Editorial Feature

Exploring Advancements in Cryo-Electron Microscopy for Single-Particle Analysis

Cryo-electron microscopy (cryo-EM) has revolutionized structural biology. It enables scientists to visualize proteins, nucleic acids, virus assemblies, and other macromolecules at near-atomic resolutions, unlocking new avenues for exploring the intricate mechanisms underlying biological processes.

Exploring Advancements in Cryo-Electron Microscopy for Single-Particle Analysis

Image Credit: PolakPhoto/Shutterstock.com

What is Cryo-EM?

Cryo-EM is an advanced imaging technique for studying bio-molecular structures and complexes at near-atomic resolution.

It involves rapidly freezing samples in a thin layer of vitreous ice below -150 °C using cryogenic liquids like ethane to preserve their natural structures. The frozen samples are then imaged using a transmission electron microscope, capturing 2D projections from various angles.

These images are computationally combined to reconstruct a 3D model of the sample at near-atomic resolution, which is particularly useful for studying large and complex biomolecular assemblies.

The key advantage of cryo-EM lies in its ability to study biological molecules in their near-native state without requiring crystallization, which can potentially disrupt their natural conformations or interactions.

This makes cryo-EM particularly well-suited for studying large and dynamic biomolecular complexes, membrane proteins, and macromolecular assemblies that have traditionally been challenging for other structural biology techniques like X-Ray crystallography or nuclear magnetic resonance (NMR) spectroscopy.1        

Technological Advancements in Cryo-EM

Recent years have witnessed remarkable advancements in cryo-EM technology, both in hardware and software, propelling the field to new heights of resolution and throughput.

Hardware Developments

One of the most significant hardware developments has been the introduction of direct electron detectors. These specialized cameras can directly detect incoming electrons, providing higher signal-to-noise ratios and improved contrast compared to traditional charge-coupled device (CCD) cameras.

Additionally, direct electron detectors enable the acquisition of images as a series of movie frames rather than a single exposure. This has proven invaluable for correcting sample movements and mitigating the effects of radiation damage, two major challenges that have historically limited the achievable resolution in cryo-EM.

The development of brighter and more coherent electron sources and the introduction of phase plates have also improved the quality, resolution, and contrast of cryo-EM images. These advancements are particularly beneficial for studying smaller proteins and complexes, which often suffer from low contrast in traditional cryo-EM imaging.2

Software Innovations

Parallel to hardware advancements, significant progress has been made in the software domain, enabling faster and more accurate data processing and analysis. Advanced image processing algorithms, such as Bayesian and maximum-likelihood approaches, have been developed to address challenges like conformational heterogeneity and low signal-to-noise ratios inherent in cryo-EM data.

Software tools like RELION and cryoSPARC have revolutionized the field by providing user-friendly interfaces and robust algorithms for particle picking, classification, and high-resolution structure determination. These tools leverage deep and machine learning techniques to improve the efficiency and accuracy of data analysis, enabling researchers to extract valuable structural information from noisy cryo-EM data.3

How are AI and ML Revolutionizing Microscopy?

Impact on Single-Particle Analysis

Enhanced Resolution and Structural Insights   

One of the most significant impacts has been the achievement of near-atomic resolution structures for a wide range of biological complexes. Recent studies have revealed the intricate details of membrane proteins, such as the TRPV1 ion channel, shedding light on their molecular mechanisms and potential therapeutic targets.2

Previously, researchers used lower-resolution cryo-EM maps and fit crystal structures of individual components into them. Now, they can directly determine the atomic structures of complexes like spliceosomes and ribosomes, gaining insights into their functional mechanisms, conformational changes, and regulatory processes during various biological processes.4

These discoveries have revolutionized our understanding of fundamental cellular processes and have opened new avenues for exploring potential therapeutic interventions.

Accelerated Structure Determination

Improved hardware, software, and automation have dramatically accelerated structure determination in cryo-EM. Structures that once took years to solve can now be determined in weeks or months, enabling researchers to explore a broader range of biological systems and accelerating the pace of discovery.

This has been especially valuable in structural genomics, where cryo-EM helps quickly determine the structures of entire proteomes or protein families, providing insights into their functions and roles in disease.3

Challenges and Limitations

A persistent challenge in time-resolved and standard cryo-EM is the presence of compositional and conformational heterogeneity in the molecules under study. To address this, researchers increasingly use techniques like classification with signal subtraction and focused 3D alignment, leveraging the contrast improvements from direct electron detection. These methods can sort molecules based on local variability, including movements of individual helices.

