPCR instruments at clinical trial sites, sponsors can avoid centralized sample shipping, simplify global study design, and more rapidly generate data. Frequent sampling also provides detailed insight into tumor kinetics and treatment response, potentially enabling earlier assessments of drug activity.
Looking ahead, QIAGEN anticipates MRD evolving beyond detection toward biological characterization. Emerging single-cell technologies, supported by QIAGEN’s recent acquisition of Parse Biosciences, could reveal why residual disease persists by distinguishing resistant cell populations and non-genetic resistance mechanisms. Watts emphasized that future clinicians will not only ask whether MRD is present, but “why it persists and which pathways sustain it,” signaling a shift toward more precise, biology-driven intervention strategies.
The expanding ecosystem
Beyond ultrasensitive and single-cell approaches, a growing number of companies are contributing complementary technologies that are broadening how MRD is detected, characterized, and monitored across cancer types.
Twist Bioscience, for example, has developed scalable target enrichment solutions for MRD monitoring that support highly personalized approaches to disease surveillance. Its MRD Rapid 500 Panel enables fast design and manufacture of customized capture panels using silicon-based DNA synthesis. By offering panels that range from dozens to hundreds of tumor-specific probes and fast turnaround times, this approach allows researchers to assess adjuvant treatment response at a genomic level while remaining compatible with established NGS library preparation and hybrid capture workflows.
Whole-genome sequencing-based plasma assays are also playing an expanding role in solid tumor MRD detection. Labcorp offers a plasma-based assay for colorectal cancer that uses whole genome sequencing to identify ctDNA associated with MRD. This approach enables the detection of recurrence at a molecular level before clinical symptoms, biological markers, or radiographic evidence emerge, creating an opportunity for earlier and more proactive intervention.
In hematologic malignancies, ultrasensitive liquid biopsy platforms are demonstrating the ability to dramatically shorten the time required to detect residual disease. For instance, Foresight Diagnostics has developed a ctDNA-based MRD platform that achieves exceptionally high sensitivity across multiple cancers. In patients with large B-cell lymphoma, this approach can detect ctDNA immediately after treatment, rather than waiting for months or even years for disease recurrence to become apparent through PET or CT imaging.
Comprehensive NGS-based MRD solutions are also advancing in myeloid malignancies. Thermo Fisher Scientific offers an integrated research-use testing solution that combines highly sensitive DNA and RNA assays on a single sequencing platform. This enables the simultaneous assessment of single-nucleotide variants, insertions and deletions, and gene fusions alongside streamlined informatics and reporting designed to simplify MRD data interpretation in research settings.
Meanwhile, dPCR continues to play a crucial role in MRD monitoring, where absolute quantification and extreme sensitivity are required. Bio-Rad Laboratories has long supported droplet dPCR technologies that are well suited for tracking low-abundance disease markers. These capabilities are particularly valuable in both hematologic malignancies and solid tumors, where MRD signals in blood can be vanishingly small yet clinically meaningful.
Pre-analytical precision
As MRD assays push detection limits ever lower, pre-analytical steps such as sample collection and cell-free DNA (cfDNA) extraction become increasingly important.
Scientist, NEB
As one example, Anagha Kadam, PhD, applications and product development scientist at New England Biolabs (NEB), highlights how the Monarch Mag Cell-free DNA Extraction Kit addresses crucial challenges in liquid-biopsy workflows and MRD research.
This kit is a magnetic bead-based solution designed for the reproducible isolation of circulating cfDNA from biofluids like plasma, urine, and cerebrospinal fluid. “The kit can be used to isolate cfDNA for discovery and detection workflows, including ctDNA profiling, cancer biomarker discovery, and oncology diagnostics research,” Kadam explained. This technology efficiently recovers cfDNA fragments in the typical sizes of 150–300 base pairs, and even as small as 50 base pairs, while remaining compatible with common anticoagulant and preservative collection tubes. According to Kadam, “The silica-coated magnetic beads, combined with optimized buffer chemistry, help ensure maximum binding and recovery of cfDNA in manual or automation formats.”
Sensitivity and reproducibility are especially crucial for MRD applications. “A cfDNA isolation method that is compatible with different sample types, and that faithfully isolates cfDNA, is a key consideration when establishing MRD workflows,” Kadam noted. She added that the kit delivers “reproducible, high-quality cfDNA yields from different biofluid samples, without additional post-extraction cleanups,” enabling consistent fragment profiles while saving time. When integrated with NEB’s sequencing and amplification tools, the kit supports streamlined, end-to-end workflows for generating high-quality data from challenging clinical samples.
From waiting to watching
For Mary Royal, MRD testing has not eliminated uncertainty, but has transformed it.
Instead of waiting passively for scans, she feels engaged in her care. Instead of fearing every appointment, she has access to information that helps her understand what is happening inside her body in near real time.
“I want to know what to do next,” she said. “I don’t want to just sit around waiting for something when I have the ability to see things early on.”
As MRD technologies continue to mature, the desire to replace waiting with knowledge is becoming central to modern oncology. MRD is no longer just a research endpoint or laboratory metric. It is becoming a bridge between science and survivorship, offering patients, clinicians, and researchers a clearer signal in the noise of uncertainty.
And sometimes, that signal is a simple zero—small, powerful, and profoundly reassuring.
