A health worker from the Mfou District Hospital collects a blood sample for the study. [Sandrine Nsango, PhD]
Malaria is one of the deadliest infectious diseases on the planet, afflicting people for the entirety of recorded history. In places where malaria is most common, it’s shaped human evolution by driving higher rates of a protective genetic change. Two copies of the genetic change cause sickle cell disease, but one copy helps protect people from malaria. More specifically, sickle cell hemoglobin (HbS) confers protection against symptomatic malaria caused by Plasmodium falciparum.
Now, a new study has found that people with the sickle cell variant are almost exclusively infected by a distinct variety of malaria parasites, which might contribute to their lower rates of disease.
“Sickle cell trait may protect against malaria, not only by changing how the human host responds to infection, but also by shaping which parasites are able to survive and establish infection,” says Sandrine Nsango, PhD, associate professor of molecular biology at the University of Douala and researcher at the Centre Pasteur du Cameroun, Head of the Department of Biomedical Sciences, Faculty of Science, University of Bertoua.
Previous work had discovered the unusual strain of malaria by investigating the rare cases where people with the sickle cell variant develop disease symptoms. The new research extends these findings to asymptomatic infections, in which people are infected by the parasite but don’t get sick. While less visible than the disease itself, asymptomatic infections were extremely common, affecting three-quarters of the healthy study population.
HbS carriers with symptomatic malaria harbor parasites enriched for P. falciparum sickle-associated alleles (Pfsa+). While these may partially overcome HbS-mediated protection, their role in asymptomatic infections is unclear. Researchers analyzed the genetics of over 2,246 healthy schoolchildren in a region of high malaria transmission in Mfou, Cameroon, as well as the malaria parasites infecting them. The team found that people with and without the sickle cell variant tend to be infected by genetically different varieties of malaria parasites.
In addition, specific varieties of the parasite infect people with and without the sickle cell variant at similar overall rates. They write, “analyzing the parasite Pfsa and the human HbS genotypes for 1,701 asymptomatic P. falciparum infections confirmed that heterozygous HbS (HbAS) and homozygous non-HbS (HbAA) genotypes have similar rates of asymptomatic infection.” However, despite being infected at similar rates, people carrying the sickle cell variant are much less likely to develop symptomatic malaria.
The researchers found that which parasites are present in an asymptomatic infection differs based on if the infected person has the sickle cell variant. They write, “the HbS genotype is strongly associated with parasites carrying the Pfsa+ alleles at the Pfsa1 and Pfsa3 loci, indicating a selective advantage for these alleles in HbS carriers.” These findings suggest that the sickle cell variant influences which parasites can infect a person, whether or not they develop disease symptoms.
People with the sickle cell variant are just as likely to become infected—albeit with a different variety of the parasite—but they’re a tenth as likely to develop harmful disease. These observations suggest that the biological characteristics of this parasite variety may contribute, at least in part, to the protection against disease conferred by the sickle cell trait.
To understand why people with sickle cell don’t get disease as often—and use that knowledge to develop better therapies—researchers should not only look at the biology of the sickle cell variant but also investigate the differences in the malaria parasite.
Nsango says that their findings emphasize that malaria is an example of a continued evolutionary interaction between humans and the parasite. “The sickle cell mutation is one of the clearest examples of how infectious diseases can shape human evolution, but our findings suggest that humans also shape the evolution of the parasite in return,” Nsango says. “Understanding these interactions may help us anticipate how malaria parasites adapt in the future and design more effective strategies for malaria control.”
Studies of the immune system show that two people born on the same day can have immune systems that age differently, leaving some older adults with resilient defense systems and others with dysfunctional ones that put them at increased risk for age-related diseases. Now a new study led by scientists at Washington University School of Medicine in St Louis, Nationwide Children’s Hospital in Ohio, and King’s College London provides a map of human immune aging that could help explain these different trajectories. Details are published in a new Immunity paper titled “Single-cell analyses of global cohorts outline determinants of immune aging and link the ratio of GZMK⁺ to GZMB⁺ Tem cells to health trajectories.”
According to the paper, a ratio between two immune cell types can reveal whether someone stays healthy or may face risk of future disease. To get to this insight, the scientists first analyzed approximately 12.4 million individual immune cells from the blood of over 2,600 mainly healthy adults ages 20 to 90-plus across eight cohorts in North America, the United Kingdom, Asia, and Australia.
Their analysis showed that while the blood of younger people was uniformly enriched with naive immune cells that had not been exposed to specific pathogens and remained adaptable to new infections or vaccines, older immune systems were more diverse. Specifically, some older participants had a greater abundance of immune cells that reflect inflammation.
To map this shift, the researchers placed each participant on a spectrum based on the ratio between two types of white blood cells: specifically, granzyme B-producing cells and granzyme K-producing cells. Some white blood cells, known as effector memory CD8 T cells, use granzymes to destroy target cells or coordinate immune responses. While granzyme B-producing cells serve as direct destroyers of diseased cells, granzyme K-producing cells have been less studied and may play a role in boosting immune responses.
