Tag: Genomics

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From Multi-Omics to Digital Twins: A Data-Driven Future for Precision Medicine

First coined over a decade ago in the aerospace industry to describe a digital replica of a physical object, the concept of a “digital twin” has since found its way into medicine, where it refers to the simulation of a patient’s unique biology. Drawing on multiple layers of patient health data, these computer models promise to predict how a person’s health will evolve over time and how they will respond to any given intervention.

Digital twins represent a transformative shift in medicine, moving from reactive health interventions toward preventive strategies. While this technology is still in early stages, it is already being used to guide personalized cancer treatment, simulate the outcomes of cardiology interventions, and manage complex metabolic diseases like diabetes. However, most applications today are closer to small-scale digital models of a specific tissue or condition rather than a complete digital twin that dynamically adapts to real-world data from each simulated patient.

Overhead of young woman using fitness app on smartphone and smartwatch to monitor training progress after exercising at home
Credit: Oscar Wong / Getty Images

A convergence of rapid technological advances across multi-omics and artificial intelligence (AI) is priming the development of powerful computational models that can capture intricate biological processes beyond the capabilities of any of their predecessors. As large-scale multi-omics datasets are increasingly combined with clinical and real-time physiological data, digital twins are laying the foundation for a more precise and individualized understanding of human health.

Exploring uncharted territory

Digital twins could have a particularly meaningful impact in areas of medicine where knowledge is limited and currently available technologies have fallen short. One such area is rare diseases. Although rare diseases collectively affect more than 300 million people worldwide, each of the over 7,000 conditions covered under this definition only affects a small number of patients—sometimes even just a single person. This scarcity makes it difficult to study the underlying biology and hinders the development of much-needed treatments and diagnostics.

Ellen M. McDonagh
Ellen M. McDonagh, PhD
Group Team Lead
European Bioinformatics Institute

“We can use digital twins to address the fact that, with a rare disease, you might only have a handful of patients with that diagnosis,” said Ellen M. McDonagh, PhD, group team lead at the European Bioinformatics Institute (EMBL-EBI) in the U.K. and translational informatics director at Open Targets.

Through a project funded by the Chan Zuckerberg Initiative, McDonagh’s team is developing digital twins of human tissues that combine multi-omics data with a patient’s clinical history and additional phenotype data. Their approach begins by modeling biological processes in healthy tissue, and then bringing in data from common diseases affecting the same tissue to train AI models to predict patterns of dysfunction. This would allow researchers to feed the algorithm data from patients with rare diseases to better understand the underlying biological mechanisms driving each condition.

Integrating diverse layers of multi-omics data will be critical to achieving a more comprehensive understanding of the molecular basis of these rare conditions. In some countries, including the U.K., patients with rare diseases routinely undergo whole-genome or whole-exome sequencing as part of diagnostic testing. However, many of the identified genetic variants remain difficult to interpret with limited current knowledge. By combining genomics with other modalities such as transcriptomics, proteomics, and metabolomics, researchers can develop a more complete picture of the underlying molecular interactions and better determine the relevance of these previously uncharacterized variants.

On this front, a major challenge lies in collecting and integrating data across a wide range of modalities, cohorts, and institutions. To address this, McDonagh’s team is actively developing workflows to standardize data collected from the scientific literature, public datasets, and research environments, enabling more reliable comparisons across datasets and facilitating their integration into digital twin models.

This work also involves efforts to fill gaps in the data, as not all data modalities will be available for every patient. For instance, a computer model could predict what the transcriptomic profile will look like based on genomics data, and vice versa.

“We are benchmarking different methods that can help with predicting missing data, but also evaluating how confident we are in those predictions,” said McDonagh. Knowing which biological processes can be predicted with high confidence, and which cannot, can help researchers draw more robust conclusions and guide future data collection efforts.

As digital twin models keep growing and becoming more refined, they will enable the identification of new therapeutic targets and diagnostic markers, while also forecasting the precise effects an intervention will have on a given person. McDonagh highlights their potential to develop more personalized treatment plans for each patient, adding that, “Monitoring patients over time, one could also predict whether a patient might develop resistance to a given drug and switch them to an alternative treatment.”

Integrating real-time data

Integrating multi-omics data with physiological measurements, obtained from continuous sensors and wearable devices, could help digital twins take a significant step forward in accurately simulating complex and dynamic biological processes. In turn, this could help advance healthcare from a reactive model to a more proactive approach.

Tadao Ooka
Tadao Ooka, MD, PhD
Associate Professor
University of Yamanashi

“Today, much of medicine begins after a disease has become clinically apparent,” said Tadao Ooka, MD, PhD, associate professor at the University of Yamanashi in Japan. “In contrast, preemptive medicine aims to detect subtle biological changes before symptoms or irreversible damage occur, and to intervene earlier through lifestyle, environmental, pharmacological, or behavioral approaches.”

Achieving such a transformative shift could significantly reduce the burden of chronic diseases such as diabetes, cardiovascular disease, and neurodegenerative disorders. This is becoming an increasingly urgent goal in aging societies, including Japan, where preventing health decline and extending healthy life expectancy are currently major public health priorities.

Ooka’s lab is developing digital twins that integrate patient data from longitudinal multi-omics, wearables, and lifestyle questionnaires. Through Taomics, a company he co-founded, Ooka is also building a platform to collect longitudinal data from patients and healthy individuals. This data is used to create digital twins that can provide users with personalized health recommendations while informing drug discovery and identifying target populations for a more precise approach to clinical development.

