There was a time when criminals with identical twins could commit serious crimes and still get away with them.Diseases that cause nerve cells in the brain to die over time are known as neurodegenerative diseases. These cells, known as neurons, are vulnerable to DNA damage because they are highly active cells that require large amounts of energy. Neural DNA damage is a normal part of aging as changes to the DNA sequence, called mutations, build up over time.
Previous scientists have shown that neurons from people with the neurodegenerative diseases Alzheimer’s disease (AD), frontotemporal dementia (FTD), and amyotrophic lateral sclerosis (ALS) accumulate unusually high numbers of changes in the DNA sequence, called somatic mutations. However, researchers don’t know what causes the high number of mutations or whether the same process causes them in all 3 diseases.
To answer these questions, researchers at Boston Children’s Hospital and Harvard Medical School analyzed brain tissue from people who died with and without AD, FTD, or ALS. They compared the processes that cause DNA mutations in the 3 diseases by first accessing the DNA inside individual neurons from healthy and diseased postmortem brain tissue.
To do this, they broke apart the brain tissue and added a fluorescent marker that identified the center of each neuronal cell where DNA is housed, called its nucleus. They used a sorting machine to separate the nuclei from the rest of the cell material to access the neuron’s DNA. Using a DNA sequencing method called single-cell whole genome sequencing, the team then sequenced each neuron’s DNA to identify mutations.
They analyzed 159 neurons from people with AD, 61 neurons from people with FTD, 77 neurons from people with ALS, and 232 neurons from healthy individuals. This allowed the team to compare the number and types of DNA mutations between diseased and control (non-diseased) neurons. The researchers found that compared to the control neurons, which typically accumulate only 200 to 300 mutations over an 80-year lifespan, neurons from all 3 diseases contained an unusually high number of 2-base-pair deletions, often exceeding 1,000 mutations per cell.
When a biological process damages DNA, it leaves behind its own unique signature. By identifying these signatures, scientists can identify the biological process that likely caused the damage. Since these 2-base-pair deletions were so frequent in diseased neurons, the researchers wanted to know whether they were left behind by a biological process. The researchers used a machine learning algorithm to scan DNA sequences for recurring mutation patterns and identified a signature in neuronal DNA, which they named ID-4.
When the researchers compared the ID-4 signature with a database of previously characterized signatures, they found that it was associated with a DNA repair protein called TOP1. TOP1 temporarily cuts and rejoins DNA to relieve the tension that builds up during normal cellular activity. TOP1 typically detaches from the DNA after completing this process. However, if TOP1 fails to detach, it breaks one of the two DNA strands.
To test whether abnormal TOP1 activity was breaking the DNA, the researchers isolated DNA from control and diseased neurons. They visualized the DNA on a gel, where intact DNA forms bands while broken DNA spreads out into smears. The more fragmented the DNA is, the more intense the smear appears. Using a machine that quantified the intensity of smears, the researchers found that DNA from diseased neurons produced smears that were up to 3.2 times more intense than those from control neurons. They also found that neurons with more DNA fragmentation had more ID-4 signatures.
Based on these findings, the researchers concluded that abnormal TOP1 activity created frequent single-strand DNA breaks. They proposed that some of these breaks are repaired incorrectly, eventually leading to widespread DNA breaks in all 3 neurodegenerative diseases.
Overall, the researchers found that neurons from these 3 neurodegenerative diseases accumulated excessive DNA mutations despite differences in the disease symptoms and underlying causes. Their findings also provided evidence that abnormal TOP1 activity contributes to this mutation accumulation. Based on these findings, they stated that preventing TOP1 from becoming trapped on DNA, or improving the DNA repair process that removes trapped TOP1, could be a promising way to treat a wide range of neurodegenerative diseases.
The post Common DNA damage could cause neurodegenerative diseases appeared first on Sciworthy.
In recent years, advances in ancient DNA sequencing have redefined our understanding of human evolution, revealing that our species did not emerge in isolation but repeatedly interbred with close evolutionary relatives like Neanderthals and Denisovans.
Yet one of the field’s greatest mysteries has remained unanswered: What if some of our ancestors also encountered extinct human groups whose DNA has never been recovered from fossils? Now, a recent study published in Science suggests researchers may finally have found a way to detect those long-lost relatives.
Instead of relying on ancient bones or preserved genomes, researchers developed a powerful computational approach capable of identifying genetic traces of previously unknown archaic humans using only the genomes of living people.
Applying the technique to modern populations around the world, the researchers uncovered evidence that an unidentified “ghost” hominin contributed DNA to the ancestors of all living humans before the great migration out of Africa.
Researchers also found new evidence providing evidence for the existence of an even older “super-archaic” human lineage whose genetic legacy may survive today through Denisovan ancestry in Oceania.
