CryoEM Characterization

Specializing in LNP, liposome, AAV, and other viral vectors. Our cutting-edge technology and experienced team ensure comprehensive insights into size, shape, and composition, supporting advancements in drug formulation and gene therapy

Introducing NanoSMART, our unique Advantage in CryoEM characterization

NanoSMART, an AI cryoEM system, automatically identifies nanoparticle features in images from various electron microscopes

It generates masks and size distribution data based on specific criteria. With one click, users receive a comprehensive report, and the system optimizes recognition by enhancing clarity in lower quality images
Size Distribution

Recognition Effect of
Liposome Types

The right figure illustrates the recognition effect of Liposome types at different particle densities. There are two sets of cases in this type, demonstrating the results when the particle quantity is relatively high and relatively sparse, with two photos in each set


From the comparison of the images, it can be observed that the particle quantity does not affect the overall recognition results. Ice debris in the carrier background is accurately excluded, and the multi-layered structure in the darker parts of the image is also identified



Recognition Effect of
Liposome Types

The right figure depicts the recognition effect of images of LNP types under different brightness and contrast conditions. In the first set, the brightness is low, and the contrast is high, with closely packed particles but clear edges. In the second set, the brightness is high, and the contrast is low, with sparse particles but relatively blurry edges

LNP - 1

Upon observation, it can be seen that the overall recognition results of the two types of images are very close. The difference lies in the fact that in the first image, a few particles with slightly blurred edges or overlapping particles are filtered and excluded by the filter, while in the second image, although there are many particles with edges close to the background color

LNP - 2


Recognition Effect of
VLP Types

NanoSMART performs well in the recognition of VLP type images, which are ultra-small targets. Compared to ordinary Liposomes or LNP types, VLP types usually have a large number of small targets at low magnifications. The system is designed with independent sampling modes and filtering rules for this type, enabling the model to perform more precise image recognition and segmentation.

VLP - 1

In the first set of images on the right, the number of particles is between 800 and 900, with only a few overlapping particles deviating from the VLP shape characteristics being filtered out. The overall recognition rate is close to 100%.

VLP - 2


Statistical Dimension

The right figure uses a set of Liposome type projects as an example to showcase the system's data analysis capabilities and user-friendly data presentation features beyond basic nanoparticle recognition.
ScreenShoot 1This functionality is divided into two parts: the overall project data summary, presenting all valuable information about this set of images in different ways, and the detailed information for individual images, including modules for free preview, particle editing, color differentiation, and independent data display boards.

Project Data

The project data summary page consists of three main modules: the particle size/circularity/bilayer membrane histogram display area, the multidimensional statistics area, and the table display area. These modules convey comprehensive statistical information about the project from the overall to the individual level, from numerical data to graphical representations, and from quantity to proportion.
ScreenShoot 1Switchable histogram dimensions: circularity, bilayer membrane, etc.ScreenShoot 1Particle size distribution table: The complete table has different images on the X-axis and particle size distribution intervals (nm) on the Y-axis.ScreenShoot 1Using NanoSMART, Shuimu will be able to provide more efficient characterization services and deliver to customers clearer and more detailed reports.

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Founded in 2017, Shuimu BioSciences aims to bring the power of cryo-EM to innovative therapeutics developers.


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