“One major objective is to identify biological pathways related to insulin resistance and metabolic dysfunction,” he added. “The goal is not only to predict risk, but also to understand which behaviors or interventions may improve a person’s molecular and metabolic state.”

While multi-omics data can tell researchers what is happening within the body at the molecular level, continuous data obtained from sensors and wearables can provide a deeper insight into what a person is experiencing in daily life, including physical activity, sleep, heart rate, and stress levels.

“The key is to connect these two layers,” said Ooka. “Together, they allow us to move from general advice to personalized, testable, and adaptive recommendations. For example, if a person’s sleep, physical activity, or dietary pattern changes, we can examine how their inflammatory, metabolic, or insulin resistance-related protein signatures change afterward. Conversely, if a molecular pathway appears to be deteriorating, [sensor] data may help identify the behavioral or environmental context behind that change.”

Across all medical specialties, Ooka expects digital twins to make the greatest early impact in diseases where progression is continuous, multifactorial, and strongly influenced by the patient’s lifestyle and environment. These include metabolic diseases, which develop over many years and are shaped by interactions between genetics, environment, and behavioral patterns. Oncology will also be particularly relevant given the complexity of treatment response and resistance processes at the molecular level.

To reach these ambitious goals, however, a number of challenges must be addressed. In addition to ensuring the data used to train digital twin models is robust and reliable, implementation needs to be carefully planned so that digital twins can adapt to and integrate into real-world clinical workflows, reimbursement systems, regulatory frameworks, and ethical governance structures.

“The goal should be to create systems that benefit the broader population,” explained Ooka. “We need to ensure that prediction does not become discrimination, that data is handled securely, and that people receive understandable and actionable recommendations.”

Towards dynamic predictions

In the future, experts expect to see digital twins that integrate multi-omics data with wearable, imaging, clinical, and environmental data to capture the full complexity of human biology, becoming intelligent decision-support platforms. This progress will be underpinned by continued improvements in multi-omics technology, with the coming decade being primed for advances in longitudinal data collection and spatial multi-omics. Coupled with increasingly lower prices, this technology is expected to become much more accessible to researchers and clinicians alike.

Kyung-In Jang
Kyung-In Jang, PhD
Associate Professor
Daegu Gyeongbuk Institute of Science and Technology (DGIST)

“While omics data were once confined to laboratory analysis, emerging wearable technologies now allow real-time detection of certain metabolites and protein markers,” wrote Kyung-In Jang, PhD, associate professor at the Daegu Gyeongbuk Institute of Science and Technology (DGIST) in South Korea. “These innovations support integrating omics into everyday health monitoring, contributing to the accessibility and responsiveness of precision healthcare.”

Within the next decade, McDonagh expects to see the first translational applications of digital twins in the clinic, whether to support diagnosis, patient stratification in clinical trials, or predicting how a patient will respond to a given treatment. “It really does open the door to being able to identify new targets that are causing disease in rare disease patients, but also in more complex, common diseases,” she said. “Ultimately, digital twins will help in the development of new, safer, more effective treatments and more personalized medicine.”

Going forward, Ooka expects medical applications of digital twins to evolve in stages, starting with smaller, disease-specific models, and later becoming large-scale tools that can predict future outcomes and enable patients to alter their disease trajectories through personalized interventions. This evolution will go beyond purely technical improvements, potentially shaking the foundations of healthcare systems as we know them today.

“The field will require new ecosystem models, not only new analytical technologies,” said Ooka. “Medical digital twins cannot be built by academia, industry, hospitals, or technology companies alone. They require long-term participant engagement, trusted data governance, scientific rigor, clinical relevance, and business sustainability.”

Ooka has been actively working on setting up such an ecosystem in Japan through the COI-NEXT initiative, bringing together universities, regional companies, and global partners to return insights derived from their data to local communities.

Futuristic Data Skyline: Neon Bar Cityscape Visualization for Tech, Analytics, and Innovation
Credit: Alllex / Getty Images

“Ultimately, I would like to create a system in which individuals can receive personalized health recommendations based on their own longitudinal biological data,” he concluded. “This means moving beyond one-time testing toward a continuous feedback loop: measure, interpret, intervene, and re-measure. At the same time, such a platform could contribute to pharmaceutical research by connecting real-world human biology, lifestyle, and molecular data in a way that supports more precise and efficient drug development.

“My hope is that digital twins will help create a future where healthcare is no longer centered only on diagnosing and treating disease, but on continuously supporting each person’s optimal health throughout life.”

 

Clara Rodríguez Fernández is a science journalist specializing in biotechnology, medicine, deeptech, and startup innovation. She previously worked as a reporter at Sifted and editor at Labiotech, and she holds an MRes degree in bioengineering from Imperial College London.

The post From Multi-Omics to Digital Twins: A Data-Driven Future for Precision Medicine appeared first on Inside Precision Medicine.

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The Hong Kong Genome Project is a flagship initiative for precision medicine in Chinese populations

Nature Medicine, Published online: 12 June 2026; doi:10.1038/s41591-026-04421-7

The Hong Kong Genome Project established a genome sequencing database that provides improved diagnoses for patients and more efficient, population-tailored carrier status screening. Actionable pharmacogenomic variants were identified in almost all participants, informing drug prescriptions. This work establishes a genomic resource and a transferable model for equitable precision medicine in underrepresented populations worldwide.

