Category: Insights

  • The Top 10 Questions Shaping the Future of Optogenetic Biomanufacturing

    The Top 10 Questions Shaping the Future of Optogenetic Biomanufacturing

    Maximilian Hoerner
    Maximilian Hoerner, PhD

    The emergence of increasingly complex biologics such as multi‑specific antibodies, cytokines, fusion proteins, and other structurally demanding modalities has exposed the limits of conventional bioprocessing. Many of these molecules are difficult to express at commercially viable yields, and their quality attributes can be highly sensitive to the cellular environment. As a result, productivity, consistency, and cost remain major constraints across the industry.

    Traditional cell line development (CLD) relies on constitutive promoters that drive static expression throughout development and production. Because production is always on, cells must divide and manufacture protein simultaneously, even when the product is burdensome or toxic. While this model worked relatively well for easy-to-express monoclonal antibodies, it does not work well for next-generation biologics, where the inherent trade-off between higher specific productivity and rapid cell growth, both competing for the same cellular resources, is increased. Light‑controlled gene expression using optogenetics is emerging as a credible alternative.

    This article explores the ten most important questions facing optogenetic biomanufacturing as it moves from proof‑of‑concept demonstrations toward mainstream adoption in modern bioprocessing.

    1. What is optogenetics, and why is it relevant to biologics manufacturing?

    Optogenetics uses light‑responsive proteins to control specific cellular functions. In biomanufacturing, the key application is regulating gene expression with light instead of relying on constitutive promoters or chemical inducers. When light‑controlled gene expression systems are engineered into production cell lines, illumination at defined wavelengths can switch expression on, off, or to intermediate levels.

    This is important because it introduces a fundamentally different control modality. Conventional systems provide static expression, whereas optogenetics enables dynamic, reversible, and tunable control. As biologics become more complex and more sensitive to expression conditions, having a way to precisely modulate transcription directly, rather than perturbing the entire cell by modulating process parameters such as temperature, will become increasingly valuable.

    2. What core limitation in biologics manufacturing does light‑based control address?

    The main limitation is the inherent competition between cell growth and protein production. In constitutive systems, cells must divide and manufacture protein at the same time, drawing on the same finite pool of metabolic resources. This creates a structural trade‑off: clones that produce strongly have a slow doubling time and/or do not grow to viable densities, while clones that grow well often produce only modest titers. This trade‑off becomes much more pronounced for challenging molecules such as multi‑specific antibodies, cytokines, and fusion proteins that are aggregation‑prone or place a significant burden on the host cell.

    Optogenetics addresses this by separating the growth and production phases. Cells can grow in an uninduced, low-burden state and then transition into a productive state upon illumination. As light acts directly on transcriptional regulators in a dose-dependent manner, expression can be tuned precisely rather than modulated indirectly through temperature shifts or coarsely and irreversibly by chemical induction. Chemical inducers can also negatively affect cell performance through off-target effects and are difficult to remove during downstream processing, which has limited their adoption in mammalian biomanufacturing. The direct, tunable control by light also enables more deliberate modulation of pathways that influence critical quality attributes such as post-translational modifications (PTMs), which is increasingly important for complex biologics and biosimilars.

    3. How does optogenetic light exposure work, and is it safe for cells and media?

    Optogenetics uses light as an informational cue rather than an energy source. Because photoreceptors respond to brief, low-intensity pulses and stay “on” for a tunable period of time before reverting to the off state, optogenetic systems can maintain precise control without continuous or high-power illumination.

    The wavelengths used in bioprocessing are in the visible range and non-ionizing, operating well under established phototoxicity thresholds. At these levels, light does not harm cells or DNA, nor does it disrupt cellular physiology. Although certain media components are known to be light-sensitive, the illumination used for optogenetic induction has not shown detrimental effects on cellular performance.

    4. What is the impact of optogenetics on lab operations?

    Optogenetic workflows use standard lab equipment with the addition of illumination devices compatible with existing infrastructure, such as plates, shake flasks, or bioreactors. The main operational difference occurs during pool generation and single-cell cloning, where cells must remain uninduced. This is achieved by working under “safe light” conditions, visible wavelengths that do not activate the specific optogenetic system but allow the operator to handle the cell culture. Only small areas of the lab need to be adapted for this purpose. For all other operations, including biomanufacturing, local interventions to shield cells from room light are typically sufficient.

    5. How many cellular functions can be controlled with light in a single cell line?

    Current systems can independently regulate cellular functions using two or three different wavelengths. For example, one color may control production while another modulates a critical quality‑related pathway. Functions that require coordinated timing can be linked to the same wavelength. As genetic circuit design advances, the number of independently controllable functions is expected to increase.

    6. When might optogenetically manufactured biologics reach clinical and commercial stages?

    Optogenetic cell line development is already being explored for multiple programs. Regulatory agencies have established pathways for evaluating emerging bioprocess technologies, and optogenetic systems are beginning to move through these mechanisms. If early development proceeds as expected, optogenetically controlled processes could support clinical filings in the next two years, with commercial adoption following thereafter.

    7. Can optogenetics be integrated into existing facilities?

    Yes. Optogenetic manufacturing does not require new bioreactor designs; illumination hardware can be added to standard single‑use or stainless‑steel systems. For single‑use bags, light is delivered externally through the bag without breaching the sterility barrier; for stainless‑steel tanks, cultures can be circulated through illuminated loops. No cleanroom redesign is required.

    8. How does optogenetic manufacturing scale to larger bioreactors?

    Scaling optogenetic systems depends on three factors: the sensitivity of the photoreceptors, the lifetime of their activated state in the dark, and the mixing and light-scattering characteristics of the bioreactor. Modern optogenetic systems for biomanufacturing are designed to respond to very low light intensities, and once activated, they gradually return to the off state in the dark at a characteristic rate, typically over tens of minutes.