Next, the researchers evaluated whether participants’ positions on the spectrum, which corresponds to a specific type of CD8 T cell, could dictate their risk of getting sick. In other words, they looked at whether having more of one type of CD8 T cell has an impact on the person’s health as they age. To answer that question, the scientists turned to the UK Biobank.
While that dataset includes protein information from the participants, it lacks counts of CD8 T cells. To get around this hurdle, the scientists used a smaller dataset of seemingly healthy adults that included data on CD8 T cell counts and blood proteins to create a computer model to map out the protein tracks left behind when either granzyme B-producing or granzyme K-producing cells dominated. They then applied this model to the baseline blood protein samples of 50,000 participants from the UK Biobank, calculating their position on the immune cell spectrum.
Using this approach, the scientists were able to draw out some useful insights for their work. Specifically, they found that healthy adults whose immune system leaned heavily toward granzyme B cells at baseline tipped the scale toward a higher risk of death a decade later compared to those with more granzyme K cells. The granzyme B-dominant group also was more likely to develop chronic conditions, including Type 2 diabetes, hypertension, liver disease, and renal failure, among others, over the following decade.
“A higher level of granzyme B cells in a healthy state means the immune system might be already reacting to something,” said Marina Terekhova, MD, a professor in pathology & immunology at WashU Medicine and one of the co-authors on the paper. “It isn’t a formal disease diagnosis, but it may indicate the body is off track.”
A healthy immune system gradually shifts toward granzyme K cells over time, while an immune system that leans toward granzyme B cells signals unhealthy aging. Because individuals can reach this granzyme B state much earlier in life, their immune systems age on different trajectories, explaining why two people born in the exact same year can face different risks for chronic disease and death decades later.
These findings point to “clues in the blood that can tell us whether someone is on a healthy or unhealthy aging trajectory,” said Maxim Artyomov, PhD, the alumni endowed professor of pathology and immunology at WashU Medicine and one of the corresponding authors on the paper. Artyomov and his lab are now adapting their research into a simple blood test that can be processed using standard equipment. “Identifying specific biological red flags in the immune system could pave the way for early-warning tests that spot disease risks years before a clinical diagnosis.”
Molecular movie of penicillin biosynthesis. X-ray free electron laser experiments on isopenicillin N synthase microcrystals capture structural snapshots of key intermediates during the penicillin forming reaction. The film strip illustrates progression through the catalytic cycle, with the monocyclic β-lactam intermediate and reactive high valent ferryl iron species highlighted at the center of the molecular movie. [University of Oxford/Greg Stewart/SLAC National Accelerator Laboratory]
Researchers from the University of Oxford and international collaborators have revealed previously unseen rapid chemical stages in the formation of β-lactam antibiotics such as penicillin, offering insights that could help to support future antibiotic development.
Rather than relying on static X-ray crystallographic structures of the enzyme, the Oxford University team, collaborating with partners from Diamond Light Source, Lawrence Berkeley National Laboratory, PAL-XFEL, and SLAC National Accelerator Laboratory, followed the reaction in real time. To do this they used X-ray free-electron lasers (XFEL) to observe the enzyme isopenicillin N synthase, (IPNS) as it converts its linear peptide substrate into the β-lactam ring system of penicillin.
The study brought together expertise in structural biology, spectroscopy, chemistry, computation and enzyme engineering. The approach, they say, provides a powerful way to study fleeting chemical intermediates that have previously been inaccessible, both in antibiotic biosynthesis and in other biologically important enzyme reactions.
The team captured several rapid stages in the reaction. These included a thioaldehyde intermediate formed just before the β-lactam ring is created, and a monocyclic β-lactam intermediate, representing the first ring-shaped structure on the way to forming the complete penicillin scaffold. These steps provide the clearest picture yet of how the IPNS enzyme assembles the penicillin scaffold. Subtle movements throughout the enzyme help guide these chemical steps, showing that both the enzyme’s shape and its chemistry work together to control penicillin formation.
The results show, in unprecedented detail, how the enzyme IPNS achieves an exceptionally complex transformation in a single step. Christopher Schofield, PhD, Professor of Chemistry, University of Oxford, said, “Penicillin has shaped modern medicine, but there is still much to learn about how nature builds this important antibiotic structure. By capturing these fleeting steps, we can better understand how enzymes control complex chemistry with remarkable precision … As rates of antimicrobial resistance continue to rise, understanding this process will ultimately help us make existing antibiotics more efficient and design new antibiotic structures.”
Allen M Orville, PhD, group leader of the XFEL Hub, further commented, “The XFEL Hub at Diamond brought specialist expertise in time-resolved X-ray methods and XFEL-based structural biology to this study. Working closely with our colleagues in Oxford and at partner facilities, we helped bring together the experimental approaches needed to capture these fleeting stages of enzyme catalysis. The results show the power of combining complementary expertise and X-ray techniques to move beyond static structures and reveal how enzymes work in real time. It is a great example of how time-resolved structural biology can uncover new principles of enzyme function and, ultimately, inform the design of new catalysts and therapeutics.”
