Radically different
Why does getting family health insurance through a job cost as much as buying a new car? How did a whistleblower take on an insurance contractor? And who might be the new Food and Drug Administration commissioner?
We discuss all that and more on this week’s episode of “The Readout LOUD,” STAT’s weekly biotech podcast.
Crossing the blood-brain barrier and avoiding off-target effects are two important challenges for gene therapies designed to target diseases of the brain. Now, developers of a gene therapy delivery platform that pairs specially engineered adeno-associated viruses (AAVs) with a delivery strategy that harnesses the brain’s fluid transport pathways claim that their approach addresses both issues and could help pave the way to new treatments for neurological disorders like multiple sclerosis, Huntington’s disease, and rare pediatric white matter disorders.
Details of the work were published recently in a Nature Biotechnology paper titled “Efficient targeting of human glial progenitor cells in vivo with engineered AAV vectors and glymphatic delivery.” The research was done by scientists at University of Rochester Medicine and the University of Copenhagen.
While the platform can deliver therapeutic genes broadly throughout the brain, it preferentially targets human glial cells. Steve Goldman, MD, PhD, lead author of the study and co-director of the Center for Translational Neuromedicine at URochester, has spent his career studying glial cells and elucidating their role in disease progression and recovery. Previously, his lab developed human glial progenitor cell models and investigated the link between glial dysfunction and neurological disease. For example, in Huntington’s disease, his lab has shown that healthy human glial progenitor cells could outcompete and replace diseased cells in the brain.
“Over the last decade, we’ve learned that many neurological disorders involve glial dysfunction as a major driver of disease,” Goldman said. “That realization has created an urgent need for tools that can safely and efficiently deliver therapies to these cells throughout the brain.”
The current study gets scientists one step closer to that goal. Digging into the details, Goldman and his colleagues engineered a library of modified AAV5 viral vectors by making small changes to the vectors’ capsids. They then screened the vectors in mice whose brains were transplanted with human glial progenitor cells and tracked their movements to identify which ones most effectively infected the human glial progenitor cells and their descendants including astrocytes and oligodendrocytes.
“Human cells display different molecular signatures than mouse cells, and cells behave differently in the brain than they do in a dish,” Goldman explained. “By selecting vectors under biologically relevant conditions, we were able to identify candidates with a strong preference for human glia.”
Next, the team turned their efforts to studying how best to distribute the AAVs throughout the brain. For that, they turned to the glymphatic system, the brain’s network of fluid-filled pathways used to clear metabolic waste by circulating cerebrospinal fluid through the brain. They delivered the engineered AAVs into the cisterna magna, a fluid-filled compartment at the base of the brain, while using hypertonic treatment to enhance fluid uptake into the network. This approach spread the vectors broadly throughout the brain tissue while largely avoiding the blood-brain barrier, and reducing exposure to peripheral organs like the liver.
“The glymphatic system is changing the way we think about brain drug delivery,” Goldman said. “Rather than trying to force therapies across the blood-brain barrier from the bloodstream, we can use the brain’s own transport pathways to distribute them more effectively where they are needed.”
Immediate targets for this approach are pediatric lysosomal storage diseases and other inherited disorders in which glial cells lack critical enzymes. Essentially, diseases of the brain’s white matter with well-defined biological targets. Further down the road, the approach could support novel therapies for multiple sclerosis, age-related white matter loss, and Huntington’s, among other neurodegenerative disorders where glial dysfunction is involved.
“We envision a future in which vectors can be designed for specific diseases and specific cell populations,” Goldman said. His lab is already exploring whether they can use artificial intelligence to design viral capsids that have specific targeting characteristics. “This study shows that by combining targeted vector engineering with glymphatic delivery, we can begin to build that future.”
The post Engineered AAVs Harness Glymphatic System to Reach Brain Targets in Mice appeared first on GEN – Genetic Engineering and Biotechnology News.
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A father fights to create and then save his daughter’s gene therapy, the White House nears a decision on an FDA chief, and the Trump administration pushes drugmakers to reshore generics manufacturing.
With $70 million and seemingly every fiber of his being, Matt Wilsey built a gene therapy company from scratch — recruiting Nobel laureates and biotech veterans to will an experimental treatment into existence for his daughter Grace. The treatment, for the ultra-rare disorder NGLY1 deficiency, landed her back in the hospital before she slowly began to recover, STAT’s Jason Mast and Matt Herper write.
Agrigenomics is, to use an analogy, spring looking toward summer. Wielding modern genomics tools fine-tuned to agriculture, along with AI-driven insights from field technologies and satellites in low Earth orbit, traditional agriculture is transitioning to precision farming and the bounty it brings. Insights are compressing 30 years of field trials into a single growing season and enabling growers to express specific traits without introducing foreign genes and without hit-and-miss Mendelian hybridization.