    Since stirred-tank bioreactors mix continuously and scatter light throughout the vessel, cells receive sufficient illumination over the photoreceptor’s characteristic activation lifetime even at large volumes. Simulation studies and early pilot-scale experiments up to the 200 L scale suggest that optogenetic activation can be achieved at commercially relevant scales, including stainless steel and single-use systems.

    9. What has convinced established players to evaluate this technology?

    Biologics development and manufacturing are intentionally conservative, with new technologies adopted only when they demonstrate clear, reproducible benefits and maintain patient safety. For any emerging bioprocess platform, the threshold for evaluation is data showing that it can deliver meaningful improvements without introducing new risks or operational complexity.

    In comparative studies, optogenetic induction has produced more than two‑fold increases in titer for standard monoclonal antibodies as well as for more difficult‑to‑express therapeutic proteins. These gains have been achieved while maintaining or improving critical quality attributes, including those influenced by folding, assembly, and post‑translational modification.

    As the next generation of therapeutics become increasingly complex, leading biopharma and CDMO players see the need for novel biomanufacturing platforms capable of meeting that complexity without compromising titers, and for that reason several key players are actively evaluating optogenetic technology for potential integration.

    10. Where is optogenetic biomanufacturing headed in the next decade?

    If current trends continue, optogenetic control may become a standard tool for managing expression, quality, and other cellular functions in biologics manufacturing. The ability to modulate gene expression dynamically opens possibilities for more efficient production of complex therapeutics, more predictable quality profiles, and more flexible facility design. As the technology matures, it is likely to be adopted across CDMOs, biotech, and biopharma companies seeking greater control over their manufacturing processes.

    Maximilian Hoerner, PhD, is the head of optogenetics at Prolific Machines

  • Multiomics in 2026 and Beyond

    Multiomics in 2026 and Beyond

    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?

    10x Genomics MultiPro Human Discovery Panel
    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 illustration
    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.”

    DNA, RNA, and protein workflow
    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
    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.”

    >19,000 genes and the 64-plex CosMx Human Immuno-Oncology Protein panel” width=”300″ height=”159″  ><figcaption id=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

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
  • How COVID, Characterization, and Commerce Are Driving an RNA Revolution

    How COVID, Characterization, and Commerce Are Driving an RNA Revolution

    The technology underpinning RNA vaccines1 and therapeutics2 has been around for decades. However, until recently, only a handful of products were on the market.

    Everything changed in 2020 when mRNA vaccines proved3 to be effective against SARS-CoV-2, the virus that causes COVID-19. In the years since, many more RNA products entered the clinic as developers recognized both their therapeutic and commercial potential.

    The pandemic also kick-started efforts to optimize RNA manufacturing that will likely shape the sector for decades to come, according to David Sokolowski, global workflow manager for nucleic acid therapeutics at Cytiva.

    “The COVID vaccines heralded the rapid industrialization of RNA and LNP [lipid nanoparticle] technologies, and due to the nature of a pandemic, large amounts of vaccine safety data were generated. However, it remains a nascent industry with limited applications commercially available, and this space is where developers are rushing to build.

    “Manufacturing good quality RNA and LNP became easier with the proliferation of high-quality reports from successful studies, trials, and campaigns occurring all over the world.

    “Now, we’re supporting the development of therapeutics with even grander aspirations and increasingly exotic biology—whether they are encoding and producing mAb via RNA, or enabling next-generation in vivo cell therapy, LNP-mediated therapeutics have no shortage of potential advances ahead,” he continued.

    Manufacturability

    The key to developing any RNA-based product—be it a vaccine or a therapeutic—is to ensure it can be produced in bulk in a cost-effective and efficient manner, says Luis Avila, PhD, a scientist at services firm Novoprotein Scientific.

    “RNA design fundamentally determines both manufacturability and biological performance, but optimizing for one often comes at the expense of the other.” The best approach is to design for manufacturability from the start,” he says, adding “the right template architecture can contribute with optimized promoter design, transcription initiation sequences, encoded polyA tails, transcript boundaries, and sequence composition to improve IVT [in vitro transcription] performance while minimizing problematic secondary structures.”

    “Combined with other factors, such as engineered RNA polymerases, optimized reaction chemistries and advanced capping technologies, these design strategies will improve RNA yield, integrity, and translational performance all while reducing the generation of dsRNA,” he tells GEN.

    The type of RNA being made should be the key consideration in any assessment of manufacturability, according to Avila, who cites the differences between messenger RNA and self-amplifying RNA to illustrate the point.

    “For mRNA, coding-sequence composition, transcript length, GC content, and secondary structure strongly influence IVT efficiency. Whereas optimized UTRs [untranslated regions], cap structures, and poly(A) tails primarily govern RNA stability and translational efficiency.

    “For saRNA, the incorporation of the large alphavirus replicase cassette substantially increases transcript length and structural complexity, thus making IVT more challenging while enabling intracellular RNA amplification that dramatically lowers the therapeutic dose required,” he says.

    Producing circular RNA—a covalently closed-loop structure that is stable and resistant to exonuclease degradation—can be even harder, according to Avila.

    “circRNA presents an even greater manufacturing challenge because linear IVT is followed by an additional circularization step whose efficiency remains a major bottleneck. Following circularization, translation relies on efficient cap-independent initiation through optimized IRES [internal ribosome entry site] elements and RNA architecture.”

    Characterization

    So, designing an RNA therapeutic or vaccine with manufacturing in mind is critical. However, the ability to realize that optimized blueprint on the factory floor is entirely dependent on being able to characterize the product in real-time, as it is being made.

    And, unfortunately for the industry, RNA characterization is a complex challenge, says Matthew Ranaghan, PhD, applications scientist at analysis technology firm Refeyn.

    TwoMP with model
    Analyzing multiple RNA molecules in a single pass reduces the experimentation burden on RNA developers. Being able to visualize characteristics within minutes—as shown here—would benefit any developer, says Refeyn. [Refeyn]

    “Thorough Characterization of RNA is critical to ensuring the safety, stability, and efficacy of next-generation RNA therapeutics, yet it remains a significant analytical bottleneck.”