Ever since penicillin was developed into a working drug at the University of Oxford in the early 1940s, β-lactam antibiotics have been among the most important medicines for treating bacterial infections. Their activity depends on a special ring—the β-lactam ring—a highly strained chemical ring system that interferes with bacterial cell wall synthesis, ultimately causing the cell wall to fail and the bacteria to die. “Isopenicillin N synthase (IPNS) catalyses the biosynthesis of isopenicillin N (IPN), forming the fused four-membered β-lactam and five-membered thiazolidine ring system that is characteristic of the penicillins,” the authors explained.
Rising rates of antimicrobial resistance are undermining the effectiveness of existing antibiotics. Today, one in six bacterial infections is resistant to antibiotics, the authors noted, citing WHO figures. With too few new antibiotics in development, understanding how nature builds these molecules is an important step towards replenishing the antibiotic pipeline.
Scientists have been studying how nature constructs this β-lactam ring for decades, but the key fleeting reaction intermediates have been too difficult to observe directly.
To capture the reaction in real time, the researchers used a system in which thousands of tiny droplets containing anaerobic enzyme microcrystals were deposited onto a moving 2 mm wide tape. As the tape entered an oxygen filled chamber, oxygen rapidly diffused into the crystals and initiated the reaction simultaneously across the sample. By precisely controlling the speed of the tape, the researchers could determine how long each crystal reacted before reaching the X-ray interaction point, where an ultrafast XFEL pulse recorded an atomic resolution snapshot.
Combining thousands of these snapshots allowed the team to build a frame-by-frame “molecular movie” of penicillin biosynthesis. This method allows individual reaction intermediates that exist only for tiny fractions of a second to be observed at atomic resolution, and under physiological temperature and pressure, before they disappear.
The newly reported findings help to resolve a long-standing mechanistic question that has remained unanswered for more than four decades. The work also showed that water molecules inside the enzyme play a key role in guiding the reaction. The combined results of their studies, they noted, “… reveal unexpected insight into the IPNS reaction mechanism including the vital roles of active-site water molecules.”
The study ultimately builds on the work of Nobel prize-winning Oxford chemist Dorothy Hodgkin, PhD, who first solved the structure of penicillin in 1945 using X-ray crystallography.
Beyond penicillin, IPNS belongs to a large family of iron-dependent oxygenase enzymes involved in human biology, including those that enable us to sense and respond to changes in oxygen availability. The mechanistic insights from this work could therefore have implications beyond antibiotics, offering new principles for enzyme engineering and catalyst design.
“These findings provide structural insight into IPNS catalysis, inform on the wider Fe(II) oxygenase superfamily and highlight the power of X-ray free-electron laser methods for resolving transient enzymatic intermediates,” the team stated in their paper. First author Patrick Rabe, PhD, Wellcome Career Development Award Investigator, University of Oxford, said, “These insights matter because they can inform how we think about antibiotic biosynthesis and future strategies for developing antibacterial medicines. The ability to capture structural snapshots over milliseconds to seconds of reaction time allows us to connect enzyme motion, iron chemistry and water-mediated proton transfer. This gives us a much richer view of how IPNS controls a difficult chemical transformation. By understanding this process in atomic detail, we can begin to think about engineering these enzymes to produce new or improved antibiotic scaffolds.”
Multiomics analysis combines data from two or more omics layers—such as genomics, transcriptomics, proteomics, and epigenomics. However, published criticisms of multiomics approaches have highlighted concerns about data integration and a tendency toward false-positive findings.1–3
What additional roadblocks might accompany ambitious multiomics projects in 2026? Could any bottlenecks emerge in cost, sample preparation, data integration, or AI implementation? We recently interviewed experts from five companies to gain their perspectives. Here is what they had to say.
Sample preparation
“I sound like a broken record because I can’t stop talking about the importance of sample prep in multiomics work,” says Michael Easterling, PhD, vice president of MALDI/Imaging at Bruker Daltonics.
Easterling stresses challenges such as section-to-section variability, internal standards, FFPE samples, and long sample runs that complicate downstream algorithms. “For multiomics imaging, these issues are not as well defined as some of the bulk methods that have been around much longer,” he notes.
Scott Jelinsky, PhD, senior director of R&D at Thermo Fisher Scientific, agrees. “Different molecular classes have different requirements for sample collection, storage, and preparation,” he says. “Therefore, careful experimental design and standardization are essential.”
Easterling mentions the consequences of inadequate freezing during biopsies, which can disrupt cell structures and lead to ice crystal artifacts. “It is important to really make sure that you have a solid workflow to correctly harvest tissue and provide a good chain of custody from the point that it’s harvested all the way to the analyzer.”
What are some tips for multiomics researchers hoping to prepare their samples in an optimal way? “Community networking is key here,” argues Easterling. “I would say get involved with the overall spatial imaging community and network and find out exactly how to do these things.”