This intensive application of science starts with comprehensive soil analyses and extends, not only to the major row crops, but also to such lesser-studied areas as fruits and forestry.
Healthy, productive crops all need a good growing environment. “Soil is the most complex, beautiful system on the planet,” Nate Kelly, CEO, Miraterra, points out. Yet, “The soil measurement system in place today is quite archaic,” with manual systems and complex chemistries.
Miraterra’s lead technology is the Digitizer. With shifted-excitement Raman difference spectroscopy as its core, it also employs lasers and advanced signal processing to remove the noise that previously made Raman spectroscopy untenable for soil samples. Robust modeling transforms those data into a molecular fingerprint of soil to 30 ppm, with significantly enhanced resolution anticipated. The Digitizer shortens analysis time from hours to minutes, and makes toxic chemical analysis unnecessary. “Today, we measure texture, organic matter, and carbon and—soon—nutrients,” Kelly says.
After acquiring Trace Genomics and its large soil microbiology data set last year, the company can identify approximately 400,000 different biological components of soil, “things like nitrogen fixers, phosphorus solubilizers, pathogens, and nematodes,” Kelly says.
As its capabilities increase, Miraterra plans to measure soil chemistry, texture, and biological content. It already accesses public data, satellite imaging, and LiDAR to develop reports, such as nutrient flow, on plots as small as 10 square meters. By adding AI to identify patterns predictive of pest infestations and disease prevalence, he says the company expects to correlate soil health with plant conditions and pathogens.
“Soil is too complex [chemically and biologically] to stick a simple probe in the ground and try to get a reading,” Kelly says. “My aspiration is to cut the cost [of soil analysis] by about 90%, to democratize measurement,” Kelly says. “That means turning a $40 analysis into one that costs less than $5.”
CRISPR technology is being applied to agrigenomics with the same revolutionary results it created for medicine. As the basis of Pairwise’s Fulcrum
Platform, it is expanding agrigenomics well beyond the major crops of corn and soybeans.
Fulcrum technology produced the world’s first seedless blackberry—now in field trials—and a more compact blackberry plant that is commercialized in the Americas. With this compact Fontana blackberry, “Growers can plant more blackberry plants per acre, which helps them be more efficient and produce more yield,” Ryan Bartlett, PhD, CTO, says. A thornless blackberry plant is on the horizon.
The goal is to “make growers more sustainable and efficient, and to give consumers the opportunity to consume more healthy things, like blackberries,” Bartlett says. Fast, precision editing will also help plant breeders quickly adjust to climate change.
Fulcrum features plant-specific CRISPR editing that fine-tunes gene expression rather than simply turning genes on or off. The platform includes additional gene editing tools, enzymes, and trait libraries to help breeders design and test changes and quickly advance specific phenotypes into field trials.
Bartlett calls this “traditional breeding on an accelerated basis.” With Fulcrum, these new blackberries were produced in about three years. Using traditional Mendelian breeding would have taken 10 to 40 years.
The speed comes from genome editing that generates the desired phenotype in one cycle, eliminating the need for generations of crosses to develop the features growers want.
Pairwise is also working with “additional permanent crops like cherries,” Bartlett says. “There haven’t been a lot of advancements in the past 50 years.” That may soon change, as Pairwise is working with Sun World to develop a pitless cherry.
“Accelerating timelines and lowering the cost of crop improvement [will] unlock expansive opportunities in agriculture, new species, new geographics, and new traits,” Brad Zamft, PhD, CEO and co-founder of Heritable Agriculture, says.
Heritable focuses on breeding indoor vegetables and forestry, although it also develops fruits and row crops with strategic partners. “The majority of the unmet need comes from placing existing plants in the right places and determining what crosses would make them better,” Zamft says.
Consider strawberries. “We think we’re going to bring a new strawberry variety to the market—start to finish—within four years. The status quo is around a decade.” Heritable can identify desired traits, determine the genes involved, and validate them in growing plants within 18 months, versus 3 to 12 years using traditional methods. Currently, Heritable is adapting genetics from Consorzio Italiano Vivaisti’s (CIV’s) strawberries to grow indoors in Canada.
To speed the process, Heritable uses digital field trials and an AI engine developed in-house specifically for agrigenomics. Potential applications are global. “We have scalable integration of weather and soil to 10-meter resolution anywhere in the world,” Zamft says.