“Admixture between modern humans and extinct hominins has shaped the genomes of present-day individuals, but reconstructing this history has been constrained by the scarcity of archaic samples and unadmixed outgroup populations,” researchers write. “We introduce TRACE, a reference- and outgroup-free approach that uses features of ancestral recombination graphs to identify archaic ancestry.”
Past research has sequenced only a handful of high-quality archaic human genomes, all from Eurasia. While those genomes have revealed that most people outside Africa carry roughly 1 to 2 percent Neanderthal DNA and that many Asian and Oceanian populations also inherited Denisovan ancestry, scientists have long suspected those known groups represented only part of humanity’s evolutionary history.
Previous genetic studies had hinted that modern humans, especially African populations, contained fragments of DNA from unidentified archaic relatives. However, because no fossil genomes exist for those populations, proving their existence has been extraordinarily difficult.
The new method, called TRACE, short for “TRacking Archaic Contributions via ARG Estimation,” takes a fundamentally different approach. Rather than comparing modern DNA against known Neanderthal or Denisovan genomes, it reconstructs ancestral recombination graphs. These enormous evolutionary family trees trace how pieces of DNA have been inherited over hundreds of thousands of years.
These ancestral graphs preserve signatures left behind when deeply divergent human lineages interbred. Such events create unusually long branches and distinctive genetic patterns that can persist in descendants for hundreds of thousands of years. By searching for those signatures, TRACE can identify archaic ancestry even when no reference genome exists.
To test the system, the researchers first ran extensive computer simulations modeling ancient human populations and known episodes of Neanderthal interbreeding. The method consistently revealed high accuracy while producing very low false-positive rates.
The team then applied TRACE to genomes from the 1000 Genomes Project, encompassing hundreds of individuals representing populations across Africa, Europe, Asia, and elsewhere.
The program successfully recovered well-established Neanderthal ancestry in Europeans and Asians, along with Denisovan ancestry concentrated primarily in Asian populations. More intriguing, however, was the discovery of an additional category of DNA that matched neither Neanderthals nor Denisovans.
TRACE identified between roughly 0.5 and 1.1 percent “ghost” ancestry across all modern populations examined. The genetic evidence suggests these segments originated from an unknown archaic human lineage that diverged from our ancestors roughly 830,000 years ago, before later contributing DNA to the ancestors of modern humans.
Perhaps most surprising was where that ancestry appeared.
Rather than being confined to African populations, as several earlier hypotheses proposed, the researchers found remarkably similar ghost ancestry in both Africans and non-Africans.
Most of the segments detected outside Africa were shared with African populations, suggesting the interbreeding event occurred before modern humans started dispersing across the globe roughly 50,000 to 70,000 years ago.
The findings point toward a previously unrecognized chapter of human evolution in which an unidentified human population mixed with the common ancestors of everyone alive today.
The researchers also explored where these ancient genetic fragments survive in the modern genome.
They found ghost ancestry spread across more than 70 percent of the accessible human genome, although it was far from evenly distributed. Some regions contained unusually high concentrations of ghost DNA, including genes involved in immune function and metabolism.
Even more unexpectedly, ghost ancestry persisted inside genomic regions previously believed to be largely devoid of archaic DNA.
These regions, known as “Neanderthal and Denisovan ancestry deserts,” have often been interpreted as places where natural selection eliminated archaic genetic variants because uniquely modern human versions proved advantageous.
The presence of ghost ancestry inside many of those same regions suggests the story may be more complicated. Rather than rejecting all archaic DNA equally, natural selection may have acted differently depending on which ancient human lineage contributed the genes.
The study also ventured even farther back in evolutionary time.
Focusing on genomes from Papua New Guinea, Vanuatu, and the Santa Cruz Islands, the researchers searched for evidence of “super-archaic” ancestry—genetic contributions from a lineage that diverged long before Neanderthals, Denisovans, and modern humans separated.
Previous studies had suggested Denisovans themselves may have inherited DNA from such an ancient population. TRACE now provides additional support for that hypothesis.
The team identified deeply divergent genetic segments embedded explicitly within Denisovan-derived DNA in Oceanian populations. Based on their analyses, those ancient lineages may have split from the ancestors of modern humans approximately 1.77 million years ago.
While the researchers stress this represents a conservative estimate of surviving super-archaic ancestry, the findings strengthen the idea that modern humans indirectly inherited DNA from a much older human lineage through Denisovan interbreeding.
The identity of both the ghost lineage and the super-archaic population remains unknown.
The ghost population may represent Middle Pleistocene human groups or African populations of Homo heidelbergensis that encountered the ancestors of modern humans before the Out-of-Africa migration. For the older super-archaic lineage, the researchers note that Homo erectus remains one plausible candidate given its estimated divergence time and broad geographic distribution.
