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Related Concept Videos

Cryo-electron Microscopy01:28

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Conventional electron microscopy (EM) involves dehydration, fixation, and staining of biological samples, which distorts the native state of biological molecules and results in several artifacts. Also, the high-energy electron beam damages the sample and makes it difficult to obtain high-resolution images. These issues can be addressed using cryo-EM, which uses frozen samples and gentler electron beams. The technique was developed by Jacques Dubochet, Joachim Frank, and Richard Henderson, for...
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Transmission electron microscopy (TEM) can be used to determine the 3D structure of biological samples with the help of techniques such as electron microscope tomography and single-particle reconstruction. While single-particle reconstruction can examine macromolecules and macromolecular complexes in vitro conditions only, tomography permits the study of cell components or small cells in vivo.
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Updated: Nov 18, 2025

A Robust Single-Particle Cryo-Electron Microscopy cryo-EM Processing Workflow with cryoSPARC, RELION, and Scipion
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DRPnet: automated particle picking in cryo-electron micrographs using deep regression.

Nguyen Phuoc Nguyen1, Ilker Ersoy2, Jacob Gotberg3

  • 1Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA.

BMC Bioinformatics
|February 9, 2021
PubMed
Summary
This summary is machine-generated.

A new deep learning method, Deep Regression Picker Network (DRPnet), automates particle picking in cryo-electron microscopy (cryoEM) images. DRPnet significantly reduces processing time and improves accuracy for high-resolution 3D reconstructions.

Keywords:
3D reconstructionAutopickingConvolutional neural networkCryoEMDeep learningElectron microscopyImage segmentationParticle pickingRegressionSingle particle analysisSingle particle reconstruction

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Area of Science:

  • Structural Biology
  • Biophysics
  • Computational Biology

Background:

  • Automated particle picking in cryo-electron microscopy (cryoEM) is crucial for single particle analysis.
  • CryoEM data presents challenges like low signal-to-noise, variable particle characteristics, and artifacts.

Purpose of the Study:

  • To develop a deep learning-based particle picking network for automated detection of protein particle centers in cryoEM micrographs.
  • To improve the efficiency and accuracy of particle selection in cryoEM image processing.

Main Methods:

  • A double convolutional neural network (CNN) cascade, termed Deep Regression Picker Network (DRPnet).
  • The first CNN (fully convolutional regression network) generates a distance map for particle center probability.
  • A second CNN refines detections to minimize false positives.

Main Results:

  • DRPnet effectively identifies particles with varying sizes, shapes, and grayscale patterns.
  • Pretrained on one dataset, DRPnet performs well on others without retraining.
  • Outperforms RELION and state-of-the-art networks in recall, precision, and F-measure.
  • Achieves better particle picking performance with reduced user interaction and processing time.

Conclusions:

  • DRPnet offers significant time savings compared to manual and template-based picking.
  • It excels with challenging datasets, including low-contrast or clumped particles.
  • Enables higher resolution 3D reconstructions, even with fewer particles or unknown symmetry.