Increasingly accurate correlations of genes to phenotypes shorten the development timeline, Zamft adds. For example, three of the top eight genes Heritable’s team discovered for flowering time in corn proved causative, and three of three in a flavor project for leafy greens. In contrast, he cited a literature analysis that reported that out of 1,671 unique genes that were field-tested across multiple traits, only 22 were identified as validated leads.
To produce and commercialize these innovations, Heritable relies on an alliance of partners in which each partner brings vital strengths to the endeavor, from genomics through production, distribution, and sales. The company also engages in trait development with seed developers and growers, and recently launched a software-as-a-service platform to enable users to predict plant performance under multiple variables.
The company is also active in commercial forestry, using AI to help breeders make placement and breeding decisions that could shorten the breeding cycle by more than half.
Verinomics built its foundation on plant genomics, computational biology, and trait mapping to enable breakthroughs in multiple crops. Most recently, it developed Nonpareil+, a self-pollinating Nonpareil almond variety that may increase per-acre productivity by 30%.
That anticipated productivity gain is directly tied to self-pollination. Because the Nonpareil variety—which is the dominant commercial almond variety in California—is self-incompatible, it requires cross-pollination. That, in turn, requires planting alternating rows of other almond varieties as pollinators and then renting beehives (at about $400 per acre) to pollinate the orchard.
“There’s no way to create a self-compatible Nonpareil using conventional breeding,” Stephen Dellaporta, PhD, founder of Verinomics and professor at Yale University, points out. Therefore, using Verinomics’ Genesis gene editing platform, “we identified the self-incompatibility gene and edited it to a self-compatible gene.” The resultant Nonpareil+ is genetically identical to the Nonpareil except that it is self-pollinating.
Growers are beginning to plant it this year, with the expectation of reducing or eliminating the need for multi-variety almond orchards and bee hives. If it works as expected, growers can harvest all the trees at once, rather than needing separate harvests for multiple varieties.
Almonds aren’t the only crop on Dellaporta’s mind. Back in the lab, “we just completed 60 whole genome sequences, assemblies, and annotations,” he says. The company works with partners to address concerns in multiple crops, making adaptations without introducing foreign DNA.
The bacterial disease known as citrus greening is an example. It’s killed millions of acres of citrus trees, according to the U.S. Department of Agriculture. “There’s no known resistance within the domesticated citrus germplasm,” Dellaporta says. Crossing a wild-type, resistant citrus with a domesticated citrus would take decades to produce a resistant, domesticated citrus, he points out. “Gene editing may create a modification to allow the plant to be resistant without losing varietal identity.”
Precision farming can bring incredible benefits to agrigenomics, just as precision genomics is doing for medicine. As agrigenomics begins to bear fruit, developers can look forward to a genomics-fueled bounty very, very soon.
The post Ag Genomics Begins to Bear Fruit appeared first on GEN – Genetic Engineering and Biotechnology News.
Thanks to booming sales of blockbuster obesity and diabetes treatments, the amount that Americans spent on prescription drugs this year is on track to surpass $1 trillion, according to a report released in April by the American Society of Health-System Pharmacists (ASHP).
That would be a sharp, above-inflation 9.3% increase from the $915 billion in U.S. drug sales recorded last year, one of the fastest one-year percentage jumps ever recorded by the group. That 12.7% jump is higher than the one-year increases achieved by healthcare costs, and growth in the overall economy.
Even more eye-opening: Nearly one-third of prescription drug spending growth came from the $132 billion spent on a single category, namely glucagon-like peptide 1 (GLP-1) receptor agonists indicated for obesity/wight management and for type 2 diabetes.
“GLP-1s have fundamentally reshaped the drug-spending landscape,” Eric Tichy, PharmD, MBA, lead author of the report and division chair of supply chain management at Mayo Clinic. “And we are still on the steep part of the curve.”
According to ASHP, the 2025 and projected 2026 leaps in drug spending reflect more medications being used by more patients, with spending growth being predicted to range from 10–12% overall.
That would appear to explain the robust increases in sales by every one of the top 10 best-selling prescription drugs (based on 2025 sales) featured in this GEN A-List. Top-selling drugs are ranked based on sales or revenue reported for 2025 by biopharma companies in press announcements, annual reports, investor materials, and/or conference calls. Each drug is listed by name, sponsor(s), 2025 sales, 2024 sales, and the percentage change between those years.
The total 2025 aggregate value of the top 10 best-selling drugs was $188.136 billion, up 21.5% from $154.888 billion in 2024—and more than double (up 127.5% over 10 years from the $82.694 billion generated by 2016’s top 10 sellers, as reported by GEN.