Another practical consideration, according to Jelinsky, is planning the workflow from the beginning so that sample collection, aliquoting, storage, and analytical requirements are compatible across different assays. “Decisions made early in the study can have a significant impact on what can be measured and how confidently the different datasets can be interpreted together,” he notes.
Data integration and analysis
Multiomics technologies generate vast amounts of high-resolution data across modalities that often behave differently from a statistical perspective, notes Najiba Mammadova, PhD, associate director of product management at 10x Genomics. “Multiomics assays have advanced incredibly quickly, and now the analysis and interpretation tools need to keep pace,” she notes.
Mammadova specifically highlights the challenges of computationally aligning multiple omics from different cells or tissues. After all, how can researchers be certain that they are measuring the same cells and biological features?
The 10x Genomics MultiPro Human Discovery Panel, which integrates into existing Flex scRNA-seq workflows, is the largest antibody-based, single-cell protein panel, enabling integrated analysis of 326 protein targets. [10x Genomics]
“That’s why I think measuring multiple modalities from the same cell or tissue is so important,” she adds. “It reduces the need to reconstruct the biology after the fact and gives researchers a more direct view of what’s happening in the sample.”
Vanee Pho-Conners, PhD, vice president of global marketing at Mission Bio, agrees that data integration is often a major hurdle with multiomics work. “Each omics layer has its own data structure, distributions, and variabilities, so combining them isn’t plug-and-play,” she explains. Multiomics analysis also often requires cross-disciplinary expertise in biostatistics, machine learning, and biology—capabilities many labs lack in-house.
“It’s important to ask how you can combine these data sets in a clean way where you preserve all the information, reduce the noise, and start to make biological inferences,” adds Easterling. “It’s not something that’s routine today, but the situation is changing rapidly, and new ways to integrate these datasets of varying dimensionality are starting to appear.”
Timing is yet another important consideration, notes Jelinsky. Proteins, RNA, and metabolites may exhibit different kinetics and respond on different timescales, so measurements collected at the same time may not always align. “Understanding those temporal relationships is essential when integrating and interpreting the results,” he says.
Thermo Fisher’s multiomics solutions include services as well as products for sample preparation, separation, acquisition, and analysis. [Thermo Fisher]
AI and multiomics
“I think the rate-limiting multiomics questions are shifting from ‘Can we measure this?’ to ‘Can we interpret it?’ And that’s where AI has enormous potential,” notes Mammadova. “We’re generating increasingly rich, high-dimensional datasets, and AI can help researchers identify patterns, interpret those data more quickly, and ultimately build models that can predict things like disease progression or treatment response.”
However, Easterling warns that even the most advanced AI technologies often cannot overcome challenges associated with inadequate sample preparation. “Unfortunately, if a model’s trained on an inconsistently prepared section, it’ll give you a confident biological story that’s completely wrong. And the reader of this data may have no way to tell.”
The DNA, RNA, and protein workflow on Tapestri’s single-cell platform. Each cell is encapsulated in its own droplet, and a unique molecular barcode (specific to each cell) gets attached to each DNA, RNA, and protein molecule. [Mission Bio]
As an example of recent AI advances, Easterling highlights multiomic workflows pioneered at the Medical University of South Carolina for single-cell analysis. The group has developed an AI algorithm that assists lasers in targeting individual cells rather than clusters of cells.4 Pho-Conners also emphasizes how Mission Bio is using machine learning to jointly cluster DNA, RNA, and protein—referred to as triomics—from the same cell. “We are using a combination of triomics and AI-driven integration to establish disease resistance. This really goes beyond correlations using a single omics layer,” she says.
Although Jelinsky is generally optimistic about AI advances in multiomics, he foresees fundamental limitations. “I see AI as a tool that can help researchers navigate complexity and generate better hypotheses rather than as a replacement for scientific judgment. AI does not remove the need for rigorous experimental design or biological validation,” he stresses.
Challenges with extracellular vesicles
Distinct multiomics challenges also exist in analyzing extracellular vesicles (EVs), small membrane spheres released by cells that contain multiple omics (DNA, RNA, protein, metabolites, and lipids).
“EVs are how cells talk to each other, and a living cell sends up to ten thousand a day. This makes them good biomarkers,” says Pierre Arsène, founder and CEO of Mursla Bio.
However, with traditional EV analysis, each potential omics layer is measured from EVs derived from an unknown blend of cells. There is usually no way to know whether a protein signal and an RNA signal came from the same tissue, for instance.
Mursla’s biomarker discovery workflow for tissue-specific extracellular vesicles. Antibody-coated magnetic beads capture hepatocyte-derived vesicles from under two milliliters of plasma, leaving behind extracellular vesicles from other tissues. [Mursla Bio]
“Tissue-specific extracellular vesicles (TS-EVs) are how we recover that record,” explains Arsène. Mursla’s technology begins with a blood draw. Liver-specific EVs are then captured using antibodies against proprietary surface markers (more specifically, hepatocyte-EV markers). Finally, proteomics and RNA sequencing are performed on two milliliters of plasma for biomarker discovery.