Mike May, PhD, is a freelance writer and editor with more than 30 years of experience. He earned an MS in biological engineering from the University of Connecticut and a PhD in neurobiology and behavior from Cornell University. He worked as an associate editor at American Scientist, and he is the author of more than 1,000 articles for clients that include GEN, Nature, Science, Scientific American, and many others. In addition, he served as the editorial director of many publications, including several Nature Outlooks and Scientific American Worldview.
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New Single-Cell Platform Expands View of Immune Function in Cancer Research
A newly developed single-cell sequencing approach, dubbed CIPHER-Seq, is designed to capture a more complete picture of immune cell behavior—an advance that could sharpen how researchers study responses to immunotherapy and mechanisms of resistance. The technique is described in a paper in Nature Scientific Reports.
Co-senior investigator Justin Taylor, MD, from the Sylvester Comprehensive Cancer Center at the University of Miami described the method as an effort to bridge a longstanding gap in single-cell analysis: the inability to simultaneously measure intracellular immune activity alongside gene expression and surface markers in the same cells.
“The main difference…is we’re trying to also look at the intracellular proteins,” Taylor said. “A lot of current approaches can measure proteins on the surface of the cell and RNA, but they can’t go inside the cell without disrupting the RNA.”
That limitation has been particularly consequential in immuno-oncology, where understanding immune cell function—especially cytokine production—is critical. Cytokines, which are typically secreted outside the cell, are central to defining T cell activation states and functional subtypes, but are difficult to capture alongside RNA using standard workflows.
CIPHER-Seq, or Cytokine Intracellular Protein High‑throughput Expression with RNA sequencing, addresses this by introducing a carefully optimized permeabilization step that allows antibodies to enter the cell without degrading RNA. At the same time, the protocol uses the Golgi stop reagent to trap cytokines inside cells, enabling their measurement.
“So instead of just what type of cell,” Taylor explained, “you can ask how they’re activated—are they secreting cytokines?”
Five layers of data in a single assay
The platform integrates five distinct data layers: cell surface markers, RNA sequencing, intracellular proteins, cytokines, and sample multiplexing via hashing antibodies. This multiomic approach builds on earlier technologies such as CITE-seq but extends them into intracellular territory.
Technically, the method relies on commercially available reagents and widely used sequencing platforms. Antibodies from multiple vendors can be used, and no proprietary components are required—an intentional design choice to encourage adoption.
“We’re not trying to sell it,” Taylor said. “There’s nothing proprietary about the protocol…you can buy all the reagents separately. It’s really about how we put them together and optimize the timing.”
Timing, in fact, proved critical during development. Excessive permeabilization can degrade RNA or induce cellular stress, while insufficient exposure prevents antibodies from entering the cell. The team iteratively optimized these conditions to preserve both RNA integrity and intracellular protein detection.
Reducing technical artifacts
Beyond enabling new measurements, CIPHER-Seq may also improve data quality by reducing technical artifacts. In benchmarking experiments using identical donor samples, the researchers observed that standard single-cell workflows induced higher levels of stress-related gene expression—signals that could be mistakenly attributed to biological processes.
“When we compared CIPHER-Seq to other methods…we found less stress,” Taylor said. “The same sample, same donor—just different processing. The other assays showed higher mitochondrial and metabolic stress markers.”
This finding has particular relevance for cancer studies, where cellular stress is often interpreted as a hallmark of disease or treatment response. If assay-induced stress is not accounted for, it could confound conclusions about tumor biology or immune activation.
“If you’re doing research on cancer patients getting immunotherapy, and one of your readouts is stress on the T cells,” Taylor added, “you might attribute that to the cancer—but maybe that’s from your technique.”
Applications in immuno-oncology
The primary envisioned applications for CIPHER-Seq lie in immuno-oncology, including studies of checkpoint inhibitors, CAR T-cell therapies, and bispecific antibodies. By enabling detailed profiling of T-cell subsets based on cytokine production, the method could help clarify how immune cells behave in different therapeutic contexts.
One potential use case, not yet demonstrated in the current study, would involve analyzing peripheral blood samples from patients before and after immunotherapy to compare immune activation states between responders and non-responders.
“That would be kind of the ideal use case,” Taylor said. “You could compare T cells in responders versus non-responders, or look at patients who develop resistance.”
Such analyses could ultimately help identify biomarkers of response or resistance, informing the development of targeted interventions.
“The whole point is to try to improve outcomes for patients,” he said. “If you can identify a resistant T cell marker, then you might develop a treatment targeting that.”
Why single-cell resolution matters
A key rationale for the approach is the need to detect rare immune cell populations that may drive treatment outcomes. Bulk sequencing methods average signals across many cells, potentially masking critical subsets.
“When you do bulk sequencing, it’s a mixture of all the cells,” Taylor noted. “You might miss rare subsets—and for immunotherapy, those rare cells might be very important.”
Path to clinical translation
While CIPHER-Seq is currently positioned as a research tool, Taylor sees a plausible path toward clinical application, drawing parallels to earlier sequencing technologies that were once considered impractical.
“When I started, people said whole genome sequencing would never work in patients,” he said. “And the same for RNA sequencing—that it was too unstable. But now both are routine.”
He anticipates similar skepticism around single-cell approaches but believes those barriers may also fall.
“Right now, people might say single-cell sequencing is too expensive or too technical,” Taylor said. “But I think that will change.”
For now, the team’s priority is encouraging adoption within the research community. By publishing the full protocol and relying on accessible reagents, they hope other groups will apply, refine, and extend the method.
“Our hope is that people start using it,” Taylor said. “Maybe they optimize it further for their own applications.”
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