    “Current methods are limited to various types of chromatography, which struggle with poor resolution for large RNAs, or mass spectrometry, which requires digesting samples into smaller oligos with RNases. While both have advantages, they demand extensive and time-consuming method development.”

    Traditional RNA analysis usually consists of separate assays looking at specific characteristics. For example, size exclusion chromatography is used to assess aggregation while enzyme-linked immunosorbent assays are used to check for trace amounts of double-stranded RNA.

    One promising alternative is mass photometry (MP), a technique that can analyze RNA molecules within minutes based on only nanograms of material within minutes in a single experiment. There are multiple potential advantages according to Ranaghan.

    “A single readout replaces the need for multiple instruments, drastically reducing hands-on time, reagent costs, and the need for extensive method development across different platforms. A single readout also enables at-line process monitoring, allowing for immediate go/no-go decisions during bioprocessing and purification.”

    “We envision the faster speed and higher resolution of MP to enable its use as a multi-attribute tool for at-line assessment of the various critical quality attributes (CQAs) of RNA therapeutics,” he says.

    “We have already demonstrated how MP can rapidly assess integrity, fragments, multimer, and purity in a single measurement. We are also actively building out more applications,” Ranaghan adds.

    Commercial concerns

    Manufacturers of RNA products also need to develop commercialization plans as early as possible—in parallel with process development—to ensure they reach the patients who need them and garner the revenues needed to fund future research.

    Rajiv Gangurde
    Rajiv Gangurde, PhD
    Vice President
    Parexel International

    And, for some firms, developing such plans can be a steep learning curve, says Rajiv Gangurde, PhD, vice president of chemistry, manufacturing, and controls (CMC) for cell and gene therapies at contract research organization Parexel International.

    “Like many newer modalities, RNA-based therapies typically emerge out of academia or start-up biotech, and transitioning from academia to industry can be challenging due to differences in objectives, differences in expertise, fundraising, etc.”

    “In addition, newer modalities require heavier than usual investment in CMC activities to establish proof-of-concept as well as manufacturability. There is heavy reliance on external partners, creating capability and capacity constraints.

    “Most importantly,” Gangurde continued, “a phase-appropriate CMC strategy aimed at global approvals can significantly derisk development and help minimize costs and timelines.”

    Yet preparing a CMC strategy that will win approval in multiple markets is no small undertaking, particularly, as Gangurde points out, because regulatory guidelines for RNA-based therapies are still evolving.

    Another challenge is the lack of consistency. For instance, the U.S. Food and Drug Administration (FDA) and EU EMA use different definitions and guidelines for CMC.

    Gangurde told us, “Harmonization of guidelines and expectations across the two agencies will be hugely beneficial to developers of RNA therapies. For example, while both agencies recognize potency/efficacy assessment as a critical quality attribute (CQA), there are some differences in method validation requirements, timing, and regulatory expectations across clinical phases.”

    “While the FDA accepts real-world experience and surrogate endpoints, particularly for rare or life-threatening diseases, EMA requires more comprehensive clinical data and demonstration of long-term efficacy for approval. The EMA also mandates an appropriate degree of method validation at each stage of development, whereas the FDA does not expect validation of the potency assays during Phase I,” he added.

    The product component designations used by regulatory agencies can also differ. For example, the lipid nanoparticle for the COVID-19 vaccine, Spikevax, consists of four lipids: SM-102, PEG2000-DMG, DSPC, and cholesterol.

    The FDA designates each of these components as a “drug substance,” whereas the EMA designates them as “novel excipients”—each designation has differing data submission requirements.

    Continuous process

    In the future, RNA manufacturing methods are likely to keep improving, according to Novoprotein’s Avila, who says continuous production processes are likely to have a major impact.

    “One of the most exciting developments is the emergence of continuous or flow-based IVT manufacturing. This has the potential to improve productivity, reduce costs, and enable more consistent large-scale RNA production through continuous enzyme utilization and process integration.”

    “As equally transformative are advances in engineered low-dsRNA T7 RNA polymerases, improved co-transcriptional capping chemistries, and integrated continuous purification. All of which will enable improvements in RNA quality, simplify downstream processing, and accelerate GMP manufacturing,” he added.

    TwoMP- Auto with model
    RNA characterization is a challenge using current technologies. Refeyn claims its mass photometry system—seen here at lab in Waltham, Massachusetts—reduces the number of assays required. [Refeyn]

    This view is shared by Ranaghan from Refeyn, who cites the shift toward continuous flow manufacturing as one of the most exciting advances in the field.

    “Moving away from traditional batch processing to a continuous flow model is highly logical because it minimizes the time that the fragile mRNA product spends exposed to active reaction components that drive fragmentation and aggregation. This shift not only improves overall yield and intactness, especially for massive transcripts like saRNA, but it also creates a mandate for faster, real-time analytics.”

    For Parexel’s Gangurde, efforts to make production more efficient and sustainable will drive innovation, with the WHO’s recently launched mRNA Technology Transfer Programme Phase 2.04 being a case in point.

    “This is a major global initiative aimed at scaling up commercially viable mRNA-based vaccine and therapeutic production in low- and middle-income countries—where the need for these therapies is most critical.”

    Launched in 2021 in collaboration with the Medicines Patent Pool (MPP), the initiative has enabled 15 partners across Latin America, Africa, Eastern Europe, and Asia to receive foundational mRNA technology.

    Phase 2.0 focuses on building local manufacturing capacity so countries can make mRNA vaccines and therapeutics for future outbreaks and diseases like influenza, TB, HIV, malaria, dengue, and leishmaniasis, as well as oncology drugs and mAbs.

    Gangurde says, “By decentralizing production, the program reduces reliance on a few high-income countries for mRNA supply, supporting global access for mRNA-based therapies.”

