Oxford Nanoimaging (ONI) recently launched the Aplo Scope super-resolution microscope that pushes imaging precision to 20 nm. The Aplo Scope integrates lasers, optics, chemistry, and software in a compact footprint, helping researchers capture and analyze molecular interactions at the nanoscale. With its user-friendly design and portability, ONI aims to simplify super-resolution microscopy and potentially reduce the time needed to validate drug targets in early-stage pharmaceutical research.
Part of the impetus behind launching the product—which ONI CEO Paul Scagnetti has called the “biggest launch” in the company’s history—was its focus on democratization. This means breaking down the traditional barriers of cost, complexity, and specialized expertise that have long limited access to super-resolution microscopy, a technique with a Nobel Prize link. “How can you unleash a technique so that 30,000 scientists can do it?” Scagnetti asked.
[Oni.bio]
Why super-resolution matters
Traditional microscopy struggles to visualize structures below the diffraction limit of roughly 200–300 nm, but many critical cellular processes occur at tens-of-nanometers scales. In drug discovery, these blind spots can lead to an overreliance on proxy measures and trial-and-error approaches. Speaking of the trial-and-error dynamic, Scagnetti pointed to the historically limited ability to directly see the basis for a drug candidate’s mechanism of action. By enabling 20 nm–scale visualization, ONI’s Aplo Scope aims to “turn the lights on” in these dark, sub-diffraction territories.
As alluded to earlier, by enabling 20 nm–scale visualization, ONI’s Aplo Scope aims to “turn the lights on” in these dark, sub-diffraction territories. Directly seeing how potential therapies engage targets could help shorten the path to validated drug candidates and reduce failures. The Aplo Scope’s product page mentions it can image down to 15 nm.
[Oni.bio]
Ease of set up
Another unique attribute of the Aplo Scope is how quickly it can be set up. “I think I’ve seen somebody do it in 20 minutes. But, you know, I tell people it’s an hour,” Scagnetti said. “The record is four minutes,” added Jason Jell, vice president of marketing at ONI.
“I mean, to give a sense of it, I came from, you know, electron microscopy and sequencers. These are multi-day, multi-day things,” Scagnetti said. Jell added that they regularly run the Aplo Scope on folding tables at events and still get good data.
Traditionally, super-resolution microscopy systems required highly controlled environments with vibration-isolated air tables and dedicated dark rooms. The Aplo Scope breaks this paradigm by operating effectively on standard laboratory benches. “This goes on a standard lab bench,” Scagnetti said. That said, basic environmental controls remain important. “You need it to be a responsible lab, right?” Scagnetti continued. “It can’t go from 100 degrees to zero…”
Real-world applications
A growing number of early adopters are turning to ONI’s Aplo Scope to address diverse research questions in drug discovery, disease biology, and advanced therapeutics. Researchers at the Center for Genomic Regulation (CRG) in Barcelona, Spain, for example, have used single-molecule localization microscopy (SMLM) to visualize chromatin fiber loops as small as 45 nm. “I started using SMLM to study chromatin structure in somatic and stem cells several years ago. I have been extensively applying it to investigate 3D genome organization and chromatin fiber looping, which are fundamental features underlying various cell phenotypes, including normal, diseased, or drug-induced states,” said Maria Pia Cosma, Senior Scientist and ICREA Research Professor at CRG, in a press release. “Recently, we utilized SMLM data of chromatin signatures to train a deep-learning model capable of identifying induced pluripotent stem cells, cancer cells, and virus-infected cells. This approach shows great potential for future cancer diagnosis and patient monitoring, as it requires very few cells to train the model.”
[Oni.bio]
Other groups emphasize SMLM’s ability to clarify molecular mechanisms that underlie disease. Lorenzo Albertazzi, Associate Professor in the TU/e Department of Biomedical Engineering, noted how directly visualizing biological and chemical phenomena “has completely transformed our research and allowed us to solve critical challenges in biomedical science.” “The potential of this technique extends far beyond its current applications and single-molecule localization microscopy (SMLM) helps us understand disease mechanisms, advance drug discovery, and enable patient stratification.”
Pharmaceutical scientists see similar value. Alexey Rak, Department Head of Bio Structure and Biophysics at Sanofi, described single-molecule imaging as offering “SMLM microscopy has transformative potential for drug discovery by providing nanoscale insights into biomolecular interactions, mechanisms, and dynamics. Its incredible resolution, combined with single-molecule sensitivity, enables drug hunters to characterize targets and molecules with exceptional precision that is close to the structural biology one. While challenges remain in live-cell imaging and high-throughput scalability, emerging technologies and AI-driven approaches will further elevate the role of SMLM in developing the next generation of therapeutics.”
[Oni.bio]
The role of AI in super-resolution imaging
As super-resolution imaging generates increasingly large datasets, AI and machine learning are emerging as valuable tools for analyzing spatial patterns, classifying cell phenotypes, and predicting therapeutic outcomes. In practice, AI already powers image segmentation, automated feature extraction, and structural data–based drug-target predictions. “Everyone says AI models are going to change drug discovery, and they probably are,” said Scagnetti. “But what do you train those models on? You have to train them on real biology.”
Capturing nanoscale data in living cells offers more than simple snapshots; it reveals the organization and distribution of proteins, lipids, and other biomolecules in normal versus diseased cells. These details can inform more robust AI models, helping researchers move beyond trial-and-error screening. “You can train them on DNA. You can train them on variants,” Scagnetti continued. “But you also have to train them on, for instance, how proteins are organized in a normal cell versus a cell that’s different.”
Such insights can accelerate hit-to-lead optimization and help pinpoint failure points earlier in drug development—critical in an industry where late-stage attrition remains high. As AI models become increasingly sophisticated, real-world super-resolution data may supply a deeper mechanistic understanding, potentially lowering failure rates and getting therapies to patients more rapidly. “It’s shocking how quickly [AI has] gone from proof of concept to something that’s relatively easy to imagine being implemented,” Scagnetti added.
Filed Under: Drug Discovery
