Lipid-Nano-Particle Characterization

Harnessing AI for Statistically Significant Cryo-EM Insights

ATEM's Breakthrough in LNP Technology

Transforming LNP Characterization

ATEM leads the way in lipid nanoparticle (LNP) characterization by transitioning from traditional, manual analysis to a groundbreaking quantitative approach. Utilizing AI-powered Cryo-EM, ATEM provides precise insights into LNP size, shape, and distribution—key factors that influence drug delivery efficacy and stability.

AI-Enhanced Efficiency

Our AI-driven method streamlines the entire analysis process, making it faster, more accurate, and cost-effective. This innovation not only supports large-scale stability studies but also offers our clients significant economic advantages by optimizing resource use and reducing time to market.

Key Insights into the Structure to Function Principle

Understanding the intricate relationship between LNP morphology and its functional efficacy is central to our analysis. By detailing how structural attributes like size and shape directly impact the effectiveness of drug delivery, ATEM provides essential insights for optimizing nanoparticle design and formulation, aligning with the principles of structure to function in nanoparticle technology.

Adhering to Regulatory Standards

ATEM’s AI LNP solution is in full compliance with the 2022 FDA recommendations and guidance for industry regarding Drug Products, Including Biological Products, that Contain Nanomaterials. Our method ensures comprehensive analysis, covering size distribution, shape, and morphological characterization. It is capable of handling high cryoprotectant concentrations and is suitable for analyzing a wide array of substances, including mRNA and siRNA, in their undiluted, native states.

Leading with Innovation

ATEM’s AI-enhanced Cryo-EM characterization method sets a new standard in the field, combining scientific accuracy with economic efficiency. Our approach accelerates the development of nanoparticle-based medicines, pushing the boundaries of what’s possible in drug delivery technology.

AI-powered statistically significant cryo-EM LNP characterization
Highest precision and accuracy
Offering a highly scalable characterization solution
Benefits of cryo-EM LNP characterization Unveils true morphological heterogeneity (blebbed, split particles) Highly scaleable AI powered solution Offers fast turnaround times even for large batches Statistically significant, highly robust and accurate results
Applications Formulation optimization Stability testing (e.g. Forced degradation studies) Batch-to-batch quality control Highest precision and accuracy

Gain significant AI enabled statistical insights into key LNP CQAs

Moving the cryo-em from a qualitative method to a quantitative approach

Automated Particle Detection & Classification

At ATEM we have developed a machine learning based algorithm to transform cryo-EM LNP images into quantitative morphological insights. For precise, reproducible and accurate characterization of thousands of LNP particles.

Cryo-EM characterization recommended by FDA

The FDA in its most recent industry guidance for “Drug Products, Including Biological Products, that Contain Nanomaterials – Guidance for Industry” (April 2022) recommends using cryo-EM for the characterization of multiple CQAs (Critical Quality Attributes).

ATEM now provides these readouts in a single assay at highest statistical robustness in a label free process.

Areas of application

Formulation Development

Gain valuable insights to decipher the in-depth characteristics of your most promising formulations or obtain previously inaccessible perspectives on challenges.

Process Development

Better understand bottlenecks in your scale-up or downstream processes and their effect on LNP drug product. Gain the ability to more effectively address and mitigate them.

Batch-to-Batch Control

Ensure the most advanced understanding into batch-to-batch consistency to ensure your processes and products always control for highest LNP drug product integrity.  

Strengthen your IND Filing

Extend your IND filing with quantitative LNP cryo-EM data to showcase product quality, production consistency and in-depth understanding of its characteristics.

Characterized Lipid-Nano-Particle CQAs

LNP Size & Size Distribution Characterization

ATEM’s machine learning algorithm can characterize the size and the size distribution of the LNP at single particle precision.

LNP Morphology Classification

The machine learning model classifies the particles into the main observed particle morphologies of Solid Core, Biphasics Split and Biphasic Dense (Blebbed) particles.

LNP Aspect Ratio

The Aspect Ratio (Shape Ratio) identifies the shape of the LNP, relative to a perfect sphere. Cryo-EM is the only method to provide this level of insights.

More questions?

Method Robustness & Reproducibility

Statical Significance

Based on a random draw study we can see that only 2.000+ particles the statistical significance becomes robust. This is important when drawing conclusions about less frequently present morphological classes.

< 1.0 nm
95% Confidence Interval
(Avg. Particle Diameter, 5000 particles)


Based on technical replicates of the same sample have been analyzed. And the result shows a Standard Deviation of sub 1 nm across all replicates. Additional information can be obtained from the application note.

< 1.0 nm
Standard Deviation
(Avg. Particle Diameter, 5000 particles)


Based on benchmarking the results of a trained human operator against the results generated by the machine learning model show human like results (>96% accuracy). Technical replicates demonstrate a 1-2% standard deviation on classification accuracy.

Ø Classifiaction Accuracy

(Avg. Particle Diameter, 5000 particles)

standard Turnaround: 2-4 weeks

Process & Business Model

Price per sample

Price per analyzed image data set for up to 500 images

Option 2 is aims at enabling partners with own cryo-EM hardware or established collaborations to benefit from transforming their images into quantitative insights. ATEM also offers the option to sell its specialized grids that enable up to 80% data collection success on the first grid.

Key LNP Sample Requirements

0.1 – 2.0 mg / ml
5 – 25 mg / ml
≤ 20% w/v
< 400 nm 
≥ 50 µl

Resources and Insights

Lipid Nanoparticles
LNP meets high-throughput Cryo-EM: Reliably quantifying LNP morphology The never-ending ...
10 April 2024
Structural Biology
REMSCHEID, NORTH RHINE-WESTPHALIA, GERMANY, March 1, 2023. ATEM Structural Discovery ...
31 August 2023
Structural Biology
ATEM presented cryo-EM in Charles River’s webinar featuring recent innovations ...
31 August 2023

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Our AI-powered Cryo-EM Lipid-Nano-Particle analysis

Fast. Simple. Powerful.

High-tech Cryo-EM data acquisition

Integrating proven and robust sample preparation with state of the art Cryo-EM

Rationalize decisions with AI-powered data analysis

Leveraging our propriatary AI algorithms, we convert images into quantitative data (5.000 particles), delivering actionable, quantitative insights for your program

Key Features

At single particle level you gain insights into LNP size, heterogeneous morphological classes and shape distribution

Sample requirements

We require at least >2 mg/ml of total lipid content with a sample volume of 100 μl, ideally aliquoted into at least 2 tubes
Additionally, provide 2 ml of sample buffer for dilution during the sample preparation
Your LNP’s diameter should not exceed a diameter of 400 nm and should contain <5% of protein, free nuclid acid or other macromolecular contaminations
Your samples should have at least <10% of large organic solutes (i.e. PEG, sugars, etc in the buffer and <0.1% of residual free lipids or detergent in solution

We prepare everything else. We receive your sample, then optimise and test it before returning the data in a personalised way to meet your needs.

Sample amounts
100 μl, ideally aliquoted into at least 2 tubes
Turnaround times
Results are delivered within 4 weeks
Quantitative (5.000 particles) report with representative images