Although the new method cannot yet identify specific fossil species, it provides researchers with an entirely new tool for investigating chapters of human evolution that may never yield recoverable ancient DNA.
Because DNA older than roughly one million years is extraordinarily unlikely to survive outside permanently frozen environments, computational approaches like TRACE are becoming increasingly important for reconstructing humanity’s deepest evolutionary history.
“We find evidence for at least one ghost introgression predating the OOA expansion that contributed ancestry to all present-day populations,” researchers write. “These results point to multiple layers of introgression—both direct and mediated through other archaic groups—shaping modern human genomes.”
The study, “Recovering signatures of archaic hominin introgression using ancestral recombination graphs,” was published in the journal Science.
Tim McMillan is a retired law enforcement executive, investigative reporter and co-founder of The Debrief. His writing typically focuses on defense, national security, the Intelligence Community and topics related to psychology. You can follow Tim on Twitter: @LtTimMcMillan. Tim can be reached by email: tim@thedebrief.org or through encrypted email: LtTimMcMillan@protonmail.com
A new analysis from a large, single-centre cohort suggests that when immune checkpoint inhibitors are given can meaningfully shape outcomes for patients with advanced cancer. The study, reported in British Journal of Cancer, examines overall survival in relation to the timing of therapy administration, moving beyond the usual question of whether immunotherapy works to asking how its scheduling may alter the odds.
Immune checkpoint inhibitors—antibodies designed to release brakes on anti-tumour T cells—have transformed treatment across multiple malignancies. Yet real-world clinical pathways are complex. Patients may begin therapy at different stages, after varying lengths of prior treatment, or under different clinical conditions that can affect both immune readiness and tumour biology.
In the cohort analysis, investigators assessed survival endpoints while stratifying by the timing of checkpoint inhibitor administration. The approach integrates observational timing variables with survival outcomes, allowing researchers to estimate whether early versus later use corresponds to measurable differences in longevity. While such designs cannot fully eliminate confounding factors—such as baseline disease severity or prior treatment intensity—the large sample and single-centre context help reduce heterogeneity.
The results indicate that overall survival is not uniform across timing categories. In practice, this means that clinical decisions about sequencing immunotherapy—relative to other systemic treatments and the patient’s progression trajectory—may influence how long patients live after starting checkpoint blockade.
Biologically, timing may affect the balance between tumour immune visibility and immune suppression. Administering therapy when tumour antigen presentation and T-cell infiltration are more favourable could support stronger initial responses. Conversely, later administration might occur after immune escape mechanisms have already hardened, potentially narrowing the window for immunological control.
Clinically, the findings encourage oncologists to treat sequencing as an active variable rather than a passive scheduling detail. Optimizing when immunotherapy enters the treatment course could improve benefit for future patients, particularly in settings where multiple lines of therapy compete.
The authors emphasize that further work, ideally incorporating prospective designs and richer biomarker data, is needed to confirm whether timing itself is the causal driver. Still, the study provides timely evidence that “when” checkpoint inhibitors are administered can matter alongside “whether.”
Overall, the viral science news takeaway is clear: immune checkpoint therapy timing may be a hidden lever in advanced cancer care, and refining it could help translate immunotherapy’s potential into more consistent survival gains.
Subject of Research: Timing of immune checkpoint inhibitor administration and overall survival in advanced cancer.
Article Title: Overall survival according to timing of immune checkpoint inhibitors administration in patients with advanced cancer: results from a large single-centre cohort analysis.
Article References: Bosetti, T., Kennedy, O.J., Califano, R. et al. Overall survival according to timing of immune checkpoint inhibitors administration in patients with advanced cancer: results from a large single-centre cohort analysis. Br J Cancer (2026). https://doi.org/10.1038/s41416-026-03494-y
DOI: https://doi.org/10.1038/s41416-026-03494-y
Image Credits: AI Generated
In a groundbreaking advancement that could reshape the landscape of molecular biology, researchers have unveiled a high-resolution method for single-cell sequencing of trans-spliced mRNA, setting new benchmarks in cellular transcriptomics. This state-of-the-art technique, detailed in a recent publication by Cosentino and colleagues, promises unprecedented insight into the complex mechanisms governing RNA splicing and gene expression at a single-cell level, a domain critical for understanding diverse biological processes and disease states.
Traditional RNA sequencing methods often struggle to capture the nuances of trans-splicing—an RNA processing event where exons from different pre-mRNA molecules are joined together. This unique form of splicing has been relatively underexplored, largely due to technical limitations in distinguishing trans-spliced variants from the more common cis-spliced transcripts. The innovation presented in this protocol addresses these challenges head-on by incorporating enhanced molecular tagging and amplification strategies to faithfully preserve the native architecture of trans-spliced mRNAs.
