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Blog post   |   8/07/2025

What makes super-resolution microscopy essential for nanoparticle research?

Author: Alex Kukreja

Understanding nanoscale biological structures like extracellular vesicles (EVs) and lipid nanoparticles (LNPs) is essential for advancing research in cell communication, targeted drug delivery, and nanomedicine. But analyzing these tiny, complex particles has always posed challenges, particularly when it comes to visualizing their morphology, measuring their size, and quantifying what’s inside them.

In a recent webinar hosted by ONI, Dr. Alex Kukreja shared how researchers are tackling these challenges using super-resolution microscopy and automated workflows. This article breaks down some of the key takeaways from the session, which you can watch at the bottom of this page.

Why image EVs and LNPs at the nanoscale?

Extracellular vesicles are membrane-bound particles secreted by cells and known to play key roles in intercellular communication1. Lipid nanoparticles, on the other hand, are synthetic delivery vehicles widely used in mRNA-based vaccines and other gene therapies2. Despite their biological and clinical importance, their small size (typically under 200 nm) makes them difficult to analyze using conventional microscopy or bulk measurement techniques.

To gain meaningful insights into individual particles, researchers are increasingly turning to single-molecule localization microscopy (SMLM), a form of super-resolution imaging that surpasses the optical diffraction limit3. With these tools, scientists can detect and analyze individual nanoparticles with nanometer-level precision—unlocking detailed information about size, morphology, and molecular cargo.


Challenges in analyzing EVs and LNPs—and how to solve them

A key point raised in the webinar was that sample preparation, imaging, and analysis each come with their own obstacles. For example, detecting cargo inside EVs typically requires permeabilizing the vesicle membrane, which can compromise structural integrity. But by comparing permeabilized and non-permeabilized samples, we showed how signal spikes reveal true cargo presence. This approach, enabled by careful staining and super-resolution imaging, allows researchers to differentiate between background signal and real cargo localization.

To support this kind of analysis, tools like the EV Profiler Kit can help standardize the workflow—providing a membrane stain (Pan-EV) and cargo-specific staining reagents, along with software-assisted automation to guide image acquisition and reconstruction.

The AutoEV feature in ONI’s CODI software, for instance, helps streamline analysis by automatically identifying chip lanes, optimizing focus and illumination, and compiling field-of-view images into quantitative reports. These reports include biomarker distribution and particle size data, giving researchers a fast and detailed look at their samples.


Imaging LNPs: Similar needs, unique chemistry

While EVs and LNPs share similarities in scale and function, we explained that their biochemical makeup is different enough that the same chip chemistry couldn’t be used for both. That led to the development of a dedicated LNP Profiler Kit, which includes a membrane stain specific to PEG (polyethylene glycol)—a lipid component commonly used in LNP formulations.

Here, too, researchers want to understand particle size, shape, encapsulated cargo, and any surface ligands used for targeting. But LNPs are often sensitive to processing steps like permeabilization. So for cargo detection, the LNP kit uses a membrane-permeable stain—meaning the vesicles stay intact. The trade-off is that the stain is diffraction-limited, not super-resolved, which introduces new challenges in analysis.

To address that, ONI’s analysis pipeline uses machine learning to correlate blurred cargo signals with super-resolved membrane data. As we demonstrated, the software is trained to confidently call cargo-positive particles, exclude ambiguous ones, and deliver results that support high-confidence quantification.

The kit also includes a super-resolution ligand stain for quantifying surface targeting ligands, which can be essential in evaluating LNP performance in therapeutic contexts.


Automation and accessibility in imaging workflows

One of the final points in the webinar was how automation is helping democratize access to high-performance imaging. Tools like AutoLNP mirror the functionality of AutoEV, providing walk-away acquisition, real-time image reconstruction, and built-in analysis tools—all integrated into the same workflow. These systems are helping reduce barriers for labs that want to conduct advanced nanoparticle research without the need for extensive microscopy expertise.

 

Watch the full webinar

june web 1-2

 

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References

  1. Théry C, Witwer KW, Aikawa E, et al. Minimal information for studies of extracellular vesicles 2018 (MISEV2018). J Extracell Vesicles. 2018;7(1):1535750. doi: 10.1080/20013078.2018.1535750
  2. Hou, X., Zaks, T., Langer, R. et al. Lipid nanoparticles for mRNA delivery. Nat Rev Mater 6, 1078–1094 (2021). doi: 10.1038/s41578-021-00358-0
  3. Betzig E, Patterson GH, Sougrat R, et al. Imaging intracellular fluorescent proteins at nanometer resolution. Science. 2006;313(5793):1642-1645. doi: 10.1126/science.1127344