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Related Concept Videos

Micropatterning Transmission Electron Microscopy Grids to Direct Cell Positioning within Whole-Cell Cryo-Electron Tomography Workflows09:53

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The goal of this protocol is to direct cell adhesion and growth to targeted areas of grids for cryo-electron microscopy. This is achieved by applying an anti-fouling layer that is ablated in user-specified patterns followed by deposition of extra-cellular matrix proteins in the patterned areas prior to cell...
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Cryo-electron tomography (cryo-ET) enables 3D visualization of cellular ultrastructure at nanometer resolution, but manual segmentation remains time-consuming and complex. We present a novel workflow that integrates advanced virtual reality software for segmenting cryo-ET tomograms, showcasing its effectiveness through the segmentation of mitochondria in mammalian...
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Cryo-Electron Microscopy Screening Automation Across Multiple Grids Using Smart Leginon07:52

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Cryo-electron microscopy (cryoEM) multi-grid screening is often a tedious process that demands hours of attention. This protocol shows how to set up a standard Leginon collection and Smart Leginon Autoscreen to automate this process. This protocol can be applied to the majority of cryoEM holey foil...
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Cryo-electron Microscopy01:28

Cryo-electron Microscopy

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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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Cryo-Electron Tomography Remote Data Collection and Subtomogram Averaging08:55

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The present protocol describes high-resolution cryo-electron tomography remote data acquisition using Tomo5 and subsequent data processing and subtomogram averaging using emClarity. Apoferritin is used as an example to illustrate detailed step-by-step processes to achieve a cryo-ET structure at 2.86 Å...
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High-resolution Single Particle Analysis from Electron Cryo-microscopy Images Using SPHIRE13:28

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This paper presents a protocol for processing cryo-EM images using the software suite SPHIRE. The present protocol can be applied for nearly all single particle EM projects that target near-atomic...
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Related Experiment Video

Updated: Jan 20, 2026

Micropatterning Transmission Electron Microscopy Grids to Direct Cell Positioning within Whole-Cell Cryo-Electron Tomography Workflows
09:53

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Methods for merging data sets in electron cryo-microscopy.

Max E Wilkinson1, Ananthanarayanan Kumar1, Ana Casañal1

  • 1MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge Biomedical Campus, Cambridge CB2 0QH, England.

Acta Crystallographica. Section D, Structural Biology
|September 4, 2019
PubMed
Summary
This summary is machine-generated.

Merging electron cryo-microscopy (cryo-EM) data improves 3D reconstructions for challenging biological samples. This study presents two methods to combine cryo-EM datasets with different pixel sizes, aiding structural biology research.

Keywords:
RELIONcryo-EMmerging of datasingle-particle processing

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Author Spotlight: Enhancing Cryo-Electron Microscopy by Automated Data Collection and Analysis Techniques
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Area of Science:

  • Structural Biology
  • Biophysics
  • Microscopy

Background:

  • Electron cryo-microscopy (cryo-EM) is vital for biological macromolecule structure determination.
  • Flexible or heterogeneous samples often require extensive cryo-EM data collection under varied conditions.
  • Merging datasets enhances 3D reconstruction quality but faces challenges due to differing pixel sizes.

Purpose of the Study:

  • To describe two methods for combining cryo-electron microscopy (cryo-EM) datasets.
  • To address the challenge of merging cryo-EM data acquired with different pixel sizes.
  • To estimate the impact of rescaling factor errors on data merging outcomes.

Main Methods:

  • Calculation of a rescaling factor from independent cryo-EM datasets.
  • Application of two distinct methods to merge cryo-EM data with varying pixel sizes.
  • Estimation of the effects of rescaling factor inaccuracies on merged data.

Main Results:

  • Successful development of two methods for merging cryo-EM datasets with different pixel sizes.
  • Quantification of the impact of rescaling factor errors on the quality of 3D reconstructions.
  • Demonstration that merging data can overcome limitations of individual datasets.

Conclusions:

  • The presented methods provide a practical guideline for combining cryo-EM data from the same microscope and detector types.
  • Data merging is a valuable strategy for improving cryo-EM structure determination, especially for difficult samples.
  • Accurate rescaling is crucial for successful cryo-EM data integration.