    References

    1. Beyrer C. The Long History of mRNA Vaccines. Johns Hopkins Bloomberg School of Public Health; 2021. Accessed August 12, 2026.
    2. Chatterjee S, Bhattacharya M, Lee SS, Chakraborty C. An insight of different classes of RNA-based therapeutic, nanodelivery and clinical status: Current landscape. Current Research in Biotechnology 2023;6:100150-100150. doi:10.1016/j.crbiot.2023.100150
    3. Parvin N, Mandal TK, Joo SW. The Impact of COVID-19 on RNA Therapeutics: A Surge in Lipid Nanoparticles and Alternative Delivery Systems, Systems. Pharmaceutics 2024; 16(11): 1366. Doi: 10.3390/pharmaceutics16111366
    4. World Health Organization: WHO. mRNA Technology Transfer Programme’s Phase 2.0 discussed with partners on the sidelines of G20 Summit. Who. June 13, 2025. Accessed August 12, 2026.
  • Spatial Biology Moves Toward Translation

    Spatial Biology Moves Toward Translation

    If “Spatial Biology” conjures images of a science conducted in zero gravity aboard the International Space Station, think again. As one of the most rapidly advancing areas in biomedical research and precision medicine, spatial biology probes the inner landscape of intact cells and tissues down to single-molecule resolution. By preserving location and organization, instead of dissociating tissue into component parts, this discipline maps fine architecture, cellular neighborhoods, and individual molecules.

    Maintaining that context is driving new ways of characterizing disease progression, identifying biomarkers, advancing therapeutics, and monitoring treatment responses. Fueling these advances is an expanding toolbox that encompasses high-plex imaging, spatial transcriptomics and proteomics, multiomic profiling, and increasingly sophisticated computational analyses. We consulted industry leaders about how the field is progressing, barriers that remain, and their vision for what is needed to propel spatial biology into its next phase.

    Scaling spatial discovery

    While genomics and single-cell sequencing profile molecular features and cellular states, spatial biology answers questions that neither can address, such as where those features and cells are located and how they are organized.

    Kamila Belhocine
    Kamila Belhocine, PhD
    Senior Director
    10x Genomics

    “When you dissociate a tissue into individual cells, you lose something fundamental: where those cells were and how they were organized relative to one another,” observes Kamila Belhocine, PhD, senior director of spatial biology at 10x Genomics. She continues: “Spatial biology puts that context back. In cancer, for example, knowing that a particular immune cell population is present within a tumor sample is useful. But knowing whether those cells have infiltrated the tumor core, are sitting at its margin, or are excluded from it altogether can tell you something very different about the biology, and potentially, about response to treatment. The same principle applies to understanding tissue architecture in cases like development and cell differentiation, and disease progression.”

    Several barriers have prevented spatial biology from going mainstream in the biopharma/biotech industry. Belhocine says, “I think about four things: speed, reliability, cost, and interpretability. Spatial needs to be fast enough to fit into routine research workflows, robust enough to generate consistent results at scale, affordable enough to use across large studies and, ultimately, straightforward enough to turn the resulting data into biological insight. The first three are fundamentally technology and engineering challenges. Interpretability is harder because it also requires accumulating evidence and validating which spatial signatures are truly meaningful and actionable.”

    However, according to Belhocine, single-cell technologies had to address similar issues. “A decade ago, single-cell analysis was largely a specialized technique. Continued innovation made it easier, more scalable, more robust, and dramatically more cost-effective, opening it to a much broader community and enabling much larger studies. We think spatial is following a similar trajectory.”

    Gary Latham
    Gary Latham, PhD
    Chief Technology Officer
    Bio-Techne

    Belhocine says the company’s newest spatial instrument, Atera, which will be shipping in the second half of the year, is an important step in that progression. “This technology combines whole-transcriptome spatial profiling with single-cell sensitivity and subcellular resolution at a massive scale designed for much larger studies.”

    Looking ahead, Belhocine predicts, “As spatial technologies become more scalable, robust and cost-effective, researchers can apply them across much larger patient cohorts to identify biomarkers and signatures of treatment response. Additionally, AI will undoubtedly accelerate that trajectory even further.”

    Spatial multiomics

    Gary Latham, PhD, CTO, Bio-Techne, provides another perspective on the role of integrating protein analysis and gene expression in spatial biology. “A fuller understanding of tissue biology requires seeing where proteins and genes are expressed, how they relate, and how they interact within the tissue architecture.”

    Human breast cancer
    Automated spatial biology systems enable high-throughput, hyperplexed analysis of tissue samples. Human breast cancer sample stained with 105-plex multiomics panel on COMET. The image shows a HER2+ tumor with HER+ cells and immune infiltrates. Markers shown: HER2 (white), collagen I (teal), CD45 (red), and CD38 (yellow). [Bio-Techne]

    This molecular multiplexing can provide a more complete view of tissue biology. “Spatial biology is unique from other technologies because it takes us from seeing what is there to understanding what is where,” says Latham. He continues, “This is critical because biology can’t be understood as a parts list. Instead, it is a dynamic system of interactions and networks where location defines meaning. While single-cell sequencing can tell you which cell types exist and their molecular states, spatial biology tells you how those cells are organized, how they may be interacting, and how these relationships may influence disease biology and clinical outcomes.”

    Latham indicates that the field is working to overcome several challenges. “There is a need for reproducible, standardized automation for today’s biomarkers and tomorrow’s multiomic precision medicine. Many spatial biology workflows remain exploratory and manual during early discovery phases. As programs move toward translational research, the focus shifts to robustness, reproducibility, and standardized, validated workflows. Ultimately, clinical adoption requires validated panels, automation, and throughput suited to routine testing.”