Just missing the top 10 at #11 was Novo Nordisk’s Wegovy®, the glucagon-like peptide 1 (GLP-1) receptor agonist indicated for weight loss in obese or overweight adults. Treatments ranked number 11 through number 15 in 2025 generated between approximately $8.4 billion and $12.2 billion in revenues. In addition to Wegovy, best sellers Nos. 12-15 include:
(nivolumab / nivolumab and hyaluronidase-nvhy), marketed by Bristol Myers Squibb (BMS) worldwide except Japan, South Korea, and Taiwan, where Ono Pharmaceutical markets the drug.Three drugs that ranked between #11 and #15 on last year’s A-List based on 2024 sales placed lower this year: Eylea/Eylea HD (aflibercept) from Regeneron Pharmaceuticals and Bayer, which ranked No. 17 in 2025 sales; Gardasil/Gardasil 9 (Human Papillomavirus Quadrivalent (Types 6, 11, 16, and 18) Vaccine, Recombinant/Human Papillomavirus 9-valent Vaccine, Recombinant) from Merck & Co., now No. 27; and Humira® (adalimumab) from AbbVie, which had long been the top-selling drug for years until being surpassed by Keytruda® but is now No. 32.
Eylea and Humira now face competition from biosimilars, while Merck halted Gardasil shipments to China last year, citing declining sales.
1. Keytruda® / Keytruda Qlex 1
(pembrolizumab / pembrolizumab and berahyaluronidase alfa-pmph) Merck & Co.
2025 Sales: $31.680 billion 1
2024 Sales: $29.482 billion
% Change: +7.5%
2. Mounjaro®
(tirzepatide) Eli Lilly
2025 Sales: $22.965 billion
2024 Sales: $11.540 billion
% Change: +99.0%
3. Eliquis®
(apixaban) Bristol Myers Squibb and Pfizer
2025 Sales: $22.404 billion ($14.443 billion BMS + $7.961 billion Pfizer)
2024 Sales: $20.699 billion ($13.333 billion BMS + $7.366 billion Pfizer)
% Change: +8.2%
4. Ozempic®
(semaglutide) Novo Nordisk
2025 Sales: $19.611 billion (DKK 127.089 billion)
2024 Sales: $18.570 billion 2 (DKK 120.342 billion)
% Change: +5.6%
5. Dupixent®
(dupilumab)3 Sanofi and Regeneron Pharmaceuticals
2025 Sales: $18.124 billion (€15.714 billion)
2024 Sales: $15.077 billion 4 (€13.072 billion)
% Change: +20.2%
6. Skyrizi®
(risankizumab-rzaa) AbbVie
2025 Sales: $17.562 billion
2024 Sales: $11.718 billion
% Change: +49.9%
7. Darzalex® / Darzalex Faspro®
(daratumumab / daratumumab and hyaluronidase-fihj) Johnson & Johnson and Genmab 5
2025 Sales: $14.351 billion 5
2024 Sales: $11.670 billion 5
% Change: +23.0%
8. Biktarvy®
(bictegravir, emtricitabine, and tenofovir alafenamide) Gilead Sciences
2025 Sales: $14.334 billion
2024 Sales: $13.423 billion
% Change: +6.8%
9. Jardiance family
(empagliflozin, monotherapy and in combinations with linagliptin and metformin) 6 Boehringer Ingelheim and Eli Lilly
2025 Sales: $13.563 billion ($10.132 billion [€8.785 billion] Boehringer Ingelheim + $3.431 billion Eli Lilly] 6
2024 Sales: $12.979 billion ($9.638 billion [€8.357 billion] Boehringer Ingelheim + $3.341 billion Eli Lilly) 6
% Change: +4.5%
10. Zepbound®
(tirzepatide) Eli Lilly
2025 Sales: $13.542 billion
2024 Sales: $4.926 billion
% Change: +174.9%
References
, a subcutaneous injectable immunotherapy consisting of pembrolizumab and berahyaluronidase alfa, and which like Keytruda is indicated to treat multiple types of cancer.The post Top 10 Best-Selling Drugs 2026 appeared first on GEN – Genetic Engineering and Biotechnology News.
Virtual cell models that enable the prediction of cell behavior across scales and biological contexts are rapidly emerging at the forefront of drug discovery.
Tom Sercu, PhD, vice president of AI and engineering at Biohub, points to a clinician-scientist studying a rare autoimmune disease as an example of how AI models could reshape translational medicine. Starting from a patient’s genome, researchers could use virtual cells to predict how major immune cell types behave in disease versus healthy states. The result offers an invaluable tool across target and mechanism-of-action discovery, patient stratification, toxicity prediction, and therapeutic development.
