title: Simulating cryo electron tomograms of crowded cell cytoplasm for assessment of automated particle picking.
abstract: BACKGROUND
  Cryo-electron tomography is an important tool to study structures of macromolecular complexes in close to native states. A whole cell cryo electron tomogram contains structural information of all its macromolecular complexes. However, extracting this information remains challenging, and relies on sophisticated image processing, in particular for template-free particle extraction, classification and averaging. To develop these methods it is crucial to realistically simulate tomograms of crowded cellular environments, which can then serve as ground truth models for assessing and optimizing methods for detection of complexes in cell tomograms.
  
  
  RESULTS
  We present a framework to generate crowded mixtures of macromolecular complexes for realistically simulating cryo electron tomograms including noise and image distortions due to the missing-wedge effects. Simulated tomograms are then used for assessing the template-free Difference-of-Gaussian (DoG) particle-picking method to detect complexes of different shapes and sizes under various crowding and noise levels. We identified DoG parameter settings that maximize precision and recall for detecting particles over a wide range of sizes and shapes. We observed that medium sized DoG scaling factors showed the overall best performance. To further improve performance, we propose a combination strategy for integrating results from multiple parameter settings. With increasing macromolecular crowding levels, the precision of particle picking remained relatively high, while the recall was dramatically reduced, which limits the detection of sufficient copy numbers of complexes in a crowded environment. Over a wide range of increasing noise levels, the DoG particle picking performance remained stable, but dramatically reduced beyond a specific noise threshold.
  
  
  CONCLUSIONS
  Automatic and reference-free particle picking is an important first step in a visual proteomics analysis of cell tomograms. However, cell cytoplasm is highly crowded, which makes particle detection challenging. It is therefore important to test particle-picking methods in a realistic crowded setting. Here, we present a framework for simulating tomograms of cellular environments at high crowding levels and assess the DoG particle picking method. We determined optimal parameter settings to maximize the performance of the DoG particle-picking method.
authors.affiliation: Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, 1050 Childs Way, Los Angeles, CA, 90089, USA.
authors.forename: Long
authors.initials: L
authors.lastname: Pei
authors.affiliation: Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, 1050 Childs Way, Los Angeles, CA, 90089, USA.
authors.forename: Min
authors.initials: M
authors.lastname: Xu
authors.affiliation: Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, 1050 Childs Way, Los Angeles, CA, 90089, USA.
authors.forename: Zachary
authors.initials: Z
authors.lastname: Frazier
authors.affiliation: Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, 1050 Childs Way, Los Angeles, CA, 90089, USA. alber@usc.edu.
authors.forename: Frank
authors.identifier: http://orcid.org/0000-0003-1981-8390
authors.initials: F
authors.lastname: Alber
mesh_terms: D002477:Cells / Q000737:chemistry* / Q000378:metabolism / Q000648:ultrastructure; D020285:Cryoelectron Microscopy / Q000379:methods*; D003593:Cytoplasm / Q000378:metabolism*; D055032:Electron Microscope Tomography / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D046911:Macromolecular Substances / Q000737:chemistry* / Q000648:ultrastructure
chemical_list: D046911:Macromolecular Substances
keywords: Cryo-electron tomography; Macromolecular crowding; Particle picking; Visual proteomics