    Human prostate cancer
    Automated spatial biology systems enable high-throughput, hyperplexed analysis of tissue samples. Human prostate cancer sample stained with a 105-plex multiomics panel on COMET. The image shows tissue fibrosis associated with cancer-associated fibroblasts (CAFs). Markers shown: COL1A1 RNA (yellow), E-Cadherin (white), alpha-SMA (blue), and FAP (magenta). [Bio-Techne]

    According to Latham, Bio-Techne is helping address these barriers in several ways. Its COMET Suite provides an end-to-end automated platform for hyperplex immunofluorescence and multiomics staining, imaging, and analysis of tissue samples. Additionally, its RNAscope portfolio enables same-section multiomics, with 80,000 RNA probes spanning 450 species and compatibility with multiple automated staining instruments.

    Latham believes the spatial multiomics approach can connect gene and protein expression with tissue architecture within the same cells and microenvironment, revealing biological relationships and linking state to function that no single modality can uncover.

    He says several areas are seeing the fastest adoption of spatial biology. “Oncology and immuno-oncology continue to dominate spatial biology applications, while immunology and neuroscience represent the next-most prominent areas of focus. Biodistribution of gene therapies is an especially high-impact application: spatial multiomics can localize an AAV vector, transgene mRNA, or oligonucleotide therapeutics such as ASOs and siRNAs to specific cells, then measure changes in the target or downstream gene expression and the associated protein phenotype.”

    Integrated discovery & translation

    Spatial biology is increasingly moving beyond simply identifying where cells are located to understanding how cellular neighborhoods, cell–cell interactions, and biological pathways shape disease biology. As Thomas Campbell, PhD, senior director of product management for Bruker Spatial Biology, explains, “Biology is inherently multi-dimensional, and no single spatial technology can or will capture every aspect of that complexity. The field needs multiple fit-for-purpose approaches that allow researchers to ask different questions and connect insights across DNA, RNA, proteins, and cellular function.” This ability to examine biology from complementary perspectives is becoming increasingly important as spatial research moves from discovery toward translation.

    According to Campbell, understanding these context-dependent differences is driving the field. “The next phase of spatial biology will be about using high-plex discovery to develop focused, quantitative assays that can be deployed across large cohorts. One of the biggest challenges in translational biopharma is the discovery-to-translation gap. A study may identify dozens or hundreds of potentially relevant biomarkers, but a translational or clinically deployable assay demands a much smaller, highly reproducible panel.”

    Thus, defining a clinically relevant spatial signature requires insights across the genome, transcriptome, and proteome. Campbell says Bruker Spatial Biology’s portfolio comprises platforms spanning multiple omic layers—including DNA, RNA, proteins, post-translational modifications, miRNA and live cell function—“because we recognize that no single modality can fully resolve the biological complexity that drives disease.”

    Thomas Campbell
    Thomas Campbell, PhD
    Senior Director
    Bruker Spatial

    That integrated approach is reflected in several of the company’s platforms. CosMx® Spatial Molecular Imager (SMI) provides a view of the whole transcriptome at subcellular resolution, while enabling the simultaneous measurement of RNA and protein within the same tissue section. The CellScape™ XR platform provides quantitative spatial proteomics using flexible assay chemistry. Meanwhile, the GeoMx® Digital Spatial Profiler (DSP) profiles the whole transcriptome and more than 1,200 proteins from distinct tissue compartments. Campbell says that, together, these three platforms “provide complementary views of biology and, when used together, enable a more complete understanding of cellular function across multiple omic layers.”

    Campbell sees this transition from discovery to translation as one of the most important opportunities for spatial biology. “Whether it’s in oncology, immunology, or neuroscience applications, the future of spatial biology is not simply just about generating more data; it’s translating increasingly sophisticated spatial discoveries into assays that can answer focused biological and clinical questions across large numbers of samples,” he says. “That requires a portfolio of complementary technologies. High-plex discovery platforms can reveal the complexity and heterogeneity of disease, while more focused and quantitative approaches can translate those findings into reproducible biomarkers and clinically relevant assays. Ultimately, the value of spatial biology will be measured by how effectively we can connect discovery to clinically actionable insights.”

    From plex to precision

    “Historically, spatial biology involved a fundamental tradeoff between plex, sensitivity, and resolution—researchers had to sacrifice one to gain another,” suggests Mike Miller, PhD, CTO and CPO at Quanterix. He says that is now shifting. “With today’s tools, those tradeoffs have narrowed considerably. The current challenge becomes building a platform that delivers reproducibility, rigor, and clinical-grade integrity at scale.”

    The company is addressing those needs in several ways. Miller reports: “Our Akoya PhenoCycler-Fusion platform enables researchers to visualize and quantify 100+ proteins in a single tissue section at single-cell resolution.” The platform uses molecular barcoding chemistry. Miller elaborates: “Molecular barcoding is a way of identifying and measuring multiple proteins in a tissue while avoiding many of the limitations of traditional immunohistochemistry and conventional multiplexing approaches.”

    Mike Miller, PhD
    Mike Miller, PhD
    Chief Technology Officer
    Quanterix

    This approach combines three main components: an antibody that recognizes the target of interest; a unique molecular barcode, consisting of a short oligonucleotide sequence attached to the antibody; and a complementary oligonucleotide carrying a fluorescent reporter that binds to the barcode, enabling target visualization and quantification. “The key advantage is that the fluorescent reporter can be reversibly hybridized and removed without removing the antibody or repeatedly staining and stripping the tissue. This enables a cyclical imaging workflow.”

    The company also recently introduced its PhenoCode2.0 chemistry as part of its new molecular barcoding kit, “which lets researchers reliably barcode virtually any antibody of their choice.” Miller continues: “This means researchers can easily expand and customize their biomarker panels to address new hypotheses, incorporate emerging biomarkers, and scale between unbiased discovery and targeted validation, accelerating the path to new insights.”

    Beyond expanding what can be measured, Miller believes the next phase of spatial biology will focus on translating high-plex discoveries into larger, clinically relevant studies. “That’s ultimately what will separate platforms going forward, bridging the gap between generating a striking image in discovery and generating a measurement that holds up in a clinical trial or diagnostic setting.”