“The great thing about TS-EVs is that they arrive addressed, carrying surface proteins specific to the sending cell and sealed behind a membrane that shields proteins and RNA from the enzymes that would destroy them in blood.”
EvoLiver, Mursla’s blood test for liver cancer surveillance in cirrhotic patients, holds an FDA Breakthrough Device Designation. It analyzes three proteins and five microRNAs derived from hepatocytes. Proteins provide information about tumor surface phenotype and metabolic reprogramming, while microRNAs reveal the regulatory programs driving that phenotype.
As with so many other multiomics platforms, Arsène stresses the importance of TS-EV sample preparation. “Blood handling standards for EVs lag well behind standard liquid biopsy, so our clinical work relied mostly on prospective collection under a common protocol rather than using legacy biobanks.”
The future of multiomics
Mammadova highlights the potential for cost-related challenges in multiomics, particularly as the field moves toward larger studies. “Making these technologies more scalable and cost-effective will be critical to expanding their use across research,” she adds.
“However, I see more overall challenges than roadblocks in multiomics work,” she concludes. “They’re the kind of problems the field works through as technologies mature.”
Detection of the whole transcriptome along with over 64 proteins in a human breast cancer FFPE tissue section using Bruker’s CosMx spatial molecular imager. [Bruker Spatial Biology]
Spatial multiomic layers will also become increasingly critical for decoding the complexity of disease biology, predicts Prajan Divakar, PhD, Bruker Spatial Biology vice president of product management. “Investigators continue to demonstrate the need for more complete spatial datasets, and we are seeing clear applications and momentum for clinical translation,” he says. Divakar highlights Bruker’s CosMx® spatial molecular imager, which measures the whole transcriptome and over 64 proteins.
Finally, Arsène argues that simply adding more layers of analysis without biological resolution is unlikely to advance multiomics research in the future. For instance, in the largest study of a blood-based cancer test ever run (involving around 50,000 participants), incorporating an additional protein layer in advanced adenoma increased the sensitivity only modestly—from about 15% to 16%.5 “More and more layers allow you to gain dimensions, but you end up losing coherence. And no cohort in the world is large enough to train that coherence back computationally.”
Jelinsky agrees that the value of multiomics will lie not in generating more data, but in selecting complementary measurements that address a specific biological question. “I think the future of multiomics will be less about generating the largest possible number of measurements and more about determining which combination of measurements provides the most meaningful biological insight,” he says.
References
Behrens LMP, Fernandes GDS, Gonçalves GF, et al. Limitations and opportunities in multi-omics integration for neurodevelopmental, neurodegenerative and psychiatric disorders: A systematic review. Neuroscience. 2026;599:76-93. doi: 10.1016/j.neuroscience.2026.01.019.
Hayes CN, Nakahara H, Ono A, Tsuge M, Oka S. Genes (Basel). From Omics to Multi-Omics: A Review of Advantages and Tradeoffs. 2024;15(12):1551. doi: 10.3390/genes15121551.
Baião AR, Cai Z, Poulos RC, et al. A technical review of multi-omics data integration methods: from classical statistical to deep generative approaches. Brief Bioinform. 2025;26(4):bbaf355. doi: 10.1093/bib/bbaf355.
Dressman JW, Bayram MF, Angel PM, Drake RR, Mehta AS. Single-Cell Multiomic MALDI-MSI Analysis of Lipids and N-Glycans through Affinity Array Capture. Anal Chem. 2025;97(24):12493-12502. doi: 10.1021/acs.analchem.4c06233.
Shaukat A, Burke CA, Chan AT, et al. Clinical Validation of a Circulating Tumor DNA-Based Blood Test to Screen for Colorectal Cancer. JAMA. 2025;334(1):56-63. doi: 10.1001/jama.2025.7515.
The team from the Science study was led by co-senior authors Jay Shendure, PhD, professor of genome sciences at the University of Washington School of Medicine and a Howard Hughes Medical Institute investigator, and Chengxiang Qiu, PhD, a molecular and systems biologist at Dartmouth College. Their approach centered on DNA Typewriter, a lineage-tracing technology developed by Shendure and Junhong Choi, PhD, that uses a cell’s DNA as a recording medium.
The researchers redesigned the system’s recording “tape” to make its history easier to recover from individual cells, then inserted it into the genome of a fertilized mouse egg. As cells divided, “a burst of editing unequivocally marks the daughters of the first cleavage, which serve as inline replicates,” the authors wrote in the Science paper. Because daughter cells inherited the marks already written before acquiring new ones, shared patterns revealed common ancestry and the order of developmental branching.