Yet, building a virtual cell is not an easy feat.
“Transformative AI in biology does not come from algorithms alone, but when models are trained on large-scale, high-quality, openly accessible datasets,” says Sercu. To capture complex biology, such data must span model systems and organisms, interventional and observational methods, and diverse cellular states.
To support this mission, Biohub announced a $500 million commitment to the Virtual Biology Initiative in April. The five-year campaign will accelerate the generation of technologies and multi-modal datasets needed to power virtual cell models.
Similar to how more than 253,000 experimentally determined molecular structures in the Protein Data Bank (PDB), assembled over five decades, became foundational training data for modern AI protein-structure prediction, Sercu sees an analogous moment for cellular biology.
“We do not yet have the equivalent of the PDB for cells,” he emphasized. “The Virtual Biology Initiative seeks to change that.”
Today’s virtual cell developers reflect on what’s needed for these models to predict complex biology and overhaul drug discovery.
Much of the industry has defined the virtual cell as transcriptome models that predict how perturbations alter gene expression across cellular contexts.
Among the increasingly crowded ecosystem, Arc Institute’s first-generation virtual cell model, STATE, predicts how stem cells, cancer cells, and immune cells respond to drugs, cytokines, or genetic perturbations. In March, billion-dollar-backed, Xaira Therapeutics unveiled X-Cell, the first scaling law demonstrator in the virtual cell domain, sizing up to a whopping 4.9 billion parameters. These models aim to generalize to unseen biological contexts by training on causal single-cell RNA sequencing (scRNA-seq) data.
To train X-Cell, Xaira has spent its initial years building what the company describes as “the largest genome-wide CRISPRi Perturb-seq dataset ever reported.” Named X-Atlas/Pisces, the dataset is composed of 25.6 million cells across seven screens and 16 biological contexts.
Ginkgo Datapoints, the AI platform division of Ginkgo Bioworks, looks toward bulk transcriptomics rather than a single-cell approach.
“Just like how models benefit from diversity in training data, we as an industry benefit from having diversity of approaches,” said John Androsavich, PhD, general manager at Ginkgo Datapoints. The Datapoints team applies high-throughput automation to create diverse biological datasets, including cell perturbations, antibody developability, and ADME small molecule developability data, to support AI model training for life science partners.
In March, Ginkgo Datapoints delivered the first data release of the Virtual Cell Pharmacology Initiative (VCPI). Approximately 2,280 small molecules were profiled in full dose response using DRUG-seq, a scalable arrayed transcriptomics assay measuring chemical perturbations.
In contrast to scRNA-seq, which covers approximately 1,500 genes per cell, DRUG-seq captures nearly 10,000 genes per condition with higher signal-to-noise to optimize insights for pharmacology. Notably, VCPI has exclusively focused on THP-1, a human monocytic cell line widely used across immunology, oncology, and inflammatory disease research, to understand drug action.
While the crowding around scRNA-seq has largely been driven by the pursuit of scale, “a cell is not only its RNA,” tempers Hani Goodarzi, PhD, core investigator at Arc Institute. He emphasizes that cells are complex systems shaped by multiple layers of biology beyond gene expression alone, including protein abundance, chromatin state, spatial organization, metabolism, and post-translational regulation.
A useful analogy comes from large language models (LLMs), which became powerful as text provided an exceptionally scalable substrate for training trillions of tokens. Yet, text alone is an incomplete representation of human communication.
“The lesson is not that one modality is sufficient forever,” says Goodarzi, “but that a single high-quality, scalable modality can support general representations when the training corpus is large.”
Emma Lundberg, PhD, co-founder and CSO at GenBio AI, is worried about the “streetlight effect.”
“We’re scaling what we can and not necessarily what we should,” she says.
GenBio AI seeks to develop world models that cross multiscale biology. Instead of concentrating on one data modality, the company’s so-called “AI-Driven Digital Organism” grows expertise in embedding, tokenizing, and training models across scales, from the molecular layer to regulatory networks. Rather than undergo internal data generation, GenBio AI focuses on public data and partnerships to power the company’s models.
Lundberg, who is also associate professor of bioengineering and pathology at Stanford University, argues that models can guide the field to which data modalities to pursue. As an example, models that incorporate biological priors, such as protein-protein interactions, can achieve noticeable improvements in predictive performance.
Spatial and temporal data also capture critical dimensions of biological function that sequence data alone cannot resolve. According to the Human Protein Atlas, roughly 60% of human genes encode proteins that localize to multiple cellular compartments, often carrying out distinct functions depending on context.