  • Trends in NGS

    Trends in NGS

    Next-generation sequencing (NGS) has matured into an essential part of modern drug development and diagnostic laboratories, and its steadily dropping costs are making it affordable for many facilities that a few years ago couldn’t justify the expense.

    The coming years will build on that foundation by extracting more and different types of data from samples, leveraging AI both upstream and downstream, and streamlining workflow. The entire workflow—not just the sequencing itself—will become more cost effective, industry experts tell GEN.

    The next wave

    “The next major breakthrough in sequencing technology will likely come not from the sequencer alone, but from the ability to extract more meaningful biological insights from every sample,” Karen B. Chapman, PhD, associate VP, R&D Genomics, Agilent Technologies, says.

    Karen B. Chapman
    Karen B. Chapman, PhD
    Associate VP
    Agilent

    She envisions “the convergence of improved long-read sequencing, intelligent assay design, AI-enabled analysis, and integrated automation across sample preparation, target enrichment, quality control, sample tracking, and data interpretation.”

    Multiomics assays will likely be the next big thing.

    Mark Van Oene
    Mark Van Oene
    President and CEO
    PacBio

    “Sequencing has traditionally focused on the four canonical bases, but DNA molecules contain much more information, including methylation, damage, and secondary structure, that can reveal different layers of biology,” Mark Van Oene, president and CEO, PacBio, points out. “We see sequencing moving toward a rapid test that can deliver comprehensive, highly accurate results within hours, enabling scientists and clinicians to make decisions faster.”

    The experts at New England Biolabs (NEB) concur. “That’s one of the next big opportunities in sequencing: to gather different data types and provide them in a way that allows them to be interpreted usefully in the context of the human genome,” Andrew Barry, associate director, product portfolio management, NEB, says.

    Such multiomics assays will “enable richer and more comprehensive biological insights,” Lakmal Jayasinghe, PhD, CSO, Oxford Nanopore Technologies, says. “The next major breakthrough will be the ability to analyze DNA, RNA, and proteins on a common sensing platform along with epigenetics, epitranscriptomics, and post-translational modifications of proteins. The next frontier is understanding how that code is regulated and executed in real biological systems. We see proteomics as a key part of that future.”

    Lakmal Jayasinghe
    Lakmal Jayasinghe
    CSO
    Oxford Nanopore Technologies

    As the industry pushes current boundaries, it is also working to improve sequencing performance through parallel workflows and automation that can accommodate a broad range of samples. Opportunities exist “to increase throughput, improve fidelity, and simplify workflows,” Jayasinghe adds.

    To that end, Keerthana “Kit” Krishnan, director, NGS applications and product development, NEB, points out: “Library prep may need a bit more work.” On the upstream side, she identifies sample extraction and sample prep as areas with opportunities for improvement. Downstream opportunities, she says, are in data analysis.

    For NGS to become a routine clinical tool, however, those analyses must be clinically meaningful and linked to follow-up actions, she says. She envisions NGS assay kits that are tailored to specific clinical applications.

    AI adds value today

    “The real breakthrough,” Chapman of Agilent Technologies points out, “will be…in delivering deeper, more actionable biological insights in a way that is economically viable, operationally practical, and accessible to a broad range of laboratories and users. AI will play a critical role in enabling this vision, unlocking new possibilities that lower computational and labor costs while enhancing accuracy and increasing operational speed.”

    sequencing
    As sequencing costs decline, steadily improving technology makes sequencing affordable for an increasing number of users. Photo provided by Nanopore. [Oxford Nanopore Technologies]

    Across the board, instrument developers are leveraging AI both upstream and down. For example:

    • PacBio uses AI upstream to transform raw sequencing data into HiFi reads and to detect DNA modifications. Downstream applications include tertiary analysis, which Van Oene says can be used “to identify and prioritize genetic variants associated with rare disease.”
    • NEB’s bioinformatics team deploys AI for coding support, while the automation team is exploring its application to streamline multi-instrument automation processes for developers and for field application scientists to anticipate pitfalls customers may face. AI is also becoming an important tool in enzyme discovery.
    • Agilent uses AI to learn from large datasets and develop ways to improve probe placement and target-enrichment design to enhance specificity, coverage and uniformity. Other applications may include “improving base calling, variant detection, structural variant characterization, methylation assessment and clinical interpretations,” Chapman says.
    • Oxford Nanopore uses AI to help build more accurate models and to directly detect DNA and RNA modifications. Looking ahead, “AI will become increasingly important in helping scientists, clinicians, and biopharma companies interpret complex multiomics datasets,” Jayasinghe says.
    • Illumina has embedded AI tools in its secondary analysis pipeline and throughout its software and informatics portfolio. It has also created the peer-reviewed Primate, Splice and Promoter AI genomic models.

    Future AI value

    Algorithms are moving “beyond simple rule-based design and increasingly learn from large datasets to optimize enrichment performance,” Chapman says. “Over time, these approaches could evolve into fully-AI-driven panel design systems that automatically optimize probe selection, predict assay performance, minimize sequencing costs, and continuously improve…as more data becomes available.”

    “Looking ahead, the most transformative role for AI may be guiding researchers toward the right biological signals to measure, rather than merely processing larger volumes of data,” she continues. “AI can help to identify the most relevant genomic regions, uncover intricate molecular patterns, integrate genomic and methylation information, and bridge genomics discoveries with protein, cellular, and metabolic biology.”

    Andrew Barry
    Andrew Barry
    Associate Director
    New England Biolabs

    As AI becomes increasingly ubiquitous, explainable AI will become a requirement, these experts agree. The ability to interrogate, assess, and validate the AI’s reasoning is imperative not only for on-instrument applications such as base-calling, but especially for tertiary analysis where understanding the evidence behind a result is critical.

    As Barry says, “To use AI effectively, we have to know what’s going on underneath the hood.”

