Single particle analysis is a method in structural biology that uses electron microscopes to determine the three-dimensional structure of biological molecules. It allows scientists to visualize proteins, viruses, and other large molecular complexes at near-atomic detail. This technique is conceptually similar to creating a detailed 3D model of an object by taking thousands of 2D photographs from many different angles and then computationally combining them. The goal is to visualize the architecture of the biological machinery that drives life.
This method is part of a field known as cryo-electron microscopy, or cryo-EM. The “cryo” prefix refers to the use of extremely low temperatures to preserve the samples in a near-native state. This approach has become a mainstream tool for visualizing molecules that are too large or flexible for other methods.
The Single Particle Analysis Workflow
The first step involves taking a solution containing millions of identical copies of a molecule, such as a protein, and preparing it for the microscope. A tiny droplet of this solution is applied to a small metal grid, which acts as the sample holder. This grid is then blotted with filter paper to create an extremely thin layer of the sample.
This prepared grid then undergoes a process called vitrification, which is a form of rapid freezing. The grid is plunged into a cryogen like liquid ethane, which is cooled by liquid nitrogen. This flash-freezing process cools the sample so quickly that water molecules do not have time to form structured, damaging ice crystals. Instead, the water forms a glass-like solid known as vitreous ice, which preserves the natural shape of the biological particles suspended within it.
Once vitrified, the sample grid is transferred into a transmission electron microscope (TEM). Inside the microscope’s high-vacuum environment, an electron beam is directed through the frozen sample. As electrons pass through the molecules, they scatter and create a projection image on a detector, similar to how an X-ray machine creates an image of bones. The microscope automatically captures tens of thousands of these 2D images, called micrographs, each showing particles frozen in random orientations.
Computational Reconstruction of 3D Structures
After the micrographs are collected, the process moves to a computer for the computational phase. The first task is “particle picking,” where software scans each micrograph to identify and extract the images of individual molecules. This step results in a massive dataset containing hundreds of thousands, or even millions, of single-particle images, each with a low signal-to-noise ratio.
These extracted particle images are then computationally grouped and sorted in a step called 2D classification. The software aligns the noisy images and categorizes them into classes based on their shared orientation. Once grouped, all the images within a class are averaged together. This averaging process enhances the molecule’s structural signal while canceling out random background noise, resulting in a clean 2D image of a specific molecular view.
Finally, these high-quality 2D class averages, each representing a different viewpoint, are used to build the 3D model. Sophisticated algorithms determine the precise orientation of each 2D class average in three-dimensional space. The software then combines these views to reconstruct a high-resolution 3D density map of the molecule, which can reveal near-atomic details.
Visualizing Molecular Machines in Action
Biological molecules are dynamic machines that change shape to carry out their functions. A strength of single particle analysis is its ability to capture molecules in their different functional shapes, known as conformations. Because the flash-freezing process traps molecules in whatever shape they were in at that instant, a single sample contains a population of molecules in various conformational states.
During computational image processing, the millions of particle images can be sorted by subtle differences in their structure in addition to their orientation. This allows researchers to separate particles that are in different functional states. For example, if a molecular machine opens and closes to perform its job, the software can group all the “open” state particles together and all the “closed” state particles together. This is an advantage over other techniques that may only show an average state.
By reconstructing separate 3D models for each of these sorted groups, scientists can create what are effectively stop-motion snapshots of a molecule in action. Instead of just seeing what the machine looks like, researchers can understand the mechanical movements that drive its biological function, such as how an enzyme binds its target or how a channel protein opens to let ions pass.
Impact on Drug Discovery and Disease Research
The ability of single particle analysis to reveal detailed molecular structures has significantly impacted medical research, particularly in the development of new drugs and therapies. By providing a 3D picture of a disease-related protein, scientists can design drugs that bind precisely to it to either block its function or modify its activity. This structure-based drug design is more efficient than traditional trial-and-error methods.
A prominent example of its impact was seen during the COVID-19 pandemic. Researchers used single particle analysis to rapidly determine the high-resolution structure of the SARS-CoV-2 spike protein, the part of the virus that attaches to human cells. This structural information was important for the design of mRNA vaccines and antibody therapies, as it revealed the exact targets for an immune response. Understanding the spike’s structure in its different states helped create vaccines that stabilize it in the form that elicits the most effective antibodies.
The technique is also advancing the study of neurodegenerative diseases like Alzheimer’s and Parkinson’s, which are associated with the misfolding and aggregation of proteins in the brain. Single particle analysis allows researchers to visualize the structures of these harmful protein clumps, offering clues as to how they form and cause damage to nerve cells. The technique has also been used to determine the structures of cellular components like the ribosome, a complex machine that builds proteins and is a target for many antibiotics.