In a May preprint posted on bioRxiv, Lundberg and colleagues introduced ProtiCelli, a deep generative model that visualizes the spatial organization of nearly the entire proteome within individual cells. By training on 1.23 million images from the Human Protein Atlas, the model simulates microscopy images for 12,800 human proteins while also generalizing to unseen cell types and drug perturbations absent from training.
Cellular Intelligence is developing a universal virtual cell signaling model designed to simulate cell-state transitions over time, with the goal of expanding the possibilities of regenerative medicine. By learning the underlying “grammar” through which sequences of signaling cues drive cell differentiation, these models aspire to enable the on-demand generation of any cell type.
Less than one percent of known human cell types can be reliably produced for downstream applications in cell therapy. As only 20 fundamental molecular signaling pathways give rise to thousands of cell states, researchers face an unfathomably large search space when engineering a particular cell type.
“Every cell that we discover or optimize opens a slew of potential applications,” said Micha Breakstone, CEO and co-founder of Cellular Intelligence. “One could spend a decade and tens of millions of dollars on painstaking trial-and-error to differentiate a new cell type, or solve this problem in one fell swoop, much like AlphaFold for the protein folding challenge.”
The company’s platform leverages a semi-permeable capsule technology, which selectively retains cells and large analytes while being freely accessible to media, enzymes, and reagents. The method enables high-throughput assays combining live-cell culture with genome-wide readouts. Millions of time-varying signal combinations are tested on human stem cell differentiation in parallel, providing 1,000 times higher efficiency than traditional methods.
In May, Cellular Intelligence advanced as a Phase II-ready clinical company after entering an agreement with Novo Nordisk to acquire STEM-PD, an allogeneic cell therapy program for Parkinson’s disease with Fast Track Designation. The deal comes six months after Novo announced its strategic exit from the cell therapy space. The start-up’s AI cell signaling models will address protocol development, one of the biggest obstacles preventing cell therapies from clinical impact.
“Novo selected Cellular Intelligence as the right partner because the next major challenge for complex cell therapy programs is not only the biology,” says Breakstone. “It is manufacturing scale-up, comparability, clinical logistics, and commercial readiness.”
As the diversity of virtual cell models targets new dimensions of complex biology, every approach takes another step closer toward clinical impact.
The post Virtual Cells Go Multiscale to Predict Complex Biology appeared first on GEN – Genetic Engineering and Biotechnology News.
Within complex biological samples, the most important cells are often the hardest to find. Identifying, characterizing, and isolating rare or functionally-distinct populations has become central to modern cell biology, translational research, and cell therapy manufacturing. Among the most powerful tools for this task is fluorescence-activated cell sorting (FACS), a specialized form of flow cytometry that physically separates heterogeneous cell mixtures into defined subpopulations based on fluorescent labeling. Compared with methods such as magnetic-activated cell sorting (MACS), FACS offers higher-resolution analysis and greater flexibility for characterizing diverse, multi-parameter populations.
Flow cytometry itself measures the physical and chemical properties of individual cells as they pass single-file through a laser interrogation point. Although the technology emerged in the mid-1960s, the term “flow cytometry” replaced the earlier “pulse cytophotometry” in the late 1970s as fluorescence-based analysis and physical cell sorting became increasingly intertwined. Using fluorescently-labeled antibodies or probes, investigators can identify and isolate specific populations, including T cells, B cells, cancer cells, stem cells, and genetically-engineered cells.
As cell sorting applications continue to expand, instrument selection increasingly depends on experimental and manufacturing demands. Considerations include fluorochrome capacity, sterility requirements, aerosol containment, throughput, scalability, and budget. GEN spoke with industry leaders about how next-generation sorting platforms are addressing these evolving challenges.
Single-cell isolation has traditionally relied on complex FACS systems or labor-intensive manual methods. These approaches can result in low cell viability, cross-contamination, and loss of cell integrity. Brendan Yee, director of cellular analysis at Bio-Techne, summarizes, “Traditional FACS sorters are expensive, difficult to use, and can take a significant amount of time to set up. Manual limiting dilution is based on Poisson distribution, where 60% of wells will be empty, about 30% will have one cell, and 10% will have multiple cells under the best circumstances.”
To address these challenges, Yee says the company developed the Pala platform, which integrates key components of traditional flow cytometry while focusing on being a faster and easier platform for single cell dispensing. “Within minutes, users can initialize the system, identify sorting parameters, and dispense single cells into a microtiter plate. The intuitive and simple software was designed so that all lab members could quickly learn to use the system.”