    Continuing, overall cost reduction

    The lab cost of genome sequencing broke $100 two years ago, and some labs are advertising 30x whole-genome sequencing and analysis for about $500. And, Van Oene from PacBio says, “There is potential to drive the cost of whole-genome sequencing down by another order of magnitude.”

    For example, Van Oene says that increasing sample throughput offers a major opportunity “to reduce instrument amortization by spreading the cost of the instrument across many more genomes.” Other opportunities are emerging through computing advances that speed processing and analysis as well as through improvements in compute logistics, such as data compression, which reduces storage requirements, and cloud computing, which reduces hardware requirements and expands access to data.

    “The sequencing part of it is becoming a smaller part of the total cost of a workflow,” Fiona Kaper, PhD, VP, head of assay R&D, Illumina, points out.

    So, rather than focusing solely on reducing the cost of sequencing itself, NGS innovators increasingly are targeting efficiencies across the entire workflow, from improving sequencing performance and optimizing library preparation to expanding multiplexing capabilities that generate more insight from every run. “Ultimately,” Chapman, Agilent Technologies, says, “the industry’s focus will shift from the cost of sequencing a genome to the cost of delivering a clinically meaningful answer.”

    “There’s still some meat left on the bone in terms of how you can improve some workflows,” Barry, NEB, observes.

    Scalability is one example. The challenge of sequencing and analyzing a genome varies by size, and solutions need to address that. Therefore, scaling the upstream environment to meet the need is a key consideration for some instrument makers, along with logistical enhancements, such as lyophilization and assay formats, to improve ease of use globally.

    Multiomics and multimodal assays two other examples. “We see a lot more customers starting to layer in additional information like proteomics, transcriptomics, and spatial and single cell analysis,” Kaper says.

    Kit Krishnan
    Kit Krishnan
    Director
    New England Biolabs

    This trend suggests a future, modular, environment in which scientists can select and optimize the assays that best address their questions. “Having a black box [solution]…really doesn’t work for everyone,” Krishnan underscores.

    Further cost reductions, these experts agree, will stem from automating and consolidating workflows, effectively multiplexing assays and developing multimodal assays to generate more actionable information with each run. Such simplification extends to library preparation, which has expanded from DNA and RNA to include proteomics and data from spatial and single-cell analysis.

    Instruments developers also are reducing the quantities of reagents needed. Consequently, Van Oene says, “Reducing reagent volumes in library preparation and sequencing will lower consumable costs as well as shipping and storage requirements—which people don’t usually think about when they think about the cost of a genome.”

    “Time is also a form of cost,” Kaper says. The industry recognizes that by increasing data quality, capabilities, and sequencing speed to help its users do more, better, and faster.

    “The goal,” as Jayasinghe of Oxford Nanopore Technologies says, “is to help customers derive meaningful answers faster, with fewer steps and greater efficiency.”

    Ubiquitous sequencing

    Fiona Kaper
    Fiona Kaper, PhD
    Vice President
    Illumina

    Five to ten years from now, NGS may well be the core technology for research and clinical labs both, Kaper, Illumina, predicts. “Anything that has to do with the genome, like oncology, could really benefit from technologies that interrogate the genome and also the epigenome, proteome, etc.,” she says.

    “Ultimately, the goal is not simply a cheaper genome, but a more informative genome, at a much lower cost,” Van Oene concludes.

  • Science Speeds Up Its Fight Against Prions

    Science Speeds Up Its Fight Against Prions

    Prions have never played by the rules. They aren’t viruses or bacteria. They’re simply proteins folded into the wrong shape, and that structural mistake can cause devastating problems.

    As Alison Armstrong, PhD, global head of scientific & regulatory consultancy, BioReliance Contract Testing Services, Merck KGaA, Darmstadt, Germany, puts it: “Prions are infectious forms of naturally occurring proteins that can alter healthy proteins and lead to neuronal cell death.” As she adds, “They present a potential health risk through animal-to-human transmission, although human-to-human transmission is rare.”

    One prion can trigger a chain reaction, coaxing neighboring proteins to misfold too, and the damage spreads through the brain like a rumor nobody can stop. Prion diseases are called transmissible spongiform encephalopathies (TSEs), and they include bovine spongiform encephalopathy (BSE), better known as mad cow disease, and Creutzfeldt-Jakob disease (CJD). Once symptoms of CJD appear, a person’s decline is often fast, and it is always fatal.

    For scientists, drug developers, and biomanufacturers, that strange biology creates a second, more practical problem: prions are brutally hard to eliminate, detect, and study.

    “For products with a risk of prion contamination, purification processes must be able to eliminate the pathogen,” Armstrong says. “Because methods for directly detecting prions in raw materials are limited by sensitivity, prion-clearance studies have become an industry-standard approach for demonstrating that downstream purification steps can remove or inactivate transmissible spongiform encephalopathy agents.”

    Unique infectious agent

    Prion disease, spongiform encephalopathy
    Credit: UCSF / Getty Images

    Human prion diseases are exceedingly rare. CJD is the most common, and variant CJD (vCJD)—historically linked to eating beef from cattle infected with BSE—is rarer still. Only four vCJD cases have ever been reported in the United States, all in people believed to have been exposed abroad, but rarity doesn’t mean the risk disappears.

    Acquired prion disease can, in unusual circumstances, follow medical procedures involving contaminated equipment or human tissue. That possibility also looms over pharmaceutical manufacturing, particularly wherever human- or animal-derived materials enter the production line.

    “Industry and regulatory authorities address these challenges through risk assessment and controls around animal-derived materials used in biologics manufacturing,” Armstrong says. “A TSE risk assessment typically evaluates factors such as the animal species, tissue source, country of origin, tissue classification, manufacturing controls, and sourcing from countries with negligible or controlled BSE risk. These measures, combined with appropriate manufacturing and quality controls, help mitigate the risk of transmissible prion diseases.”

    For biological products carrying any plausible contamination risk, manufacturers can use prion-clearance studies to test whether individual purification or manufacturing steps actually remove or inactivate prions. Researchers deliberately spike scaled-down versions of a production process with prion material, then measure what’s left afterward.