DNA Typewriter is injected into the genetic material of a fertilized mouse egg. This technology taps into the genetic material in the fertilized egg and resulting embryonic cells as a recording device of the cells’ histories. It tracks their relationships as the embryo develops. [Kyle O’Conner/Seattle Hub for Synthetic Biology]
“Most biological measurements are based on either live imaging, which is limited by the fact that most animal tissues are not transparent, or genomics, which are destructive and only measure a single timepoint,” Shendure said. “Recording techniques like the ones in these studies enable measurements over time including in settings that we can’t directly visualize.”
The experiment was attempted in 100 fertilized eggs and yielded 10 embryos for examination. One embryo carried the most informative record, allowing the team to trace nearly every profiled cell back to one of the first two cells formed after the fertilized egg divided. Although one founding cell produced more descendants, the two branches generated diverse cell types in nearly equal proportions.
The ordered recording also allowed the researchers to estimate when cell types diverged from shared developmental paths. Blood and retinal cells committed relatively early, whereas cells forming the skin’s outer layer committed later. The results indicate that cell identity is not fixed in a single coordinated step; instead, different lineages become restricted on distinct schedules.
Earlier lineage-tracing approaches often relied on DNA-cutting enzymes that could damage cells, erase previous records, exhaust recording capacity, or leave unordered marks whose chronology had to be inferred. “DNA Typewriter avoids all of this,” said co-first author Haedong Kim, PhD, a postdoctoral scientist in genome sciences at UW Medicine. “It writes without fully severing the DNA, keeps recording relatively steadily, and writes everything in strict order, so we can record cell lineages at much higher resolution for a longer time.”
In the Cell paper, Jonathan Weissman, PhD, and colleagues used a prime-editing system called PEtracer to map lineage dynamics across more than 1.4 million cells from 16 mouse embryos. Together with the DNA Typewriter study in Science, the work demonstrates that mammalian cellular family trees can be reconstructed at unprecedented scale. “It’s really proof of principle that we can do what was done with the roundworm in 1983, but for mammals like you and me,” Weissman said.
The work provides a framework for investigating how normal organs form and where development can go awry. More complete lineage records could help researchers study congenital malformations, neurodevelopmental and genetic disorders, and cancer, as well as guide stem cell engineering. As Shendure explained, “Recording techniques like the ones in these studies enable measurements over time including in settings that we can’t directly visualize.” Scaling the methods across additional embryos and experimental conditions could support more quantitative—and eventually predictive—models of mammalian development.
The Era of Modality Convergence: Why Drug Development Is Being Rewritten
The life sciences industry is entering a new era, defined not by a single breakthrough technology but by the convergence of many. Artificial intelligence is accelerating drug discovery, cell and gene therapies are expanding treatment possibilities, and antibody engineering continues to evolve beyond traditional monoclonal antibodies. Meanwhile, mRNA platforms, viral vectors, antibody-drug conjugates (ADCs), multispecific antibodies, and other advanced therapeutic modalities are transforming how developers approach previously intractable diseases. Taken individually, each of these innovations represents a significant advancement. Together, they are reshaping the drug development landscape.
For decades, therapeutic development followed a familiar path: identify a target, optimize a lead candidate, advance through preclinical studies, and move into clinical evaluation. While the science was challenging, the overall framework remained relatively consistent. Today, that framework is evolving rapidly. Developers are no longer limited to choosing between small molecules and conventional biologics. Instead, they have access to a growing toolkit of therapeutic technologies, each with unique advantages, manufacturing requirements, analytical challenges, and regulatory considerations. At the same time, advances in computational biology and AI-driven discovery are generating more candidates than ever before.
As a result, the challenge is no longer simply finding promising molecules. Increasingly, success depends on identifying candidates that can be manufactured, scaled, and translated into viable therapies. Decisions once reserved for later stages, including developability, manufacturability, analytical characterization, and clinical strategy, must now be addressed much earlier in the process. Drug development is becoming a continuous, interconnected journey rather than a series of separate steps.
The boundaries between traditional disciplines are also beginning to blur. Cell therapies may incorporate viral vectors. Gene therapies may leverage lipid nanoparticles. Antibody programs may evolve into multispecific or conjugated formats. The future of drug development is not defined by any single modality, but by the ability to integrate technologies effectively.
Organizations that combine discovery expertise, modality-specific knowledge, CMC capabilities, and manufacturing execution will be best positioned to navigate this increasingly complex environment. The articles that follow examine key trends driving this transformation, from AI-enabled discovery and experimental validation to advanced cell, gene, and biologic platforms, and explore how the next generation of therapies will move from concept to clinic.
The Advanced Research Projects Agency for Health, (ARPA-H), an agency within the United States’ Department of Health and Human Services (HHS), recently announced awards and teams involved in its BioStabilization Systems (BoSS) program, an initiative that aims to develop new methods for producing, storing, and transporting cell-based biologic medicines at room temperature. ARPA-H has committed to provide up to $87 million across the four-year program. This round of funding will support Phase I of the program.
The teams have been tasked with pursuing new technologies that confer long-term, shelf-life stability to cell-based therapies and biologics making it possible to store and ship them without refrigeration. To accomplish this goal, BoSS teams will work across two technical areas. They will work on interventions to stabilize cells for storage and then reactivate them on demand as well as on creating novel processing systems that ensure these interventions are efficient and scalable for manufacturing.