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Origins of First Eukaryotes Linked to Contributions from Multiple Bacteria and Giant Viruses

All cells in animals, plants, fungi, and protists share a fundamental characteristic, in that they are eukaryotic cells. These are essentially complex cells with specialized internal compartments. The cells that make up our bodies are no exception.

How this type of cell emerged is one of the great questions in biology. For decades, the dominant explanation has placed acquisition of the mitochondrion as the ultimate turning point. It’s thought that an archaeon established a symbiotic relationship with a bacterium, which eventually became the mitochondrion, and this alliance opened the door to cellular complexity.

A study led by Toni Gabaldón, PhD, an ICREA researcher at IRB Barcelona and the Barcelona Supercomputing Center-Centro Nacional de Supercomputación (BSC-CNS) now rethinks this view. While the research does not deny the central role of the mitochondrion, it suggests that the origin of complex cells was a longer, more gradual and more collaborative process than had previously been thought. Challenging the idea that cellular complexity emerged from a single evolutionary encounter, the study results point instead to a gradual process of interactions among different microorganisms that lasted for millions of years. The findings identify contributions from several bacteria, in addition to the one that gave rise to the mitochondria, and suggest that giant viruses may have acted as vehicles for genetic transfer.

“For a long time, we have explained the origin of complex cells as a story with two main protagonists: an archaeon and the bacterium that gave rise to the mitochondrion,” said Gabaldón. “Our study suggests that this narrative is incomplete and that there were more actors on stage, including other bacterial groups and giant viruses that may have facilitated gene exchange.” The team published their findings in Nature, in a paper titled “Gene ancestries reveal diverse microbial associations during eukaryogenesis.”

“The origin of eukaryotes remains a central enigma in biology,” the authors wrote. Unlike studies with dinosaurs, the origin of eukaryotes cannot be reconstructed from visible bones or fossils. It likely occurred about two billion years ago in microscopic organisms, of which barely any direct traces remain. “The current consensus on eukaryogenesis revolves around scenarios that always involve an endosymbiotic relationship with extensive gene transfer between an alphaproteobacterial endosymbiont and a host with an Asgard archaeal ancestry,” the team noted. However, the footprints of this evolution are still present in today’s genomes.

To trace them, the team approached the problem as a form of computational molecular archaeology, using the computing power of the MareNostrum series of supercomputers to analyze public genomic data spanning biodiversity as a whole.

The researchers first reconstructed the repertoire of gene and protein families of the last common ancestor of all eukaryotes, known as LECA (last eukaryotic common ancestor). “Our analysis provided a revised reconstruction of the last eukaryotic common ancestor (LECA) proteome, in which we traced the phylogenetic origin of each protein family,” they wrote. The investigators then analyzed its evolutionary origin by comparing these families against databases containing tens of thousands of bacterial, archaeal, and viral genomes.

“We are trying to reconstruct a story that took place billions of years ago and for which we have no direct fossils. That is why we have been very conservative: we only kept the most robust evolutionary signals—those with a strength comparable to the signals already accepted for the ancestral archaeon and for the bacterium that gave rise to the mitochondrion,” explain study co-authors Moisès Bernabeu, PhD, Saioa Manzano-Morales, PhD, and Marina Marcet-Houben, PhD, who are researchers in the Comparative Genomics group led by Gabaldón at IRB Barcelona and the BSC.

After more than five years of work using complex mathematical models and processing large volumes of genomic sequences, the team was able to detect signals that would otherwise have remained invisible.

Beyond the mitochondrion, the study identifies two particularly relevant bacterial signals: Myxococcota and Planctomycetota. The former are related to metabolic functions, including processes linked to lipids and membranes. The latter are bacteria known for their structural complexity, featuring internal compartments that are unusual for bacterial organisms. “Transfers from these donors have been identified in earlier studies, including small-scale detailed ones such as the acquisition of some steroid biosynthesis enzymes from Myxococcota,” the team stated.

Their analyses indicate that these contributions did not happen all at once. Planctomycetota appear as an older signal, whereas Myxococcota and the bacterium that gave rise to the mitochondrion show signals that are closer in time. “We found compelling evidence for multiple waves of horizontal gene transfer from diverse bacterial donors, with some likely to have preceded mitochondrial endosymbiosis,” the scientists suggested.

One of the most unexpected findings of the study is that some genes integrated during the early evolution of eukaryotes appear to come from giant viruses, specifically Nucleocytoviricota. These viruses have genomes that are much larger than those of most known viruses, and they infect single-celled eukaryotic organisms.

The authors propose that these viruses could have acted as vehicles for genetic transfer between microorganisms coexisting in the same ecosystem, facilitating exchanges that helped shape the ancestral genome of eukaryotic cells. “Our results confirm and expand earlier results supporting sizeable gene flow from diverse prokaryotic ancestors preceding the LECA4, and uncover a role for viruses as potential mediators of such transfers,” the scientists stated.

This vision fits with the idea that the ancestors of eukaryotic cells lived in environments rich in microbial communities, such as microbial mats, where different microorganisms coexist in layers under varying chemical conditions. In this context, genetic exchanges would have allowed them to acquire new biological capabilities over time. “Microorganisms are known to form complex communities such as microbial mats or complex biofilms, of which viruses also form active part, and it is reasonable to consider that the ancestors of the LECA lived in such complex environments,” they stated.