    Texcell, for instance, describes seeding prion material into crude starting material or mock manufacturing vessels, then tracking removal or inactivation through each subsequent step. Similar studies check whether cleaning procedures between production batches actually destroy prion protein left behind on equipment.

    On paper, it sounds simple: put prions in, run the process, see what comes out. Measuring that convincingly is another matter entirely.

    The measurement problem

    A clearance study is only as trustworthy as the assay behind it.

    ViruSure, whose staff have worked in prion safety since the BSE crisis of the 1990s, relies on the hamster-adapted 263K scrapie strain for many of its studies—a model it describes as accepted by regulatory authorities. The company runs quantitative Western blot assays to check whether manufacturing steps remove or inactivate prion protein, alongside in vivo bioassays for determining prion titer.

    Animal bioassays remain the benchmark, but they are slow, expensive, and nearly impossible to scale. And an assay built on rodent-adapted prions isn’t a stand-in for rapidly experimenting on the real, infectious human thing.

    That distinction matters most in drug discovery. A compound that performs beautifully against an experimental prion model has no guarantee of working against actual human disease.

    A new experimental system might finally close that gap.

    Making human prions grow in cells

    Parmjit Jat, PhD, a professor of molecular biology at University College London, and his colleagues report developing EKV cells: a humanized mouse-cell model capable of supporting robust, indefinite propagation of sporadic CJD (sCJD) prions.

    That solves a problem that has dogged the field for decades. As Jat and his co-authors put it, research into human prion biology and therapeutics “has been severely constrained by the inability to propagate human prions in dividing cells,” leaving scientists dependent on “costly and slow animal bioassays.”

    The team reports that EKV cells reliably generate high-titer infectious sCJD prions. Lysates from those cells triggered lethal prion disease in humanized mice that was clinically and neuropathologically indistinguishable from disease caused by sCJD-infected human brain tissue, which gives a strong signal that the model is capturing the real thing, not a rough approximation of it. Using the cell line, the researchers built the Human Prion Assay (HPA) which quantifies sCJD infectivity with sensitivity they report as comparable to the gold-standard mouse bioassay.

    The crucial difference is time. The HPA compresses the experimental timeline “from years to weeks,” the scientists note.

    A renewable, cell-based model makes experiments more reproducible and far more scalable. Instead of leaning entirely on infected brain tissue or months-long animal studies, scientists can now investigate disease mechanisms and screen candidate therapies directly against infectious human prions.

    It also hands researchers a faster route to arguably the most important question in the field: Can an established prion infection actually be cleared, once it has taken hold?

    To show the platform could actually screen drugs, Jat’s team dosed infected cells with ICSM18, an anti-prion protein monoclonal antibody previously shown to work against nonhuman prions. The result was potent, dose-dependent clearance of human PrPSc in infected EKV cells. An isotype control antibody, used as a check, did nothing.

    These scientists point out that earlier therapeutic screens built on murine prions turned up compounds like quinacrine that later failed in human trials. Infected EKV cells, by contrast, could support high-throughput screening of small-molecule inhibitors against genuine infectious human prions from the start.

    The gap between a years-long assay and one that produces answers in weeks could reshape the scale of that screening. More compounds, more concentrations, more shots on goal become experimentally realistic. Failures get weeded out sooner. Promising candidates move faster.

    Attacking from another direction

    As some researchers build better models, others are widening what counts as a viable drug target in the first place.

    Most therapeutic strategies have focused on blocking the conversion of normal cellular prion protein, PrPC, into its pathological form, PrPSc. Scientists at the laboratory of prion biology at the Scuola Internazionale Superiore di Studi Avanzati in Italy tried something different: reducing prion burden without touching either protein directly.

    Their strategy grew out of an observation that the protease inhibitor SERPINA3, also known as SerpinA3n, is upregulated during prion disease. The researchers identified a small molecule, ARN1468, that inhibits those serpins and reduced prion load in chronically infected cells.

    That compound’s low bioavailability, however, ruled out in vivo studies in prion-infected mice. Even so, the work is proof of concept that a drug can target the biological machinery around prion accumulation rather than the prion protein itself. The researchers call it “a novel and effective approach to the treatment of prion disease.”

    That broader strategy could become especially useful once paired with scalable, human-relevant experimental systems. Researchers could investigate not just molecules that physically bind prion proteins, but the cellular pathways that decide whether misfolded proteins accumulate, persist, or vanish.

    Breaking the bottleneck

    Prion clearance in biomanufacturing and therapeutic drug discovery can look like two unrelated fields. They aren’t. Both are wrestling with the same core problem: scientists need a reliable way to prove that infectious prions have actually been removed.

    For manufacturers, that means stress-testing purification and cleaning processes and quantifying how well they eliminate a remarkably resilient contaminant. For therapeutic researchers, it means building systems where a genuine human prion infection can be established, measured, and cleared, rather than approximated.

    Prions remain exceptionally difficult targets. The distance between clearing PrPSc in a dish and treating a patient with a rapidly progressive, fatal neurodegenerative disease is enormous, and new cell assays will complement rather than replace rigorous animal and validation studies.

    But the tools available to scientists shape the questions they can realistically ask. For decades, prion research has been bottlenecked by exactly that—the sheer time and difficulty of studying the pathogen at all. Human prions resisted scalable propagation, infectivity studies leaned almost entirely on animals, and drug candidates built on nonhuman models kept failing to translate. A system that can propagate, quantify, and experimentally clear genuine human sCJD prions would change that math.

    The most important advance here, then, might not be any single antibody, small molecule, or assay. It’s the possibility of a faster experimental loop—one where scientists can generate human prions, measure them, challenge them with candidate treatments, and get an answer within weeks instead of years.

    For one of biology’s most stubborn infectious agents, simply making the experiments faster might turn out to be one of the most powerful forms of progress available.