This week, members of the Reversible Ambient Temperature (RAB) team said that they have been awarded up to $7.3 million initially from ARPA-H. The RAB team includes representatives from Draper, a non-profit research, development, and manufacturing company, which serves as the administrative lead,and Likarda, a biotechnology company that develops hydrogel technologies for delivering and preserving cell therapies and biologics, which serves as the technical lead for the team. Other members of the team include scientists from CaseBioscience, CPSI Biotech, Fresenius Kabi, MiNK Therapeutics, and University of Colorado.
For their part, this team will use AI-assisted design of experiments to optimize formulation, quiescence induction parameters, hydrogel composition, and recovery media as well as freeze-assisted droplet drying to achieve a stable product. Specifically, the RAB team’s biostabilization strategy, led by Likarda, will focus on simultaneous control of cell state, physical environment, and the cellular stress responses activated during freezing, dehydration, storage, and recovery. Meanwhile, the bioprocessing engineering team, led by Draper, will convert the manual steps of biostabilization into an automated, commercially viable workflow and will develop custom instruments to scale up the processes.
Likarda will contribute its proprietary Core-Shell Spherification hydrogel technology, which is designed to encapsulate and protect living cells while maintaining viability and function. CaseBioscience will provide expertise in clinical media formulation, biopreservation, and cellular metabolism to the development and evaluation of novel approaches for long-term ambient stabilization of therapeutic cells.
“The RAB team brings together deep expertise in cryoprotection, encapsulation, and cell state, with a focus on protecting cell viability throughout the stabilization and reanimation process, and the team has direct experience bringing products to market,” said Jason Fiering, distinguished technical staff at Draper. “Working in parallel, our interdisciplinary engineering team will develop the customized instruments needed to transfer these processes to clinical manufacturing environments.”
In his comments, John Baust, PhD, an expert in biopreservation and collaborator on the project with CPSI Biotech noted that “the concept of preserving living cells in a dry state has been pursued for more than two decades, but significant scientific and engineering barriers have yielded limited success.” The BoSS program is an opportunity “to tackle this from multiple scientific and engineering directions simultaneously” that if successful “could represent a paradigm shift in how biologics are preserved, stored, and distributed.”
Other teams announced by ARPA-H include one led by DesiCorp. This team includes members from the University of Louisville, University of Texas at Austin, Via Therapeutics, VeriSIM Life, and Ossium Health. They will leverage high-throughput screening to identify biocompatible formulations that slow cellular metabolism and AI-assisted process optimization to develop a thin-film freeze-drying (TFFD) system that stabilizes cells at room temperature.
DesiCorp will lead integration of the overall workflow by using a high-throughput screening platform to rapidly identify biocompatible formulations that enable cells to survive TFFD and resume function after rehydration. Meanwhile, UT Austin researchers will help optimize the TFFD critical process parameters and composition/material attributes, while also contributing expertise in aseptic processing and scaling up the technology.
A third team is led by the University of California, Davis and includes scientists from Mayo Clinic, Case Western Reserve University, University of Georgia, and Sersense. The team will induce a low metabolic state in cells using light-responsive gels and microfluidic processing techniques to produce cells that are stable at room temperature.
Recently, that team announced that it had been awarded up to $6 million, initially, from ARPA-H to fund its project dubbed CYBORGEL. Their efforts will leverage research done in the laboratory of Cheemeng Tan, PhD, a professor of biomedical engineering at UC Davis. Previously, he and his collaborators formed water-rich polymer networks inside living cells to create cell-material hybrids. This process, called intracellular gelation, forms a gel inside the cell and is capable of halting cell division while preserving cellular function. Tan and his team dubbed this cellular product a “cyborg” since the gelation process combines living cells with synthetic materials.
Through CYBORGEL, the collaborators will investigate whether this intracellular gelation process can be extended to reduce the cost and simplify the storage, transportation and delivery of cellular therapies. The researchers will also test whether CYBORGEL cells remain therapeutically useful after being returned to their original state. For instance, one study will test the effectiveness of reanimated CAR T cells for existing clinical trials on cancer immunotherapies. Another will test it on placental mesenchymal stem cells used to treat spina bifida, a medical intervention being developed by UC Davis researchers.
As part of the initiative, the American Type Culture Collection (ATCC) has been tapped to serve as the IV&V partner. ATCC will provide working cell banks of government-selected cell types that will be used for demonstrations. They will also provide unbiased assessments of both the viability and function of the preserved products and the design features of the preservation systems.
The first phase of the BoSS program kicked off in September and is expected to run for 15 months. Over the four-year multi-phase program, the teams’ approaches will be evaluated against increasingly stringent benchmarks for cell viability, production speed, and shelf-life stability. If successful, work under the BoSS program could enable lower costs for cell-based drugs, reduce the risk of therapeutic and pharmaceutical product losses, and enable domestic reserves for public health preparedness.