The study addresses one of the major questions in biology: how the complexity of the cells that form our bodies came to be. By reconstructing the genetic traces of that process, the work provides a new perspective on a key episode in the history of life: the origin of the cellular lineage to which animals, plants, fungi, and protists belong. “Taken together, our results suggest that ancient eukaryotes may have originated within complex microbial ecosystems through a succession of diverse associations that left a footprint of horizontally transferred genes.”

The paper expands on a line of research initiated by Gabaldón in 2016, when he published a study in Nature that already suggested the mitochondrion might have been acquired relatively late in the process of eukaryotic origins. Now, with much more genomic data available and more powerful computational tools, the team has been able to analyze in greater detail which other organisms left their mark on that common ancestor.

“All genomes preserve traces of their history. In the case of eukaryotes, those traces tell us of ancient alliances between microorganisms. Understanding them helps us answer a very profound question: what we are and where we come from,” commented Gabaldón.

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Ultrasensitive HPV DNA Blood Test Could Help Personalize Head and Neck Cancer Treatment

Researchers from Mass General Brigham Cancer Institute have shown that HPV-DeepSeek, an ultrasensitive blood test that detects human papillomavirus (HPV) DNA, could predict which patients with HPV-associated head and neck cancer are most likely to benefit from adjuvant treatment after surgery.

“Right now, the decision to give adjuvant radiation or chemoradiation after surgery hinges almost entirely on TNM staging and surgical pathology,” explained senior author Daniel Faden, MD, director of the Head and Neck Cancer Genomics and Liquid Biopsy Program at Mass General Brigham Cancer Institute.

This means that surgeons and oncologists look at features like the number of involved lymph nodes, whether there’s extranodal extension, surgical margin status, and perineural invasion or lymphovascular invasion.

“The problem is, these are static, backward-looking variables,” Faden told Inside Precision Medicine. “They tell us what was in the tumor specimen, but they don’t tell us whether the patient has microscopic disease left behind after surgery.”

This can result in some patients receiving more treatment than they need, with potential unnecessary side effects, and others receiving less treatment than they should and later having their cancer come back.

Previous work has shown that circulating tumor (ct)HPV DNA is a promising biomarker for predicting head and neck cancer recurrence, but current assays, using droplet digital polymerase chain reaction (ddPCR), lack the sensitivity needed for detecting minimal residual disease (MRD) after surgery.

Faden and team therefore used their experience of studying HPV to build HPV-DeepSeek, a whole genome sequencing (WGS) assay that detects ctHPV DNA in blood samples.

He noted that the assay differs from most currently available MRD assays as it is not tumor-informed and does not require building a bespoke panel from each patient’s tumor. This makes HPV-DeepSeek tissue agnostic, and means the assay has a lower cost and faster turnaround times than tumor-informed WGS approaches.

Faden and team evaluated the performance of HPV-DeepSeek in the Clear-HPVca study, which included 103 patients (mean age 62 years, 89% men) with stage I to IV HPV-positive head and neck cancer who were treated with surgery.

The authors report in Science Translational Medicine that among 74 patients with ctHPV DNA status available during the postsurgery period (four days to six months after surgery, before adjuvant therapy), 23% were MRD-positive.

After a median follow-up of 27 months, patients with ctHPV DNA detected had worse outcomes than those with no ctHPV DNA. Specifically, the two-year disease-free survival rate was 60% among the patients who were MRD-positive, compared with 100% for those who were MRD-negative, and the two-year overall survival rates were 73% and 98%, respectively.

The researchers also found that HPV-DeepSeek could predict which patients benefit from adjuvant treatment; the recurrence rate was 27% among 15 MRD-positive patients who received adjuvant treatment compared with 100% among the two MRD positive patients who did not receive adjuvant treatment.

Conversely, there was no benefit of adjuvant treatment among MRD-negative patients; in this group, the recurrence rate was 3% among the 37 MRD-negative patients who received adjuvant therapy and 0% among the 21 MRD-negative patients who did not receive adjuvant therapy.

Furthermore, the assay identified molecular recurrence an average of seven months, and up to 17.5 months, earlier than clinical recurrence. With ddPCR, molecular recurrence was only detected four months earlier than clinical recurrence.

Although the team knew that HPV-DeepSeek was more sensitive than ddPCR, Faden described the size of the lead-time as striking.

“It tells us we’re detecting recurrence at a disease burden that’s genuinely clinically meaningful and actionable,” he said. “We had some patients where molecular detection preceded clinical detection by over a year. That kind of window creates real opportunities—time to discuss salvage surgery or systemic therapy before a patient ever develops symptomatic recurrence.”

Faden and team are now planning interventional trials that will investigate whether using HPV-DeepSeek to guide adjuvant therapy decisions improves outcomes and reduces toxicity.

Faden hopes that the assay will eventually be used to guide personalized treatment decisions.

“As a surgeon who treats this disease, not just a scientist who studies it, this is where I get genuinely excited—because it’s a shift in philosophy, not just methodology,” he said. “What HPV-DeepSeek does is make disease biology—specifically, whether there’s residual disease—visible in real time.”