The power of single-cell sequencing lies in its ability to discern cellular heterogeneity that bulk sequencing methods average out. By applying this resolution to trans-splicing events, the new protocol enables researchers to map how individual cells harness this process to diversify their transcriptomes. This can reveal regulatory layers previously masked in population-level analyses, providing clues about cell type-specific functions, developmental trajectories, and responses to environmental stimuli.
Central to the protocol’s success is its meticulous capture of the transcript’s 5’ and 3’ ends, allowing unambiguous identification of trans-spliced junctions. The approach leverages novel enzymatic reactions and ligation steps carefully optimized to maintain sequence integrity, enabling high-fidelity reconstruction of splicing landscapes. Moreover, the method integrates seamlessly with established single-cell RNA sequencing platforms, widening accessibility and potential for widespread adoption.
The practical implications of this development are vast. Researchers can now probe the role of trans-splicing in normal physiology and pathogenesis with precision. Given that aberrant RNA splicing is implicated in various cancers and genetic disorders, understanding trans-splicing patterns at the single-cell level could illuminate new biomarkers and therapeutic targets. Furthermore, tissues with complex cell compositions, such as the brain or immune system, stand to benefit profoundly from this refined analytical lens.
Implementing this protocol requires meticulous experimental execution. Steps include cell isolation, mRNA extraction under conditions preserving RNA integrity, strategic reverse transcription with specialized primers, as well as a series of purification and amplification cycles designed to enrich for trans-spliced sequences. Each phase has been rigorously characterized to maximize sensitivity and specificity, ensuring reproducible outcomes across diverse sample types.
Bioinformatic analysis pipelines accompanying the protocol are equally sophisticated. They employ advanced algorithms capable of distinguishing genuine trans-splicing events from experimental artifacts or sequencing errors. These computational tools facilitate high-confidence annotation of transcripts, visualization of splicing diversity, and quantification of junction abundance at single-cell resolution, pushing the analytical frontier forward.
Beyond capturing static snapshots, the technology opens pathways to dynamic studies. Scientists can track how trans-splicing activity varies during cellular differentiation, in response to external cues, or throughout disease progression. This temporal dimension adds a vital layer of understanding to RNA biology’s functional repertoire, potentially revealing regulatory checkpoints amenable to therapeutic modulation.
The introduction of this high-resolution sequencing method also underscores the increasing convergence of experimental and computational biology. It reflects a growing recognition that unraveling complex molecular phenomena demands integrated approaches combining cutting-edge laboratory techniques with robust data science. This synergy not only accelerates discovery but democratizes access to intricate biological insights.
Intriguingly, this approach may shed light on evolutionary aspects of splicing. Differences in trans-splicing prevalence and patterns across species and cell types could inform hypotheses about RNA processing’s adaptive significance. Delineating these evolutionary narratives may provide broader context for interpreting functional data and guiding synthetic biology applications.
Moreover, the methodology’s adaptability hints at future expansions. For example, coupling this protocol with spatial transcriptomics could map trans-spliced mRNAs within tissue architecture, or integrating with proteomics could link transcript variants to protein isoform expression. Such multidimensional analyses hold promise for unraveling the intricate web connecting genotype to phenotype.
As research workflows adopt this technology, anticipated challenges include managing increased data complexity and scaling up throughput for large single-cell atlases. Nonetheless, the benefits—unveiling the hidden layers of transcriptomic regulation with unprecedented clarity—far outweigh these hurdles. The authors’ thorough validation in diverse biological contexts allays concerns about adaptability and robustness.
Ultimately, this state-of-the-art protocol propels trans-splicing research to new heights, furnishing the scientific community with a versatile toolset to decode RNA’s role in health and disease at unparalleled resolution. It exemplifies a milestone where technological ingenuity meets biological inquiry, heralding a new era in transcriptomic sciences.
As this methodology gains traction, it is poised to catalyze transformative discoveries, deepen our molecular understanding, and inspire follow-up innovations. The potential ripple effects extend from basic science to clinical diagnostics, underlining the enduring impact of pioneering single-cell approaches in unlocking cellular complexity comprehensively.
Subject of Research: High-resolution single-cell sequencing of trans-spliced mRNA
Article Title: High-resolution single-cell sequencing of trans-spliced mRNA
Article References:
Cosentino, R.O., Keneskhanova, Z., Esser, S. et al. High-resolution single-cell sequencing of trans-spliced mRNA. Nat Protoc (2026). https://doi.org/10.1038/s41596-026-01373-7
DOI: https://doi.org/10.1038/s41596-026-01373-7
Image Credits: AI Generated
New DNA evidence reveals bed bugs tracked human expansion from ancient caves to modern cities.
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