Sophie, a 12-year-old dog, isn’t sure what to make of the much larger Homer after Dog Aging Project co-lead and ASU Professor Noah Snyder-Mackler delivered his lecture to residents at Mirabella at ASU on Thursday, August 6, 2026. Sophie lives at Mirabella with her owner, Bonnie Kasel. Snyder-Mackler and his fellow researchers study various health markers in dogs as they age. [Charlie Leight/ASU News]
Domestic dogs offer a useful model for understanding the biology of aging because their lifespans vary dramatically with body size. Large dogs tend to be shorter lived than their smaller counterparts, but why? Headed by a team at Arizona State University, a study of nearly 900 dogs now suggests that the answer may be written into the animals’ epigenomes.
Research professor Noah Snyder-Mackler, PhD, at Arizona State University School of Life Sciences, and colleagues developed an epigenetic clock that predicted mortality in dogs and showed that epigenetic aging is fastest early in life. The study results indicated that male dogs and larger dogs undergo accelerated molecular aging, with male dogs exhibiting pronounced DNA methylation (DNAm) changes on the X chromosome, and larger animals exhibiting methylation changes at transposable elements (TEs), stretches of DNA that can influence genome stability and gene regulation.
Snyder-Mackler is senior and corresponding author of the researchers’ published paper in Science (“Epigenetic aging and transposon dysregulation reflect size-related lifespan compression in dogs,”) in which they concluded, “Our findings broadly establish companion dogs as a translational model for uncovering molecular mechanisms of lifespan variation, epigenomic instability, and age-related disease.”
Genome-wide DNA methylation in 894 dogs from the Dog Aging Project reveals accelerated aging in larger dogs and faster loss of repression of transposable elements. [Blaise Mariner & Brianah McCoy]
Aging is a universal process, but its pace and biological mechanisms substantially differ between individuals within even the same species, the authors wrote. “Whether shorter-lived individuals simply die earlier or instead experience accelerated biological aging throughout life remains unclear,” they noted. And while comparative studies across species have identified mechanisms associated with exceptional longevity, they may not be as well suited to identifying the molecular drivers of aging variation. “Domestic dogs provide a model for addressing this challenge because body size strongly predicts lifespan, with larger breeds living substantially shorter lives than smaller breeds despite sharing many physiological and environmental conditions,” the team continued.
In fact, smaller breeds can live nearly twice as long as larger breeds. And companion dogs live in human environments, consume commercial diets, and receive routine medical care, making them a relevant translational model for studying how genetic background and environmental exposures shape aging, the investigators pointed out.
Professor and School of Life Sciences Associate Director Noah Snyder-Mackler poses for portraits with his two dogs, 7-year-old Homer, bottom, and 4-year-old Juno, top, on the steps of Old Main on the ASU Tempe campus on Monday morning, September 28, 2026. Snyder-Mackler’s recent research focuses on dog epigenetics. [Deanna Dent/Arizona State University]
But how intrinsic factors such as size and biological sex influence variation in dog aging and lifespan remains unclear. “It is well established that size and sex influence lifespan in dogs, but it remains unclear whether shorter-lived dogs experience accelerated epigenetic aging,” they stated. Previous research has demonstrated that DNAm is a useful indicator of biological aging, with “epigenetic clocks” offering a way to understand the factors that accelerate or decelerate biological aging. “DNA methylation (DNAm) has emerged as a robust molecular biomarker of age, with ‘epigenetic clocks’ offering a quantitative framework for identifying factors that accelerate or decelerate biological age.”
For their newly reported study Snyder-Mackler, together with first author Blaise Mariner, PhD, and colleagues generated 1,640 methylomes from a cohort of 894 dogs in the Dog Aging Project. They combined the molecular data with detailed genetic and demographic data. The results indicated that molecular aging occurs most rapidly early in a dog’s life. The study also found that larger and male dogs—both of which have shorter lifespans than their respective counterparts—age more quickly at the molecular level.
The authors identified different epigenetic patterns underlying these effects. According to the findings, sex-related changes were concentrated on the X chromosome, while body-size-related changes were especially prominent in TEs. “Thus, size and sex, two axes associated with shorter expected lifespan in dogs, appear to shape aging through partially distinct epigenetic architectures,” they stated. “Larger dogs show greater methylation loss at TEs with age, consistent with increased immune remodeling and genomic instability … These data address a major gap in comparative epigenomics by providing evidence that, within a species, demographic groups with shorter expected lifespans show faster epigenetic aging.”
Detection of the whole transcriptome along with over 64 proteins in a human breast cancer FFPE tissue section using Bruker’s CosMx spatial molecular imager. [Bruker Spatial Biology]
![Genome-wide DNA methylation in 894 dogs from the Dog Aging Project reveals accelerated aging in larger dogs and faster loss of repression of transposable elements. [Blaise Mariner & Brianah McCoy]](https://www.genengnews.com/wp-content/uploads/2026/10/low-res-3-300x169.jpeg)