He added: “If you can accurately detect MRD early after surgery, you can make treatment decisions based on that biology [and] the downstream implications are significant. You reduce unnecessary toxicity in patients who don’t need more treatment. You potentially intensify treatment in patients with residual disease. And you can rethink surveillance—if a patient remains MRD-negative after treatment, can we move to less frequent imaging and potentially spare them years of follow-up CT scans?”

“We’re not there yet clinically, but this is a first step—a research finding with real promise. It’s the beginning of a conversation about how to personalize care.”

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Updated Amplification Tool Rapidly Detects Mycoplasma

Nucleic acid amplification techniques (NAAT) are recommended by pharmacopoeias in the United States, the EU, and Japan as an alternative to traditional culture and indicator cell methods used to detect Mycoplasma in biological products. Used for in-process controls and release testing, NAAT is an improvement over traditional methods that take up to 28 days, but attempts to balance broad species coverage and sensitivity favor one over the other.

Recently, Chinese researchers developed a NAAT that overcomes those challenges, according to a recent paper by scientists at the National Institute for Food and Drug Control, Yeasen Biotechnology, and Xi’an Jiaotong-Liverpool University.

Rather than surpassing existing technologies, “It optimizes the core pain points of mainstream multiplex NAATs,” encompassing their advantages and forming others, senior author Xiaoliang Sun, PhD, scientist, genomics division, Yeasen Biotechnology, tells GEN.

For example, for cell therapy products, Sun says this NAAT technique, “cuts mycoplasma tests from a 28-day culture to several hours, enabling same-day batch release and avoiding cell product expiry scrap.” It also “accelerates finished-product release for short shelf-life recombinant proteins and monoclonal antibodies.”

The three pairs of primer-probe sets in this assay cover 183 Mollicutes species by targeting Mycoplasma-specific conserved regions. “This fills the detection gap [experienced by] some rare strains…and makes it suitable for trace contamination screening in biopharmaceutical production,” Sun points out.

To objectively assess this method, they compared it to traditional 165 rRNA degenerate PCR, strain-specific NAATs, and mainstream multiplex NAATs cited in the literature.

The analysis shows the new assay offers “single-copy detection sensitivity (validated by 10 pharmacopoeia standard strains), no cross-reactivity (validated with 14 non-Mycoplasma genera and six engineered cell lines), short amplicons of 100 to 200 bp, amplification efficiency of 95–105%, and excellent repeatability,” Sun says. This ensures specificity and improves detection consistency. Consequently, the method “meets the strain detection requirement of Chinese and European pharmacopoeia for full-process regulatory scenarios.

“The detection process is compatible with existing qPCR platforms without requiring special equipment and can be directly applied to full-process scenarios, such as raw material screening, cell bank verification, and finished product release,” Sun continues, “achieving more comprehensive compliance.”

There are limitations, though. “Detection performance for unrecorded Mycoplasma strains from extreme environments or highly variable subspecies has not been verified… leaving a potential detection gap,” he cautions. Additionally, “The use of three pairs of primer-probe sets increases the reagent cost per sample.” Therefore, scientists may best use this method for initial screening, followed by digital PCR confirmation.

For biopharmaceutical manufacturers, Sun says this assay’s main advantages are “comprehensiveness, stability, and practicability.” Future work is envisioned to expand the assay’s applicability parameters and robustness.

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10x Genomics Acquires Proteintech Genomics, Expanding Proteomics Capablities

10x Genomics has acquired Proteintech Genomics for an undisclosed price, the companies said, in a deal driven by the buyer’s commitment to expanding its multiomics presence—in this case, by strategically expanding its proteomics capabilities.

The acquisition is intended to bring together 10x’s expertise in scalable single-cell and spatial biology platforms with Proteintech Genomics’ capabilities in protein detection. Founded in 2022, Proteintech Genomics specializes in life science technologies enabling researchers to apply single-cell and spatial multiomics tools toward discovery. The company develops fully optimized and highly multiplexed proteomic assays designed for ready integration into various single-cell and spatial analysis application workflows.

By helping researchers harmonize RNA and protein analysis, Proteintech Genomics has positioned itself as an alternative to the multiple vendors and their workflows traditionally needed to accomplish both analyses, as researchers increasingly combine transcriptomic- and proteomics-based data to gain complementary insights into cellular identity, state, and function.

“Proteintech Genomics strengthens our capabilities in one of the most exciting and rapidly evolving areas of biology: proteomics,” Michael Schnall-Levin, 10x’s CTO, chief strategy officer, and founding scientist, told GEN. “While transcriptomic technologies have advanced tremendously over the last decade, we believe there is still significant opportunity to push protein-based analysis much further, particularly in combination with RNA and other analytes.”

“We believe the future of biological analysis will increasingly integrate single-cell, proteomic, and spatial information,” Schnall-Levin added. “This acquisition reflects our conviction that proteomics will be an important part of that future and expands our ability to support richer multiomic workflows across our portfolio.”

Among Proteintech Genomics’ technologies is its Human Discovery Panel, which, according to the company, is the largest antibody-based single-cell protein panel. The panel allows researchers to simultaneously profile 347 DNA-barcoded antibodies covering 325 distinct protein targets, alongside transcriptomic measurements.

“Significant opportunities”

“We see significant opportunities to continue expanding both plex and content over time. One of the exciting aspects of bringing Proteintech Genomics into 10x is the ability to invest more deeply in future proteomic innovation,” Schnall-Levin explained. “While we aren’t announcing specific products today, directionally we’re interested in enabling higher-plex measurements, broader biological content, and more accessible workflows. Those are all areas where we see substantial opportunity going forward.”