During operation, cells are loaded into a sterile, disposable microfluidic cartridge pressurized to less than two pounds per square inch (PSI;13.8 kPa, kilopascals). Yee reports, “Traditional flow cytometers are pressurized up to 35 PSI, which has been demonstrated to reduce viability, especially with sensitive cell lines such as induced pluripotent stem cells.”
Approximately the size of a desktop printer, the Pala cell sorter can be operated inside a tissue culture hood. The system is offered with a dual laser configuration, up to six photomultiplier tubes (PMTs) for fluorescence detection, and two photodiodes for light-scatter detection. Yee notes, “The use of a two-laser design allows for up to 11 different fluorophores to be used and therefore flexibility in the design of the experiment.”
Speed also matters for cell viability. After a brief sorting parameter set-up, a high-speed valve pushes target cells into a channel where a one-microliter droplet is dispensed into a microtiter well. For a 96-well microtiter plate, the process takes about two minutes and approximately six minutes for a 384-well plate.
The system simplifies single-cell applications, including cell line development, single-cell genomics, CRISPR editing, antibody discovery, and rare-cell isolation. Yee reports, “We are starting to see an expanded interest in the Pala platform for use with mass spectrometry and single-cell proteomics.”
As cell sorting moves beyond specialized core facilities into broader laboratory settings, a persistent challenge has been balancing performance with usability. This is particularly true when isolating rare or dim cell populations without further complicating operations. Instruments that deliver high sensitivity often demand extensive expertise, limiting adoption across multidisciplinary teams.
“The CytoFLEX SRT is designed to bring advanced cell sorting into a more routine, accessible, and automation-ready workflow,” says James McCracken, PhD, portfolio product manager, Beckman Coulter Life Sciences. Built on the CytoFLEX platform (i.e., the same underlying optical detection system, fluidics, and software architecture), the SRT system offers users the CytoFLEX analyzer’s high fluorescence and scatter sensitivity alongside a familiar software interface, enabling resolution of complex or low-abundance populations while supporting broader adoption and easier training.
McCracken also highlights, “A key advantage is that automation programming is built into the instrument, helping laboratories integrate the CytoFLEX SRT into automated workflows with less upfront complexity. The compact benchtop design supports integration into connected lab environments, with flexible sorting into tubes, slides, and multi-well plates. This helps researchers connect sorted cells to downstream applications such as culture, liquid handling, single-cell genomics, transcriptomics, proteomics, or functional testing.” Additional capabilities include up to 15 fluorescence parameters, four-way sorting, and the capability to support sort logic across multiple streams.
McCracken summarizes, “With automated setup and quality control features, the system helps reduce workflow complexity and support reliable, repeatable sorting. For scientists, that means less time spent managing the instrument and more time spent moving from complex cell biology to downstream insights.”
The expansion of cell sorting into translational and higher-risk biological applications has elevated the issue of biosafety alongside performance. Researchers must not only balance purity, yield, and viability, but also mitigate risks associated with aerosol generation and operator exposure, especially when working with unfixed or human-derived samples. These considerations can further complicate sorting procedures already requiring technical precision and specialized expertise.
Eric Diebold, PhD, vice president and general manager of instruments and informatics at Waters Biosciences (formerly BD Biosciences), a division of Waters Corp, says the BD FACSAria Fusion cell sorter incorporates biosafety directly into the instrument. He explains, “With its fully integrated Class II biosafety cabinet and aerosol management design, FACSAria Fusion enables high-speed, high-purity sorting of unfixed or higher-risk samples while meeting stringent operator and sample protection requirements.”
According to Diebold, “The system is typically deployed in high‑complexity research environments such as academic core facilities, pharmaceutical R&D, and translational research labs, where maximum flexibility, high‑speed sorting, deep multicolor resolution, and integrated biosafety are required to confidently interrogate complex or rare populations.”
Other instruments include BD FACSAria III and FACSMelody systems. Diebold reports, “All are built on BD’s distinctive fixed-alignment, gel-coupled cuvette flow cell-based sorter that is a historical differentiator that delivers stream stability, high sensitivity, and day-to-day reproducibility beyond the stream-in-air sorters.”
Another innovative instrument is the BD FACSDiscover S8 cell sorter, which employs spectral flow cytometry (i.e., full-spectrum flow cytometry) to capture the entire emission spectrum of fluorochromes rather than specific wavelength bands. Diebold explains, “The S8 sorter represents a significant step forward by integrating spectral flow cytometry with real‑time imaging, enabling simultaneous measurement of phenotype, morphology, and spatial features at the point of sort. This added imaging context allows researchers to visually confirm cell populations, resolve heterogeneous or ambiguous subsets, improve doublet discrimination, and sort based on characteristics that extend beyond fluorescence alone—capabilities that are increasingly important for complex translational studies and early process development.”