The inherent intra-particle flexibility also remains a limiting factor in cryo-EM, which can lead to blurring and difficulty in achieving high-resolution structures. Overcoming this challenge is essential for advancing cryo-EM's ability to study complex biomolecular structures.5

Future Outlook and Potential Applications

Technological advances in single-particle cryo-EM have transformed structural biology, enabling the study of previously challenging targets like integral membrane proteins and large, dynamic complexes. These advancements have led to a rapid pace of atomic structure determination and attracted researchers from diverse fields.

Ongoing efforts, such as the integration of cryo-EM with other techniques like NMR spectroscopy, X-Ray crystallography, and machine learning, have the potential to accelerate structure-based drug discovery by providing atomic-level insights into protein-drug interactions, leading to the design of more potent and selective therapeutics and streamlining drug development.3

More from AZoOptics: Ultrasonic Techniques for Lithium-Ion Battery Diagnostics

References and Further Reading

  1. Doerr, A. (2016). Single-particle cryo-electron microscopy: a brief overview of how to solve a macromolecular structure using single-particle cryo-electron microscopy (cryo-EM). Nature methods. doi.org/10.1038/nmeth.3700
  2. Benjin, X., Ling, L. (2020). Developments, applications, and prospects of cryo‐electron microscopy. Protein Science. doi.org/10.1002/pro.3805
  3. Cheng, Y. (2018). Single-particle cryo-EM—How did it get here and where will it go. Science. doi.org/10.1126/science.aat4346
  4. Fica, SM., Nagai, K. (2017). Cryo-electron microscopy snapshots of the spliceosome: structural insights into a dynamic ribonucleoprotein machine. Nature structural & molecular biology. doi.org/10.1038/nsmb.3463
  5. Frank, J. (2017). Advances in the field of single-particle cryo-electron microscopy over the last decade. Nature protocols. doi.org/10.1038/nprot.2017.004

Disclaimer: The views expressed here are those of the author expressed in their private capacity and do not necessarily represent the views of AZoM.com Limited T/A AZoNetwork the owner and operator of this website. This disclaimer forms part of the Terms and conditions of use of this website.

Owais Ali

Written by

Owais Ali

NEBOSH certified Mechanical Engineer with 3 years of experience as a technical writer and editor. Owais is interested in occupational health and safety, computer hardware, industrial and mobile robotics. During his academic career, Owais worked on several research projects regarding mobile robots, notably the Autonomous Fire Fighting Mobile Robot. The designed mobile robot could navigate, detect and extinguish fire autonomously. Arduino Uno was used as the microcontroller to control the flame sensors' input and output of the flame extinguisher. Apart from his professional life, Owais is an avid book reader and a huge computer technology enthusiast and likes to keep himself updated regarding developments in the computer industry.


Please use one of the following formats to cite this article in your essay, paper or report:

  • APA

    Ali, Owais. (2024, June 20). Exploring Advancements in Cryo-Electron Microscopy for Single-Particle Analysis. AZoOptics. Retrieved on July 15, 2024 from https://www.azooptics.com/Article.aspx?ArticleID=2637.

  • MLA

    Ali, Owais. "Exploring Advancements in Cryo-Electron Microscopy for Single-Particle Analysis". AZoOptics. 15 July 2024. <https://www.azooptics.com/Article.aspx?ArticleID=2637>.

  • Chicago

    Ali, Owais. "Exploring Advancements in Cryo-Electron Microscopy for Single-Particle Analysis". AZoOptics. https://www.azooptics.com/Article.aspx?ArticleID=2637. (accessed July 15, 2024).

  • Harvard

    Ali, Owais. 2024. Exploring Advancements in Cryo-Electron Microscopy for Single-Particle Analysis. AZoOptics, viewed 15 July 2024, https://www.azooptics.com/Article.aspx?ArticleID=2637.

Tell Us What You Think

Do you have a review, update or anything you would like to add to this article?

Leave your feedback
Your comment type

While we only use edited and approved content for Azthena answers, it may on occasions provide incorrect responses. Please confirm any data provided with the related suppliers or authors. We do not provide medical advice, if you search for medical information you must always consult a medical professional before acting on any information provided.

Your questions, but not your email details will be shared with OpenAI and retained for 30 days in accordance with their privacy principles.

Please do not ask questions that use sensitive or confidential information.

Read the full Terms & Conditions.