The Human Discovery Panel is also designed to support integrated analysis of intracellular proteins, cell surface proteins, and transcriptomic profiles within sequencing-compatible workflows. The panel is compatible with 10x’s Flex chemistry, including the Flex Apex assay, the company’s fastest-growing single-cell assay.

“Proteintech Genomics brings innovative protein detection technologies, including the Human Discovery Panel, as well as deep expertise in protein biology, antibody panel development, and assay design. Together, we believe we can move faster, pursue more ambitious product development efforts, and make high-quality multiomic workflows more accessible and scalable for researchers,” Schnall-Levin said.

He said one attraction to 10x of combining with Proteintech Genomics was the combination it offered to 10x of technical performance and ecosystem fit: “Proteintech Genomics has been a longtime 10x partner, and its products were purpose-built to work with 10x workflows. Our customers are already using these technologies together today, which gives us a strong foundation to innovate more quickly and bring even more powerful multiomic capabilities to the field.”

Through its acquisition of Proteintech Genomics, 10x aims to achieve its vision of enabling multimodal biological analysis across its single-cell and spatial platforms—including protein capabilities planned for the Atera spatial platform, unveiled during the American Association for Cancer Research (AACR) conference.

Atera can run up to 800 1 cm2 whole transcriptome samples (FFPE and fresh frozen) per year, with flexible run configurations, and a greater than 5 cm² imageable area per slide (for greater than 2,000 mm² total tissue per run when using all four slides). The Atera WTA (Whole Transcriptome Analysis) panel targets more than 18,000 genes, with stackable customization of 1,000-gene Atera Select panels available now—and optional stacking of up to three 1,000-gene panels coming in the future.

“We’ve already spoken publicly about future protein capabilities planned for Atera, and this acquisition strengthens our ability to deliver on that vision while continuing to advance integrated approaches across both our single-cell and spatial platforms, Schnall-Levin said.

Higher-plex proteomics challenge

10x’s interest in higher-plex proteomics makes sense since it is the fastest-growing submarket in the $11 billion core proteomic space, set to grow by double digits, according to Leerink Partners. But a Leerink analyst cautioned that the company will find it challenging to grow in the space as it absorbs costs associated with Atera and Flex Apex.

“TXG leveraging itself to higher-plex proteomics is helpful,” Puneet Souda, senior managing director, life science tools and diagnostics, and a senior research analyst with Leerink, wrote in a research note. “We believe the deal accelerates higher-plex proteomics content for Atera, which adds to its long-term value. TXG can also bundle single-cell proteomic kits, which is a small but growing area of single-cell research.”

“Nonetheless, we still expect the digestion of Flex Apex and Atera-driven freezing/impact to weigh on near-term growth, particularly in a challenged academic funding backdrop,” Souda added.

Investors did not appear to share that concern, as 10x shares dipped 0.6% as of 12:21 p.m. ET, to an even $29.00 from $29.18 at Monday’s close. The share fluctuated between $28.93 and $30.87 during Tuesday morning trading.

Ci Chu, senior vice president, AI-enabled discovery for Xaira Therapeutics, said in a statement included within 10x’s announcement that the acquisition reflected the importance of integrating protein biology with single-cell and spatial technologies.

“Biology is bigger than transcriptomics alone,” Chu stated. “Bringing scalable protein measurements into single cell and spatial biology is an important step toward richer, more predictive views of cellular state—and ultimately, virtual cell models that better reflect the complexity of living systems.”

Potential clinical applications

In addition to facilitating expansion in multiomics, the acquisition of Proteintech Genomics could help 10x achieve another longer-term goal, namely, looking beyond its traditional focus on research tools for academic, government, and industry customers, by strengthening its clinical diagnostic offerings.

10x expanded into clinical diagnostics during the J.P. Morgan 44th Annual Healthcare Conference, when the company announced three partnerships with top-tier institutions: Brigham and Women’s Hospital and Dana-Farber Cancer Institute, both in Boston, as well as the New York-based Cancer Research Institute. 10x also committed to building its own CLIA-certified laboratory within about a year, with the goal of enabling clinical deployment of diagnostics that will come up with such collaborations.

Integrating Proteintech Genomics tools into clinical settings will be a longer-term priority since the company’s offerings are for research use only and not intended for diagnostics procedures.

“Protein measurements are highly relevant to many translational research applications, particularly in areas such as immunology, oncology, and neurology. Researchers in academia and biopharma increasingly want to combine protein and RNA measurements to better understand cellular function, therapeutic response, and disease biology,” Schnall-Levin said. “Longer term, we believe single-cell and spatial proteomics are likely to play a meaningful role in diagnostics.”

10x and Proteintech Genomics did not disclose the price or other financial terms of the acquisition, though 10x said it believes the transaction “will not meaningfully impact its near-term financial outlook.”

10x finished the first quarter with a net loss of $13.47 million, less than half its $34.358 million net loss of Q1 2025, on revenue that shrunk 3% year-over-year, to $150.843 million from $154.883 million in the first three months of last year. The company reported $490.285 million in cash and cash equivalents as of March 31, up 3% from $473.966 million as of December 31, 2025.