Successful cell therapies begin with high-quality, functional cells. Thus, GMP-compliant cell sorting focuses on producing consistent, sterile cell populations suitable for clinical applications under tightly controlled regulatory conditions. Therapeutic applications include isolation of stem cells, CRISPR-edited cells, and T cells that will be engineered with chimeric antigen receptors.
“Clinical cell manufacturing demands the highest standards of safety, precision, and reliability,” notes Sudheer Gambheer, PhD, global product manager for flow cytometry cell sorting portfolio, Miltenyi Biotec. He continues, “The key point is that many of the traditional droplet-based FACS limitations become amplified and intertwined under GMP constraints.”
To address these challenges, the company has developed MACS® GMP Tyto® Consumables to enable GMP-compliant multiparameter cell sorting using the MACSQuant® Tyto Instruments. Employing gentle, microchip-based sorting within a closed-cartridge system, the MACSQuant Tyto Family of cell sorters is designed to preserve cell viability and functionality. Gambheer reports, “In the closed cartridge, cells are kept sterile at all times while never coming into contact with the instrument.”
The system employs GMP-compliant consumables, including unique single-use cartridges in a closed environment. A microfluidic chip housing an ultra-fast mechanical valve (30,000 actuations/sec) lies at the heart of the technology. During sorting, cells flow through the microchip under low air pressure. Lasers detect target cells based on fluorescence and scatter as they flow through the microchannel. Non-target cells pass into the negative collection chamber. When a target cell is detected, a magnetic pulse activates a solenoid to open the valve, redirecting the cell into a positive-sorting chamber. The valve then resets, ready to isolate the next target cell.
From a regulatory perspective, Gambheer notes that the MACS GMP Tyto Consumables come with extensive supporting documentation for regulatory submissions. “Miltenyi Biotec is one of the only vendors providing an end-to-end workflow for easy integration into the GMP environment. This includes everything from GMP antibodies, buffers, and cartridges to the 21 CFR Part 11 software module, simplifying sorting compliance with secure electronic records and signatures.”
As cell sorting continues moving from specialized core facilities into translational research and therapeutic manufacturing, future platforms will likely emphasize automation, richer cellular characterization, biosafety, and standardized workflows alongside sensitivity and throughput. Increasingly, the challenge is no longer simply identifying rare cells, but isolating them reproducibly, gently, and at a clinically relevant scale.
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Nature Biotechnology, Published online: 09 July 2026; doi:10.1038/s41587-026-03211-3
From virtual patients to simulated cells and organs-on-chips, digital twins are forecasting disease and guiding drug development. Scientists now must figure out how to define, validate and deploy these models.
Nature Biotechnology, Published online: 09 July 2026; doi:10.1038/s41587-026-03188-z
PALACE combines homology features and deep learning to improve phage assembly from metagenomics.
Texas Biomedical Research Institute signed a cooperative research and development agreement (CRADA) with the U.S. Department of War to help protect service members and civilians from emerging and high-consequence biological threats.
The agreement with the Capability Program Executive for Chemical, Biological, Radiological, and Nuclear Defense (CPE CBRND), which manages the nation’s investments in chemical, biological, radiological, and nuclear (CBRN) defense diagnostics and medical countermeasures (MCMs), establishes a collaborative framework to accelerate the development, testing, and validation of medical countermeasures while strengthening national preparedness, according to Cory Hallam, PhD, professor and executive vice president of Applied Science and Innovation at Texas Biomed.
“We are proud to offer our unique scientific capabilities to support the nation’s biodefense mission,” said Hallam. “Texas Biomed excels in reducing the time between scientific insight and operational impact, which is critical for proactive preparedness and responding quickly when threats emerge.”
The CRADA establishes a framework for Texas Biomed and CPE CBRND to collaborate on MCM projects that protect global populations, including U.S. soldiers serving at home and abroad. The agreement recognizes Texas Biomed’s operational impact capabilities, including high containment research and adherence to Good Laboratory Practice standards that support FDA regulatory requirements.
The Institute is also home to one of the nation’s seven National Primate Research Centers, offering expertise in translational studies and model development.
The CRADA framework is intended to ensure that critical resources for biological threats, whether from naturally emerging diseases or engineered risks, are continuously ready and available on demand, rather than episodically, continued Hallam.
“This is a long-term investment in resilience,” added Larry Schlesinger, MD, president and CEO of Texas Biomed. “As biological threats continue to evolve, partnerships like this are increasingly essential. Under this agreement, we have the potential to build new joint capabilities that will serve the nation not just today, but for decades to come.”
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