Locating Macromolecular Assemblies in Cells by 2D Template Matching with cisTEM

  1. Bronwyn A Lucas
  2. Benjamin A Himes
  3. Liang Xue
  4. Tim Grant
  5. Julia Mahamid
  6. Nikolaus Grigorieff  Is a corresponding author
  1. Howard Hughes Medical Institute, United States
  2. University of Massachusetts Medical School, United States
  3. EMBL, Germany
  4. European Molecular Biology Laboratory, Germany

Abstract

For a more complete understanding of molecular mechanisms, it is important to study macromolecules and their assemblies in the broader context of the cell. This context can be visualized at nanometer resolution in three dimensions (3D) using electron cryo-tomography, which requires tilt series to be recorded and computationally aligned, currently limiting throughput. Additionally, the high-resolution signal preserved in the raw tomograms is currently limited by a number of technical difficulties, leading to an increased false-positive detection rate when using 3D template matching to find molecular complexes in tomograms. We have recently described a 2D template matching approach that addresses these issues by including high-resolution signal preserved in single-tilt images. A current limitation of this approach is the high computational cost that limits throughput. We describe here a GPU-accelerated implementation of 2D template matching in the image processing software cisTEM that allows for easy scaling and improves the accessibility of this approach. We apply 2D template matching to identify ribosomes in images of frozen-hydrated Mycoplasma pneumoniae cells with high precision and sensitivity, demonstrating that this is a versatile tool for in situ visual proteomics and in situ structure determination. We benchmark the results with 3D template matching of tomograms acquired on identical sample locations and identify strengths and weaknesses of both techniques, which offer complementary information about target localization and identity.

Data availability

All the code used for the 2D template matching has an open source license and is freely available from the cisTEM github repository, https://github.com/timothygrant80/cisTEM. The images of M. pneumoniae analyzed for this work, as well as the 70S reconstructions have been deposited in the EMPIAR and EMDB databases, https://www.ebi.ac.uk/pdbe/emdb/empiar/ and https://www.ebi.ac.uk/pdbe/emdb,

The following data sets were generated

Article and author information

Author details

  1. Bronwyn A Lucas

    Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
    Competing interests
    No competing interests declared.
  2. Benjamin A Himes

    RNA Therapeutics Institute, University of Massachusetts Medical School, Worcester, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7777-0298
  3. Liang Xue

    Structural and Computational Biology Unit, EMBL, Heidelberg, Germany
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4368-2526
  4. Tim Grant

    Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4855-8703
  5. Julia Mahamid

    European Molecular Biology Laboratory, Heidelberg, Germany
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6968-041X
  6. Nikolaus Grigorieff

    RNA Therapeutics Institute, University of Massachusetts Medical School, Worcester, United States
    For correspondence
    niko@grigorieff.org
    Competing interests
    Nikolaus Grigorieff, Reviewing editor, eLife.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1506-909X

Funding

European Research Council (760067)

  • Julia Mahamid

Howard Hughes Medical Institute (N/A)

  • Bronwyn A Lucas
  • Benjamin A Himes
  • Nikolaus Grigorieff

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Edward H Egelman, University of Virginia, United States

Version history

  1. Received: March 30, 2021
  2. Accepted: June 9, 2021
  3. Accepted Manuscript published: June 11, 2021 (version 1)
  4. Version of Record published: June 22, 2021 (version 2)

Copyright

© 2021, Lucas et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Bronwyn A Lucas
  2. Benjamin A Himes
  3. Liang Xue
  4. Tim Grant
  5. Julia Mahamid
  6. Nikolaus Grigorieff
(2021)
Locating Macromolecular Assemblies in Cells by 2D Template Matching with cisTEM
eLife 10:e68946.
https://doi.org/10.7554/eLife.68946

Share this article

https://doi.org/10.7554/eLife.68946

Further reading

    1. Cell Biology
    2. Structural Biology and Molecular Biophysics
    Bronwyn A Lucas, Kexin Zhang ... Nikolaus Grigorieff
    Research Advance Updated

    Previously, we showed that high-resolution template matching can localize ribosomes in two-dimensional electron cryo-microscopy (cryo-EM) images of untilted Mycoplasma pneumoniae cells with high precision (Lucas et al., 2021). Here, we show that comparing the signal-to-noise ratio (SNR) observed with 2DTM using different templates relative to the same cellular target can correct for local variation in noise and differentiate related complexes in focused ion beam (FIB)-milled cell sections. We use a maximum likelihood approach to define the probability of each particle belonging to each class, thereby establishing a statistic to describe the confidence of our classification. We apply this method in two contexts to locate and classify related intermediate states of 60S ribosome biogenesis in the Saccharomyces cerevisiae cell nucleus. In the first, we separate the nuclear pre-60S population from the cytoplasmic mature 60S population, using the subcellular localization to validate assignment. In the second, we show that relative 2DTM SNRs can be used to separate mixed populations of nuclear pre-60S that are not visually separable. 2DTM can distinguish related molecular populations without the need to generate 3D reconstructions from the data to be classified, permitting classification even when only a few target particles exist in a cell.

    1. Structural Biology and Molecular Biophysics
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    Research Article

    In their folded state, biomolecules exchange between multiple conformational states that are crucial for their function. Traditional structural biology methods, such as X-ray crystallography and cryogenic electron microscopy (cryo-EM), produce density maps that are ensemble averages, reflecting molecules in various conformations. Yet, most models derived from these maps explicitly represent only a single conformation, overlooking the complexity of biomolecular structures. To accurately reflect the diversity of biomolecular forms, there is a pressing need to shift toward modeling structural ensembles that mirror the experimental data. However, the challenge of distinguishing signal from noise complicates manual efforts to create these models. In response, we introduce the latest enhancements to qFit, an automated computational strategy designed to incorporate protein conformational heterogeneity into models built into density maps. These algorithmic improvements in qFit are substantiated by superior Rfree and geometry metrics across a wide range of proteins. Importantly, unlike more complex multicopy ensemble models, the multiconformer models produced by qFit can be manually modified in most major model building software (e.g., Coot) and fit can be further improved by refinement using standard pipelines (e.g., Phenix, Refmac, Buster). By reducing the barrier of creating multiconformer models, qFit can foster the development of new hypotheses about the relationship between macromolecular conformational dynamics and function.