Proteintech Genomics is a subsidiary of Proteintech Group, a developer of high-plex proteomic solutions for single-cell and spatial applications; both companies are privately held.

San Diego-based Proteintech Genomics’ workforce consists of what Schnall-Levin described as “a small team of eight people,” including CEO Kristopher Nazor, PhD.

“We’re going to keep that team,” Schnall-Levin declared.

Added Nazor: “From day one, Proteintech Genomics was built around the belief that RNA and protein measurements are most powerful when used together.”

“Because our technologies were designed to integrate with 10x workflows, joining the 10x team feels like a natural next chapter,” Nazor continued. “We are excited about the opportunity to accelerate innovation together and expand access to integrated multiomic approaches for researchers around the world.”

The post 10x Genomics Acquires Proteintech Genomics, Expanding Proteomics Capablities appeared first on GEN – Genetic Engineering and Biotechnology News.

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Beyond the Genome: Five Emerging Leaders in Epigenetics Diagnostics

Epigenetics is increasingly powering cancer diagnostics and liquid biopsies. These emerging private companies are spurring the market with structural DNA, fragmentomics, and DNA methylation tests.

Our understanding of the role of epigenetics in disease is growing rapidly, driven by rapid advances in sequencing technology and computing.

Epigenetic processes such as DNA methylation, histone modifications, and non-coding RNA expression can interact with genomic changes to cause cancer. Therefore, diagnostics can detect early signs of disease by screening for these epigenetic signals.

Players including Illumina, Agilent Technologies, and Roche Diagnostics are leading the global market for epigenetic diagnostics, which is expected to swell by 15.5% per year from $17 billion in 2024 to $39 billion by 2030.

Growth is being driven by the increasing affordability of genome sequencing; the integration of AI tools in data analysis; growing investments; and soaring demand for liquid biopsies—noninvasive cancer tests based on blood and urine samples.

One of the first diagnostics with an epigenetic component to be approved by the U.S. Food and Drug Administration (FDA) was Exact Sciences’ (now part of Abbott Laboratories) ColoGuard® noninvasive stool test for colorectal cancer in 2014.

Since then, epigenetics diagnostics have already been generating M&A activity, with deals in the space including Cardio Diagnostics of the U.S. going public via a merger with special purpose acquisition company Mana Capital in 2022; the takeover of Ireland’s EpiCapture by compatriot Trinity Biotech in 2024; the 2023 acquisition of Germany’s Epigenomics AG by U.S.-based New Day Diagnostics; and U.S. Agilent’s acquisition of Avida Biomed, also in 2023.

There is also corporate venture interest with giants like Illumina Ventures, the Labcorp Venture Fund, and Lilly Asia Ventures making investments in small startups.

Check out below for our take on the most promising privately-owned players in the epigenetics diagnostics space, based on their investor attraction and market potential.

 

1. Arima Genomics

Founded: 2015 | Headquarters: Carlsbad, California

arima genomics logo

Arima Genomics was spun out of UC San Diego and developed research tools to pinpoint the 3D structure of DNA in cells.

However, the company pivoted to cancer diagnostics after its assay discovered vital clues on how to treat a teenage girl with glioblastoma in 2022.

Arima’s Hi-C technology involves locking the DNA structure in place via crosslinking. DNA strands are then cut with enzymes and labeled with a marker called biotin. Arima uses a process called proximity ligation to connect DNA strands that were physically close together into a single strand, and then sequences the resulting molecule.

Last year, Arima launched a lymphoma test that is delivered via the firm’s laboratory testing service, certified by the U.S. Clinical Laboratory Improvement Amendments (CLIA) program. The test is designed to be used to help patient management by discovering gene fusions and rearrangements for 417 genes in different types of lymphoma.

The test helps to fill in the gaps left by the gold standard, fluorescent in situ hybridization, which can be time- and resource-intensive and lead to conflicting results.

Arima raised $22 million in a Series C round led by Illumina Ventures in 2025 and appointed a former venture partner from Illumina Ventures as CEO. The firm is using the proceeds to launch a pipeline of clinical assays in cancer.

Arima also closed a partnership with Fox Chase Cancer Center earlier this year to co-develop diagnostic tests for lymphoma and sarcoma.

 

2. DELFI Diagnostics

Founded: 2019 | Headquarters: Baltimore, Maryland

Delfi logo

DELFI Diagnostics was founded on an “aha” moment at Johns Hopkins University School of Medicine when a group of researchers aimed to overcome the high costs and low sensitivity of traditional liquid biopsies.

The breakthrough involved hunting for the certain way cell-free DNA fragments appear in the blood. Healthy cells and cancer cells package their DNA in different patterns, reflecting changes in the cell’s genomic and epigenomic machinery.

Using this method of “fragmentomics,” DELFI’s technology can tap into orders of magnitude more data than traditional methods.

DELFI’s product FirstLook Lung uses artificial intelligence (AI) and fragmentomics technology to screen a blood sample for signs of lung cancer. It is designed as an adjunct tool to check whether patients are eligible for lung cancer screening, and is regulated under the CLIA program.

The startup’s other product, DELFI-Tumor Fraction (DELFI-TF), allows pharmaceutical companies to track the effectiveness of a cancer therapy based on a sample of less than one milliliter of plasma.

Delfi raised $5.5 million in a seed round when it was founded, with investors including Menlo Ventures and Illumina Ventures.

The startup subsequently raised a $100 million Series A round led by OrbiMed in 2021, a $225 million Series B round led by DFJ Growth in 2022, and a $34 million debt round last year.

 

3. Element Biosciences

Founded: 2017 | Headquarters: San Diego, California

Element Biosciences logo

Element Biosciences was co-founded by three former Illumina employees who dreamed of democratizing access to genomic sequencing.

The company markets devices designed to sequence genetic information at a lower cost and higher performance than traditional next-generation sequencing. These include AVITI™—its flagship benchtop sequencer—and AVITI24, which can simultaneously analyze DNA, RNA, proteins, and phosphorylated proteins.

The company generated $25 million in revenue in 2023, partly driven by orders of AVITI.

The technology, based on a process called Avidite Base Chemistry (ABC™), uses a dye-labeled polymer to bind genetic material and produce sequencing data with the need for fewer reagents than traditional sequencing.

Element is working with epigenetics specialists to boost their research offerings, including Dovetail Genomics and biomodal.

The company has also formed collaborations with diagnostics providers to enhance their offerings, including Revvity’s neonatal genetic tests and Medicover Genetics’ tests for hereditary cancers, metabolic and cardiovascular disorders, infertility, and neonatal diseases.

The company plans to market a clinical diagnostics-focused sequencing product called AVITI Dx, with EU approval expected this year in the form of a CE In Vitro Diagnostic (IVD) mark.

Element Biosciences has raised more than $680 million since it was founded, including a $277 million Series D round in 2024. The asset manager, Wellington Management, led the oversubscribed round, with participation from Samsung Electronics, Fidelity, and more.

This year, Element plans to commercialize a benchtop device, called VITARI, that can sequence a whole genome at high quality for just $100.

 

4. Nucleix

Founded: 2008 | Headquarters: Rehovot, Israel

Nucleix logo

Nucleix was initially founded to use epigenetics to trace falsified DNA in forensic investigations. Although the technology worked well, the management team decided to pivot to cancer screening.

Nucleix’s kits involve screening for specific cancer-linked DNA methylation patterns using polymerase chain reaction (PCR) tests.

The company also uses machine learning to construct biomarker panels best suited to the application of interest.

Nucleix’s Bladder EpiCheck® urine test is designed to detect the recurrence of bladder tumors based on changes in DNA methylation. It can also be used to support standard diagnostics when detecting bladder cancer in cases where malignancy is suspected.

The test has a CE mark in Europe and FDA 510(k) clearance in the U.S. for bladder cancer recurrence, meaning it can be marketed as substantially equivalent to another device in the U.S. market.

Nucleix is also developing a blood test for detecting lung tumors based on their DNA methylation signatures.

The company raised a $55 million funding round led by RA Capital Management in 2021, with participation from investors including BlackRock and corporate venture firm Lilly Asia Ventures. It followed up with a $22 million extension round in 2022.

In 2024, Nucleix sealed a strategic partnership with A. Menarini Diagnostics, part of the Italian Menarini Group, to bring its Bladder EpiCheck test to the European market.

 

5. Precede Biosciences

Founded: 2021 | Headquarters: Boston, Massachusetts

precede biosciences logo

Precede was established by a team comprising Dana Farber Cancer Institute researchers and the venture capital firm 5AM Ventures.

Precede is developing blood tests that measure signals of disease based on the genomic and epigenomic characteristics of cell-free DNA shed into the blood by tumors.

For example, the company tracks gene transcription activity and DNA methylation based on as little as one milliliter of plasma. It can then use machine learning to interpret the results and predict the optimal treatment for each patient.

Precede collaborates with drugmakers to harness its technology to inform the development of next-generation radioligand therapies and antibody-drug conjugates, which depend on the knowledge of target expression and pathway activity rather than single genomic alterations.

The research-focused product Precede Bio Insight™ is designed to track the progress of cancer, with data spanning breast and prostate cancer.

The second product, Precede Bio Dx™, also allows clinicians to select patients for clinical trials based on the blood test results.

The company emerged from stealth mode with $57 million in 2023, and followed up with a Series B round worth $83.5 million in January this year to fund the scaling of its technology as it gains commercial traction.

Among the B round’s syndicate were corporate venture investors Labcorp Venture Fund and Lilly Asia Ventures, and existing investor Illumina Ventures.

 

Jonathan Smith, PhD, is a freelance science journalist based in the U.K. and Spain. He previously worked in Berlin as a reporter and news editor at Labiotech, a website covering the biotech industry. Prior to this, he completed a PhD in behavioral neurobiology at the University of Leicester and freelanced for the U.K. organizations Research Media and Society of Experimental Biology. He has also written for medwireNews, Biopharma Reporter, and Outsourcing Pharma.

The post Beyond the Genome: Five Emerging Leaders in Epigenetics Diagnostics appeared first on Inside Precision Medicine.

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Effects of SGLT2 inhibition on incident heart failure in carriers of cardiomyopathy-associated genetic variants

Nature Medicine, Published online: 08 June 2026; doi:10.1038/s41591-026-04439-x

In a whole-exome sequencing analysis, the beneficial effects of the SGLT2 inhibitor dapagliflozin in reducing the risk of future heart failure hospitalization in individuals with type 2 diabetes were markedly greater in individuals who carried a cardiomyopathy-associated genetic variant compared with noncarriers, suggesting a personalized preventative therapy based on genetic information.